motif

motif contains many functions to process glycans in various ways and use this processing to analyze glycans via curated motifs, graph features, and sequence features. It contains the following modules:

draw

drawing glycans in SNFG style

GlycoDraw

 GlycoDraw (glycan:str, vertical:bool=False, compact:bool=False,
            show_linkage:bool=True, dim:float=50,
            highlight_motif:str|None=None, highlight_termini_list:list=[],
            highlight_linkages:list[int]|None=None,
            reverse_highlight:bool=False, repeat:bool|int|str|None=None,
            repeat_range:list[int]|None=None, draw_method:str|None=None,
            filepath:str|pathlib.Path|None=None, suppress:bool=False,
            per_residue:list=[], pdb_file:str|pathlib.Path|None=None,
            alt_text:str|None=None, libr:dict|None=None,
            reducing_end_label:str|None=None, restrict_vocab:bool=False)

Renders glycan structure using SNFG symbols or chemical structure representation

Type Default Details
glycan str IUPAC-condensed glycan sequence
vertical bool False Draw vertically
compact bool False Use compact style
show_linkage bool True Show linkage labels
dim float 50 Base dimension for scaling
highlight_motif str | None None Motif to highlight
highlight_termini_list list [] Terminal positions (from ‘terminal’, ‘internal’, and ‘flexible’)
highlight_linkages list[int] | None None Which linkages to highlight in a different color; indices, starting from 0, in glycan
reverse_highlight bool False Whether to highlight everything EXCEPT highlight_motif
repeat bool | int | str | None None Repeat unit specification (True: n units, int: # of units, str: range of units)
repeat_range list[int] | None None Repeat unit range
draw_method str | None None Drawing method: None, ‘chem2d’, ‘chem3d’
filepath str | pathlib.Path | None None Output file path
suppress bool False Suppress display
per_residue list [] Per-residue intensity values (order should be the same as the monosaccharides in glycan string)
pdb_file str | pathlib.Path | None None only used when draw_method=‘chem3d’; already existing glycan structure
alt_text str | None None Custom ALT text for accessibility
libr dict | None None Can be modified for drawing too exotic monosaccharides
reducing_end_label str | None None Label to be drawn connected to the reducing end
restrict_vocab bool False Whether only tokens present in libr can be drawn
Returns Any Drawing object 
GlycoDraw("Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Neu5Gc(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)][GlcNAc(b1-4)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc",
         highlight_motif = "GlcNAc(b1-?)Man")

annotate_figure

 annotate_figure (svg_input:str, scale_range:tuple[int,int]=(25, 80),
                  compact:bool=False, glycan_size:str='medium',
                  filepath:str|pathlib.Path='',
                  scale_by_DE_res:pandas.core.frame.DataFrame|None=None,
                  x_thresh:float=1, y_thresh:float=0.05,
                  x_metric:str='Log2FC')

Replaces text labels with glycan drawings in SVG figure

Type Default Details
svg_input str Input SVG file path
scale_range tuple (25, 80) Min/max glycan dimensions
compact bool False Use compact style
glycan_size str medium Glycan size preset (‘small’, ‘medium’, ‘large’)
filepath str | pathlib.Path Output file path
scale_by_DE_res pandas.core.frame.DataFrame | None None Differential expression results (motif_analysis.get_differential_expression)
x_thresh float 1 X metric threshold
y_thresh float 0.05 P-value threshold
x_metric str Log2FC X axis metric (‘Log2FC’, ‘Effect size’)
Returns str | None Modified SVG code

plot_glycans_excel

 plot_glycans_excel (df:pandas.core.frame.DataFrame|str|pathlib.Path,
                     folder_filepath:str|pathlib.Path,
                     glycan_col_num:int=0, scaling_factor:float=0.2,
                     compact:bool=False)

Creates Excel file with SNFG glycan images in a new column

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame or filepath with glycans
folder_filepath str | pathlib.Path Output folder path
glycan_col_num int 0 Glycan column index
scaling_factor float 0.2 Image scaling
compact bool False Use compact style
Returns None

analysis

downstream analyses of important glycan motifs

get_pvals_motifs

 get_pvals_motifs (df:pandas.core.frame.DataFrame|str,
                   glycan_col_name:str='glycan',
                   label_col_name:str='target', zscores:bool=True,
                   thresh:float=1.645, sorting:bool=True,
                   feature_set:list[str]=['exhaustive'],
                   multiple_samples:bool=False,
                   motifs:pandas.core.frame.DataFrame|None=None,
                   custom_motifs:list[str]=[])

Identifies significantly enriched glycan motifs using Welch’s t-test with FDR correction and Cohen’s d effect size calculation, comparing samples above/below threshold

Type Default Details
df pandas.core.frame.DataFrame | str Input dataframe or filepath (.csv/.xlsx)
glycan_col_name str glycan Column name for glycan sequences
label_col_name str target Column name for labels
zscores bool True Whether data are z-scores
thresh float 1.645 Threshold to separate positive/negative
sorting bool True Sort p-value dataframe
feature_set list [‘exhaustive’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
multiple_samples bool False Multiple samples with glycan columns
motifs pandas.core.frame.DataFrame | None None Modified motif_list
custom_motifs list [] Custom motifs if using ‘custom’ feature set
Returns DataFrame DataFrame with p-values, FDR-corrected p-values, and Cohen’s d effect sizes for glycan motifs 
glycans = ['Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc',
           'Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcOPN(b1-6)GlcOPN',
          'Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'Glc(b1-3)Glc(b1-3)Glc']
label = [3.234, 2.423, 0.733, 3.102, 0.108]
test_df = pd.DataFrame({'glycan':glycans, 'binding':label})

print("Glyco-Motif enrichment p-value test")
out = get_pvals_motifs(test_df, 'glycan', 'binding').iloc[:10,:]
Glyco-Motif enrichment p-value test
  motif pval corr_pval effect_size
4 GlcNAc 0.038120 0.205849 1.530905
8 Man 0.054356 0.234990 1.390253
24 Man(a1-?)Man 0.060923 0.234990 1.308333
22 Man(a1-3)Man 0.034212 0.205849 1.196586
14 GlcNAc(b1-4)GlcNAc 0.019543 0.175885 1.168815
23 Man(a1-6)Man 0.019543 0.175885 1.168815
25 Man(b1-4)GlcNAc 0.019543 0.175885 1.168815
7 Kdo 0.328790 0.479672 -0.811679
2 Glc 0.644180 0.668956 -0.811679
21 Man(a1-2)Man 0.177461 0.479672 0.772320

get_representative_substructures

 get_representative_substructures
                                   (enrichment_df:pandas.core.frame.DataFr
                                   ame)

Constructs minimal glycan structures that represent significantly enriched motifs by optimizing for motif content while minimizing structure size using subgraph isomorphism

Type Details
enrichment_df DataFrame Output from get_pvals_motifs
Returns list Up to 10 minimal glycans containing enriched motifs

get_heatmap

 get_heatmap (df:pandas.core.frame.DataFrame|str|pathlib.Path,
              motifs:bool=False, feature_set:list[str]=['known'],
              transform:str='', datatype:str='response',
              rarity_filter:float=0.05, filepath:str|pathlib.Path='',
              index_col:str='glycan', custom_motifs:list[str]=[],
              return_plot:bool=False, show_all:bool=False, **kwargs:Any)

Creates hierarchically clustered heatmap visualization of glycan/motif abundances

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path Input dataframe or filepath (.csv/.xlsx)
motifs bool False Analyze motifs instead of sequences
feature_set list [‘known’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
transform str Transform data before plotting
datatype str response Data type: ‘response’ for quantitative values or ‘presence’ for presence/absence
rarity_filter float 0.05 Min proportion for non-zero values
filepath str | pathlib.Path Path to save plot
index_col str glycan Column to use as index
custom_motifs list [] Custom motifs if using ‘custom’ feature set
return_plot bool False Return plot object
show_all bool False Show all tick labels
kwargs Any
Returns Optional None or plot object if return_plot=True 
glycans = ['Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc',
           'Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN4P',
           'Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'Glc(b1-3)Glc(b1-3)Glc']
label = [3.234, 2.423, 0.733, 3.102, 0.108]
label2 = [0.134, 0.345, 1.15, 0.233, 2.981]
label3 = [0.334, 0.245, 1.55, 0.133, 2.581]
test_df = pd.DataFrame([label, label2, label3], columns = glycans)

get_heatmap(test_df, motifs = True, feature_set = ['known', 'exhaustive'])

plot_embeddings

 plot_embeddings (glycans:list[str],
                  emb:dict[str,numpy.ndarray]|pandas.core.frame.DataFrame|
                  None=None, label_list:list[typing.Any]|None=None,
                  shape_feature:str|None=None,
                  filepath:str|pathlib.Path='', alpha:float=0.8,
                  palette:str='colorblind', **kwargs:Any)

Visualizes learned glycan embeddings using t-SNE dimensionality reduction with optional group coloring

Type Default Details
glycans list List of IUPAC-condensed glycan sequences
emb dict[str, numpy.ndarray] | pandas.core.frame.DataFrame | None None Glycan embeddings dict/DataFrame; defaults to SweetNet embeddings
label_list list[typing.Any] | None None Labels for coloring points
shape_feature str | None None Monosaccharide/bond for point shapes
filepath str | pathlib.Path Path to save plot
alpha float 0.8 Point transparency
palette str colorblind Color palette for groups
kwargs Any
Returns None Keyword args passed to seaborn scatterplot 
df_fabales = df_species[df_species.Order == 'Fabales'].reset_index(drop = True)
plot_embeddings(df_fabales.glycan.values.tolist(), label_list = df_fabales.Family.values.tolist())
Download completed.

characterize_monosaccharide

 characterize_monosaccharide (sugar:str,
                              df:pandas.core.frame.DataFrame|None=None,
                              mode:str='sugar',
                              glycan_col_name:str='glycan',
                              rank:str|None=None, focus:str|None=None,
                              modifications:bool=False,
                              filepath:str|pathlib.Path='', thresh:int=10)

Analyzes connectivity and modification patterns of specified monosaccharides/linkages in glycan sequences

Type Default Details
sugar str Monosaccharide or linkage to analyze
df pandas.core.frame.DataFrame | None None DataFrame with glycan column ‘glycan’; defaults to df_species
mode str sugar Analysis mode: ‘sugar’, ‘bond’, ‘sugarbond’
glycan_col_name str glycan Column name for glycan sequences
rank str | None None Column name for group filtering
focus str | None None Row value for group filtering
modifications bool False Consider modified monosaccharides
filepath str | pathlib.Path Path to save plot
thresh int 10 Minimum count threshold for inclusion
Returns None 
characterize_monosaccharide('Rha', rank = 'Kingdom', focus = 'Fungi', modifications = True)

get_differential_expression

 get_differential_expression
                              (df:pandas.core.frame.DataFrame|str|pathlib.
                              Path, group1:list[str|int],
                              group2:list[str|int], motifs:bool=False,
                              feature_set:list[str]=['exhaustive',
                              'known'], paired:bool=False,
                              impute:bool=True, sets:bool=False,
                              set_thresh:float=0.9,
                              effect_size_variance:bool=False,
                              min_samples:float=0.1,
                              grouped_BH:bool=False,
                              custom_motifs:list[str]=[],
                              transform:str|None=None, gamma:float=0.1,
                              custom_scale:float|dict=0,
                              glycoproteomics:bool=False,
                              level:str='peptide', monte_carlo:bool=False,
                              random_state:int|numpy.random._generator.Gen
                              erator|None=None)

Performs differential expression analysis using Welch’s t-test (or Hotelling’s T2 for sets) with multiple testing correction on glycomics abundance data

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (col 1) and abundance values in subsequent columns
group1 list Column indices/names for first group
group2 list Column indices/names for second group
motifs bool False Analyze motifs instead of sequences
feature_set list [‘exhaustive’, ‘known’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
paired bool False Whether samples are paired
impute bool True Replace zeros with Random Forest model
sets bool False Identify clusters of correlated glycans
set_thresh float 0.9 Correlation threshold for clusters
effect_size_variance bool False Calculate effect size variance
min_samples float 0.1 Min percent of non-zero samples required
grouped_BH bool False Use two-stage adaptive Benjamini-Hochberg
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
custom_scale float | dict 0 Ratio of total signal in group2/group1 for an informed scale model (or group_idx: mean(group)/min(mean(groups)) signal dict for multivariate)
glycoproteomics bool False Whether data is from glycoproteomics
level str peptide Analysis level for glycoproteomics
monte_carlo bool False Use Monte Carlo for technical variation
random_state int | numpy.random._generator.Generator | None None optional random state for reproducibility
Returns DataFrame DataFrame with log2FC, p-values, FDR-corrected p-values, and Cohen’s d/Mahalanobis distance effect sizes 
test_df = glycomics_data_loader.human_skin_O_PMC5871710_BCC

res = get_differential_expression(test_df, group1 = [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39],
                                  group2 = [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40], motifs = True, paired = True)
res
You're working with an alpha of 0.044390023979542614 that has been adjusted for your sample size of 40.
Glycan Mean abundance Log2FC p-val corr p-val significant corr Levene p-val Effect size Equivalence p-val
8 GalOS(b1-3)GalNAc 0.159900 -0.930987 0.000848 0.005641 True 0.940575 -0.884455 1.000000
0 H_antigen_type2 0.247156 -0.689804 0.002058 0.005641 True 0.940575 -0.797572 1.000000
1 Internal_LacNAc_type2 2.328696 0.467133 0.002552 0.005641 True 0.940575 0.776382 1.000000
9 GlcNAc6S(b1-6)GalNAc 1.046247 0.894961 0.002820 0.005641 True 0.940575 0.766511 1.000000
12 Neu5Ac(a2-3)Gal 12.345115 0.250839 0.004655 0.007449 True 0.940575 0.716794 1.000000
2 Terminal_LacNAc_type2 2.440640 -0.475258 0.007864 0.010485 True 0.940575 -0.664152 1.000000
14 Neu5Ac(a2-8)Neu5Ac 0.038663 -0.635598 0.018773 0.021455 True 0.940575 -0.574518 1.000000
10 Neu5Ac 16.553504 0.165081 0.048618 0.048618 False 0.940575 0.471179 1.000000
6 Gal 18.541780 0.111858 0.073880 0.073880 False 0.940575 0.422987 0.739562
4 Oglycan_core1 7.956860 0.162683 0.084723 0.084723 False 0.940575 0.406721 0.739562
11 Gal(b1-3)GalNAc 12.726196 0.095097 0.133433 0.133433 False 0.940575 0.350569 0.739562
7 GalNAc 12.886096 0.085435 0.168975 0.168975 False 0.940575 0.319743 0.739562
13 Neu5Ac(a2-6)GalNAc 4.169726 -0.118513 0.312657 0.312657 False 0.940575 -0.231928 0.739562
3 Disialyl_T_antigen 3.790085 -0.073129 0.543318 0.543318 False 0.940575 -0.138395 0.739562
5 Mucin_elongated_core2 4.769337 -0.012072 0.918651 0.918651 False 0.940575 -0.023143 0.739562

get_volcano

 get_volcano (df_res:pandas.core.frame.DataFrame|str|pathlib.Path,
              y_thresh:float=0.05, x_thresh:float=0, n:int|None=None,
              label_changed:bool=True, x_metric:str='Log2FC',
              annotate_volcano:bool=False, filepath:str='', **kwargs:Any)

Creates volcano plot showing -log10(FDR-corrected p-values) vs Log2FC or effect size

Type Default Details
df_res pandas.core.frame.DataFrame | str | pathlib.Path DataFrame from get_differential_expression with columns [Glycan, Log2FC, p-val, corr p-val]
y_thresh float 0.05 Corrected p threshold for labeling
x_thresh float 0 Absolute x metric threshold for labeling
n int | None None Sample size for Bayesian-Adaptive Alpha
label_changed bool True Add text labels to significant points
x_metric str Log2FC x-axis metric: ‘Log2FC’ or ‘Effect size’
annotate_volcano bool False Annotate dots with SNFG images
filepath str Path to save plot
kwargs Any
Returns None Displays volcano plot 
get_volcano(res)
You're working with a default alpha of 0.05. Set sample size (n = ...) for Bayesian-Adaptive Alpha Adjustment

get_coverage

 get_coverage (df:pandas.core.frame.DataFrame|str|pathlib.Path,
               filepath:str='')

Visualizes glycan detection frequency across samples with intensity-based ordering

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (col 1), abundances in columns
filepath str Path to save plot
Returns None 
test_df = pd.concat([test_df.iloc[:, 0], test_df[test_df.columns[1:]].astype(float)], axis = 1)

get_coverage(test_df)

get_pca

 get_pca (df:pandas.core.frame.DataFrame|str|pathlib.Path,
          groups:list[int]|pandas.core.frame.DataFrame|None=None,
          motifs:bool=False, feature_set:list[str]=['known',
          'exhaustive'], pc_x:int=1, pc_y:int=2, color:str|None=None,
          shape:str|None=None, size:str|None=None,
          filepath:str|pathlib.Path='', custom_motifs:list[str]=[],
          transform:str|None=None, rarity_filter:float=0.05)

Performs PCA on glycan/motif abundance data with group-based visualization

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (col 1), abundances in columns
groups list[int] | pandas.core.frame.DataFrame | None None Group labels (e.g., [1,1,1,2,2,2,3,3,3]) or metadata DataFrame with ‘id’ column
motifs bool False Analyze motifs instead of sequences
feature_set list [‘known’, ‘exhaustive’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
pc_x int 1 Principal component for x-axis
pc_y int 2 Principal component for y-axis
color str | None None Column in metadata for color grouping; recommended to be categorical
shape str | None None Column in metadata for shape grouping; recommended to be categorical
size str | None None Column in metadata for point size control; recommended to be scalar
filepath str | pathlib.Path Path to save plot
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
rarity_filter float 0.05 Min proportion for non-zero values
Returns None 
get_pca(test_df, motifs = True, groups = [1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2])

get_pval_distribution

 get_pval_distribution
                        (df_res:pandas.core.frame.DataFrame|str|pathlib.Pa
                        th, filepath:str|pathlib.Path='')

Creates histogram of p-values from differential expression analysis

Type Default Details
df_res pandas.core.frame.DataFrame | str | pathlib.Path Output DataFrame from get_differential_expression
filepath str | pathlib.Path Path to save plot
Returns None 
get_pval_distribution(res)

get_ma

 get_ma (df_res:pandas.core.frame.DataFrame|str|pathlib.Path,
         log2fc_thresh:int=1, sig_thresh:float=0.05,
         filepath:str|pathlib.Path='')

Generates MA plot (mean abundance vs log2 fold change) from differential expression results

Type Default Details
df_res pandas.core.frame.DataFrame | str | pathlib.Path Output DataFrame from get_differential_expression
log2fc_thresh int 1 Log2FC threshold for highlighting
sig_thresh float 0.05 Significance threshold for highlighting
filepath str | pathlib.Path Path to save plot
Returns None 
get_ma(res)

get_glycanova

 get_glycanova (df:pandas.core.frame.DataFrame|str|pathlib.Path,
                groups:list[typing.Any], impute:bool=True,
                motifs:bool=False, feature_set:list[str]=['exhaustive',
                'known'], min_samples:float=0.1, posthoc:bool=True,
                custom_motifs:list[str]=[], transform:str|None=None,
                gamma:float=0.1, custom_scale:float=0, random_state:int|nu
                mpy.random._generator.Generator|None=None)

Performs one-way ANOVA with omega-squared effect size calculation and optional Tukey’s HSD post-hoc testing on glycomics data across multiple groups

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (col 1) and abundance values in columns
groups list Group labels for samples (e.g., [1,1,1,2,2,2,3,3,3])
impute bool True Replace zeros with Random Forest model
motifs bool False Analyze motifs instead of sequences
feature_set list [‘exhaustive’, ‘known’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
min_samples float 0.1 Min percent of non-zero samples required
posthoc bool True Perform Tukey’s HSD test post-hoc
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
custom_scale float 0 Ratio of total signal in group2/group1 for an informed scale model (or group_idx: mean(group)/min(mean(groups)) signal dict for multivariate)
random_state int | numpy.random._generator.Generator | None None optional random state for reproducibility
Returns tuple (ANOVA results with F-stats and omega-squared effect sizes, post-hoc results) 
test_df2 = glycomics_data_loader.HIV_gagtransfection_O_PMID35112714

anv, ph = get_glycanova(test_df2, [1,1,1,1,2,2,2,2,3,3,3,3], motifs = False)
anv
You're working with an alpha of 0.06364810000741428 that has been adjusted for your sample size of 12.
Glycan F statistic p-val corr p-val significant Effect size
0 Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 3.337354 0.082356 0.255515 False 0.182074
1 Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc 2.264846 0.159697 0.255515 False 0.107511
2 Neu5Ac(a2-3)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]Ga... 2.987894 0.101120 0.255515 False -0.097792
3 Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 4.902553 0.036295 0.255515 False 0.159186
7 Neu5Ac(a2-3)Gal(b1-4)GlcNAc6S(b1-6)[Neu5Ac(a2-... 2.442388 0.142124 0.255515 False -0.072202
4 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3/6)[GlcNAc(b1-... 1.346603 0.307886 0.368114 False 0.270963
5 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 1.288259 0.322100 0.368114 False 0.031955
6 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Neu5Ac(a2-3)... 0.292927 0.752927 0.752927 False 0.026720
8 Neu5Ac(a2-3)Gal(b1-3)GalNAc 0.000000 1.000000 1.000000 False 0.120779

get_meta_analysis

 get_meta_analysis (effect_sizes:numpy.ndarray|list[float],
                    variances:numpy.ndarray|list[float],
                    model:str='fixed', filepath:str='',
                    study_names:list[str]=[])

Performs fixed/random effects meta-analysis using DerSimonian-Laird method for between-study variance estimation, with optional Forest plot visualization

Type Default Details
effect_sizes numpy.ndarray | list[float] List of Cohen’s d/other effect sizes
variances numpy.ndarray | list[float] Associated variance estimates
model str fixed ‘fixed’ or ‘random’ effects model
filepath str Path to save Forest plot
study_names list [] Names corresponding to each effect size
Returns tuple (combined effect size, two-tailed p-value) 
get_meta_analysis([-8.759, -6.363, -5.199, -3.952],
                 [7.061, 4.041, 2.919, 1.968])
(-5.326913553837341, 3.005077298112724e-09)

get_time_series

 get_time_series (df:pandas.core.frame.DataFrame|str|pathlib.Path,
                  impute:bool=True, motifs:bool=False,
                  feature_set:list[str]=['known', 'exhaustive'],
                  degree:int=1, min_samples:float=0.1,
                  custom_motifs:list[str]=[], transform:str|None=None,
                  gamma:float=0.1, custom_scale:float|dict=0)

Analyzes time series glycomics data using polynomial regression

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with sample IDs as ‘sampleID_timepoint_replicate’ in col 1 (e.g., T1_h5_r1)
impute bool True Replace zeros with Random Forest model
motifs bool False Analyze motifs instead of sequences
feature_set list [‘known’, ‘exhaustive’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
degree int 1 Polynomial degree for regression
min_samples float 0.1 Min percent of non-zero samples required
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
custom_scale float | dict 0 Ratio of total signal in group2/group1 for an informed scale model (or group_idx: mean(group)/min(mean(groups)) signal dict for multivariate)
Returns DataFrame DataFrame with regression coefficients and FDR-corrected p-values 
t_dic = {}
t_dic["ID"] = ["D1_h5_r1", "D1_h5_r2", "D1_h5_r3", "D1_h10_r1", "D1_h10_r2", "D1_h10_r3", "D1_h15_r1", "D1_h15_r2", "D1_h15_r3"]
t_dic["Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc"] = [0.33, 0.31, 0.35, 1.51, 1.57, 1.66, 2.11, 2.04, 2.09]
t_dic["Fuc(a1-2)Gal(b1-3)GalNAc"] = [0.78, 1.01, 0.98, 0.88, 1.11, 0.72, 1.22, 1.00, 0.54]
t_dic["Neu5Ac(a2-6)GalNAc"] = [0.11, 0.09, 0.14, 0.02, 0.07, 0.10, 0.11, 0.09, 0.08]
get_time_series(pd.DataFrame(t_dic).set_index("ID").T)
You're working with an alpha of 0.0694557066556809 that has been adjusted for your sample size of 9.
Glycan Change p-val corr p-val significant
0 Fuc(a1-2)Gal(b1-3)GalNAc -0.009300 0.415220 0.633796 False
1 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 0.005395 0.422530 0.633796 False
2 Neu5Ac(a2-6)GalNAc -0.001835 0.843457 0.843457 False

get_jtk

 get_jtk (df_in:pandas.core.frame.DataFrame|str|pathlib.Path,
          timepoints:int, interval:int, periods:list[int]=[12, 24],
          motifs:bool=False, feature_set:list[str]=['known', 'exhaustive',
          'terminal'], custom_motifs:list[str]=[],
          transform:str|None=None, gamma:float=0.1,
          correction_method:str='two-stage')

Identifies rhythmically expressed glycans using Jonckheere-Terpstra-Kendall algorithm for time series analysis

Type Default Details
df_in pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (first column), then groups arranged by ascending timepoints
timepoints int Number of timepoints (each must have same number of replicates)
interval int Time units between experimental timepoints
periods list [12, 24] Timepoints per cycle to test
motifs bool False Analyze motifs instead of sequences
feature_set list [‘known’, ‘exhaustive’, ‘terminal’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
correction_method str two-stage Multiple testing correction method
Returns DataFrame DataFrame with JTK results: adjusted p-values, period length, lag phase, amplitude 
t_dic = {}
t_dic["Neu5Ac(a2-3)Gal(b1-3)GalNAc"] = [0.433138901, 0.149729209, 0.358018822, 0.537641256, 1.526963756, 1.349986672, 0.75156406, 0.736710183]
t_dic["Gal(b1-3)GalNAc"] = [0.919762334, 0.760237184, 0.725566662, 0.459945797, 0.523801515, 0.695106926, 0.627632047, 1.183511209]
t_dic["Gal(b1-3)[Neu5Ac(a2-6)]GalNAc"] = [0.533138901, 0.119729209, 0.458018822, 0.637641256, 1.726963756, 1.249986672, 0.55156406, 0.436710183]
t_dic["Fuc(a1-2)Gal(b1-3)GalNAc"] = [3.862169504, 5.455032837, 3.858163289, 5.614650335, 3.124254095, 4.189550337, 4.641831312, 4.19538484]
tps = 8  # number of timepoints in experiment
periods = [8]  # potential cycles to test
interval = 3  # units of time between experimental timepoints
t_df = pd.DataFrame(t_dic).T
t_df.columns = ["T3", "T6", "T9", "T12", "T15", "T18", "T21", "T24"]
get_jtk(t_df.reset_index(), tps, interval, periods = periods)
You're working with an alpha of 0.22004505213567527 that has been adjusted for your sample size of 1.
Significance inflation detected. The CLR/ALR transformation possibly cannot handle this dataset. Consider running again with a higher gamma value.             Proceed with caution; for now switching to Bonferroni correction to be conservative about this.
Molecule_Name Adjusted_P_value Period_Length Lag_Phase Amplitude significant
0 Gal(b1-3)GalNAc 0.037499 8 12 0.785714 False
1 Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 0.037499 8 12 0.785714 False
2 Neu5Ac(a2-3)Gal(b1-3)GalNAc 0.431049 8 9 0.500000 False
3 Fuc(a1-2)Gal(b1-3)GalNAc 0.694185 8 9 0.428571 False 
get_jtk(t_df.reset_index(), tps, interval, periods = periods, motifs = True, feature_set = ['terminal'])
You're working with an alpha of 0.22004505213567527 that has been adjusted for your sample size of 1.
Molecule_Name Adjusted_P_value Period_Length Lag_Phase Amplitude significant
0 Terminal_Gal(b1-3) 0.059080 8 3 0.642857 True
1 Terminal_Neu5Ac(a2-6) 0.059080 8 12 0.642857 True
2 Terminal_Neu5Ac(a2-?) 0.059080 8 12 0.714286 True
3 Terminal_Neu5Ac(a2-3) 0.216933 8 9 0.428571 True
4 Terminal_Fuc(a1-2) 0.386476 8 3 0.285714 False

get_biodiversity

 get_biodiversity (df:pandas.core.frame.DataFrame|str|pathlib.Path,
                   group1:list[str|int], group2:list[str|int],
                   metrics:list[str]=['alpha', 'beta'], motifs:bool=False,
                   feature_set:list[str]=['exhaustive', 'known'],
                   custom_motifs:list[str]=[], paired:bool=False,
                   permutations:int=999, transform:str|None=None,
                   gamma:float=0.1, custom_scale:float|dict=0, random_stat
                   e:int|numpy.random._generator.Generator|None=None)

Calculates alpha (Shannon/Simpson) and beta (ANOSIM/PERMANOVA) diversity measures from glycomics data

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (col 1), abundances in columns
group1 list First group column indices or group labels
group2 list Second group indices or additional group labels
metrics list [‘alpha’, ‘beta’] Diversity metrics to calculate
motifs bool False Analyze motifs instead of sequences
feature_set list [‘exhaustive’, ‘known’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
custom_motifs list [] Custom motifs if using ‘custom’ feature set
paired bool False Whether samples are paired
permutations int 999 Number of permutations for ANOSIM/PERMANOVA
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
custom_scale float | dict 0 Ratio of total signal in group2/group1 for an informed scale model (or group_idx: mean(group)/min(mean(groups)) signal dict for multivariate)
random_state int | numpy.random._generator.Generator | None None optional random state for reproducibility
Returns DataFrame DataFrame with diversity indices and test statistics 
res = get_biodiversity(test_df, group1 = [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39],
                                  group2 = [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40], motifs = True, paired = True)
res
You're working with an alpha of 0.044390023979542614 that has been adjusted for your sample size of 40.
Metric Group1 mean Group2 mean p-val Effect size corr p-val significant
0 simpson_diversity 0.876756 0.874348 0.000443 -0.948203 0.000443 True
1 shannon_diversity 2.244523 2.225758 0.001255 -0.846077 0.001255 True
2 Beta diversity (PERMANOVA) NaN NaN 0.003003 43.641775 0.003003 True
3 Beta diversity (ANOSIM) NaN NaN 0.004004 0.146395 0.004004 True
4 species_richness 15.000000 15.000000 1.000000 0.000000 1.000000 False

get_SparCC

 get_SparCC (df1:pandas.core.frame.DataFrame|str|pathlib.Path,
             df2:pandas.core.frame.DataFrame|str|pathlib.Path,
             motifs:bool=False, feature_set:list[str]=['known',
             'exhaustive'], custom_motifs:list[str]=[],
             transform:str|None=None, gamma:float=0.1,
             partial_correlations:bool=False)

Calculates SparCC (Sparse Correlations for Compositional Data) between two matching datasets (e.g., glycomics)

Type Default Details
df1 pandas.core.frame.DataFrame | str | pathlib.Path First DataFrame with glycans in rows (col 1) and abundances in columns
df2 pandas.core.frame.DataFrame | str | pathlib.Path Second DataFrame with same format as df1
motifs bool False Analyze motifs instead of sequences
feature_set list [‘known’, ‘exhaustive’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
partial_correlations bool False Use regularized partial correlations
Returns tuple (Spearman correlation matrix, FDR-corrected p-value matrix) 
df1 = glycomics_data_loader.time_series_N_PMID32149347
df2 = glycomics_data_loader.time_series_O_PMID32149347
df1 = pd.merge(df1, df2[['ID']], on = 'ID', how = 'inner')
df2 = pd.merge(df2, df1[['ID']], on = 'ID', how = 'inner')
df1 = df1.set_index(df1.columns.tolist()[0]).T.reset_index()
df2 = df2.set_index(df2.columns.tolist()[0]).T.reset_index()

corr, pval = get_SparCC(df1, df2, motifs = True, transform = "CLR")
sns.clustermap(corr)
You're working with an alpha of 0.04787928055709467 that has been adjusted for your sample size of 31.

get_roc

 get_roc (df:pandas.core.frame.DataFrame|str|pathlib.Path,
          group1:list[str|int], group2:list[str|int], motifs:bool=False,
          feature_set:list[str]=['known', 'exhaustive'],
          paired:bool=False, impute:bool=True, min_samples:float=0.1,
          custom_motifs:list[str]=[], transform:str|None=None,
          gamma:float=0.1, custom_scale:float|dict=0,
          filepath:str|pathlib.Path='', multi_score:bool=False,
          random_state:int|numpy.random._generator.Generator|None=None)

Calculates ROC curves and AUC scores for glycans/motifs or multi-glycan classifiers

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with glycans in rows (col 1), abundances in columns
group1 list First group indices/names
group2 list Second group indices/names
motifs bool False Analyze motifs instead of sequences
feature_set list [‘known’, ‘exhaustive’] Feature sets to use; exhaustive, known, terminal1, terminal2, terminal3, chemical, graph, custom, size_branch
paired bool False Whether samples are paired
impute bool True Replace zeros with Random Forest model
min_samples float 0.1 Min percent of non-zero samples required
custom_motifs list [] Custom motifs if using ‘custom’ feature set
transform str | None None Transformation type: “CLR” or “ALR”
gamma float 0.1 Uncertainty parameter for CLR transform
custom_scale float | dict 0 Ratio of total signal in group2/group1 for an informed scale model (or group_idx: mean(group)/min(mean(groups)) signal dict for multivariate)
filepath str | pathlib.Path Path to save ROC plot
multi_score bool False Find best multi-glycan score
random_state int | numpy.random._generator.Generator | None None optional random state for reproducibility
Returns list[tuple[str, float]] | dict[typing.Any, tuple[str, float]] | tuple[sklearn.linear_model._logistic.LogisticRegression, float] (Feature scores with ROC AUC values) 
get_roc(test_df, group1 = [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39],
                                  group2 = [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40], motifs = True, paired = True)
[('GlcNAc6S(b1-6)GalNAc', 0.7599999999999999),
 ('Internal_LacNAc_type2', 0.715),
 ('Neu5Ac(a2-3)Gal', 0.6699999999999999),
 ('Neu5Ac', 0.65),
 ('Oglycan_core1', 0.6224999999999999),
 ('Gal', 0.6),
 ('Gal(b1-3)GalNAc', 0.5874999999999999),
 ('GalNAc', 0.585),
 ('Mucin_elongated_core2', 0.4975),
 ('Disialyl_T_antigen', 0.465),
 ('Neu5Ac(a2-6)GalNAc', 0.45),
 ('Neu5Ac(a2-8)Neu5Ac', 0.37000000000000005),
 ('H_antigen_type2', 0.2625),
 ('Terminal_LacNAc_type2', 0.2625),
 ('GalOS(b1-3)GalNAc', 0.2475)]

get_lectin_array

 get_lectin_array (df:pandas.core.frame.DataFrame|str|pathlib.Path,
                   group1:list[str|int], group2:list[str|int],
                   paired:bool=False, transform:str='')

Analyzes lectin microarray data by mapping lectin binding patterns to glycan motifs, calculating Cohen’s d effect sizes between groups and clustering results by significance

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with samples as rows and lectins as columns, first column containing sample IDs
group1 list First group indices/names
group2 list Second group indices/names
paired bool False Whether samples are paired
transform str Optional log2 transformation
Returns DataFrame DataFrame with altered glycan motifs, supporting lectins, and effect sizes 
lectin_df = lectin_array_data_loader.A549_influenza_PMID33046650
get_lectin_array(lectin_df, [5,6,7], [8,9,10])
Lectin "Ab-LeB-1" is not found in our annotated lectin library and is excluded from analysis.
Lectin "APA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "APP" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Blood Group B [CLCP-19B]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Blood Group H2" is not found in our annotated lectin library and is excluded from analysis.
Lectin "CA19-9 [121SLE]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "CCA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "CD15 [ICRF29-2]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "CD15 [MY-1]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "CD15 [SP-159]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Forssman" is not found in our annotated lectin library and is excluded from analysis.
Lectin "IAA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "IRA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Le X [P12]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Lewis A [7LE]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Lewis B [218]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "Lewis Y [F3]" is not found in our annotated lectin library and is excluded from analysis.
Lectin "LFA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "LPA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "MNA-M " is not found in our annotated lectin library and is excluded from analysis.
Lectin "MUC5Ac Ab" is not found in our annotated lectin library and is excluded from analysis.
Lectin "PMA" is not found in our annotated lectin library and is excluded from analysis.
Lectin "PTA_1" is not found in our annotated lectin library and is excluded from analysis.
Lectin "PTA_2" is not found in our annotated lectin library and is excluded from analysis.
Lectin "SNA-S" is not found in our annotated lectin library and is excluded from analysis.
Lectin "SNA-V" is not found in our annotated lectin library and is excluded from analysis.
Lectin "VFA" is not found in our annotated lectin library and is excluded from analysis.
motif named_motifs lectin(s) change score significance
39 Neu5Ac(a2-6)Gal(b1-3)GlcNAc [Internal_LacNAc_type1] PSL, SNA, TJA-I, BDA, BPA, WGA_1, WGA_2 down 11.32 highly significant
38 Neu5Ac(a2-6)Gal(b1-4)GlcNAc [Internal_LacNAc_type2] PSL, SNA, TJA-I, BDA, BPA, ECA, RCA120, Ricin ... down 10.81 highly significant
7 Man(a1-2) [] ASA, Con A, CVN, HHL, SVN_1, GRFT, SVN_2, SNA-... up 4.83 moderately significant
14 Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc... [Chitobiose, Trimannosylcore, Terminal_LacNAc_... CA, CAA, DSA_1, DSA_2, DSA_3, AMA, BDA, BPA, C... up 3.51 moderately significant
4 Gal(b1-3)GalNAc [Oglycan_core1] ACA, AIA, MPA, PNA_1, PNA_2, BDA, BPA up 3.48 moderately significant
43 Neu5Ac(a2-6)GalNAc(b1-4)GlcNAc [Internal_LacdiNAc_type2] SNA, CSA, SBA, VVA_1, VVA_2, WFA, BPA, ECA, ST... down 2.86 moderately significant
10 Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-4)][G... [Chitobiose, Trimannosylcore, Terminal_LacNAc_... Blackbean, Calsepa, PHA-E_1, PHA-E_2, AMA, BDA... up 2.70 moderately significant
16 Fuc(a1-2)Gal(b1-3)GalNAc(b1-4)[Neu5Ac(a2-3)]Ga... [Internal_LacNAc_type2, H_type3] Cholera Toxin, AAA, AAL, ACA, AIA, AOL, BDA, B... up 2.51 moderately significant
15 Gal(b1-3)GalNAc(b1-4)[Neu5Ac(a2-3)]Gal(b1-4)Gl... [Internal_LacNAc_type2] Cholera Toxin, ACA, AIA, BDA, BPA, CSA, ECA, L... up 2.46 moderately significant
47 GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)]Ma... [Chitobiose, Trimannosylcore, core_fucose] TL, AAL, AMA, AOL, Con A, GNA, GNL, HHL, LcH, ... up 2.36 moderately significant
18 Man(a1-6) [] Con A, GNA, GNL, HHL, NPA, SNA-II, UDA up 2.30 moderately significant
17 Man(a1-3) [] Con A, GNA, GNL, HHL, NPA, SNA-II, UDA up 2.30 moderately significant
22 Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Ma... [Chitobiose, Trimannosylcore, Terminal_LacNAc_... DSA_1, DSA_2, DSA_3, AMA, BDA, Blackbean, BPA,... up 2.05 moderately significant
46 Fuc(a1-2)Gal(b1-3)GalNAc [H_type3, Oglycan_core1] TJA-II, AAA, AAL, ACA, AIA, AOL, BDA, BPA, MPA... up 1.96 moderately significant
3 Fuc(a1-6) [] AAL, AOL, LcH, PSA up 1.70 moderately significant
34 Neu5Ac(a2-3)Gal(b1-3)GalNAc [Oglycan_core1] MAL-II, ACA, AIA, BDA, BPA, MPA, PNA_1, PNA_2,... up 1.59 moderately significant
6 Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc [Chitobiose, Trimannosylcore] AMA, Con A, GNA, GNL, HHL, NPA, SNA-II, UDA, W... up 1.58 moderately significant
11 GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)[GlcNAc(b1-6... [Chitobiose, Trimannosylcore] Blackbean, PHA-L, AMA, Con A, GNA, GNL, HHL, N... up 1.44 moderately significant
42 GlcNAc(b1-2)[GlcNAc(b1-6)]Man(a1-6)[GlcNAc(b1-... [Chitobiose, Trimannosylcore, bisectingGlcNAc] RPA, AMA, Blackbean, Con A, GNA, GNL, HHL, NPA... up 1.40 moderately significant
41 GlcNAc(b1-2)[GlcNAc(b1-4)]Man(a1-3)[GlcNAc(b1-... [Chitobiose, Trimannosylcore, bisectingGlcNAc] RPA, AMA, Con A, GNA, GNL, HHL, NPA, SNA-II, U... up 1.36 moderately significant
23 Gal(b1-4)GlcNAc [Terminal_LacNAc_type2] ECA, RCA120, Ricin B Chain, SJA, BDA, BPA up 1.05 low significance
5 GlcNAc(b1-3)GalNAc [Oglycan_core3] AIA, UEA-II, WGA_1, WGA_2 up 0.86 low significance
26 Gal(a1-3) [] GS-I_1, GS-I_2, GS-I_3, GS-I_4, MNA-G, PA-IL up 0.83 low significance
27 Gal(a1-4) [] GS-I_1, GS-I_2, GS-I_3, GS-I_4, MNA-G, PA-IL up 0.83 low significance
30 Gal(b1-4)GlcNAc(b1-3) [Terminal_LacNAc_type2] LEA_1, LEA_2, STA, BDA, BPA, ECA, RCA120, Rici... up 0.54 low significance
25 Gal(a1-3)Gal [] EEA, EEL, MOA, GS-I_1, GS-I_2, GS-I_3, GS-I_4,... up 0.51 low significance
33 Neu5Ac(a2-3)Gal(b1-4)GlcNAc [Internal_LacNAc_type2] MAA_1, MAA_2, MAL-I, BDA, BPA, ECA, RCA120, Ri... up 0.49 low significance
37 Gal(a1-3)GalNAc [] MOA, EEA, EEL, GS-I_1, GS-I_2, GS-I_3, GS-I_4,... up 0.46 low significance
20 GalNAc(a1-4) [] GHA, HAA, HPA, CSA, GS-I_1, GS-I_2, GS-I_3, GS... up 0.39 low significance
19 GalNAc(a1-3) [] GHA, HAA, HPA, CSA, GS-I_1, GS-I_2, GS-I_3, GS... up 0.39 low significance
21 GalNAc(a1-3)GalNAc(b1-3) [] DBA, SBA, CSA, GHA, HAA, HPA, VVA_1, VVA_2, WF... up 0.25 low significance
24 GalNAc(b1-4)GlcNAc [Terminal_LacdiNAc_type2] ECA, STA, CSA, SBA, VVA_1, VVA_2, WFA, BPA, WG... up 0.20 low significance
44 Fuc(a1-2)Gal(b1-4)GalNAc(b1-3) [] SNA-II, AAA, AAL, AOL, BDA, BPA, CSA, SBA, VVA... up 0.16 low significance
13 GalNAc(b1-4) [] CSA, SBA, VVA_1, VVA_2, WFA, BPA, WGA_1, WGA_2 up 0.13 low significance
12 GalNAc(b1-3) [] CSA, SBA, VVA_1, VVA_2, WFA, BPA, WGA_1, WGA_2 up 0.13 low significance
40 Fuc(a1-2)Gal(b1-4)GlcNAc [H_antigen_type2, Internal_LacNAc_type2] PTL-II, TJA-II, UEA-I, UEA-II, AAA, AAL, AOL, ... up 0.13 low significance
28 GlcNAc(a1-3) [] HAA, HPA, WGA_1, WGA_2 up 0.12 low significance
29 GlcNAc(a1-4) [] HAA, HPA, WGA_1, WGA_2 up 0.12 low significance
32 Gal3S(b1-4)GlcNAc [] MAA_1, MAA_2, MAL-I, MAL-II down 0.12 low significance
0 Fuc(a1-2) [] AAA, AAL, AOL up 0.09 low significance
36 Gal3S(b1-4) [] MAL-II down 0.08 low significance
35 Gal3S(b1-3) [] MAL-II down 0.08 low significance
49 Fuc(a1-2)Gal(b1-4)GalNAc [] UEA-II, AAA, AAL, AOL, BDA, BPA up 0.07 low significance
9 Gal(b1-4) [] BDA, BPA up 0.05 low significance
8 Gal(b1-3) [] BDA, BPA up 0.05 low significance
1 Fuc(a1-3) [] AAL, AOL, Lotus down 0.03 low significance
2 Fuc(a1-4) [] AAL, AOL down 0.03 low significance
31 GlcNAc(b1-4)GlcNAc(b1-4) [Chitobiose] LEA_1, LEA_2, WGA_1, WGA_2 down 0.01 low significance
50 GlcNAc(b1-3) [] WGA_1, WGA_2 down 0.01 low significance
51 GlcNAc(b1-4) [] WGA_1, WGA_2 down 0.01 low significance
45 GlcNAc(b1-4)GlcNAc(b1-4)GlcNAc(b1-4) [Chitobiose] STA, LEA_1, LEA_2, WGA_1, WGA_2 down 0.00 low significance
48 GlcNAc(b1-3)Gal [] UEA-II, WGA_1, WGA_2 up 0.00 low significance
52 Neu5Ac(a2-3) [] WGA_1, WGA_2 down 0.00 low significance
53 Neu5Ac(a2-6) [] WGA_1, WGA_2 down 0.00 low significance
54 Neu5Ac(a2-8) [] WGA_1, WGA_2 down 0.00 low significance

get_glycoshift_per_site

 get_glycoshift_per_site (df:pandas.core.frame.DataFrame|str|pathlib.Path,
                          group1:list[str|int], group2:list[str|int],
                          paired:bool=False, impute:bool=True,
                          min_samples:float=0.2, gamma:float=0.1,
                          custom_scale:float|dict=0, random_state:int|nump
                          y.random._generator.Generator|None=None)

Analyzes site-specific glycosylation changes in glycoproteomics data using generalized linear models (GLM) with compositional data normalization

Type Default Details
df pandas.core.frame.DataFrame | str | pathlib.Path DataFrame with rows formatted as ‘protein_site_composition’ in col 1, abundances in remaining cols
group1 list First group indices/names or group labels for multi-group
group2 list Second group indices/names
paired bool False Whether samples are paired
impute bool True Replace zeros with Random Forest model
min_samples float 0.2 Min percent of non-zero samples required
gamma float 0.1 Uncertainty parameter for CLR transform
custom_scale float | dict 0 Ratio of total signal in group2/group1 for an informed scale model (or group_idx: mean(group)/min(mean(groups)) signal dict for multivariate)
random_state int | numpy.random._generator.Generator | None None optional random state for reproducibility
Returns DataFrame DataFrame with GLM coefficients and FDR-corrected p-values 
df_milk = glycoproteomics_data_loader.human_milk_N_PMID34087070

get_glycoshift_per_site(df_milk, ['Colostrum1', 'Colostrum2', 'Colostrum3'], ['Mature1', 'Mature2', 'Mature3'])
You're working with an alpha of 0.07862467893233027 that has been adjusted for your sample size of 6.
Condition_coefficient Condition_corr_pval Condition_significant antennary_Fuc_Condition_coefficient antennary_Fuc_Condition_corr_pval antennary_Fuc_Condition_significant dHex_Condition_coefficient dHex_Condition_corr_pval dHex_Condition_significant complex_Condition_coefficient ... high_Man_Condition_significant hybrid_Condition_coefficient hybrid_Condition_corr_pval hybrid_Condition_significant Neu5Ac_Condition_coefficient Neu5Ac_Condition_corr_pval Neu5Ac_Condition_significant Hex_Condition_coefficient Hex_Condition_corr_pval Hex_Condition_significant
sp|P47710|CASA1_69 0.351306 0.000000e+00 True 0.000000 1.000000e+00 False 3.651421 0.000000e+00 True 0.000000 ... False 0.351306 0.000000e+00 True 0.351306 0.000000e+00 True -1.543583 0.000000e+00 True
sp|P01024|CO3_85 -13.741464 0.000000e+00 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... True -13.741464 0.000000e+00 True 0.000000 1.000000e+00 False 12.821387 0.000000e+00 True
sp|P10909|CLUS_103 -0.148812 0.000000e+00 True 0.000000 1.000000e+00 False -0.148812 0.000000e+00 True 4.461066 ... False -4.609878 0.000000e+00 True 4.312254 0.000000e+00 True -0.744062 0.000000e+00 True
sp|Q13410|BT1A1_55 -13.032160 1.632815e-86 True -13.621108 2.312512e-126 True 0.530115 4.007150e-01 False -4.158213 ... False -8.873947 1.136641e-42 True -17.190373 1.192223e-132 True -0.631943 1.290904e-18 True
sp|P01011|AACT_106 -0.027180 8.973881e-15 True 2.529792 0.000000e+00 True 2.502612 0.000000e+00 True -2.556972 ... False 2.529792 0.000000e+00 True -2.584152 0.000000e+00 True -0.135901 8.973881e-15 True
sp|P00709|LALBA_90 -1.256621 1.737034e-08 True -1.295395 5.342502e-01 False 7.335961 2.957124e-28 True -0.554787 ... False -0.701834 4.151344e-01 False -1.811408 4.231494e-06 True 3.643645 1.656325e-06 True
sp|P02749|APOH_253 -0.002492 2.356643e-06 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False -0.002492 ... False 0.000000 1.000000e+00 False -0.004984 3.167328e-06 True -0.012460 2.356643e-06 True
sp|P00738|HPT_241 0.001144 1.133736e-04 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.001144 ... False 0.000000 1.000000e+00 False 0.002289 1.243870e-04 True 0.005722 1.133736e-04 True
sp|P02765|FETUA_156 -0.002672 3.394518e-03 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False -0.002672 ... False 0.000000 1.000000e+00 False -0.005343 3.666080e-03 True -0.013358 3.394518e-03 True
sp|P01871|IGHM_46 -0.000319 7.588850e-02 True 0.000000 1.000000e+00 False -0.000319 1.214216e-01 False 0.000000 ... False -0.000319 9.443902e-02 False -0.000319 5.604074e-02 True -0.001594 7.588850e-02 True
sp|Q08431|MFGM_238 0.153221 1.005956e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False 0.153221 1.239337e-01 False -0.315619 6.920843e-02 True -0.010198 7.085456e-01 False
sp|P25311|ZA2G_109 0.009796 1.359146e-01 False 0.000000 1.000000e+00 False 0.009796 2.283365e-01 False -0.263924 ... False 0.273720 7.532107e-02 True -0.254128 4.310116e-02 True 0.048980 1.482705e-01 False
sp|P08571|CD14_151 0.001329 3.366071e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False 0.001329 4.084166e-01 False 0.000000 1.000000e+00 False 0.007973 2.734933e-01 False
sp|P10909|CLUS_291 -0.000684 4.277733e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False -0.000684 4.471657e-01 False -0.000684 3.381925e-01 False -0.003422 3.522839e-01 False
sp|P06858|LIPL_70 -0.000554 5.525567e-01 False 0.000000 1.000000e+00 False -0.000554 6.630680e-01 False -0.000554 ... False 0.000000 1.000000e+00 False -0.001107 4.420453e-01 False -0.002768 4.362290e-01 False
sp|P07602|SAP_101 -0.001426 5.635031e-01 False 0.000000 1.000000e+00 False -0.001426 6.731984e-01 False 0.000000 ... False -0.001426 6.311235e-01 False -0.001426 4.555478e-01 False -0.007129 4.508025e-01 False
sp|P02788|TRFL_156 -3.509734 6.172068e-01 False 0.781505 9.117647e-01 False 4.237603 3.139568e-04 True -0.558968 ... False -2.950766 4.471657e-01 False 0.726250 6.386663e-01 False 2.716540 2.734301e-01 False
sp|P02788|TRFL_497 0.282217 6.343135e-01 False 1.366091 9.117647e-01 False -1.143372 6.991406e-01 False -11.362849 ... False -13.834092 5.356118e-07 True -2.046556 2.146705e-01 False 1.888504 2.734301e-01 False
sp|P07602|SAP_215 -0.001385 6.588839e-01 False 0.000000 1.000000e+00 False -0.001385 8.235294e-01 False 0.000000 ... False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False -0.002770 6.108151e-01 False
sp|P10909|CLUS_374 0.000707 6.588839e-01 False 0.000707 9.117647e-01 False 0.001414 8.235294e-01 False 0.000000 ... False 0.000707 8.058303e-01 False 0.000707 6.386663e-01 False 0.003535 6.120552e-01 False
sp|P0C0L5|CO4B_HUMAN/sp|P0C0L4|CO4A 0.000433 6.588839e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False 0.000433 7.456105e-01 False 0.000000 1.000000e+00 False 0.003901 6.108151e-01 False
sp|P02790|HEMO_453 0.000962 6.588839e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000962 ... False 0.000000 1.000000e+00 False 0.001924 6.331523e-01 False 0.004810 6.108151e-01 False
sp|P01876|IGHA1_340 4.689804 6.588839e-01 False 0.000000 1.000000e+00 False -2.429746 3.616176e-01 False -4.685317 ... False -2.193530 6.529675e-01 False 4.191721 3.152153e-02 True -0.676016 6.927812e-01 False
sp|P0DOX2|IGA2_HUMAN/sp|P01877|IGHA2 -1.970855 6.754175e-01 False 0.000000 1.000000e+00 False -1.350375 3.616176e-01 False -5.021428 ... False -3.149798 1.501009e-01 False -2.029984 5.604074e-02 True 1.473677 2.656424e-01 False
sp|P07602|SAP_426 0.001044 6.862599e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False 0.001044 8.235294e-01 False 0.000000 1.000000e+00 False 0.005222 6.862599e-01 False
sp|P01877|IGHA2_327 -0.727920 7.386459e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False -0.727920 8.235294e-01 False 0.000000 1.000000e+00 False 0.217980 9.310474e-01 False
sp|P19652|A1AG2_HUMAN/sp|P02763|A1AG1 -0.000394 7.493023e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False -0.000394 ... False 0.000000 1.000000e+00 False -0.000788 7.493023e-01 False -0.001971 7.493023e-01 False
sp|P01833|PIGR_186 -0.001645 7.727110e-01 False 0.000000 1.000000e+00 False 0.025357 8.235294e-01 False -0.027002 ... False 0.025357 8.058303e-01 False -0.028648 6.514894e-01 False 0.017131 6.108151e-01 False
sp|P01833|PIGR_499 -2.947551 7.916424e-01 False 1.517432 9.117647e-01 False 2.930012 3.317594e-01 False -1.813514 ... False -2.813763 7.196543e-01 False -3.178778 1.651305e-01 False -0.871071 6.542124e-01 False
sp|Q08380|LG3BP_125 -0.000227 8.418318e-01 False 0.000000 1.000000e+00 False -0.000227 8.418318e-01 False 0.000000 ... False -0.000227 8.418318e-01 False -0.000227 8.418318e-01 False -0.001135 8.418318e-01 False
sp|P01591|IGJ_71 0.986725 8.438826e-01 False -1.068357 9.117647e-01 False 1.155134 3.037004e-01 False -0.341331 ... False 1.031008 6.529675e-01 False 0.292119 7.150544e-01 False -0.877081 4.362290e-01 False
sp|P01833|PIGR_421 0.364442 8.577440e-01 False 1.445412 9.117647e-01 False -0.486043 8.235294e-01 False 0.000000 ... False 0.364442 8.577440e-01 False 0.612043 4.889933e-01 False 0.129271 8.937029e-01 False
sp|P01833|PIGR_469 0.917714 8.697268e-01 False 8.789078 9.836502e-04 True -3.064088 1.096962e-02 True 14.562257 ... False 6.868686 7.890305e-02 False -4.112379 2.455828e-02 True -2.408593 2.656424e-01 False
sp|P10909|CLUS_86 -0.000104 9.648675e-01 False 0.000000 1.000000e+00 False -0.000104 9.648675e-01 False 0.000000 ... False -0.000104 9.648675e-01 False -0.000104 9.648675e-01 False -0.000518 9.648675e-01 False

34 rows × 27 columns

annotate

extract curated motifs, graph features, and sequence features from glycan sequences

annotate_glycan

 annotate_glycan (glycan:str|networkx.classes.digraph.DiGraph,
                  motifs:pandas.core.frame.DataFrame|None=None,
                  termini_list:list=[], gmotifs:list[networkx.classes.digr
                  aph.DiGraph]|None=None)

Counts occurrences of known motifs in a glycan structure using subgraph isomorphism

Type Default Details
glycan str | networkx.classes.digraph.DiGraph IUPAC-condensed glycan sequence or NetworkX graph
motifs pandas.core.frame.DataFrame | None None Motif dataframe (name + sequence); defaults to motif_list
termini_list list [] Monosaccharide positions: ‘terminal’, ‘internal’, or ‘flexible’
gmotifs list[networkx.classes.digraph.DiGraph] | None None Precalculated motif graphs for speed
Returns DataFrame DataFrame with motif counts for the glycan 
annotate_glycan("Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc")
motif_name Terminal_LewisX Internal_LewisX LewisY SialylLewisX SulfoSialylLewisX Terminal_LewisA Internal_LewisA LewisB SialylLewisA SulfoLewisA ... Mucin_elongated_core2 Fucoidan Alginate FG XX Difucosylated_core GalFuc_core DisialylLewisC RM2 DisialylLewisA
Neu5Ac(a2-3)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc 0 1 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0

1 rows × 165 columns

annotate_dataset

 annotate_dataset (glycans:list[str],
                   motifs:pandas.core.frame.DataFrame|None=None,
                   feature_set:list[str]=['known'], termini_list:list=[],
                   condense:bool=False, custom_motifs:list=[])

Comprehensive glycan annotation combining multiple feature types: structural motifs, graph properties, terminal sequences

Type Default Details
glycans list List of IUPAC-condensed glycan sequences
motifs pandas.core.frame.DataFrame | None None Motif dataframe (name + sequence); defaults to motif_list
feature_set list [‘known’] Feature types to analyze: known, graph, exhaustive, terminal(1-3), custom, chemical, size_branch
termini_list list [] Monosaccharide positions: ‘terminal’, ‘internal’, or ‘flexible’
condense bool False Remove columns with only zeros
custom_motifs list [] Custom motifs when using ‘custom’ feature set
Returns DataFrame DataFrame mapping glycans to presence/absence of motifs 
glycans = ['Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc',
           'Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN4P']
print("Annotate Test")
out = annotate_dataset(glycans)
Annotate Test
motif_name Terminal_LewisX Internal_LewisX LewisY SialylLewisX SulfoSialylLewisX Terminal_LewisA Internal_LewisA LewisB SialylLewisA SulfoLewisA H_antigen_type2 H_antigen_type1 H_antigen_type3 A_antigen A_antigen_type1 A_antigen_type2 A_antigen_type3 B_antigen B_antigen_type1 B_antigen_type2 ExtB Galili_antigen GloboH Gb5 Gb4 Gb3 3SGb3 8DSGb3 3SGb4 8DSGb4 6DSGb4 3SGb5 8DSGb5 6DSGb5 6DSGb5_2 6SGb3 8DSGb3_2 6SGb4 8DSGb4_2 6SGb5 8DSGb5_2 66DSGb5 Forssman_antigen iGb3 I_antigen i_antigen PI_antigen Chitobiose Trimannosylcore Internal_LacNAc_type1 Terminal_LacNAc_type1 Internal_LacNAc_type2 Terminal_LacNAc_type2 Internal_LacdiNAc_type1 Terminal_LacdiNAc_type1 Internal_LacdiNAc_type2 Terminal_LacdiNAc_type2 bisectingGlcNAc VIM PolyLacNAc Ganglio_Series Lacto_Series(LewisC) NeoLacto_Series betaGlucan KeratanSulfate Hyaluronan Mollu_series Arthro_series Cellulose_like Chondroitin_4S GPI_anchor Isoglobo_series LewisD Globo_series Sda SDA Muco_series Heparin Peptidoglycan Dermatansulfate CAD Lactosylceramide Lactotriaosylceramide LexLex GM3 H_type3 GM2 GM1 cisGM1 VIM2 GD3 GD1a GD2 GD1b SDLex Fuc_LN3 GT1b GD1 GD1a_2 LcGg4 GT3 Disialyl_T_antigen GT1a GT2 GT1c 2Fuc_GM1 GQ1c O_linked_mannose GT1aa GQ1b HNK1 GQ1ba O_mannose_Lex 2Fuc_GD1b Sialopentaosylceramide Sulfogangliotetraosylceramide B-GM1 GQ1aa bisSulfo-Lewis x para-Forssman core_fucose core_fucose(a1-3) GP1c B-GD1b GP1ca Isoglobotetraosylceramide polySia high_mannose Gala_series LPS_core Nglycan_complex Nglycan_complex2 Oglycan_core1 Oglycan_core2 Oglycan_core3 Oglycan_core4 Oglycan_core5 Oglycan_core6 Oglycan_core7 Xylogalacturonan Sialosylparagloboside LDNF OFuc Arabinogalactan_type2 EGF_repeat Nglycan_hybrid Arabinan Xyloglucan Acharan_Sulfate M3FX M3X 1-6betaGalactan Arabinogalactan_type1 Galactomannan Tetraantennary_Nglycan Mucin_elongated_core2 Fucoidan Alginate FG XX Difucosylated_core GalFuc_core DisialylLewisC RM2 DisialylLewisA
Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN4P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

quantify_motifs

 quantify_motifs (df:str|pandas.core.frame.DataFrame, glycans:list[str],
                  feature_set:list[str], custom_motifs:list=[],
                  remove_redundant:bool=True)

Extracts and quantifies motif abundances from glycan abundance data by weighting motif occurrences

Type Default Details
df str | pandas.core.frame.DataFrame DataFrame or filepath with samples as columns, abundances as values
glycans list List of IUPAC-condensed glycan sequences
feature_set list Feature types to analyze: known, graph, exhaustive, terminal(1-3), custom, chemical, size_branch
custom_motifs list [] Custom motifs when using ‘custom’ feature set
remove_redundant bool True Remove redundant motifs via deduplicate_motifs
Returns DataFrame DataFrame with motif abundances (motifs as columns, samples as rows) 
quantify_motifs(test_df.iloc[:, 1:], test_df.iloc[:, 0].values.tolist(), ['known', 'exhaustive'])
control_1 tumor_1 control_2 tumor_2 control_3 tumor_3 control_4 tumor_4 control_5 tumor_5 ... control_16 tumor_16 control_17 tumor_17 control_18 tumor_18 control_19 tumor_19 control_20 tumor_20
H_antigen_type2 1.347737 0.892651 2.468405 1.810795 1.589162 0.449339 2.640132 0.572828 2.763890 0.737076 ... 1.070249 0.647786 1.440912 1.810304 1.722289 1.475260 4.847788 4.552496 0.480035 0.494123
Internal_LacNAc_type2 8.845085 10.063160 13.435501 28.834006 5.585973 11.359659 11.672584 21.193308 12.734919 28.597709 ... 10.883437 17.991155 21.166792 16.161351 11.909325 29.924308 12.820872 19.107379 8.802443 10.268911
Terminal_LacNAc_type2 52.982192 13.183951 24.413523 12.870782 9.555884 9.822266 12.628910 13.916662 26.569737 10.733867 ... 18.779972 12.157928 14.828507 20.879287 27.689619 10.734756 28.328965 37.870847 14.835019 8.910804
Disialyl_T_antigen 20.803836 36.895471 32.803297 20.401157 33.971366 30.150599 37.703636 24.728411 31.798990 15.989214 ... 46.337629 39.476930 39.087708 40.348217 35.791797 22.968160 11.026029 2.613718 44.676379 46.125360
Oglycan_core1 37.329013 75.567842 59.998893 57.608119 83.293693 78.436161 73.308916 64.356888 58.197862 60.329536 ... 68.269613 68.762287 62.541874 60.699726 58.713271 58.203265 58.826129 42.904325 74.390026 79.515568
Mucin_elongated_core2 61.827277 23.247111 37.849024 41.704788 15.141858 21.181925 24.301494 35.109970 39.304656 39.331576 ... 29.663409 30.149083 35.995300 37.040638 39.598944 40.659064 41.149838 56.978227 23.637462 19.179715
Gal 163.691481 126.500106 141.895063 147.702533 115.056369 132.721945 122.804259 138.398297 141.412183 167.203077 ... 133.838024 140.218313 142.530133 139.697255 138.848449 154.791018 142.588964 157.426027 122.916027 120.555251
GalNAc 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 ... 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000 100.000000
GalOS(b1-3)GalNAc 0.843710 1.185047 2.152084 0.687093 1.564450 0.381914 2.389590 0.533142 2.497482 0.338889 ... 2.066978 1.088630 1.462826 2.259636 1.687785 1.137672 0.024033 0.117449 1.972512 1.304717
GlcNAc6S(b1-6)GalNAc 2.707913 4.438043 6.198123 6.684838 1.478960 11.921934 0.892356 3.821469 4.605009 28.210391 ... 6.241593 11.157860 7.997660 4.916252 0.937290 15.269626 1.463159 0.565249 1.251077 2.680253
Neu5Ac 80.494155 134.094482 120.708503 125.892731 128.626161 137.543517 132.135127 124.740497 118.279272 134.227059 ... 149.089683 152.360772 145.124475 140.251427 125.331418 121.962226 91.599064 72.000898 142.956534 148.579697
Gal(b1-3)GalNAc 99.156290 98.814953 97.847916 99.312907 98.435550 99.618086 97.610410 99.466858 97.502518 99.661111 ... 97.933022 98.911370 98.537174 97.740364 98.312215 98.862328 99.975967 99.882551 98.027488 98.695283
Neu5Ac(a2-3)Gal 57.345927 94.670033 83.675402 103.574200 91.775344 106.231617 90.136699 98.461821 81.110136 117.087919 ... 97.928245 109.749014 101.760261 93.222423 86.403840 96.715461 80.029183 69.040921 95.565848 99.973512
Neu5Ac(a2-6)GalNAc 23.063482 39.304399 36.644881 22.263129 36.571122 31.229766 41.628644 26.256121 37.088978 17.054227 ... 50.675599 41.982557 42.829042 46.391984 38.682564 25.118814 11.540028 2.937334 47.171520 48.274238
Neu5Ac(a2-8)Neu5Ac 0.084745 0.120050 0.388219 0.055402 0.279696 0.082135 0.369784 0.022555 0.080158 0.084913 ... 0.485839 0.629202 0.535171 0.637019 0.245015 0.127952 0.029853 0.022643 0.219166 0.331947

15 rows × 40 columns

get_k_saccharides

 get_k_saccharides (glycans:list[str]|set[str], size:int=2,
                    up_to:bool=False, just_motifs:bool=False,
                    terminal:bool=False)

Extracts k-saccharide fragments from glycan sequences with options for different fragment sizes and positions

Type Default Details
glycans list[str] | set[str] List or set of IUPAC-condensed glycan sequences
size int 2 Number of monosaccharides per fragment
up_to bool False Include fragments up to size k (adds monosaccharides)
just_motifs bool False Return nested list of motifs instead of count DataFrame
terminal bool False Only count terminal fragments
Returns pandas.core.frame.DataFrame | list[list[str]] DataFrame of k-saccharide counts or list of motifs per glycan 
glycans = ['Man(a1-3)[Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc',
           'Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
           'GalNAc(a1-4)GlcNAcA(a1-4)[GlcN(b1-7)]Kdo(a2-5)[Kdo(a2-4)]Kdo(a2-6)GlcN4P(b1-6)GlcN4P']
out = get_k_saccharides(glycans, size = 3)
  Fuc(a1-3)[GlcNAc(b1-4)]GlcNAc GalNAc(a1-4)GlcNAcA(a1-4)Kdo GlcN(b1-7)Kdo(a2-5)Kdo GlcN(b1-?)[GlcNAcA(a1-?)]Kdo GlcNAcA(a1-4)Kdo(a2-5)Kdo GlcNAcA(a1-4)[GlcN(b1-7)]Kdo Kdo(a2-4)Kdo(a2-6)GlcN4P Kdo(a2-4)[Kdo(a2-5)]Kdo Kdo(a2-5)Kdo(a2-6)GlcN4P Kdo(a2-6)GlcN4P(b1-6)GlcN4P Kdo(a2-?)Kdo(a2-?)GlcN4P Man(a1-2)Man(a1-2)Man Man(a1-2)Man(a1-3)Man Man(a1-3)Man(a1-6)Man Man(a1-3)Man(b1-4)GlcNAc Man(a1-3)[Man(a1-6)]Man Man(a1-6)Man(b1-4)GlcNAc Man(a1-?)Man(a1-?)Man Man(a1-?)Man(b1-?)GlcNAc Man(a1-?)[Xyl(b1-?)]Man Man(b1-4)GlcNAc(b1-4)GlcNAc Xyl(b1-2)Man(b1-4)GlcNAc Xyl(b1-2)[Man(a1-3)]Man Xyl(b1-2)[Man(a1-6)]Man
0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 2 2 1 1 1 1
1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 3 2 0 1 0 0 0
2 0 1 1 1 1 1 1 1 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0

get_terminal_structures

 get_terminal_structures (glycan:str|networkx.classes.digraph.DiGraph,
                          size:int=1)

Identifies terminal monosaccharide sequences from non-reducing ends of glycan structure

Type Default Details
glycan str | networkx.classes.digraph.DiGraph IUPAC-condensed glycan sequence or NetworkX graph
size int 1 Number of monosaccharides in terminal fragment (1 or 2)
Returns list List of terminal structures with linkages 
get_terminal_structures("Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc")
['Neu5Ac(a2-3)', 'Neu5Ac(a2-6)']

get_molecular_properties

 get_molecular_properties (glycan_list:list[str], verbose:bool=False,
                           placeholder:bool=False)

Retrieves molecular properties from PubChem for a list of glycans using their SMILES representations

Type Default Details
glycan_list list List of IUPAC-condensed glycan sequences
verbose bool False Print SMILES not found on PubChem
placeholder bool False Return dummy values instead of dropping failed requests
Returns DataFrame DataFrame with molecular parameters from PubChem 
out = get_molecular_properties(["Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"])
  rotatable_bond_count undefined_atom_stereo_count monoisotopic_mass isotope_atom_count atom_stereo_count molecular_weight tpsa h_bond_donor_count defined_atom_stereo_count xlogp bond_stereo_count covalent_unit_count h_bond_acceptor_count undefined_bond_stereo_count defined_bond_stereo_count heavy_atom_count charge complexity exact_mass
Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc 43 1 2222.7830048 0 57 2224.0 1070 39 56 -23.600000 0 1 62 0 0 152 0 4410 2222.7830048

get_glycan_similarity

 get_glycan_similarity (glycan1:str|networkx.classes.digraph.DiGraph,
                        glycan2:str|networkx.classes.digraph.DiGraph,
                        motifs:pandas.core.frame.DataFrame|None=None,
                        feature_set:list=['known', 'exhaustive',
                        'terminal'])

Calculates cosine similarity between two glycans based on their motif count fingerprints

Type Default Details
glycan1 str | networkx.classes.digraph.DiGraph IUPAC-condensed glycan sequence or NetworkX graph
glycan2 str | networkx.classes.digraph.DiGraph IUPAC-condensed glycan sequence or NetworkX graph
motifs pandas.core.frame.DataFrame | None None Motif dataframe (name + sequence); defaults to motif_list
feature_set list [‘known’, ‘exhaustive’, ‘terminal’] Feature types to analyze: known, graph, exhaustive, terminal(1-3), custom, chemical, size_branch
Returns float Cosine similarity between glycan1 and glycan2 
get_glycan_similarity("Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc", "Neu5Ac(a2-3)Gal(b1-4)[Neu5Ac(a2-6)]GlcNAc")
0.7276068751089989

graph

convert glycan sequences to graphs and contains helper functions to search for motifs / check whether two sequences describe the same sequence, etc.

glycan_to_nxGraph

 glycan_to_nxGraph (glycan:str,
                    libr:glycowork.glycan_data.loader.HashableDict[str,int
                    ]|None=None, termini:str='ignore',
                    termini_list:tuple[str]|None=None)

Wrapper for converting glycans into networkx graphs; also works with floating substituents

Type Default Details
glycan str Glycan in IUPAC-condensed format
libr glycowork.glycan_data.loader.HashableDict[str, int] | None None Dictionary of form glycoletter:index
termini str ignore How to encode terminal/internal position; options: ignore, calc, provided
termini_list tuple[str] | None None List of positions from terminal/internal/flexible
Returns DiGraph NetworkX graph object of glycan 
print('Glycan to networkx Graph (only edges printed)')
print(glycan_to_nxGraph('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc').edges())
Glycan to networkx Graph (only edges printed)
[(1, 0), (3, 2), (4, 1), (4, 3), (5, 4), (6, 5), (7, 6), (9, 8), (10, 7), (10, 9)]

graph_to_string

 graph_to_string (graph:networkx.classes.digraph.DiGraph,
                  canonicalize:bool=True, order_by:str='length')

Convert glycan graph back to IUPAC-condensed format, handling disconnected components

Type Default Details
graph DiGraph Glycan graph (assumes root node is the one with the highest index)
canonicalize bool True Whether to output canonicalized IUPAC-condensed
order_by str length canonicalize by ‘length’ or ‘linkage’
Returns str IUPAC-condensed glycan string 
graph_to_string(glycan_to_nxGraph('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'))
'Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'

compare_glycans

 compare_glycans (glycan_a:str|networkx.classes.digraph.DiGraph,
                  glycan_b:str|networkx.classes.digraph.DiGraph,
                  return_matches:bool=False)

Check whether two glycans are identical

Type Default Details
glycan_a str | networkx.classes.digraph.DiGraph First glycan to compare
glycan_b str | networkx.classes.digraph.DiGraph Second glycan to compare
return_matches bool False Whether to return node mapping between glycans
Returns bool True if glycans are same, False if not 
print("Graph Isomorphism Test")
print(compare_glycans('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc',
                      'Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'))
Graph Isomorphism Test
True

subgraph_isomorphism

 subgraph_isomorphism (glycan:str|networkx.classes.digraph.DiGraph,
                       motif:str|networkx.classes.digraph.DiGraph,
                       termini_list:list=[], count:bool=False,
                       return_matches:bool=False)

Check if motif exists as subgraph in glycan

Type Default Details
glycan str | networkx.classes.digraph.DiGraph Glycan sequence or graph
motif str | networkx.classes.digraph.DiGraph Glycan motif sequence or graph
termini_list list [] List of monosaccharide positions from terminal/internal/flexible
count bool False Whether to return count instead of presence/absence
return_matches bool False Whether to return matched subgraphs as node lists
Returns bool | int | tuple[int, list[list[int]]] Boolean presence, count, or (count, matches) 
print("Subgraph Isomorphism Test")
print(subgraph_isomorphism('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc',
                           'Fuc(a1-6)GlcNAc'))
Subgraph Isomorphism Test
True

generate_graph_features

 generate_graph_features (glycan:str|networkx.classes.digraph.DiGraph,
                          glycan_graph:bool=True, label:str='network')

Compute graph features of glycan or network

Type Default Details
glycan str | networkx.classes.digraph.DiGraph Glycan sequence or network graph
glycan_graph bool True True if input is glycan, False if network
label str network Label for output dataframe if glycan_graph=False
Returns DataFrame Dataframe of graph features 
generate_graph_features("Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc")
diameter branching nbrLeaves avgDeg varDeg maxDeg nbrDeg4 max_deg_leaves mean_deg_leaves deg_assort ... flow_edgeMax flow_edgeMin flow_edgeAvg flow_edgeVar secorderMax secorderMin secorderAvg secorderVar egap entropyStation
Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc 8 1 3 1.818182 0.330579 3.0 0 3.0 3.0 -1.850372e-15 ... 0.333333 0.111111 0.217778 0.007289 45.607017 20.736441 31.679285 62.422895 0.340654 -2.180184

1 rows × 49 columns

largest_subgraph

 largest_subgraph (glycan_a:str|networkx.classes.digraph.DiGraph,
                   glycan_b:str|networkx.classes.digraph.DiGraph)

Find the largest common subgraph of two glycans

Type Details
glycan_a str | networkx.classes.digraph.DiGraph First glycan
glycan_b str | networkx.classes.digraph.DiGraph Second glycan
Returns str Largest common subgraph in IUPAC format 
glycan1 = 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'
glycan2 = 'Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'
largest_subgraph(glycan1, glycan2)
'Fuc(a1-6)GlcNAc'

ensure_graph

 ensure_graph (glycan:str|networkx.classes.digraph.DiGraph, **kwargs)

Ensures function compatibility with string glycans and graph glycans

Type Details
glycan str | networkx.classes.digraph.DiGraph Glycan in IUPAC-condensed format or as networkx graph
kwargs VAR_KEYWORD
Returns DiGraph NetworkX graph object of glycan 
ensure_graph("Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc")
<networkx.classes.digraph.DiGraph>

get_possible_topologies

 get_possible_topologies (glycan:str|networkx.classes.digraph.DiGraph,
                          exhaustive:bool=False,
                          allowed_disaccharides:set[str]|None=None,
                          modification_map:dict[str,set[str]]={'6S':
                          {'Gal', 'GlcNAc'}, '3S': {'Gal'}, '4S':
                          {'GalNAc'}, 'OS': {'Gal', 'GalNAc', 'GlcNAc'}},
                          return_graphs:bool=False)

Create possible glycan graphs given a floating substituent

Type Default Details
glycan str | networkx.classes.digraph.DiGraph Glycan with floating substituent
exhaustive bool False Whether to allow additions at internal positions
allowed_disaccharides set[str] | None None Permitted disaccharides when creating possible glycans
modification_map dict {‘6S’: {‘Gal’, ‘GlcNAc’}, ‘3S’: {‘Gal’}, ‘4S’: {‘GalNAc’}, ‘OS’: {‘Gal’, ‘GalNAc’, ‘GlcNAc’}} Maps modifications to valid attachments
return_graphs bool False Whether to return glycan graphs (otherwise return converted strings)
Returns list List of possible topology strings or graphs

possible_topology_check

 possible_topology_check (glycan:str|networkx.classes.digraph.DiGraph,
                          glycans:list[str|networkx.classes.digraph.DiGrap
                          h], exhaustive:bool=False, **kwargs)

Check whether glycan with floating substituent could match glycans from a list

Type Default Details
glycan str | networkx.classes.digraph.DiGraph Glycan with floating substituent
glycans list List of glycans to check against
exhaustive bool False Whether to allow additions at internal positions
kwargs VAR_KEYWORD
Returns list List of matching glycans 
possible_topology_check("{Neu5Ac(a2-3)}Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc",
                       ["Fuc(a1-2)Gal(b1-3)GalNAc", "Neu5Ac(a2-3)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc",
                       "Neu5Ac(a2-6)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc"])
['Neu5Ac(a2-3)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc']

deduplicate_glycans

 deduplicate_glycans (glycans:list[str]|set[str])

Remove duplicate glycans from a list/set, even if they have different strings

Type Details
glycans list[str] | set[str] List/set of glycans to deduplicate
Returns list Deduplicated list of glycans 
deduplicate_glycans(["Fuc(a1-2)Gal(b1-3)GalNAc", "Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Neu5Ac(a2-3)Gal(b1-3)]GalNAc",
                     "Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)]GalNAc", "Neu5Ac(a2-6)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc"])
['Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Neu5Ac(a2-3)Gal(b1-3)]GalNAc',
 'Fuc(a1-2)Gal(b1-3)GalNAc',
 'Neu5Ac(a2-6)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc']

processing

process IUPAC-condensed glycan sequences into glycoletters etc.

min_process_glycans

 min_process_glycans (glycan_list:list[str])

Convert list of glycans into a nested lists of glycoletters

Type Details
glycan_list list List of glycans in IUPAC-condensed format
Returns list List of glycoletter lists 
min_process_glycans(['Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
                     'Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc'])
[['Man', 'a1-3', 'Man', 'a1-6', 'Man', 'b1-4', 'GlcNAc', 'b1-4', 'GlcNAc'],
 ['Man',
  'a1-2',
  'Man',
  'a1-3',
  'Man',
  'a1-6',
  'Man',
  'b1-4',
  'GlcNAc',
  'b1-4',
  'GlcNAc']]

get_lib

 get_lib (glycan_list:list[str])

Returns dictionary mapping glycoletters to indices

Type Details
glycan_list list List of IUPAC-condensed glycan sequences
Returns dict Dictionary of glycoletter:index mappings 
get_lib(['Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
                     'Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc'])
{'GlcNAc': 0, 'Man': 1, 'a1-2': 2, 'a1-3': 3, 'a1-6': 4, 'b1-4': 5}

expand_lib

 expand_lib (libr_in:dict[str,int], glycan_list:list[str])

Updates libr with newly introduced glycoletters

Type Details
libr_in dict Existing dictionary of glycoletter:index
glycan_list list List of IUPAC-condensed glycan sequences
Returns dict Updated dictionary with new glycoletters 
lib1 = get_lib(['Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc',
                     'Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc'])
lib2 = expand_lib(lib1, ['Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'])
lib2
{'GlcNAc': 0, 'Man': 1, 'a1-2': 2, 'a1-3': 3, 'a1-6': 4, 'b1-4': 5, 'Fuc': 6}

presence_to_matrix

 presence_to_matrix (df:pandas.core.frame.DataFrame,
                     glycan_col_name:str='glycan',
                     label_col_name:str='Species')

Converts a dataframe with glycan occurrence to absence/presence matrix

Type Default Details
df DataFrame DataFrame with glycan occurrence
glycan_col_name str glycan Column name for glycans
label_col_name str Species Column name for labels
Returns DataFrame Matrix with labels as rows and glycan occurrences as columns 
out = presence_to_matrix(df_species[df_species.Order == 'Fabales'].reset_index(drop = True),
                         label_col_name = 'Family')
glycan Apif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]Glc Ara(a1-2)Ara(a1-6)GlcNAc Ara(a1-2)Glc(b1-2)Ara Ara(a1-2)GlcA Ara(a1-2)[Glc(b1-6)]Glc Ara(a1-3)Gal(b1-6)Gal Ara(a1-6)Glc Araf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)Araf Araf(a1-3)Gal(b1-6)Gal D-Apif(b1-2)Glc D-Apif(b1-2)GlcA D-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]Glc D-Apif(b1-3)Xyl(b1-4)Rha(a1-2)Ara D-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-Fuc D-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-Fuc D-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-Fuc D-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-Fuc D-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-Fuc D-Apif(b1-6)Glc D-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMe D-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMe Fruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc Fruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6Ac Fruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc Fruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6Ac Fruf(b2-1)Glc3Ac6Ac Fruf(b2-1)Glc4Ac6Ac Fruf(b2-1)Glc6Ac Fruf(b2-1)[Glc(b1-2)]Glc Fruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]Glc Fruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6Ac Fruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc Fruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6Ac Fruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc Fruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6Ac Fruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc Fruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6Ac Fruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6Ac Fruf(b2-1)[Glc3Ac(b1-2)]Glc Fruf(b2-1)[Glc6Ac(b1-2)]Glc Fruf1Ac(b2-1)Glc2Ac4Ac6Ac Fuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc Fuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)Glc Fuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)Glc Fuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)Glc Fuc(a1-2)Gal(b1-4)Xyl Fuc(a1-3)[Gal(b1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAc Fuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Fuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(a1-4)Gal Gal(a1-6)Gal Gal(a1-6)Gal(a1-6)Gal Gal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)Fru Gal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)Fruf Gal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]Glc Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)Fru Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)Fruf Gal(a1-6)Gal(a1-6)Glc Gal(a1-6)Gal(a1-6)Glc(a1-2)Fru Gal(a1-6)Gal(a1-6)Glc(a1-2)Fruf Gal(a1-6)Glc(a1-2)Fru Gal(a1-6)Glc(a1-2)Fruf Gal(a1-6)Man Gal(a1-6)Man(b1-4)Man Gal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)Man Gal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man Gal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man Gal(a1-6)Man(b1-4)[Gal(a1-6)]Man Gal(b1-2)Glc Gal(b1-2)GlcA Gal(b1-2)GlcA6Me Gal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Gal(b1-2)[Xyl(b1-3)]GlcA Gal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3/6)[Gal(b1-3)GlcNAc(b1-2)Man(a1-3/6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3/6)[GlcNAc(b1-2)Man(a1-3/6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3/6)[Xyl(b1-2)][Man(a1-3/6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-3)[Fuc(a1-6)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Gal(b1-4)Gal(b1-4)Man Gal(b1-4)Gal(b1-4)ManOMe Gal(b1-4)GlcA Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Gal(b1-4)Man(b1-4)Man Gal(b1-4)Man(b1-4)Man(b1-4)Gal Gal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc Gal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1CoumOMe Gal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1FerOMe Gal(b1-4)Xyl(b1-4)Rha(a1-2)Fuc Gal(b1-4)Xyl(b1-4)Rha(a1-2)Fuc4Ac Gal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc Gal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMe Gal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucOMeOSin Gal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]Fuc Gal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc Gal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc1CoumOMe Gal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc Gal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMe GalA(a1-2)[Araf(a1-5)Araf(a1-4)]Rha(b1-4)GalA GalA(a1-4)GalA(a1-4)GalA GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)GalA GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA GalOMe(b1-2)[XylOMe(b1-3)]GlcAOMe GalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMe GalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMe GalOMe(b1-4)XylOMe(b1-4)[D-ApifOMe(b1-3)]RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMe Galf(b1-2)[Galf(b1-4)]Man Glc(a1-2)Fru Glc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Glc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Glc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Glc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcN Glc(a1-2)Rha(a1-6)Glc Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Glc(a1-4)Glc(a1-2)Rha(a1-6)Glc Glc(a1-4)Glc(a1-4)Glc(a1-6)Glc Glc(a1-4)Glc(a1-4)GlcA 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Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBz Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBz Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBz Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBz Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc6Ac(a1-2)Fru Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Coum6Ac(a1-2)Fruf1CoumOBz Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOBz Glc(b1-3)Rha1Fer(a1-4)Fruf(b2-1)GlcOBz Glc(b1-3)[Araf(a1-4)]Rha(a1-2)Glc Glc(b1-3)[Xyl(b1-4)]Rha(a1-2)D-FucOMe Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)Fru Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)Fruf Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBz Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBz Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBz Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBz Glc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc6Ac(a1-2)Fru Glc(b1-4)Glc(b1-4)Glc Glc(b1-4)Glc(b1-4)Glc(b1-4)Man Glc(b1-4)Glc6Ac(b1-3)Glc1Fer6Ac(a1-2)Fruf1FerOBz 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Rha1Fer(a1-4)Fruf(b2-1)GlcOBz RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-ol RhaOMe(a1-6)GlcOMe(b1-2)GlcOMe-ol Xyl(a1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(a1-3)Fuc(a1-4)Rha Xyl(a1-6)Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)Glc-ol Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-ol Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-ol Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-ol Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-ol Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc-ol Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc Xyl(a1-6)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc-ol Xyl(b1-2)Ara(a1-6)Glc Xyl(b1-2)Ara(a1-6)GlcNAc Xyl(b1-2)Ara(a1-6)[Glc(b1-2)]Glc Xyl(b1-2)Ara(a1-6)[Glc(b1-4)]GlcNAc Xyl(b1-2)D-Fuc(b1-6)Glc Xyl(b1-2)D-Fuc(b1-6)GlcNAc Xyl(b1-2)D-Fuc(b1-6)[Glc(b1-2)]Glc Xyl(b1-2)Fuc(a1-6)Glc Xyl(b1-2)Fuc(a1-6)GlcNAc Xyl(b1-2)Fuc(b1-6)Glc Xyl(b1-2)Fuc(b1-6)GlcNAc Xyl(b1-2)Fuc(b1-6)[Glc(b1-2)]Glc Xyl(b1-2)Gal(b1-2)GlcA6Me Xyl(b1-2)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(b1-2)Rha(a1-2)Ara Xyl(b1-2)Xyl(b1-3)[Rha(b1-2)Rha(b1-4)]Xyl Xyl(b1-2)[Glc(b1-3)]Ara Xyl(b1-2)[Glc2Ac(b1-4)]Glc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(a1-6)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc-ol Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc Xyl(b1-2)[Man(a1-3)][Man(a1-6)]Man(b1-4)ManNAc Xyl(b1-2)[Man(a1-6)]Man(a1-3)Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc Xyl(b1-2)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAc Xyl(b1-2)[Rha(a1-3)]GlcA Xyl(b1-3)Ara Xyl(b1-3)Xyl(b1-2)[Rha(a1-6)]Glc(b1-2)Glc Xyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]Glc Xyl(b1-3)Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]Glc(b1-2)Glc Xyl(b1-4)Rha(a1-2)Ara Xyl(b1-4)Rha(a1-2)D-Fuc Xyl(b1-4)Rha(a1-2)D-FucOMe Xyl(b1-4)Rha(a1-2)Fuc Xyl(b1-4)Rha(a1-2)Fuc3Ac Xyl(b1-4)Rha(a1-2)Fuc4Ac Xyl(b1-4)Rha(a1-2)Glc Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]Fuc4Ac Xyl(b1-4)Rha(a1-2)[Rha(a1-6)]Glc Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)Xyl Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl3Ac(b1-4)Xyl Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl(b1-4)Xyl Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl3Ac(b1-4)Xyl(b1-4)Xyl(b1-4)[GlcA4Me(a1-2)]Xyl3Ac(b1-4)Xyl Xyl(b1-4)Xyl(b1-4)[GlcA(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl Xyl(b1-4)[GlcAOMe(a1-2)]Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl Xyl2Ac3Ac4Ac(b1-3)Ara Xyl4Ac(b1-3)Ara XylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-ol XylOMe(b1-3)XylOMe(b1-2)[RhaOMe(a1-6)]GlcOMe(b1-2)GlcOMe-ol XylOMe(b1-4)RhaOMe(a1-2)D-FucOMe XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-6)]GlcOMe-ol Xylf(b1-2)Xyl(b1-3)[Rha(b1-2)Rha(b1-4)]Xyl [Araf(a1-3)Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal [Araf(a1-3)Gal(b1-6)]Gal(b1-3)Gal [Gal(a1-4)Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Man(b1-4)Man(b1-4)Man(b1-4)Gal(a1-6)]Man(b1-2)[Gal(a1-6)]Man(b1-2)[Gal(a1-4)Gal(a1-6)]Man(b1-4)Man [Gal(a1-6)]Man(b1-4)Man [Gal(a1-6)]Man(b1-4)Man(b1-4)Man [Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)Man [Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man [Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man [Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Gal(b1-3)Gal(b1-6)[Araf(a1-3)]Gal(b1-6)]Gal(b1-3)Gal [Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal [Gal(b1-6)Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal [Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal [Gal(b1-6)]Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc [Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc
Family                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
Fabaceae 1 4 1 3 1 1 1 0 1 3 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 1 1 2 1 1 1 1 4 2 1 1 2 2 7 7 4 4 4 4 4 2 8 4 2 5 4 2 2 1 1 1 1 0 1 1 3 1 1 2 1 1 1 1 2 5 1 1 2 2 1 1 1 1 2 1 1 1 1 1 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 1 3 1 1 1 1 1 2 2 1 3 1 5 0 0 1 3 1 1 1 2 0 0 0 0 0 0 2 1 1 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 2 0 1 1 1 1 5 1 1 0 0 0 0 1 0 0 0 0 0 0 1 3 2 0 0 0 1 1 4 6 1 1 1 1 3 4 2 1 1 1 4 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 7 2 5 1 2 1 1 1 1 1 1 1 2 1 5 1 1 1 1 1 3 1 1 1 1 4 1 1 1 1 1 5 1 11 2 1 1 1 1 1 1 1 1 1 2 1 1 1 4 6 4 4 4 1 1 5 4 1 4 1 1 0 1 1 1 7 1 1 2 3 23 6 7 0 1 9 3 4 1 3 1 1 1 1 3 3 2 1 1 1 1 1 0 2 1 1 1 1 1 1 1 1 1 1 1 0 1 2 0 1 1 1 1 2 1 1 2 1 2 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 7 1 1 1 2 3 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 3 16 18 2 1 2 1 9 13 2 1 1 3 2 1 0 0 0 0 0 0 0 2 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 4 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Fagaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Polygalaceae 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 1 2 1 2 2 1 2 1 1 1 2 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 2 2 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Quillajaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

enforce_class

 enforce_class (glycan:str, glycan_class:str, conf:float|None=None,
                extra_thresh:float=0.3)

Determines whether glycan belongs to a specified class

Type Default Details
glycan str Glycan in IUPAC-condensed nomenclature
glycan_class str Glycan class (O, N, free, or lipid)
conf float | None None Prediction confidence to override class
extra_thresh float 0.3 Threshold to override class
Returns bool True if glycan is in glycan class 
enforce_class("Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc", "O")
False

IUPAC_to_SMILES

 IUPAC_to_SMILES (glycan_list:str|list[str])

Convert list of IUPAC-condensed glycans to isomeric SMILES using GlyLES

Type Details
glycan_list str | list[str] List of IUPAC-condensed glycans or single glycan
Returns list List of corresponding SMILES strings 
IUPAC_to_SMILES(['Neu5Ac(a2-3)Gal(b1-4)Glc'])
['O1C(O)[C@H](O)[C@@H](O)[C@H](O[C@@H]2O[C@H](CO)[C@H](O)[C@H](O[C@]3(C(=O)O)C[C@H](O)[C@@H](NC(C)=O)[C@H]([C@H](O)[C@H](O)CO)O3)[C@H]2O)[C@H]1CO']

canonicalize_composition

 canonicalize_composition (comp:str)

Converts composition from any common format to standardized dictionary

Type Details
comp str Composition in Hex5HexNAc4Fuc1Neu5Ac2 or H5N4F1A2 format
Returns dict Dictionary of monosaccharide:count 
print(canonicalize_composition("HexNAc2Hex1Fuc3Neu5Ac1"))
print(canonicalize_composition("N2H1F3A1"))
{'HexNAc': 2, 'Hex': 1, 'dHex': 3, 'Neu5Ac': 1}
{'HexNAc': 2, 'Hex': 1, 'dHex': 3, 'Neu5Ac': 1}

canonicalize_iupac

 canonicalize_iupac (glycan:str)

Convert glycan from IUPAC-extended, LinearCode, GlycoCT, WURCS, Oxford, GLYCAM, GlycoWorkBench, CSDB-linear, KCF, GlyConnect IDs, and GlyTouCanIDs to standardized IUPAC-condensed format

Type Details
glycan str Glycan sequence in any supported format
Returns str Standardized IUPAC-condensed format 
print(canonicalize_iupac("NeuAc?1-36SGalb1-4GlcNACb1-6(Fuc?1-2Galb1-4GlcNacb1-3Galb1-3)GalNAc-sp3"))
print(canonicalize_iupac("WURCS=2.0/5,11,10/[a2122h-1b_1-5_2*NCC/3=O][a1122h-1b_1-5][a1122h-1a_1-5][a2112h-1b_1-5][a1221m-1a_1-5]/1-1-2-3-1-4-3-1-4-5-5/a4-b1_a6-k1_b4-c1_c3-d1_c6-g1_d2-e1_e4-f1_g2-h1_h4-i1_i2-j1"))
print(canonicalize_iupac("Ma3(Ma6)Mb4GNb4GN;N"))
print(canonicalize_iupac("α-D-Manp-(1→3)[α-D-Manp-(1→6)]-β-D-Manp-(1→4)-β-D-GlcpNAc-(1→4)-β-D-GlcpNAc-(1→"))
print(canonicalize_iupac("""RES
1b:b-dgal-HEX-1:5
2s:n-acetyl
3b:b-dgal-HEX-1:5
4b:b-dglc-HEX-1:5
5b:b-dgal-HEX-1:5
6b:a-dglc-HEX-1:5
7b:b-dgal-HEX-1:5
8b:a-lgal-HEX-1:5|6:d
9b:a-dgal-HEX-1:5
10s:n-acetyl
11s:n-acetyl
12b:b-dglc-HEX-1:5
13b:b-dgal-HEX-1:5
14b:a-lgal-HEX-1:5|6:d
15b:a-lgal-HEX-1:5|6:d
16s:n-acetyl
17s:n-acetyl
18b:b-dgal-HEX-1:5
LIN
1:1d(2+1)2n
2:1o(3+1)3d
3:3o(3+1)4d
4:4o(-1+1)5d
5:5o(-1+1)6d
6:6o(-1+1)7d
7:7o(2+1)8d
8:7o(3+1)9d
9:9d(2+1)10n
10:6d(2+1)11n
11:5o(-1+1)12d
12:12o(-1+1)13d
13:13o(2+1)14d
14:12o(-1+1)15d
15:12d(2+1)16n
16:4d(2+1)17n
17:1o(6+1)18d
"""))
Fuc(a1-2)Gal(b1-4)GlcNAc(b1-3)Gal(b1-3)[Neu5Ac(a2-3)Gal6S(b1-4)GlcNAc(b1-6)]GalNAc
Fuc(a1-2)Gal(b1-4)GlcNAc(b1-2)Man(a1-6)[Gal(b1-4)GlcNAc(b1-2)Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc
Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc
Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc
Fuc(a1-2)[GalNAc(a1-3)]Gal(b1-?)GlcNAc(a1-?)[Fuc(a1-2)Gal(b1-?)[Fuc(a1-?)]GlcNAc(b1-?)]Gal(b1-?)GlcNAc(b1-3)Gal(b1-3)[Gal(b1-6)]GalNAc

get_possible_linkages

 get_possible_linkages (wildcard:str, linkage_list:list[str]={'a2-2',
                        'a1-3', '?1-2', '?1-?', '?2-8', 'a1-5', 'a2-6',
                        'a2-5', '?1-4', 'a1-1', 'a1-6', 'a2-11', 'a1-?',
                        'b2-6', '1-6', 'b2-4', '?2-6', 'b2-8', 'b1-?',
                        'b1-5', 'b1-3', 'a1-11', 'a2-7', 'b1-4', 'b1-1',
                        'b2-2', 'a2-3', '?1-6', 'b2-7', 'a1-8', '?1-3',
                        'a1-9', 'b1-9', '?2-3', 'b1-8', '?2-?', 'a1-4',
                        'a2-8', 'b1-2', 'b1-7', 'b1-6', 'b2-5', '1-4',
                        'a2-1', 'a2-9', 'a1-7', 'a2-?', 'a2-4', 'a1-2',
                        'b2-1', 'b2-3'})

Retrieves all linkages that match a given wildcard pattern

Type Default Details
wildcard str Pattern to match, ? can be wildcard
linkage_list list {‘a2-2’, ‘a1-3’, ‘?1-2’, ‘?1-?’, ‘?2-8’, ‘a1-5’, ‘a2-6’, ‘a2-5’, ‘?1-4’, ‘a1-1’, ‘a1-6’, ‘a2-11’, ‘a1-?’, ‘b2-6’, ‘1-6’, ‘b2-4’, ‘?2-6’, ‘b2-8’, ‘b1-?’, ‘b1-5’, ‘b1-3’, ‘a1-11’, ‘a2-7’, ‘b1-4’, ‘b1-1’, ‘b2-2’, ‘a2-3’, ‘?1-6’, ‘b2-7’, ‘a1-8’, ‘?1-3’, ‘a1-9’, ‘b1-9’, ‘?2-3’, ‘b1-8’, ‘?2-?’, ‘a1-4’, ‘a2-8’, ‘b1-2’, ‘b1-7’, ‘b1-6’, ‘b2-5’, ‘1-4’, ‘a2-1’, ‘a2-9’, ‘a1-7’, ‘a2-?’, ‘a2-4’, ‘a1-2’, ‘b2-1’, ‘b2-3’} List of linkages to search
Returns set Matching linkages 
get_possible_linkages("a1-?")
{'a1-1',
 'a1-2',
 'a1-3',
 'a1-4',
 'a1-5',
 'a1-6',
 'a1-7',
 'a1-8',
 'a1-9',
 'a1-?'}

get_possible_monosaccharides

 get_possible_monosaccharides (wildcard:str)

Retrieves all matching common monosaccharides of a type

Type Details
wildcard str Monosaccharide type; options: Hex, HexNAc, dHex, Sia, HexA, Pen, HexOS, HexNAcOS
Returns set Matching monosaccharides 
get_possible_monosaccharides("HexNAc")
{'GalNAc', 'GlcNAc', 'HexNAc', 'ManNAc'}

equal_repeats

 equal_repeats (r1:str, r2:str)

Check whether two repeat units could stem from the same repeating structure

Type Details
r1 str First glycan sequence
r2 str Second glycan sequence
Returns bool True if repeats are shifted versions 
equal_repeats("Fuc2S3S(a1-3)Fuc2S(a1-4)Fuc2S3S", "Fuc2S(a1-4)Fuc2S3S(a1-3)Fuc2S")
True

get_class

 get_class (glycan:str)

Determines glycan class

Type Details
glycan str Glycan in IUPAC-condensed nomenclature
Returns str Glycan class (repeat, O, N, free, lipid, lipid/free, or empty) 
get_class("Gal(b1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc")
'N'

query

for interacting with the databases contained in glycowork, delivering insights for sequences of interest

get_insight

 get_insight (glycan:str, motifs:pandas.core.frame.DataFrame|None=None)

Print meta-information about a glycan

Type Default Details
glycan str Glycan in IUPAC-condensed format
motifs pandas.core.frame.DataFrame | None None DataFrame of glycan motifs; default:motif_list
Returns None Prints glycan meta-information 
print("Test get_insight with 'Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'")
get_insight('Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc')
Test get_insight with 'Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'
Let's get rolling! Give us a few moments to crunch some numbers.

This glycan occurs in the following species: ['Acanthocheilonema_viteae', 'Adeno-associated_dependoparvovirusA', 'Aedes_aegypti', 'Angiostrongylus_cantonensis', 'Anopheles_gambiae', 'Antheraea_pernyi', 'Apis_mellifera', 'Ascaris_suum', 'Autographa_californica_nucleopolyhedrovirus', 'AvianInfluenzaA_Virus', 'Bombus_ignitus', 'Bombyx_mori', 'Bos_taurus', 'Brugia_malayi', 'Caenorhabditis_elegans', 'Cardicola_forsteri', 'Cooperia_onchophora', 'Cornu_aspersum', 'Crassostrea_gigas', 'Crassostrea_virginica', 'Cricetulus_griseus', 'Danio_rerio', 'Dictyocaulus_viviparus', 'Dirofilaria_immitis', 'Drosophila_melanogaster', 'Fasciola_hepatica', 'Gallus_gallus', 'Glossina_morsitans', 'Haemonchus_contortus', 'Haliotis_tuberculata', 'Heligmosomoides_polygyrus', 'Helix_lucorum', 'Homo_sapiens', 'HumanImmunoDeficiency_Virus', 'Hylesia_metabus', 'Hypsibius_exemplaris', 'Lutzomyia_longipalpis', 'Lymantria_dispar', 'Macaca_mulatta', 'Mamestra_brassicae', 'Megathura_crenulata', 'Mus_musculus', 'Nilaparvata_lugens', 'Oesophagostomum_dentatum', 'Onchocerca_volvulus', 'Onchocerca_volvulus', 'Ophiactis_savignyi', 'Opisthorchis_viverrini', 'Ostrea_edulis', 'Ovis_aries', 'Pan_troglodytes', 'Pristionchus_pacificus', 'Ramazzottius_varieornatus', 'Rattus_norvegicus', 'Schistosoma_mansoni', 'SemlikiForest_Virus', 'Spodoptera_frugiperda', 'Sus_scrofa', 'Tick_borne_encephalitis_virus', 'Tribolium_castaneum', 'Trichinella_spiralis', 'Trichoplusia_ni', 'Trichuris_suis', 'Tropidolaemus_subannulatus', 'Volvarina_rubella', 'undetermined', 'unidentified_influenza_virus']

Puh, that's quite a lot! Here are the phyla of those species: ['Arthropoda', 'Artverviricota', 'Chordata', 'Cossaviricota', 'Echinodermata', 'Kitrinoviricota', 'Mollusca', 'Negarnaviricota', 'Nematoda', 'Platyhelminthes', 'Tardigrada', 'Virus']

This glycan contains the following motifs: ['Chitobiose', 'Trimannosylcore', 'core_fucose']

This is the GlyTouCan ID for this glycan: G63041RA

This glycan has been reported to be expressed in: ['2A3_cell_line', 'A549_cell_line', 'AML_193_cell_line', 'C10_cell_line', 'CHOK1_cell_line', 'CHOS_cell_line', 'COLO_205_cell_line', 'COLO_320_cell_line', 'CRL_1620_cell_line', 'Caco_2_cell_line', 'Cal-27_cell_line', 'Cervicovaginal_Secretion', 'Co_115_cell_line', 'EOL_1_cell_line', 'FaDu_cell_line', 'HCT_15_cell_line', 'HCT_8_cell_line', 'HEK293_cell_line', 'HEL92_1_7_cell_line', 'HEL_cell_line', 'HL_60_cell_line', 'HT_29_cell_line', 'KG_1_cell_line', 'KG_1a_cell_line', 'KM12_cell_line', 'Kasumi_1_cell_line', 'LS174T_cell_line', 'LS180_cell_line', 'LS411N_cell_line', 'LoVo_cell_line', 'MDA_MB_231BR_cell_line', 'ME_1_cell_line', 'ML_1_cell_line', 'MOLM_13_cell_line', 'MOLM_14_cell_line', 'MV4_11_cell_line', 'M_07e_cell_line', 'NB_4_cell_line', 'NS0_cell_line', 'OCI_AML2_cell_line', 'OCI_AML3_cell_line', 'PLB_985_cell_line', 'RKO_cell_line', 'SCC-9_cell_line', 'SCC_25_cell_line', 'SW1116_cell_line', 'SW1398_cell_line', 'SW1463_cell_line', 'SW480_cell_line', 'SW48_cell_line', 'SW620_cell_line', 'SW948_cell_line', 'T84_cell_line', 'TF_1_cell_line', 'THP_1_cell_line', 'U_937_cell_line', 'VU-147T_cell_line', 'WiDr_cell_line', 'alveolus_of_lung', 'brain', 'brain', 'cerebellar_cortex', 'cerebellar_cortex', 'cerebellar_cortex', 'cerebellar_cortex', 'cerebellum', 'colon', 'cortex', 'digestive_tract', 'digestive_tract', 'forebrain', 'gills', 'gills', 'heart', 'heart', 'heart', 'hindbrain', 'hippocampal_formation', 'hippocampus', 'hippocampus', 'hippocampus', 'hippocampus', 'iPS1A_cell_line', 'iPS2A_cell_line', 'kidney', 'liver', 'liver', 'liver', 'lung', 'mantle', 'mantle', 'metastatic_pancreatic_ductal_adenocarcinoma', 'milk', 'mucus', 'muscle_of_leg', 'nerve_ending', 'ovary', 'pancreas', 'placenta', 'prefrontal_cortex', 'prefrontal_cortex', 'prefrontal_cortex', 'prefrontal_cortex', 'primary_pancreatic_ductal_adenocarcinoma', 'prostate_gland', 'seminal_fluid', 'striatum', 'striatum', 'striatum', 'striatum', 'testicle', 'testis', 'trachea', 'urine', 'urothelium']

This glycan has been reported to be dysregulated in (disease, direction, sample): [('REM_sleep_behavior_disorder', 'down', 'serum'), ('benign_breast_tumor_tissues_vs_para_carcinoma_tissues', 'up', 'breast'), ('cystic_fibrosis', 'up', 'sputum'), ('female_breast_cancer', 'up', 'breast'), ('female_breast_cancer', 'up', 'cell_line'), ('prostate_cancer', 'up', 'prostate_cancer_biopsy'), ('thyroid_gland_papillary_carcinoma', 'up', 'serum'), ('urinary_bladder_cancer', 'down', 'urine')]

That's all we can do for you at this point!

glytoucan_to_glycan

 glytoucan_to_glycan (ids:list[str], revert:bool=False, verbose:bool=True)

Convert between GlyTouCan IDs and IUPAC-condensed glycans

Type Default Details
ids list List of GlyTouCan IDs or glycans
revert bool False Whether to map glycans to IDs; default:False
verbose bool True Whether to print missing entries; default:True
Returns list List of glycans or IDs 
glytoucan_to_glycan(['G63041RA'])
['Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc']

regex

for performing regular expression-like searches in glycans, very powerful to find complicated motifs

get_match

 get_match (pattern:str|list[str],
            glycan:str|networkx.classes.digraph.DiGraph,
            return_matches:bool=True)

Find matches for glyco-regular expression in glycan

Type Default Details
pattern str | list[str] Expression or pre-compiled pattern; e.g., “Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc”
glycan str | networkx.classes.digraph.DiGraph Glycan string or graph
return_matches bool True Whether to return matches vs boolean
Returns bool | list[str] Match results 
# {} = between min and max occurrences, e.g., "Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc"
# * = zero or more occurrences, e.g., "Hex-HexNAc-([Hex|Fuc])*-HexNAc"
# + = one or more occurrences, e.g., "Hex-HexNAc-([Hex|Fuc])+-HexNAc"
# ? = zero or one occurrence, e.g., "Hex-HexNAc-([Hex|Fuc])?-HexNAc"
# {1,} = at minimum one occurrence, e.g., "Hex-HexNAc-([Hex|Fuc]){1,}-HexNAc"
# {,1} = at maximum one occurrence, e.g., "Hex-HexNAc-([Hex|Fuc]){,1}-HexNAc"
# {2} = exactly two occurrences, e.g., "Hex-HexNAc-([Hex|Fuc]){2}-HexNAc"
# ^ = start of sequence, e.g., "^Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc"
# % = middle of sequence (i.e., neither start nor end)
# $ = end of sequence, e.g., "Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc$"
# ?<= = lookbehind (i.e., provided pattern must be present before rest of pattern but is not included in match), e.g., "(?<=Xyl-)Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc"
# ?<! = negative lookbehind (i.e., provided pattern is not present before rest of pattern and is also not included in match), e.g., "(?<!Xyl-)Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc"
# ?= = lookahead (i.e., provided pattern must be present after rest of pattern but is not included in match), e.g., "Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc(?=-HexNAc)"
# ?! = negative lookahead (i.e., provided pattern is not present after rest of pattern and is not included in match), e.g., "Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc(?!-HexNAc)"

# Example: extracting the sequence from the a1-6 branch of N-glycans
pattern = "r[Sia]{,1}-Monosaccharide-([dHex]){,1}-Monosaccharide(?=-Mana6-Monosaccharide)"
print(get_match(pattern, "GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"))
print(get_match(pattern, "GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[GalNAc(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"))
print(get_match(pattern, "GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Ac(a2-6)GalNAc(b1-4)GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"))
print(get_match(pattern, "GalNAc(b1-4)GlcNAc(b1-2)Man(a1-3)[Neu5Gc(a2-6)GalNAc(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"))
['Gal(b1-4)GlcNAc']
['GalNAc(b1-4)GlcNAc']
['Neu5Ac(a2-6)GalNAc(b1-4)GlcNAc']
['Neu5Gc(a2-6)GalNAc(b1-4)[Fuc(a1-3)]GlcNAc']

For interested users, we here compile a selection of regular expression patterns that we find useful in our own work:

  • Lewis or sialyl-Lewis structures:
    pattern = “r[Sia]{,1}-[Gal|GalOS]{1}-([Fuc]){1}-[GlcNAc|GlcNAc6S]{1}”
  • Blood groups:
    pattern = “rFuc-([Gal|GalNAc])?-Gal-GlcNAc”
  • a1-6 branch in N-glycans:
    pattern = “r[Sia]{,1}-[Hex|HexNAc]{,1}-([dHex]){,1}-[Man|GlcNAc]{1}-([.-.|.]){,1}-Mana6(?=-Manb4-GlcNAc)”
  • b1-6 branch in O-glycans (from core 2/4/6):
    pattern = “r[Sia|dHex]{,1}-[Hex|HexNAc]{,1}-([dHex]){,1}-.b6(?=-GalNAc)”
  • b1-3 branch in O-glycans (from core 1/2):
    pattern = “r[Sia]{,1}-[.]{,1}-([dHex]){,1}-.b3(?=-GalNAc)”

get_match_batch

 get_match_batch (pattern:str,
                  glycan_list:list[str|networkx.classes.digraph.DiGraph],
                  return_matches:bool=True)

Find glyco-regular expression matches in list of glycans

Type Default Details
pattern str Glyco-regular expression; e.g., “Hex-HexNAc-([Hex|Fuc]){1,2}-HexNAc”
glycan_list list List of glycans
return_matches bool True Whether to return matches vs boolean
Returns list[bool] | list[list[str]] Match results for each glycan

motif_to_regex

 motif_to_regex (motif:str)

Convert glycan motif to regular expression pattern

Type Details
motif str Glycan in IUPAC-condensed
Returns str Regular expression 
motif_to_regex("Fuc(a1-3)[Gal(b1-4)]GlcNAc(b1-?)")
'Fuca3-([Galb4]){1}-GlcNAcb?'

tokenization

helper functions to map m/z–>composition, composition–>structure, structure–>motif, and more

string_to_labels

 string_to_labels (character_string:str, libr:dict[str,int]|None=None)

Tokenize word by indexing characters in library

Type Default Details
character_string str String to tokenize
libr dict[str, int] | None None Dictionary mapping characters to indices
Returns list List of character indices 
string_to_labels(['Man','a1-3','Man','a1-6','Man'])
[None, None, None, None, None]

pad_sequence

 pad_sequence (seq:list[int], max_length:int, pad_label:int|None=None,
               libr:dict[str,int]|None=None)

Pad sequences to same length using padding token

Type Default Details
seq list Sequence to pad
max_length int Target length
pad_label int | None None Padding token value
libr dict[str, int] | None None Character library
Returns list Padded sequence 
pad_sequence(string_to_labels(['Man','a1-3','Man','a1-6','Man']), 7)
[None, None, None, None, None, 25, 25]

stemify_glycan

 stemify_glycan (glycan:str, stem_lib:dict[str,str]|None=None,
                 libr:dict[str,int]|None=None)

Remove modifications from all monosaccharides in glycan

Type Default Details
glycan str Glycan in IUPAC-condensed format
stem_lib dict[str, str] | None None Modified to core monosaccharide mapping; default:created from lib
libr dict[str, int] | None None Glycoletter to index mapping
Returns str Stemmed glycan string 
stemify_glycan("Neu5Ac9Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
'Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc'

stemify_dataset

 stemify_dataset (df:pandas.core.frame.DataFrame,
                  stem_lib:dict[str,str]|None=None,
                  libr:dict[str,int]|None=None,
                  glycan_col_name:str='glycan', rarity_filter:int=1)

Remove monosaccharide modifications from all glycans in dataset

Type Default Details
df DataFrame DataFrame with glycan column
stem_lib dict[str, str] | None None Modified to core monosaccharide mapping; default:created from lib
libr dict[str, int] | None None Glycoletter to index mapping
glycan_col_name str glycan Column name for glycans
rarity_filter int 1 Minimum occurrences to keep modification
Returns DataFrame DataFrame with stemified glycans

mask_rare_glycoletters

 mask_rare_glycoletters (glycans:list[str],
                         thresh_monosaccharides:int|None=None,
                         thresh_linkages:int|None=None)

Mask rare monosaccharides and linkages in glycans

Type Default Details
glycans list List of IUPAC-condensed glycans
thresh_monosaccharides int | None None Threshold for rare monosaccharides (default: 0.001*len(glycans))
thresh_linkages int | None None Threshold for rare linkages (default: 0.03*len(glycans))
Returns list List of glycans with masked rare elements

mz_to_composition

 mz_to_composition (mz_value:float, mode:str='negative',
                    mass_value:str='monoisotopic', reduced:bool=False,
                    sample_prep:str='underivatized',
                    mass_tolerance:float=0.5, kingdom:str='Animalia',
                    glycan_class:str='all',
                    df_use:pandas.core.frame.DataFrame|None=None,
                    filter_out:set[str]|None=None,
                    extras:list[str]=['doubly_charged'],
                    adduct:str|None=None)

Map m/z value to matching monosaccharide composition

Type Default Details
mz_value float m/z value from mass spec
mode str negative MS mode: positive/negative
mass_value str monoisotopic Mass type: monoisotopic/average
reduced bool False Whether glycans are reduced
sample_prep str underivatized Sample preparation method: underivatized/permethylated/peracetylated
mass_tolerance float 0.5 Mass tolerance for matching
kingdom str Animalia Taxonomic kingdom filter for choosing a subset of glycans to consider
glycan_class str all Glycan class: N/O/lipid/free/all
df_use pandas.core.frame.DataFrame | None None Custom glycan database
filter_out set[str] | None None Monosaccharides to ignore during composition finding
extras list [‘doubly_charged’] Additional operations: adduct/doubly_charged
adduct str | None None Chemical formula of adduct that contributes to m/z, e.g., “C2H4O2”
Returns list List of matching compositions 
mz_to_composition(665.4, glycan_class='O', filter_out={'Kdn', 'P', 'HexA', 'Pen', 'HexN', 'Me', 'PCho', 'PEtN'},
                    reduced = True)
[{'Hex': 2, 'HexNAc': 2, 'Neu5Ac': 2}]

match_composition_relaxed

 match_composition_relaxed (composition:dict[str,int],
                            glycan_class:str='N', kingdom:str='Animalia',
                            df_use:pandas.core.frame.DataFrame|None=None,
                            reducing_end:str|None=None)

Map coarse-grained composition to matching glycans

Type Default Details
composition dict Dictionary indicating composition (e.g. {“dHex”: 1, “Hex”: 1, “HexNAc”: 1})
glycan_class str N Glycan class: N/O/lipid/free
kingdom str Animalia Taxonomic kingdom filter for choosing a subset of glycans to consider
df_use pandas.core.frame.DataFrame | None None Custom glycan database
reducing_end str | None None Reducing end specification
Returns list List of matching glycans 
match_composition_relaxed({"Hex":3, "HexNAc":2, "dHex":1}, glycan_class = 'O')
['Fuc(a1-2)[Gal(a1-3)]Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
 'Fuc(a1-2)[Gal(a1-3)]Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc',
 'Gal(b1-4)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
 'Gal(?1-3/4)Gal(b1-3/4)[Fuc(a1-3/4)]GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
 'Gal(b1-4)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-3)Gal(b1-3)GalNAc',
 'Fuc(a1-2)Gal(b1-3/4)GlcNAc(b1-3)Gal(b1-3)[Gal(b1-6)]GalNAc',
 'Fuc(a1-2)Gal(b1-4)GlcNAc(b1-6)[Gal(?1-?)Gal(b1-3)]GalNAc',
 'Fuc(a1-2)[Gal(a1-3)]Gal(b1-3)GlcNAc(b1-3)Gal(b1-3)GalNAc',
 'Fuc(a1-2)[Gal(a1-3)]Gal(b1-4)GlcNAc(?1-3/4)Gal(b1-3)GalNAc',
 'Fuc(a1-3)[Gal(b1-4)]GlcNAc(b1-3)Gal(b1-4)GlcNAc(b1-3)Gal',
 'Gal(?1-?)Gal(b1-4)GlcNAc(b1-6)[Fuc(a1-2)Gal(b1-3)]GalNAc',
 'Fuc(a1-2)Gal(b1-4)GlcNAc(b1-6)[Gal(a1-3)Gal(b1-3)]GalNAc',
 'Gal(a1-3)Gal(b1-4)GlcNAc(b1-6)[Fuc(a1-2)Gal(b1-3)]GalNAc',
 'Fuc(a1-2)Gal(b1-3)Gal(b1-3)GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
 'Fuc(a1-2)Gal(b1-3)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc',
 'Gal(b1-4)Gal(b1-3)[Fuc(a1-3)[Gal(b1-4)]GlcNAc(b1-6)]GalNAc',
 'Fuc(a1-2)Gal(?1-?)Gal(b1-3/4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc',
 'Gal(a1-3)GalNAc(a1-3)[Fuc(a1-2)]Gal(b1-3)Gal(b1-3)GalNAc',
 'Man(a1-6)Glc(a1-4)GlcNAc(b1-4)[Fuc(a1-2)]Gal(b1-3)GalNAc',
 'Man(a1-6)Glc(b1-4)GlcNAc(b1-4)[Fuc(a1-2)]Gal(b1-3)GalNAc',
 'Fuc(a1-2)Gal(b1-3)GlcNAc(b1-3)Gal(b1-4)GlcNAc(b1-?)Man',
 'Gal(b1-2)Gal(a1-3)[Fuc(a1-2)]Gal(b1-3)[GlcNAc(b1-6)]GalNAc',
 'Fuc(a1-2)Gal(a1-3)Gal(a1-4)Gal(b1-3)[GlcNAc(b1-6)]GalNAc',
 'Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-6)[Gal(b1-3)]Gal(b1-3)GalNAc',
 'Fuc(a1-3)[Gal(b1-4)]GlcNAc(b1-?)Gal(b1-6)[Gal(b1-3)]GalNAc']

condense_composition_matching

 condense_composition_matching (matched_composition:list[str])

Find minimum set of glycans characterizing matched composition

Type Details
matched_composition list List of matching glycans
Returns list Minimal list of representative glycans 
match_comp = match_composition_relaxed({'Hex':1, 'HexNAc':1, 'Neu5Ac':1}, glycan_class = 'O')
print(match_comp)
condense_composition_matching(match_comp)
['Neu5Ac(a2-3)Gal(b1-3)GalNAc', 'Gal(b1-3)[Neu5Ac(a2-6)]GalNAc', '{Neu5Ac(a2-3/6)}Gal(b1-3)GalNAc', 'Neu5Ac(a2-3)[GalNAc(b1-4)]Gal', 'Gal(a1-3)[Neu5Ac(a2-6)]GalNAc', 'Neu5Ac(a2-3/6)Gal(b1-3)GalNAc', 'Neu5Ac(a2-6)Gal(b1-3)GalNAc', 'Gal(?1-3)[Neu5Ac(a2-6)]GalNAc', 'Neu5Ac(a2-3/6)Gal(?1-3)GalNAc', 'Neu5Ac(a2-?)Hex(?1-?)GalNAc', 'Neu5Ac(a2-3)Gal(?1-?)GalNAc', 'Neu5Ac(a2-3/6)GalNAc(a1-6)Gal', 'Neu5Ac(a2-6)Gal(a1-3)GalNAc', 'Gal(b1-4)[Neu5Ac(a2-6)]GalNAc', 'Neu5Ac(a2-3)GalNAc(b1-3)Gal']
['Neu5Ac(a2-3)Gal(b1-3)GalNAc',
 'Neu5Ac(a2-3/6)Gal(b1-3)GalNAc',
 'Gal(b1-3)[Neu5Ac(a2-6)]GalNAc',
 'Gal(a1-3)[Neu5Ac(a2-6)]GalNAc',
 '{Neu5Ac(a2-3/6)}Gal(b1-3)GalNAc',
 'Neu5Ac(a2-3)[GalNAc(b1-4)]Gal',
 'Neu5Ac(a2-6)Gal(b1-3)GalNAc',
 'Neu5Ac(a2-3/6)GalNAc(a1-6)Gal',
 'Neu5Ac(a2-6)Gal(a1-3)GalNAc',
 'Gal(b1-4)[Neu5Ac(a2-6)]GalNAc',
 'Neu5Ac(a2-3)GalNAc(b1-3)Gal']

mz_to_structures

 mz_to_structures (mz_list:list[float], glycan_class:str,
                   kingdom:str='Animalia',
                   abundances:pandas.core.frame.DataFrame|None=None,
                   mode:str='negative', mass_value:str='monoisotopic',
                   sample_prep:str='underivatized',
                   mass_tolerance:float=0.5, reduced:bool=False,
                   df_use:pandas.core.frame.DataFrame|None=None,
                   filter_out:set[str]|None=None, verbose:bool=False)

Map precursor masses to structures, supporting accompanying relative intensities

Type Default Details
mz_list list List of precursor masses
glycan_class str Glycan class: N/O/lipid/free
kingdom str Animalia Taxonomic kingdom filter for choosing a subset of glycans to consider
abundances pandas.core.frame.DataFrame | None None Sample abundances matrix
mode str negative MS mode: positive/negative
mass_value str monoisotopic Mass type: monoisotopic/average
sample_prep str underivatized Sample prep: underivatized/permethylated/peracetylated
mass_tolerance float 0.5 Mass tolerance for matching
reduced bool False Whether glycans are reduced
df_use pandas.core.frame.DataFrame | None None Custom glycan database
filter_out set[str] | None None Monosaccharides to ignore
verbose bool False Whether to print non-matching compositions
Returns pandas.core.frame.DataFrame | list DataFrame of structures x intensities or empty list 
mz_to_structures([674.29], glycan_class = 'O')
0 compositions could not be matched. Run with verbose = True to see which compositions.
glycan abundance
0 Neu5Ac(a2-3)Gal(b1-3)GalNAc 0
1 Neu5Ac(a2-3/6)Gal(b1-3)GalNAc 0
2 Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 0
3 Gal(a1-3)[Neu5Ac(a2-6)]GalNAc 0
4 {Neu5Ac(a2-3/6)}Gal(b1-3)GalNAc 0
5 Neu5Ac(a2-3)[GalNAc(b1-4)]Gal 0
6 Neu5Ac(a2-6)Gal(b1-3)GalNAc 0
7 Neu5Ac(a2-3/6)GalNAc(a1-6)Gal 0
8 Neu5Ac(a2-6)Gal(a1-3)GalNAc 0
9 Gal(b1-4)[Neu5Ac(a2-6)]GalNAc 0
10 Neu5Ac(a2-3)GalNAc(b1-3)Gal 0

compositions_to_structures

 compositions_to_structures (composition_list:list[dict[str,int]],
                             glycan_class:str='N', kingdom:str='Animalia',
                             abundances:pandas.core.frame.DataFrame|None=N
                             one,
                             df_use:pandas.core.frame.DataFrame|None=None,
                             verbose:bool=False)

Map compositions to structures, supporting accompanying relative intensities

Type Default Details
composition_list list List of compositions like {‘Hex’: 1, ‘HexNAc’: 1}
glycan_class str N Glycan class: N/O/lipid/free
kingdom str Animalia Taxonomic kingdom filter for choosing a subset of glycans to consider
abundances pandas.core.frame.DataFrame | None None Sample abundances matrix
df_use pandas.core.frame.DataFrame | None None Custom glycan database
verbose bool False Whether to print non-matching compositions
Returns DataFrame DataFrame of structures x intensities 
compositions_to_structures([{'Neu5Ac': 2, 'Hex': 1, 'HexNAc': 1}], glycan_class = 'O')
0 compositions could not be matched. Run with verbose = True to see which compositions.
glycan abundance
0 Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 0
1 Neu5Ac(a2-8)Neu5Ac(a2-6)[Gal(b1-3)]GalNAc 0
2 Neu5Ac(a2-3)[Neu5Ac(a2-6)]Gal(b1-3)GalNAc 0
3 Neu5Ac(a2-3)Gal(b1-4)[Neu5Ac(a2-6)]GalNAc 0 
compositions_to_structures(["H1N1A2"], glycan_class = 'O')
0 compositions could not be matched. Run with verbose = True to see which compositions.
glycan abundance
0 Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 0
1 Neu5Ac(a2-8)Neu5Ac(a2-6)[Gal(b1-3)]GalNAc 0
2 Neu5Ac(a2-3)[Neu5Ac(a2-6)]Gal(b1-3)GalNAc 0
3 Neu5Ac(a2-3)Gal(b1-4)[Neu5Ac(a2-6)]GalNAc 0

structure_to_basic

 structure_to_basic (glycan:str)

Convert glycan structure to base topology

Type Details
glycan str Glycan in IUPAC-condensed format
Returns str Base topology string 
structure_to_basic("Neu5Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
'Neu5Ac(?1-?)HexOS(?1-?)[Neu5Ac(?1-?)]HexNAc'

glycan_to_composition

 glycan_to_composition (glycan:str, stem_libr:dict[str,str]|None=None)

Map glycan to its composition

Type Default Details
glycan str Glycan in IUPAC-condensed format
stem_libr dict[str, str] | None None Modified to core monosaccharide mapping; default: created from lib
Returns dict Dictionary of monosaccharide counts 
glycan_to_composition("Neu5Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
{'Hex': 1, 'HexNAc': 1, 'Neu5Ac': 2, 'S': 1}

glycan_to_mass

 glycan_to_mass (glycan:str, mass_value:str='monoisotopic',
                 sample_prep:str='underivatized',
                 stem_libr:dict[str,str]|None=None,
                 adduct:str|float|None=None)

Calculate theoretical mass from glycan

Type Default Details
glycan str Glycan in IUPAC-condensed format
mass_value str monoisotopic Mass type: monoisotopic/average
sample_prep str underivatized Sample prep: underivatized/permethylated/peracetylated
stem_libr dict[str, str] | None None Modified to core monosaccharide mapping
adduct str | float | None None Chemical formula of adduct (e.g., “C2H4O2”) OR its exact mass in Da
Returns float Theoretical mass 
glycan_to_mass("Neu5Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
1045.2903546

composition_to_mass

 composition_to_mass (dict_comp_in:dict[str,int],
                      mass_value:str='monoisotopic',
                      sample_prep:str='underivatized',
                      adduct:str|float|None=None)

Calculate theoretical mass from composition

Type Default Details
dict_comp_in dict Composition dictionary of monosaccharide:count
mass_value str monoisotopic Mass type: monoisotopic/average
sample_prep str underivatized Sample prep: underivatized/permethylated/peracetylated
adduct str | float | None None Chemical formula of adduct (e.g., “C2H4O2”) OR its exact mass in Da
Returns float Theoretical mass 
composition_to_mass({'Neu5Ac': 2, 'Hex': 1, 'HexNAc': 1, 'S': 1})
1045.2903546

calculate_adduct_mass

 calculate_adduct_mass (formula:str, mass_value:str='monoisotopic',
                        enforce_sign:bool=False)

Calculate mass of adduct from chemical formula, including signed formulas

Type Default Details
formula str Chemical formula of adduct (e.g., “C2H4O2”, “-H2O”, “+Na”)
mass_value str monoisotopic Mass type: monoisotopic/average
enforce_sign bool False If True, returns 0 for unsigned formulas
Returns float Formula mass 
calculate_adduct_mass("C2H4O2")
60.021

get_unique_topologies

 get_unique_topologies (composition:dict[str,int], glycan_type:str,
                        df_use:pandas.core.frame.DataFrame|None=None,
                        universal_replacers:dict[str,str]|None=None,
                        taxonomy_rank:str='Kingdom',
                        taxonomy_value:str='Animalia')

Get all observed unique base topologies for composition

Type Default Details
composition dict Composition dictionary of monosaccharide:count
glycan_type str Glycan class: N/O/lipid/free/repeat
df_use pandas.core.frame.DataFrame | None None Custom glycan database to use for mapping
universal_replacers dict[str, str] | None None Base-to-specific monosaccharide mapping
taxonomy_rank str Kingdom Taxonomic rank for filtering
taxonomy_value str Animalia Value at taxonomy rank
Returns list List of unique base topologies 
get_unique_topologies({'HexNAc':2, 'Hex':1}, 'O', universal_replacers = {'dHex':'Fuc'})
['HexNAc(?1-?)[HexNAc(?1-?)]Hex',
 'HexNAc(?1-?)HexNAc(?1-?)Hex',
 'HexNAc(?1-?)Hex(?1-?)HexNAc',
 'Hex(?1-?)[HexNAc(?1-?)]HexNAc',
 'Hex(?1-?)HexNAc(?1-?)HexNAc']

get_random_glycan

 get_random_glycan (n:int=1, glycan_class:str='all',
                    kingdom:str='Animalia')
Type Default Details
n int 1 How many random glycans to sample
glycan_class str all Glycan class: N/O/lipid/free/repeat/all
kingdom str Animalia Taxonomic kingdom filter for choosing a subset of glycans to consider
Returns str | list[str] Returns a random glycan or list of glycans if n > 1 
get_random_glycan()
'Gal(b1-4)GlcNAc(b1-2)Man(a1-3/6)[GlcNAc(b1-4)Man(a1-3/6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc'