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


def GlycoDraw(
    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
)->Any: # Drawing object

Renders glycan structure using SNFG symbols or chemical structure representation

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


def annotate_figure(
    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.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')
)->str | None: # Modified SVG code

Replaces text labels with glycan drawings in SVG figure

plot_glycans_excel


def plot_glycans_excel(
    df:pandas.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
)->None:

Creates Excel file with SNFG glycan images in a new column

analysis

downstream analyses of important glycan motifs

get_pvals_motifs


def get_pvals_motifs(
    df:pandas.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.DataFrame | None=None, # Modified motif_list
    custom_motifs:list=[], # Custom motifs if using 'custom' feature set
)->DataFrame: # DataFrame with p-values, FDR-corrected p-values, and Cohen's d effect sizes for glycan motifs

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

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 0.144857 1.530905
8 Man 0.054356 0.165364 1.390253
16 Man(a1-2/3/6)Man 0.060923 0.165364 1.308333
14 Man(a1-3)Man 0.034212 0.144857 1.196586
12 Man(a1-6)Man 0.019543 0.123771 1.168815
15 Man(b1-4)GlcNAc 0.019543 0.123771 1.168815
18 GlcNAc(b1-4)GlcNAc 0.019543 0.123771 1.168815
7 Kdo 0.328790 0.496393 -0.811679
2 Glc 0.644180 0.679968 -0.811679
11 Man(a1-2)Man 0.177461 0.421470 0.772320

get_representative_substructures


def get_representative_substructures(
    enrichment_df:DataFrame, # Output from get_pvals_motifs
)->list: # Up to 10 minimal glycans containing enriched motifs

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

get_heatmap


def get_heatmap(
    df:pandas.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
)->tuple[typing.Any, list[str], pandas.DataFrame] | None: # None or (plot object, column names, transformed dataframe) if return_plot=True

Creates hierarchically clustered heatmap visualization of glycan/motif abundances

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


def plot_embeddings(
    glycans:list, # List of IUPAC-condensed glycan sequences
    emb:dict[str, numpy.ndarray] | pandas.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
)->None: # Keyword args passed to seaborn scatterplot

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

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


def characterize_monosaccharide(
    sugar:str, # Monosaccharide or linkage to analyze
    df:pandas.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
)->None:

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

characterize_monosaccharide('Rha', rank = 'Kingdom', focus = 'Fungi', modifications = True)

get_differential_expression


def get_differential_expression(
    df:pandas.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
)->DataFrame: # DataFrame with log2FC, p-values, FDR-corrected p-values, and Cohen's d/Mahalanobis distance effect sizes

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

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.
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.
Glycan Mean abundance Log2FC p-val corr p-val significant corr Levene p-val Effect size Equivalence p-val
6 Gal 18.570780 -2.810982 3.038799e-22 4.558198e-21 True 3.684477e-05 -12.047275 1.000000
11 Neu5Ac(a2-3)Gal 12.364384 -2.168027 3.371284e-22 5.056926e-21 True 6.854560e-08 -11.981208 1.000000
14 Gal(b1-3)GalNAc 12.746417 -2.286370 9.062568e-22 1.359385e-20 True 3.088705e-05 -11.369661 1.000000
10 Neu5Ac 16.580453 -2.632591 3.241244e-21 4.861866e-20 True 1.631811e-05 -10.626734 1.000000
7 GalNAc 12.906669 -2.316123 8.372261e-20 1.255839e-18 True 1.783496e-05 -8.940591 1.000000
13 Neu5Ac(a2-8)Neu5Ac 0.038743 6.249081 1.051533e-16 1.577300e-15 True 9.004822e-06 6.102326 1.000000
4 Oglycan_core1 7.969780 -1.578350 1.959493e-14 2.939239e-13 True 7.682079e-06 -4.593089 1.000000
8 GalOS 0.160252 3.935439 9.315000e-14 1.397250e-12 True 3.550473e-04 4.215097 1.000000
0 H_antigen_type2 0.247550 3.251957 8.838338e-13 1.325751e-11 True 9.729411e-06 3.718853 1.000000
5 Mucin_elongated_core2 4.776637 -0.916792 1.468039e-08 2.202059e-07 True 3.997627e-06 -2.096764 1.000000
9 GlcNAc6S(b1-6)GalNAc 1.047725 2.101894 6.500297e-08 9.750445e-07 True 7.342527e-05 1.905049 1.000000
12 Neu5Ac(a2-6)GalNAc 4.017075 -0.636913 1.990078e-05 2.985117e-04 True 9.729411e-06 -1.258696 1.000000
1 Internal_LacNAc_type2 2.332012 0.446722 1.687358e-04 2.531038e-03 True 3.997627e-06 1.043131 1.000000
3 Disialyl_T_antigen 3.796897 -0.550242 2.034837e-04 3.052256e-03 True 2.044072e-06 -1.024620 1.000000
2 Terminal_LacNAc_type2 2.444625 -0.100778 5.115432e-01 1.000000e+00 False 1.828193e-06 -0.149593 0.514385

get_volcano


def get_volcano(
    df_res:pandas.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
)->None: # Displays volcano plot

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

get_volcano(res)
You're working with a default alpha of 0.05. Set sample size (n = ...) for Bayesian-Adaptive Alpha Adjustment

get_coverage


def get_coverage(
    df:pandas.DataFrame | str | pathlib.Path, # DataFrame with glycans in rows (col 1), abundances in columns
    filepath:str='', # Path to save plot
)->None:

Visualizes glycan detection frequency across samples with intensity-based ordering

test_df = pd.concat([test_df.iloc[:, 0], test_df[test_df.columns[1:]].astype(float)], axis = 1)

get_coverage(test_df)

get_pca


def get_pca(
    df:pandas.DataFrame | str | pathlib.Path, # DataFrame with glycans in rows (col 1), abundances in columns
    groups:list[int] | pandas.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
)->None:

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

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


def get_pval_distribution(
    df_res:pandas.DataFrame | str | pathlib.Path, # Output DataFrame from get_differential_expression
    filepath:str | pathlib.Path='', # Path to save plot
)->None:

Creates histogram of p-values from differential expression analysis

get_pval_distribution(res)

get_ma


def get_ma(
    df_res:pandas.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
)->None:

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

get_ma(res)

get_glycanova


def get_glycanova(
    df:pandas.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
)->tuple: # (ANOVA results with F-stats and omega-squared effect sizes, post-hoc results)

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

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
4 Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 9.538792 0.005977 0.041838 True 0.448494
7 Neu5Ac(a2-3)Gal(b1-4)GlcNAc6S(b1-6)[Neu5Ac(a2-... 6.706481 0.016476 0.057665 True -0.052876
0 Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 2.725019 0.118763 0.169985 False 0.141106
2 Neu5Ac(a2-3)Gal(b1-3)GalNAc 2.324103 0.153551 0.169985 False 0.111983
6 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 2.676719 0.122402 0.169985 False 0.137699
5 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3/6)[GlcNAc(b1-... 2.159181 0.171420 0.171420 False 0.099422
3 Neu5Ac(a2-3)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]Ga... 2.086710 0.180073 0.180073 False 0.093789
1 Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]GalNAc 1.976577 0.194268 0.194268 False 0.085093
8 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Neu5Ac(a2-3)... 0.000000 1.000000 1.000000 False 0.352111

get_meta_analysis


def get_meta_analysis(
    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
)->tuple: # (combined effect size, two-tailed p-value)

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

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


def get_time_series(
    df:pandas.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)
)->DataFrame: # DataFrame with regression coefficients and FDR-corrected p-values

Analyzes time series glycomics data using polynomial regression

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 -0.006923 0.202954 0.202954 False
1 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 0.019568 0.091398 0.202954 False
2 Neu5Ac(a2-6)GalNAc -0.013189 0.160749 0.202954 False

get_jtk


def get_jtk(
    df_in:pandas.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
)->DataFrame: # DataFrame with JTK results: adjusted p-values, period length, lag phase, amplitude

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

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.
Molecule_Name Adjusted_P_value Period_Length Lag_Phase Amplitude significant
0 Gal(b1-3)GalNAc 0.005945 8 12 0.928571 True
1 Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 0.053172 8 12 0.642857 True
2 Neu5Ac(a2-3)Gal(b1-3)GalNAc 0.063487 8 12 0.571429 True
3 Fuc(a1-2)Gal(b1-3)GalNAc 0.265510 8 3 0.357143 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_Neu5Ac(a2-3/6) 0.014062 8 12 0.785714 True
1 Terminal_Neu5Ac(a2-6) 0.014062 8 12 0.785714 True
2 Terminal_Neu5Ac(a2-3) 0.107762 8 9 0.500000 True
3 Terminal_Fuc(a1-2) 0.265510 8 3 0.357143 False
4 Terminal_Gal(b1-3) 0.265510 8 3 0.357143 False

get_biodiversity


def get_biodiversity(
    df:pandas.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
)->tuple: # First DataFrame with diversity indices and test statistics, second with beta-diversity distance matrix

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

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  \
 0     Beta diversity (ANOSIM)          NaN          NaN  0.000000   
 1  Beta diversity (PERMANOVA)          NaN          NaN  0.000000   
 2           simpson_diversity     0.876445     0.874168  0.000520   
 3           shannon_diversity     2.242778     2.224732  0.001402   
 4            species_richness    15.000000    15.000000  1.000000   
 
    Effect size  corr p-val  significant  
 0     1.000000    0.000000         True  
 1   263.321829    0.000000         True  
 2    -0.932303    0.000520         True  
 3    -0.835249    0.001402         True  
 4     0.000000    1.000000        False  ,
 array([[ 0.        ,  2.21397052,  3.06333292, ..., 11.24499348,
         11.24499348, 11.24499348],
        [ 2.21397052,  0.        ,  2.31028564, ...,  9.50451026,
          9.50451026,  9.50451026],
        [ 3.06333292,  2.31028564,  0.        , ..., 10.63189474,
         10.63189474, 10.63189474],
        ...,
        [11.24499348,  9.50451026, 10.63189474, ...,  0.        ,
          0.        ,  0.        ],
        [11.24499348,  9.50451026, 10.63189474, ...,  0.        ,
          0.        ,  0.        ],
        [11.24499348,  9.50451026, 10.63189474, ...,  0.        ,
          0.        ,  0.        ]]))

get_SparCC


def get_SparCC(
    df1:pandas.DataFrame | str | pathlib.Path, # First DataFrame with glycans in rows (col 1) and abundances in columns
    df2:pandas.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
)->tuple: # (Spearman correlation matrix, FDR-corrected p-value matrix)

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

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


def get_roc(
    df:pandas.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
)->list[tuple[str, float]] | dict[typing.Any, tuple[str, float]] | tuple[sklearn.linear_model._logistic.LogisticRegression, float]: # (Feature scores with ROC AUC values)

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

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)

[('H_antigen_type2', 1.0),
 ('GalOS', 1.0),
 ('GlcNAc6S(b1-6)GalNAc', 1.0),
 ('Neu5Ac(a2-8)Neu5Ac', 1.0),
 ('Internal_LacNAc_type2', 0.85),
 ('Terminal_LacNAc_type2', 0.5),
 ('Neu5Ac(a2-6)GalNAc', 0.15000000000000002),
 ('Disialyl_T_antigen', 0.09999999999999998),
 ('Oglycan_core1', 0.0),
 ('Mucin_elongated_core2', 0.0),
 ('Gal', 0.0),
 ('GalNAc', 0.0),
 ('Neu5Ac', 0.0),
 ('Neu5Ac(a2-3)Gal', 0.0),
 ('Gal(b1-3)GalNAc', 0.0)]

get_lectin_array


def get_lectin_array(
    df:pandas.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
)->DataFrame: # DataFrame with altered glycan motifs, supporting lectins, and effect sizes

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

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


def get_glycoshift_per_site(
    df:pandas.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
)->DataFrame: # DataFrame with GLM coefficients and FDR-corrected p-values

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

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 Neu5Ac_Condition_coefficient Neu5Ac_Condition_corr_pval Neu5Ac_Condition_significant complex_Condition_coefficient complex_Condition_corr_pval complex_Condition_significant hybrid_Condition_coefficient ... dHex_Condition_significant high_Man_Condition_coefficient high_Man_Condition_corr_pval high_Man_Condition_significant HexNAc_Condition_coefficient HexNAc_Condition_corr_pval HexNAc_Condition_significant Hex_Condition_coefficient Hex_Condition_corr_pval Hex_Condition_significant
sp|P47710|CASA1_69 0.353257 0.000000e+00 True 0.353257 0.000000e+00 True 0.000000 1.000000e+00 False 0.353257 ... True 0.000000 1.000000 False 1.413027 0.000000e+00 True -1.563416 0.000000e+00 True
sp|P01024|CO3_85 -13.530653 0.000000e+00 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False -13.530653 ... False -13.530653 0.000000 True -27.061306 0.000000e+00 True 12.636985 0.000000e+00 True
sp|P10909|CLUS_103 -0.149909 0.000000e+00 True 4.306665 0.000000e+00 True 4.456574 0.000000e+00 True -4.606483 ... True 0.000000 1.000000 False -0.599635 0.000000e+00 True -0.749544 0.000000e+00 True
sp|Q13410|BT1A1_55 -13.100965 1.331366e-72 True -17.275380 3.955343e-111 True -4.174415 7.070812e-177 True -8.926549 ... False 0.000000 1.000000 False 12.462657 2.362957e-76 True -0.638308 6.203369e-16 True
sp|P01011|AACT_106 -0.027608 3.078766e-16 True -2.620928 0.000000e+00 True -2.593321 0.000000e+00 True 2.565713 ... True 0.000000 1.000000 False -0.110431 3.078766e-16 True -0.138038 3.848458e-16 True
sp|P00709|LALBA_90 -1.220055 1.372347e-07 True -1.773743 2.017993e-05 True -0.553687 6.915606e-01 False -0.666368 ... True 0.000000 1.000000 False -4.880222 1.372347e-07 True 3.511094 9.427322e-06 True
sp|P08571|CD14_151 0.002309 3.336465e-04 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.002309 ... False 0.002309 0.001437 True 0.004617 3.336465e-04 True 0.013851 3.336465e-04 True
sp|P07602|SAP_426 0.002851 6.752159e-03 True 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.002851 ... False 0.000000 1.000000 False 0.005702 6.752159e-03 True 0.014255 6.752159e-03 True
sp|P07602|SAP_101 -0.001653 1.856037e-01 False -0.001653 1.798929e-01 False 0.000000 1.000000e+00 False -0.001653 ... False 0.000000 1.000000 False -0.006613 1.670434e-01 False -0.008267 1.856037e-01 False
sp|P07602|SAP_215 -0.002911 2.018278e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000000 ... False 0.000000 1.000000 False -0.005822 1.834798e-01 False -0.005822 2.018278e-01 False
sp|Q08431|MFGM_238 0.144987 3.025292e-01 False -0.252363 3.799644e-01 False 0.000000 1.000000e+00 False 0.144987 ... False -0.254360 0.941176 False 0.037612 5.947780e-01 False -0.036146 3.833506e-01 False
sp|P25311|ZA2G_109 0.007570 3.250433e-01 False -0.227406 1.091050e-01 False -0.234976 1.699985e-01 False 0.242546 ... False 0.000000 1.000000 False 0.030279 3.082506e-01 False 0.037849 2.863536e-01 False
sp|P10909|CLUS_291 0.001798 3.553860e-01 False 0.001798 3.799644e-01 False 0.000000 1.000000e+00 False 0.001798 ... False 0.000000 1.000000 False 0.005395 3.300013e-01 False 0.008992 2.887511e-01 False
sp|P10909|CLUS_86 -0.000590 4.353640e-01 False -0.000590 4.375966e-01 False 0.000000 1.000000e+00 False -0.000590 ... False 0.000000 1.000000 False -0.002359 3.809435e-01 False -0.002949 3.585351e-01 False
sp|Q08380|LG3BP_125 -0.001393 5.111342e-01 False -0.001393 5.160489e-01 False 0.000000 1.000000e+00 False -0.001393 ... False 0.000000 1.000000 False -0.005570 4.510007e-01 False -0.006963 3.833506e-01 False
sp|P0C0L5|CO4B_HUMAN/sp|P0C0L4|CO4A 0.000775 5.322439e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False 0.000775 ... False 0.000775 0.941176 False 0.001550 4.731057e-01 False 0.006973 4.055191e-01 False
sp|P10909|CLUS_374 -0.001409 5.699737e-01 False -0.001409 6.709711e-01 False 0.000000 1.000000e+00 False -0.001409 ... False 0.000000 1.000000 False -0.005638 5.285468e-01 False -0.007047 5.111285e-01 False
sp|P02788|TRFL_156 -3.489318 5.699737e-01 False 0.698868 7.657590e-01 False -0.484459 8.650370e-01 False -3.004858 ... True 0.000000 1.000000 False -4.184906 1.192373e-01 False 2.754223 2.863536e-01 False
sp|P01833|PIGR_186 -0.003925 5.699737e-01 False -0.040250 6.709711e-01 False -0.036325 8.529412e-01 False 0.032400 ... False 0.000000 1.000000 False -0.015701 5.285468e-01 False 0.012773 6.584205e-01 False
sp|P01871|IGHM_46 -0.001251 5.699737e-01 False -0.001251 6.709711e-01 False 0.000000 1.000000e+00 False -0.001251 ... False 0.000000 1.000000 False -0.005002 5.285468e-01 False -0.006253 5.059774e-01 False
sp|P02788|TRFL_497 0.298762 5.699737e-01 False -1.945478 3.755496e-01 False -11.524302 2.311371e-04 True -13.834315 ... False 0.000000 1.000000 False 1.195049 5.285468e-01 False 1.832253 2.887511e-01 False
sp|P02749|APOH_253 0.000764 5.765965e-01 False 0.001528 6.709711e-01 False 0.000764 8.529412e-01 False 0.000000 ... False 0.000000 1.000000 False 0.003055 5.285468e-01 False 0.003819 5.285468e-01 False
sp|P01876|IGHA1_340 4.562124 6.627967e-01 False 4.150781 5.641617e-02 True -4.646176 8.529412e-01 False -2.153690 ... False 3.337078 0.941176 False -0.276113 7.563851e-01 False -0.665575 6.974410e-01 False
sp|P0DOX2|IGA2_HUMAN/sp|P01877|IGHA2 -2.201206 6.628949e-01 False -2.015248 1.078886e-01 False -5.152422 2.302813e-01 False -3.155824 ... False -4.195685 0.941176 False -0.303173 5.945689e-01 False 1.493354 2.863536e-01 False
sp|P06858|LIPL_70 -0.000852 6.628949e-01 False -0.001704 7.657590e-01 False -0.000852 8.529412e-01 False 0.000000 ... False 0.000000 1.000000 False -0.003409 6.290163e-01 False -0.004261 6.290163e-01 False
sp|P02790|HEMO_453 -0.000284 6.937840e-01 False -0.000568 8.094146e-01 False -0.000284 8.529412e-01 False 0.000000 ... False 0.000000 1.000000 False -0.001136 6.937840e-01 False -0.001420 6.937840e-01 False
sp|P19652|A1AG2_HUMAN/sp|P02763|A1AG1 -0.000680 7.292625e-01 False -0.001360 8.167433e-01 False -0.000680 8.529412e-01 False 0.000000 ... False 0.000000 1.000000 False -0.002719 7.292625e-01 False -0.003399 7.292625e-01 False
sp|P01877|IGHA2_327 -0.692246 7.511404e-01 False 0.000000 1.000000e+00 False 0.000000 1.000000e+00 False -0.692246 ... False 3.982010 0.941176 False -1.384492 7.511404e-01 False 0.179438 9.432450e-01 False
sp|P02765|FETUA_156 -0.000528 7.584045e-01 False -0.001057 8.167433e-01 False -0.000528 8.529412e-01 False 0.000000 ... False 0.000000 1.000000 False -0.002113 7.584045e-01 False -0.002642 7.584045e-01 False
sp|P01833|PIGR_499 -2.996462 7.877926e-01 False -3.201522 2.545075e-01 False -1.686467 8.529412e-01 False -2.734399 ... False 0.000000 1.000000 False 2.897029 3.082506e-01 False -0.906962 6.401825e-01 False
sp|P01591|IGJ_71 1.352444 7.903746e-01 False 0.215802 8.198211e-01 False -0.144731 9.420876e-01 False 1.120894 ... False 0.000000 1.000000 False 0.425201 5.285468e-01 False -0.931993 3.833506e-01 False
sp|P01833|PIGR_421 0.337457 8.662386e-01 False 0.487402 6.709711e-01 False 0.000000 1.000000e+00 False 0.337457 ... False 0.000000 1.000000 False -0.236225 8.245977e-01 False 0.026444 9.778702e-01 False
sp|P01833|PIGR_469 0.914459 8.709747e-01 False -4.184570 3.942310e-02 True 14.610788 1.012432e-04 True 6.866686 ... True 0.000000 1.000000 False 2.097292 3.427967e-01 False -2.417376 2.863536e-01 False
sp|P00738|HPT_241 0.000030 9.880762e-01 False 0.000061 9.880762e-01 False 0.000030 9.880762e-01 False 0.000000 ... False 0.000000 1.000000 False 0.000121 9.880762e-01 False 0.000152 9.880762e-01 False

34 rows × 27 columns

annotate

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

annotate_glycan


def annotate_glycan(
    glycan:str | networkx.classes.digraph.DiGraph, # IUPAC-condensed glycan sequence or NetworkX graph
    motifs:pandas.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
)->DataFrame: # DataFrame with motif counts for the glycan

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

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


def annotate_dataset(
    glycans:list, # List of IUPAC-condensed glycan sequences
    motifs:pandas.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
)->DataFrame: # DataFrame mapping glycans to presence/absence of motifs

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

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


def quantify_motifs(
    df:str | pandas.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
)->DataFrame: # DataFrame with motif abundances (motifs as columns, samples as rows)

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

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 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
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 22.219773 38.119351 34.492798 21.576036 35.006672 30.847852 39.239054 25.722979 34.591496 16.715338 ... 48.608621 40.893926 41.366216 44.132349 36.994779 23.981142 11.515995 2.819886 45.199008 46.969521
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
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

15 rows × 40 columns

get_k_saccharides


def get_k_saccharides(
    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
)->pandas.DataFrame | list[list[str]]: # DataFrame of k-saccharide counts or list of motifs per glycan

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

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)
  Kdo(a2-4)Kdo Kdo(a2-4/5)Kdo Man(a1-2)Man Man(a1-6)Man Fuc(a1-3)GlcNAc Man(a1-3)Man Man(b1-4)GlcNAc GlcNAcA(a1-4)Kdo Man(a1-2/3/6)Man Kdo(a2-5)Kdo GalNAc(a1-4)GlcNAcA Xyl(b1-2)Man GlcN(b1-7)Kdo Kdo(a2-6)GlcN4P GlcNAc(b1-4)GlcNAc GlcN4P(b1-6)GlcN4P Xyl(b1-2)Man(b1-4)GlcNAc Kdo(a2-5)Kdo(a2-6)GlcN4P Kdo(a2-4)[Kdo(a2-5)]Kdo Man(b1-4)GlcNAc(b1-4)GlcNAc GlcNAcA(a1-4)[GlcN(b1-7)]Kdo Kdo(a2-4)Kdo(a2-6)GlcN4P Xyl(b1-2)[Man(a1-3/6)]Man Fuc(a1-3)[GlcNAc(b1-4)]GlcNAc GalNAc(a1-4)GlcNAcA(a1-4)Kdo Man(a1-3/6)Man(b1-4)GlcNAc Kdo(a2-4/5)Kdo(a2-6)GlcN4P Man(a1-2/3)Man(a1-2/3/6)Man Man(a1-2)Man(a1-3)Man Man(a1-6)Man(b1-4)GlcNAc Man(a1-3)Man(b1-4)GlcNAc Kdo(a2-6)GlcN4P(b1-6)GlcN4P Xyl(b1-2)[Man(a1-6)]Man GlcN(b1-7)Kdo(a2-5)Kdo Man(a1-3)[Man(a1-6)]Man GlcNAcA(a1-4)Kdo(a2-5)Kdo Man(a1-3)Man(a1-6)Man Man(a1-2)Man(a1-2)Man Xyl(b1-2)[Man(a1-3)]Man
0 0 0 0 1 1 1 1 0 2 0 0 1 0 0 1 0 1 0 0 1 0 0 2 1 0 2 0 0 0 1 1 0 1 0 1 0 0 0 1
1 0 0 2 1 0 2 1 0 5 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 2 0 3 1 1 1 0 0 0 1 0 1 1 0
2 1 2 0 0 0 0 0 1 0 1 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 2 0 0 0 0 1 0 1 0 1 0 0 0

get_terminal_structures


def get_terminal_structures(
    glycan:str | networkx.classes.digraph.DiGraph, # IUPAC-condensed glycan sequence or NetworkX graph
    size:int=1, # Number of monosaccharides in terminal fragment (1 or higher)
)->list: # List of terminal structures with linkages

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

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


def get_molecular_properties(
    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
)->DataFrame: # DataFrame with molecular parameters from PubChem

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

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"])
  undefined_atom_stereo_count isotope_atom_count defined_atom_stereo_count bond_stereo_count undefined_bond_stereo_count defined_bond_stereo_count tpsa monoisotopic_mass xlogp molecular_weight atom_stereo_count complexity h_bond_donor_count heavy_atom_count exact_mass h_bond_acceptor_count covalent_unit_count charge rotatable_bond_count
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 1 0 56 0 0 0 1070 2222.7830048 -23.600000 2224.0 57 4410 39 152 2222.7830048 62 1 0 43

get_glycan_similarity


def get_glycan_similarity(
    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.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
)->float: # Cosine similarity between glycan1 and glycan2

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

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


def glycan_to_nxGraph(
    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
)->DiGraph: # NetworkX graph object of glycan

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

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


def graph_to_string(
    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'
)->str: # IUPAC-condensed glycan string

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

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


def compare_glycans(
    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
)->bool: # True if glycans are same, False if not

Check whether two glycans are identical

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


def subgraph_isomorphism(
    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
)->bool | int | tuple[int, list[list[int]]]: # Boolean presence, count, or (count, matches)

Check if motif exists as subgraph in glycan

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


def generate_graph_features(
    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
)->DataFrame: # Dataframe of graph features

Compute graph features of glycan or network

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


def largest_subgraph(
    glycan_a:str | networkx.classes.digraph.DiGraph, # First glycan
    glycan_b:str | networkx.classes.digraph.DiGraph, # Second glycan
)->str: # Largest common subgraph in IUPAC format

Find the largest common subgraph of two glycans

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


def ensure_graph(
    glycan:str | networkx.classes.digraph.DiGraph, # Glycan in IUPAC-condensed format or as networkx graph
    kwargs:VAR_KEYWORD
)->DiGraph: # NetworkX graph object of glycan

Ensures function compatibility with string glycans and graph glycans

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


def get_possible_topologies(
    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)
)->list: # List of possible topology strings or graphs

Create possible glycan graphs given a floating substituent

possible_topology_check


def possible_topology_check(
    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
)->list: # List of matching glycans

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

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


def deduplicate_glycans(
    glycans:list[str] | set[str], # List/set of glycans to deduplicate
)->list: # Deduplicated list of glycans

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

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(a2-6)Gal(b1-3)[Gal(b1-4)GlcNAc(b1-6)]GalNAc',
 'Fuc(a1-2)Gal(b1-3)GalNAc',
 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Neu5Ac(a2-3)Gal(b1-3)]GalNAc']

processing

process IUPAC-condensed glycan sequences into glycoletters etc.

min_process_glycans


def min_process_glycans(
    glycan_list:list, # List of glycans in IUPAC-condensed format
)->list: # List of glycoletter lists

Convert list of glycans into a nested lists of glycoletters

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


def get_lib(
    glycan_list:list, # List of IUPAC-condensed glycan sequences
)->dict: # Dictionary of glycoletter:index mappings

Returns dictionary mapping glycoletters to indices

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


def expand_lib(
    libr_in:dict, # Existing dictionary of glycoletter:index
    glycan_list:list, # List of IUPAC-condensed glycan sequences
)->dict: # Updated dictionary with new glycoletters

Updates libr with newly introduced 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


def presence_to_matrix(
    df:DataFrame, # DataFrame with glycan occurrence
    glycan_col_name:str='glycan', # Column name for glycans
    label_col_name:str='Species', # Column name for labels
)->DataFrame: # Matrix with labels as rows and glycan occurrences as columns

Converts a dataframe with glycan occurrence to absence/presence matrix

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 Glc(a1-4)GlcA(b1-2)GlcA Glc(b1-2)Ara Glc(b1-2)Ara(a1-2)GlcA Glc(b1-2)Gal(b1-2)Gal(b1-2)GlcA Glc(b1-2)Gal(b1-2)GlcA Glc(b1-2)Gal(b1-2)GlcA(b1-3)[Glc(b1-3)]Ara Glc(b1-2)Glc Glc(b1-2)Glc(a1-2)Fru Glc(b1-2)Glc(a1-2)FrufOBzOCin Glc(b1-2)Glc(b1-2)Glc Glc(b1-2)GlcA Glc(b1-2)Xyl Glc(b1-2)[Ara(a1-3)]GlcA6Me Glc(b1-2)[Ara(a1-3)]GlcAOMe Glc(b1-2)[Ara(a1-6)]Glc Glc(b1-2)[Glc(b1-3)]Glc(a1-2)Fruf Glc(b1-2)[Glc(b1-3)]Glc1Fer6Ac(a1-2)Fruf1FerOBz Glc(b1-2)[Glc(b1-3)]Glc6Ac(a1-2)Fru Glc(b1-2)[Glc6Ac(b1-3)]Glc(a1-2)Fru Glc(b1-2)[Glc6Ac(b1-3)]Glc1Fer(a1-2)Fruf1FerOBz Glc(b1-2)[Glc6Ac(b1-3)]Glc6Ac(a1-2)Fru Glc(b1-2)[Rha(a1-3)]GlcA Glc(b1-2)[Xyl(b1-2)Ara(a1-6)]Glc Glc(b1-2)[Xyl(b1-2)D-Fuc(b1-6)]Glc Glc(b1-3)Ara Glc(b1-3)Glc Glc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)Fru Glc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)Fruf Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)Fru Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)Fruf Glc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1CoumOBz 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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


def enforce_class(
    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
)->bool: # True if glycan is in glycan class

Determines whether glycan belongs to a specified class

enforce_class("Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-6)]GlcNAc", "O")
False

IUPAC_to_SMILES


def IUPAC_to_SMILES(
    glycan_list:str | list[str], # List of IUPAC-condensed glycans or single glycan
)->list: # List of corresponding SMILES strings

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

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


def canonicalize_composition(
    comp:str, # Composition in Hex5HexNAc4Fuc1Neu5Ac2 or H5N4F1A2 format
)->dict: # Dictionary of monosaccharide:count

Converts composition from any common format to standardized dictionary

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


def canonicalize_iupac(
    glycan:str, # Glycan sequence in any supported format
)->str: # Standardized IUPAC-condensed format

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


def get_possible_linkages(
    wildcard:str, # Pattern to match, ? can be wildcard
    linkage_list:list={'b2-4', 'b1-6', '?1-?', 'b2-7', 'a2-3', 'a1-1', 'b1-5', 'a2-11', 'b2-8', 'b1-4', 'a1-3', 'b2-2', 'b1-2', 'b1-3', 'a2-1', 'a1-5', '?2-6', 'a1-8', 'a1-11', '?1-3', '?1-6', 'a2-4', 'a1-2', 'a2-2', 'b2-6', 'b2-1', 'a2-8', 'b1-?', '?1-2', '?2-3', '?2-8', 'a2-6', 'a1-7', 'b1-7', '?2-?', 'a1-6', 'a2-9', 'a1-9', 'a2-?', 'b2-5', 'a1-4', 'b1-9', '1-6', 'a2-7', 'b1-1', 'a1-?', 'b1-8', 'b2-3', 'a2-5', '?1-4', '1-4'}, # List of linkages to search
)->set: # Matching linkages

Retrieves all linkages that match a given wildcard pattern

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


def get_possible_monosaccharides(
    wildcard:str, # Monosaccharide type; options: Hex, HexNAc, dHex, Sia, HexA, Pen, HexOS, HexNAcOS
)->set: # Matching monosaccharides

Retrieves all matching common monosaccharides of a type

get_possible_monosaccharides("HexNAc")
{'GalNAc', 'GlcNAc', 'HexNAc', 'ManNAc'}

equal_repeats


def equal_repeats(
    r1:str, # First glycan sequence
    r2:str, # Second glycan sequence
)->bool: # True if repeats are shifted versions

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

equal_repeats("Fuc2S3S(a1-3)Fuc2S(a1-4)Fuc2S3S", "Fuc2S(a1-4)Fuc2S3S(a1-3)Fuc2S")
True

get_class


def get_class(
    glycan:str, # Glycan in IUPAC-condensed nomenclature
)->str: # Glycan class (repeat, O, N, free, lipid, lipid/free, or empty)

Determines glycan class

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


def get_insight(
    glycan:str, # Glycan in IUPAC-condensed format
    motifs:pandas.DataFrame | None=None, # DataFrame of glycan motifs; default:motif_list
)->None: # Prints glycan meta-information

Print meta-information about a glycan

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


def glytoucan_to_glycan(
    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
)->list: # List of glycans or IDs

Convert between GlyTouCan IDs and IUPAC-condensed glycans

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


def get_match(
    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
)->bool | list[str]: # Match results

Find matches for glyco-regular expression in glycan

# {} = 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


def get_match_batch(
    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
)->list[bool] | list[list[str]]: # Match results for each glycan

Find glyco-regular expression matches in list of glycans

motif_to_regex


def motif_to_regex(
    motif:str, # Glycan in IUPAC-condensed
)->str: # Regular expression

Convert glycan motif to regular expression pattern

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


def string_to_labels(
    character_string:str, # String to tokenize
    libr:dict[str, int] | None=None, # Dictionary mapping characters to indices
)->list: # List of character indices

Tokenize word by indexing characters in library

string_to_labels(['Man','a1-3','Man','a1-6','Man'])
[None, None, None, None, None]

pad_sequence


def pad_sequence(
    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
)->list: # Padded sequence

Pad sequences to same length using padding token

pad_sequence(string_to_labels(['Man','a1-3','Man','a1-6','Man']), 7)
[None, None, None, None, None, 25, 25]

stemify_glycan


def stemify_glycan(
    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
)->str: # Stemmed glycan string

Remove modifications from all monosaccharides in glycan

stemify_glycan("Neu5Ac9Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
'Neu5Ac(a2-3)Gal(b1-3)[Neu5Ac(a2-6)]GalNAc'

stemify_dataset


def stemify_dataset(
    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
)->DataFrame: # DataFrame with stemified glycans

Remove monosaccharide modifications from all glycans in dataset

mask_rare_glycoletters


def mask_rare_glycoletters(
    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))
)->list: # List of glycans with masked rare elements

Mask rare monosaccharides and linkages in glycans

mz_to_composition


def mz_to_composition(
    mz_value:float, # m/z value from mass spec
    mode:str='negative', # MS mode: positive/negative
    mass_value:str='monoisotopic', # Mass type: monoisotopic/average
    modification:str | None=None, # Reducing end modification: reduced/2AA/2AB
    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.DataFrame | None=None, # Custom glycan database
    filter_out:set[str] | None=None, # Monosaccharides to ignore during composition finding
    deprioritized:set[str] | None={'PCho', 'HexA', 'Me'}, # Monosaccharides to use only as fallback if no other composition matches
    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"
    mass_tag:float | None=None, # Mass in Da of a reducing-end label (e.g., 137.14 for 2AA, 219.21 for 2AB+procA), subtracted from mz_value
)->list: # List of matching compositions

Map m/z value to matching monosaccharide composition

mz_to_composition(665.4, glycan_class='O', filter_out={'Kdn', 'P', 'HexA', 'Pen', 'HexN', 'Me', 'PCho', 'PEtN'},
                    modification = "reduced")
[{'Hex': 2, 'HexNAc': 2, 'Neu5Ac': 2}]

match_composition_relaxed


def match_composition_relaxed(
    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.DataFrame | None=None, # Custom glycan database
)->list: # List of matching glycans

Map coarse-grained composition to 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


def condense_composition_matching(
    matched_composition:list, # List of matching glycans
)->list: # Minimal list of representative glycans

Find minimum set of glycans characterizing matched composition

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


def mz_to_structures(
    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.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
    modification:str | None=None, # Reducing end modification: reduced/2AA/2AB
    df_use:pandas.DataFrame | None=None, # Custom glycan database
    filter_out:set[str] | None=None, # Monosaccharides to ignore
    deprioritized:set[str] | None={'PCho', 'HexA', 'Me'}, # Monosaccharides to use only as fallback if no other composition matches
    verbose:bool=False, # Whether to print non-matching compositions
    mass_tag:float | None=None, # Mass in Da of a reducing-end label (e.g., 137.14 for 2AA), subtracted from each m/z before matching
)->pandas.DataFrame | list: # DataFrame of structures x intensities or empty list

Map precursor masses to structures, supporting accompanying relative intensities

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 Fuc(a1-2)Gal(b1-4)[Fuc(a1-3)]GlcNAc 0

compositions_to_structures


def compositions_to_structures(
    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.DataFrame | None=None, # Sample abundances matrix
    df_use:pandas.DataFrame | None=None, # Custom glycan database
    verbose:bool=False, # Whether to print non-matching compositions
)->DataFrame: # DataFrame of structures x intensities

Map compositions to structures, supporting accompanying relative 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


def structure_to_basic(
    glycan:str, # Glycan in IUPAC-condensed format
)->str: # Base topology string

Convert glycan structure to base topology

structure_to_basic("Neu5Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
'Neu5Ac(?1-?)HexOS(?1-?)[Neu5Ac(?1-?)]HexNAc'

glycan_to_composition


def glycan_to_composition(
    glycan:str, # Glycan in IUPAC-condensed format
    stem_libr:dict[str, str] | None=None, # Modified to core monosaccharide mapping; default: created from lib
)->dict: # Dictionary of monosaccharide counts

Map glycan to its composition

glycan_to_composition("Neu5Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
{'Hex': 1, 'HexNAc': 1, 'Neu5Ac': 2, 'S': 1}

glycan_to_mass


def glycan_to_mass(
    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
    modification:str | None=None, # Reducing end modification: reduced/2AA/2AB
)->float: # Theoretical mass

Calculate theoretical mass from glycan

glycan_to_mass("Neu5Ac(a2-3)Gal6S(b1-3)[Neu5Ac(a2-6)]GalNAc")
1045.2903546

composition_to_mass


def composition_to_mass(
    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
    modification:str | None=None, # Reducing end modification: reduced/2AA/2AB
)->float: # Theoretical mass

Calculate theoretical mass from composition

composition_to_mass({'Neu5Ac': 2, 'Hex': 1, 'HexNAc': 1, 'S': 1})
1045.2903546

calculate_adduct_mass


def calculate_adduct_mass(
    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
)->float: # Formula mass

Calculate mass of adduct from chemical formula, including signed formulas

calculate_adduct_mass("C2H4O2")
60.021

get_unique_topologies


def get_unique_topologies(
    composition:dict, # Composition dictionary of monosaccharide:count
    glycan_type:str, # Glycan class: N/O/lipid/free/repeat
    df_use:pandas.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
)->list: # List of unique base topologies

Get all observed unique base topologies for composition

get_unique_topologies({'HexNAc':2, 'Hex':1}, 'O', universal_replacers = {'dHex':'Fuc'})
['Hex(?1-?)HexNAc(?1-?)HexNAc',
 'HexNAc(?1-?)HexNAc(?1-?)Hex',
 'HexNAc(?1-?)[HexNAc(?1-?)]Hex',
 'Hex(?1-?)[HexNAc(?1-?)]HexNAc',
 'HexNAc(?1-?)Hex(?1-?)HexNAc']

get_random_glycan


def get_random_glycan(
    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
)->str | list[str]: # Returns a random glycan or list of glycans if n > 1

Sample random glycans from the SugarBase database

get_random_glycan()
'Man(b1-2)Man(b1-2)Man(a1-2)Man(a1-2)Man'