glycanPRMQuant

glycanPRMQuant is a Python package for targeted PRM glycomics analysis from Thermo .raw or .mzML data. It extracts MS2 spectra, matches precursor ions to N-glycan compositions, generates theoretical fragments from IUPAC structures, resolves likely structures, plots chromatograms/spectra, and quantifies glycan signal by AUC.

The package can be run from a Tkinter GUI for batch processing or called programmatically from Python.

What It Does

• Reads Thermo .raw files directly with AlphaRaw, or .mzML files with pyteomics.
• Matches MS1 precursor m/z values against glycan compositions.
• Calculates precursor neutral masses from the bundled N_glycan_db.csv using glypy, grouped once per Composition.
• Generates theoretical MS2 fragments from each candidate Condensed IUPAC structure for a matched numerical composition.
• Scores candidate IUPAC structures and returns the most likely structure with the numerical composition.
• Supports configurable fragment ion series, maximum cleavage count, m/z tolerances, intensity thresholds, smoothing, and AUC boundary logic.
• Produces per-glycan MS2 CSV files, chromatograms, spectra, AUC tables, and optional Skyline transition lists.
• Runs one file or many files in parallel.

Repository Layout

• glycanPRMQuant/processmzML.py
  Single-file end-to-end pipeline: extraction, MS1 matching, MS2 matching, plotting, AUC, and optional Skyline export.
• glycanPRMQuant/spectra.py and glycanPRMQuant/thermo_raw.py Input-format dispatch and direct Thermo RAW extraction through AlphaRaw.
• glycanPRMQuant/parallelProcess.py
  Parallel multi-file runner used by the GUI and programmatic batch workflows.
• glycanPRMQuant/pipelineGUI.py
  Tkinter GUI for selecting input files, output folder, a shared glycan database, matching parameters, plotting options, and batch execution.
• glycanPRMQuant/matchMS1.py
  Precursor matching. Uses the N-glycan database by default and calculates neutral masses from grouped IUPAC compositions.
• glycanPRMQuant/matchMS2.py
  Fragment matching. Generates fragments from IUPAC candidates, matches observed fragments, and selects the best IUPAC structure.
• glycanPRMQuant/fragment_structure.py
glypy-based theoretical glycan fragmentation.
• glycanPRMQuant/calculateAUC.py
  Peak picking, integration windows, smoothing, and AUC summarization.
• glycanPRMQuant/plotFragmentIntensity.py and plotMS2spectrum.py
  Chromatogram and spectrum plotting utilities.
• glycanPRMQuant/database/N_glycan_db.csv
  Default structure database with Condensed IUPAC, Composition, and Numerical Composition columns.

Installation

(Optional) Create a new python environment:

python -m venv .venv

(Optional) Activate the environment:

# Windows
.venv\Scripts\activate

# macOS/Linux
source .venv/bin/activate

Install from PyPi:

python -m pip install --upgrade pip
pip install glycanprmquant

Check the command-line entry point and bundled database:

glycan-prmquant --help
python -c "from glycanPRMQuant.constants import DEFAULT_PRECURSOR_DB; import os; print(os.path.exists(DEFAULT_PRECURSOR_DB), DEFAULT_PRECURSOR_DB)"

The package expects Python >=3.12.

Development Install

For local development, clone the repository and install it in editable mode:

git clone https://github.com/Elquimico09/GlycanPRMQuant.git
cd GlycanPRMQuant
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

On Windows, activate the environment with:

.venv\Scripts\activate

Dependencies

Installed from pyproject.toml:

• alpharaw
• numpy
• pandas
• scipy
• matplotlib
• seaborn
• statsmodels
• scikit-learn
• openpyxl
• scienceplots
• pyteomics
• psims
• glypy
• lxml

Thermo RAW files are read directly; no mzML conversion is performed. AlphaRaw initializes its .NET runtime only when a .raw file is read, so mzML-only runs continue to use Pyteomics without loading .NET. Convert unsupported vendor formats to .mzML with a tool such as ProteoWizard msconvert.

Development Checks

Install the development extra and run the tests:

pip install -e ".[dev]"
python -m pytest
python -m build
python -m twine check dist/*

Quick Start: GUI

Run:

glycan-prmquant gui

In the GUI:

1. Select one or more Thermo .raw files or .mzML files. Do not mix formats within one batch.
2. Select an output folder.
3. Optionally select a custom glycan database. Leave the field blank to use the bundled N_glycan_db.csv. One custom database is used for both precursor and fragment matching and must contain nonblank Condensed IUPAC, Composition, and Numerical Composition columns.
4. Choose MS2 noise filtering. Automatic is the default; Off retains all positive centroids, and Manual enables an absolute intensity threshold. Set the MS1 and fragment mass tolerances.
5. Set fragment options:
   • Fragment ion series: any combination of A, B, C, X, Y, Z. Default: ABCXYZ.
   • Max cleavages: maximum number of cleavages used during theoretical fragmentation. Default: 2.
6. For a replicate batch, set the cross-run peak-consensus controls. The default aligned RT tolerance is ±0.3 minutes and a peak group must occur in at least 0.8 (80%) of the input runs. All files selected together are treated as one comparison set.
7. Choose output options and run.

You can also launch the GUI as a module:

python -m glycanPRMQuant.pipelineGUI

Quick Start: Command Line

Process one file:

glycan-prmquant run path/to/sample.raw path/to/output_dir \
  --ppm-ms1-tol 10 \
  --fragment-mass-tol 0.02 \
  --figure-filetype pdf \
  --fragment-ion-series BY \
  --fragment-max-cleavages 2

Process a folder of Thermo .raw files:

glycan-prmquant batch \
  --input-dir path/to/raw_folder \
  --output-root path/to/results \
  --workers 4

Process specific files:

glycan-prmquant batch \
  --input-files path/to/file1.raw path/to/file2.raw \
  --output-root path/to/results \
  --workers 2

Useful CLI flags:

• --precursor-db-path and --structure-db-path override the bundled N_glycan_db.csv.
• --skyline-transition writes Skyline transition lists.
• --disable-smoothing disables chromatogram/AUC smoothing.
• --disable-isobaric-resolution reports all glycan assignments whose precursor m/z values fall within the 20 ppm isobaric-resolution window. Candidate scores are still calculated, but contested assignments are not included in composition-level AUC because no winner was selected.
• --candidate-min-fragments, --candidate-min-explained-intensity, --candidate-min-score, and --candidate-min-evidence-difference control assignment acceptance and isobaric resolution. --candidate-mass-outlier-min-delta sets the minimum precursor-error separation used by audited pruning.
• --fragment-mass-tol supplies the numeric product-ion tolerance and --fragment-mass-tol-unit {da,ppm} selects its unit. The defaults remain 0.02 Da; 20 ppm is a typical high-resolution starting point, while a wider Da tolerance should be validated for low-resolution data.
• --figure-filetype {png,pdf,svg} selects the format for every chromatogram and MS2 spectrum generated by the pipeline. The default is pdf.
• --ms2-noise-filter-mode {auto,off,manual} controls product-ion denoising. Automatic filtering is the default. --intensity-threshold is used only in manual mode.
• Target-decoy validation is enabled by default. --candidate-max-q-value sets the acceptance threshold, --target-decoy-seed makes the paired decoy library reproducible, and --disable-target-decoy disables this validation.
• Multi-file runs perform cross-run RT alignment and consensus peak selection by default. --consensus-rt-tolerance controls the allowed aligned apex ΔRT in either direction (for example, 0.5 means ±0.5 min), --consensus-min-replicate-fraction controls the required run coverage, and --disable-consensus-peak-selection restores legacy glycan-level AUC consolidation.
• --quiet shows warnings/errors only.
• -v and -vv increase logging verbosity.

Quick Start: Single File

from glycanPRMQuant.processmzML import process_mzml_pipeline

process_mzml_pipeline(
    mzml_file="path/to/sample.raw",
    output_dir="path/to/output_dir",
    ppm_ms1_tol=10,
    ms2_noise_filter_mode="auto",
    fragment_mass_tol=20,
    fragment_mass_tol_unit="ppm",
    fragment_ion_series="BY",
    fragment_max_cleavages=2,
)

Quick Start: Multiple Files

On Windows, keep the if __name__ == "__main__" guard for multiprocessing.

import multiprocessing
from glycanPRMQuant.parallelProcess import run_parallel_pipeline

if __name__ == "__main__":
    multiprocessing.freeze_support()
    run_parallel_pipeline(
        input_files=[
            r"path\to\file1.raw",
            r"path\to\file2.raw",
        ],
        output_root=r"path\to\results",
        n_workers=4,
        ppm_ms1_tol=10,
        fragment_mass_tol=0.02,
        fragment_mass_tol_unit="Da",
        fragment_ion_series="ABCXYZ",
        fragment_max_cleavages=2,
    )

Custom Databases

By default, both MS1 and MS2 use the bundled N_glycan_db.csv.

You can override the database paths:

process_mzml_pipeline(
    mzml_file="path/to/sample.raw",
    output_dir="path/to/output_dir",
    precursor_db_path="path/to/N_glycan_db.csv",
    structure_db_path="path/to/N_glycan_db.csv",
)

The N-glycan structure database should include:

• Condensed IUPAC
• Composition
• Numerical Composition

Numerical Composition is validated against every condensed IUPAC structure as the concatenated HexNAc, Hex, Fuc, Neu5Ac, and Neu5Gc residue counts, in that order.

matchMS1 groups by Composition and calculates mass once per composition. matchMS2 groups by Numerical Composition and fragments each candidate IUPAC structure for that composition.

Matching Details

MS1

matchMS1 calculates neutral masses from the first parsable IUPAC structure for each unique Composition, then generates precursor adduct m/z values:

• 2H
• 3H
• 4H
• H+NH4
• 2NH4

The output includes:

• precursor_mz
• Glycan using the numerical composition ID when available
• Adduct
• database_mz
• ppm_error

MS2

matchMS2 uses the matched numerical composition to find all candidate IUPAC structures, generates theoretical fragments, and matches observed fragments by m/z tolerance.

Protonated +H and +2H product ions are considered for every precursor. For an H+NH4 precursor assignment, +NH4 and +H+NH4 product ions are also considered. For a 2NH4 precursor assignment, +NH4 and +2NH4 product ions are also considered. Product-ion matching is restricted to the adduct forms allowed by that scan's assigned precursor adduct.

For each theoretical fragment whose substructure contains NeuAc, matching also includes a methanol neutral-loss variant (-CH3OH). The neutral mass loss is 32.026215 Da, so the fragment m/z shift is divided by its charge.

It scores candidate structures by:

1. Total matched fragment count
2. Unique matched fragment count
3. Total matched fragment intensity
4. Mean absolute ppm error

The returned rows are restricted to the selected best-scoring IUPAC and include:

• Glycan
• NumericalComposition
• Composition
• IUPAC
• Fragment
• BaseFragment
• FragmentType
• fragment_annotation
• fragment_iupac
• contains_neuac
• neutral_loss
• fragment_mz
• fragment_intensity
• Charge
• Adduct
• IUPAC_match_count
• IUPAC_unique_fragments
• IUPAC_total_intensity

Automatic MS2 noise filtering

Automatic filtering operates on positive centroid peaks independently for each MS2 scan. The lower 60% of the scan's centroid intensities defines a robust low-intensity population, and the floor is:

noise floor = median + 3 * 1.4826 * MAD

Centroids must be strictly above that floor. A scan with fewer than 20 peaks borrows intensities from up to two adjacent scans on either side belonging to the same precursor-m/z cluster. If the pooled population is still too small, that scan is deliberately left unfiltered rather than applying an unreliable estimate. Precursor matching uses the unfiltered scan metadata, so denoising cannot erase a precursor assignment merely because every product ion was removed. Candidate explained-intensity scoring uses the denoised TIC; both raw and denoised TIC values and all calculated floors are retained in ms2_noise_filter_audit.csv. Input spectra should be centroided.

Denoising is used only for identification and candidate scoring. After a candidate is accepted, its observed transition list is re-extracted from the original positive centroid data. Re-extraction is restricted to the accepted precursor/adduct and chromatographic feature boundaries. Within each scan, one raw centroid can quantify at most one accepted transition and one transition can receive at most one centroid; the lowest mass-error pairing is preferred. Missing transition/scan combinations are written explicitly with zero intensity so chromatograms cannot interpolate across absent scans. The usual AUC relative-height boundary is then calculated inside the accepted feature, preventing quantification from drifting to a different chromatographic peak.

Isobaric composition scoring

After structure-level matching, numerical-composition candidates within 20 ppm are scored independently inside each detected retention-time feature. The composition scorer:

1. Deduplicates repeated matches to the same observed peak within a scan.
2. Counts distinct fragment transitions instead of matched table rows.
3. Down-weights observed peaks shared by multiple composition candidates.
4. Uses the specificity-weighted explained MS2 intensity fraction.
5. Estimates a robust signed precursor-error center and sigma from the run, then converts each candidate's error into a Gaussian likelihood relative to the best precursor match in the same feature.
6. Measures chromatographic coherence against the precursor trace, a consensus of fragments shared by the conflict set, or the MS2 TIC fallback using peak-shape correlation, apex agreement, and trace overlap.
7. Uses chromatographic coherence only when it is evaluable for every candidate in a contested feature. If any candidate is missing it, every candidate gets the same neutral coelution component.
8. Requires the chromatographic apex to have at least two acquired scans on both its rising and falling flanks. Boundary or sparsely sampled features remain auditable but are not eligible for assignment or quantification.
9. Searches a paired charge/adduct-matched shifted-fragment decoy library, performs feature-level target-decoy competition, and estimates assignment FDRs and q-values.

Fragment mass error is measured in the selected tolerance unit. Its score is still Gaussian in the median error divided by the tolerance, but its influence decreases as the effective tolerance widens. At the feature's median observed fragment m/z, the selected tolerance is converted to equivalent ppm and the reliability is:

R = min(1, sqrt(20 / equivalent_tolerance_ppm))

Thus 20 ppm or tighter receives full reliability. For example, 0.5 Da at m/z 500 is 1000 ppm and receives R = 0.141. The raw mass-accuracy weight is 0.15 * R, and all bounded-score weights are renormalized so the candidate score still spans 0-100:

W = 0.15 * R

100 / (0.85 + W) * (
    0.35 * explained-intensity component
  + 0.20 * distinct-fragment support
  + W    * fragment mass accuracy
  + 0.10 * within-feature precursor relative likelihood
  + 0.20 * feature-symmetric chromatographic coherence
)

The bounded score remains an absolute evidence-quality check. Candidate ranking and runner-up separation use a separate discriminative score:

S = 90 / (75 + 15 * R)

S * (35 * explained-intensity component
   + 20 * distinct-fragment support
   + 15 * R * fragment mass accuracy
   + 20 * feature-symmetric chromatographic coherence)
+  2 * within-feature precursor log-likelihood

This score is centered within each feature, so evidence shared equally by all candidates cancels exactly. Resolution uses the top-minus-runner-up discriminative evidence difference and no longer uses a ratio of total scores.

A candidate with zero candidate-specific fragments is pruned before ranking only when its calibrated precursor error is also farther from the best candidate by more than both 2 ppm and four calibrated sigmas. The decision is recorded in mass_outlier_pruned, mass_outlier_threshold_ppm, and candidate_rejection_reason; the row is never deleted from the audit output. If no candidate in a contested feature has a distinguishing fragment, the feature receives no_discriminating_fragment_evidence and no winner is selected.

Every assignment, including an uncontested feature, must contain an interior, adequately sampled chromatographic apex and pass the minimum fragment, explained-intensity, and bounded-score checks. Contested assignments must additionally pass the discriminative-difference rule. Otherwise they receive no_chromatographic_peak, insufficient_evidence, ambiguous, possible_coisolation, or no_discriminating_fragment_evidence and remain unselected. A sole surviving candidate after audited pruning is reported as resolved_after_mass_pruning.

For target-decoy validation, each target product ion receives a reproducible random neutral-mass shift between 1 and 30 Da; the m/z shift is divided by ion charge. Shifts overlapping any target theoretical ion within the selected Da or ppm fragment tolerance are rejected and regenerated. Target and decoy libraries preserve the same precursor assignments, fragment counts, structures, ion series, charges, adducts, and neutral-loss counts, and are searched and structure-ranked separately so decoys do not change target specificity weights. Decoys reuse the target run's precursor-error calibration because each paired decoy has the same precursor hypothesis and differs only in its shifted fragment evidence. For a shared feature, decoys also reuse the target feature's tolerance reliability so both sides of the competition give mass accuracy equal weight.

The selected target and selected decoy compete within each RT feature using the bounded candidate score. At score threshold s, the estimated FDR is:

(number of decoy-winning features at or above s + 1)
/ max(number of target-winning features at or above s, 1)

Monotone q-values are calculated across score thresholds. A preliminarily selected target is removed when the decoy out-scores it or its q-value exceeds the configured maximum. The +1 correction is deliberately conservative; at the default q-value of 0.05, at least 20 target-winning features are needed before any assignment can pass. Non-quantified fragment rows are kept in candidate_rows_not_quantified.csv.

Cross-run chromatographic peak consensus

Each selected composition/RT feature is now integrated independently before results are combined across files. Nearby adduct-level features for the same glycan are first joined into run-local chromatographic peaks. Reproducible, high-scoring glycans provide RT landmarks, and each run is mapped to the batch median retention-time scale with a robust Theil-Sen linear fit. When too few landmarks are available, the method uses a median RT shift; with no landmarks, it records an identity alignment rather than inventing a correction.

After alignment, peaks for the same glycan are grouped within the configured RT tolerance, with at most one peak from each run in a group. Each group receives:

replicate coverage = number of runs containing the peak / number of batch runs
consensus score    = median candidate score - candidate-score MAD

The median absolute deviation (MAD) penalizes a peak whose candidate score is unstable across runs. A group is eligible only when both its run coverage and, when target-decoy validation is available, its target-decoy pass fraction meet the minimum replicate fraction. Eligible groups are ranked first by coverage, then by consensus score, then by aligned-RT consistency. Only the highest ranked group is used for that glycan in combined_auc_values.csv; abundance is not part of the ranking, so a large inconsistent peak cannot win merely because it has the largest AUC. Alternative peak groups are retained in audit tables.

Important Parameters

• ppm_ms1_tol: tolerance in ppm for precursor database matching and for associating MS2 scans with matched precursors.
• mz_offset: offset applied to calculated precursor adduct m/z values.
• mass_offset: offset applied to neutral masses before precursor adduct calculation.
• ms2_noise_filter_mode: auto, off, or manual; default auto. Automatic mode estimates a robust noise floor separately for each MS2 scan.
• intensity_threshold: absolute minimum MS2 centroid intensity used only when ms2_noise_filter_mode="manual"; default 100.
• fragment_mass_tol: numeric fragment m/z tolerance value; default 0.02.
• fragment_mass_tol_unit: Da or ppm; default Da. The same unit/value is used for target matching, decoy matching, overlap prevention, near-duplicate peak clustering, and fragment mass-accuracy scoring.
• fragment_ion_series: allowed theoretical fragment ion series. Use any combination of A, B, C, X, Y, Z.
• fragment_max_cleavages: maximum number of cleavages during theoretical fragmentation.
• smoothing_window: smoothing strength/window for chromatograms and AUC.
• smoothing_method: gaussian or savgol.
• rel_height: AUC boundary relative height.
• rel_height_mode: prominence or height.
• skyline_transition: write a Skyline transition list when True.
• candidate_min_fragments: minimum distinct fragments needed to accept any assignment; default 2.
• candidate_min_explained_intensity: minimum specificity-weighted explained intensity fraction; default 0.01 (1%).
• candidate_min_score: minimum bounded candidate score; default 35.
• candidate_min_evidence_difference: minimum top-minus-runner-up difference in discriminative evidence; default 4. Common-mode evidence does not affect this difference.
• candidate_mass_outlier_min_delta: absolute floor, in ppm, for pruning a zero-specific-evidence candidate. The effective threshold is the larger of this value and four run-calibrated precursor sigmas; default 2.
• enable_target_decoy: generate and search the paired shifted-fragment decoy library; default True.
• candidate_max_q_value: maximum assignment q-value; default 0.05.
• target_decoy_seed: reproducible decoy-generation seed; default 1729.
• minimum_peak_flank_scans: minimum acquired scans required before and after a feature apex for assignment and quantification; programmatic default 2.
• enable_consensus_peak_selection: align and choose one reproducible peak group per glycan across a multi-file batch; default True.
• consensus_rt_tolerance: allowed absolute difference between an aligned peak apex and its consensus-group center, in minutes. For example, 0.5 accepts peaks within ±0.5 min; default 0.3.
• consensus_min_replicate_fraction: minimum fraction of all batch runs that must contain a peak group (and pass target-decoy validation when available); default 0.8.

Outputs

Each sample output directory can include:

• ms2_noise_filter_audit.csv One row per extracted MS2 scan with its filtering mode, estimation source, estimated noise floor, raw/retained peak counts, and raw/denoised TIC.

• selected_identification_fragment_matches.csv Denoised fragment rows that established the selected candidate assignments and accepted transition lists. These rows are identification evidence and are not used directly for AUC integration.

• quantification_reextraction_audit.csv One row per accepted feature/adduct with its feature bounds, accepted transition count, raw scan count, and detected/zero trace-point counts.

• ms1_results.csv
  Matched precursor assignments.

• ms1_results_resolved.csv
  MS1 precursor assignments that survived MS2-based isobaric resolution.

• candidate_scores.csv Candidate-level component scores, RT-feature boundaries, runner-up metrics, precursor calibration and pruning audit fields, coelution comparability, discriminative evidence, selected tolerance and equivalent ppm, mass-accuracy reliability/effective weights, feature scan/flank counts, chromatographic_peak_valid, reported/selected flags, quantification weight, and resolution status for every composition considered. Here, selected=True specifically means that the candidate is eligible for composition-level quantification.

• decoy_fragment_matches.csv Raw observed-fragment matches to the shifted-ion decoy library.

• decoy_candidate_scores.csv Candidate-level scores calculated independently from decoy matches.

• target_decoy_competitions.csv Complete feature-level target and decoy scores, competition winner, estimated FDR, q-value, and pass/fail result, including decoy-only winning features.

• candidate_rows_not_quantified.csv Matched fragment rows for losing or unresolved candidate hypotheses. These rows remain available for review but are excluded from glycan AUC outputs.

• quantification_transition_library.csv One row per accepted transition used for quantification. This table preserves the complete identification annotation (including the condensed IUPAC structure when available) and links to the compact trace files through quantification_transition_id.

• ms2_<glycan>.csv
  Raw-intensity accepted-transition traces for a numerical glycan composition, constrained to selected chromatographic features and including explicit zero points and quantification provenance. Full structure annotations are stored once per transition in quantification_transition_library.csv instead of being repeated at every scan point.

• <sample>_auc_values.csv
  Glycan-level total AUC.

• <sample>_auc_values_by_adduct.csv
  Per-adduct AUC values.

• <sample>_feature_auc_values.csv Independent AUC and scoring/target-decoy audit fields for every selected chromatographic feature; this is the input to cross-run peak consensus.

• <sample>_skyline_transitions.xlsx
  Optional Skyline transition export.

• images/*.{png,pdf,svg} Fragment chromatograms, precursor-adduct chromatograms, total chromatograms, shaded AUC plots, and averaged MS2 spectra in the selected figure format.

For multi-file runs:

• combined_auc_values.csv One consensus-selected peak group per glycan, with a separate AUC column for every run.
• consensus_peak_groups.csv Coverage, median score, score MAD, consensus score, aligned RT MAD, rank, eligibility, and selection decision for every possible peak group.
• aligned_feature_auc_values.csv Run-local peaks with raw/aligned RTs, alignment parameters, group assignment, source features/adducts, and all consensus metrics.
• combined_all_feature_auc_values.csv Wide AUC table containing selected and alternative peak groups.
• retention_time_alignment.csv Per-run slope, intercept, landmark count, residual MAD, and alignment method.

Cross-run consensus should be run on files that form a meaningful comparison set (for example technical replicates or samples expected to share the same LC method). If unrelated batches should not constrain one another, process them separately or disable consensus selection.

Notes For Packaging

Default database paths are resolved through glycanPRMQuant.resources, which supports both source-tree execution and PyInstaller-style bundled resources. The included GlycanPRMQuant.spec bundles the glycan database, AlphaRaw's Thermo assemblies, and the Python/.NET bridge needed for direct RAW access.

On Windows, build the complete GUI application from the defaultenv Conda environment with:

powershell -NoProfile -ExecutionPolicy Bypass -File .\build_windows.ps1

The script produces a versioned ZIP and SHA-256 checksum under .packaging/release/. Distribute the ZIP as the GitHub Release asset because the executable depends on the adjacent files in its onedir bundle.

Data Availability

Development and benchmarking data are available through MassIVE: MSV000101208.

The package is archived on Zenodo:

To-Do List / Work in Progress

• Finalize the visualization module allowing for gui-based visualization of quality-control data.
• Test the module on permethylated O-glycans, database already supports usage with O-glycans.
• Test the module with native glycans in negative mode, database already supports usage but needs to be validated.
• Support for Isomeric Analysis using PGC/MGC.

Contact

For issues regarding this package, bug reports, feature requests, or questions about usage, please contact:

• Vishal Sandilya
• Email: vis.sandilya@gmail.com
• ORCID: 0009-0006-5834-7404

License