Dataset Information

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

ABSTRACT:

Objective

The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons.

Materials and methods

Modifications of the PCDC data portal software were implemented to facilitate desired functionality.

Results

Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions.

Discussion

We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform.

Conclusion

Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.

SUBMITTER: Wyatt KD

PROVIDER: S-EPMC10833446 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

Wyatt Kirk D KD Graglia Luca L Furner Brian B Kang Bobae B Fitzsimons Michael M Grossman Robert L RL Volchenboum Samuel L SL

JAMIA open 20240131 1

<h4>Objective</h4>The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons.<h4>Materials and methods</h4>Modifications of the PCDC data portal software were implemented to facilitate desired ...[more]

PMID: 38304249

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Project description:Glycosylation is among the most abundant and diverse protein post-translational modifications (PTMs) identified to date. The structural analysis of this PTM is challenging because of the diverse monosaccharides which are not conserved among organisms, the branched nature of glycans, their isomeric structures, and heterogeneity in the glycan distribution at a given site. Glycoproteomics experiments have adopted the traditional high-throughput LC-MSn proteomics workflow to analyze site-specific glycosylation. However, comprehensive computational platforms for data analyses are scarce. To address this limitation, we present a comprehensive, open-source, modular software for glycoproteomics data analysis called GlycoPAT (GlycoProteomics Analysis Toolbox; freely available from www.VirtualGlycome.org/glycopat). The program includes three major advances: (1) "SmallGlyPep," a minimal linear representation of glycopeptides for MSn data analysis. This format allows facile serial fragmentation of both the peptide backbone and PTM at one or more locations. (2) A novel scoring scheme based on calculation of the "Ensemble Score (ES)," a measure that scores and rank-orders MS/MS spectrum for N- and O-linked glycopeptides using cross-correlation and probability based analyses. (3) A false discovery rate (FDR) calculation scheme where decoy glycopeptides are created by simultaneously scrambling the amino acid sequence and by introducing artificial monosaccharides by perturbing the original sugar mass. Parallel computing facilities and user-friendly GUIs (Graphical User Interfaces) are also provided. GlycoPAT is used to catalogue site-specific glycosylation on simple glycoproteins, standard protein mixtures and human plasma cryoprecipitate samples in three common MS/MS fragmentation modes: CID, HCD and ETD. It is also used to identify 960 unique glycopeptides in cell lysates from prostate cancer cells. The results show that the simultaneous consideration of peptide and glycan fragmentation is necessary for high quality MSn spectrum annotation in CID and HCD fragmentation modes. Additionally, they confirm the suitability of GlycoPAT to analyze shotgun glycoproteomics data.

Dataset Information

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

Objective

Materials and methods

Results

Discussion

Conclusion

Publications

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

Similar Datasets

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