Simplified and Unified Access to Cancer Proteogenomic Data
| Autor: | Benjamin Kimball, Samuel L. Pugh, Caleb M. Lindgren, Sadie Tayler, David W Adams, Samuel H. Payne, Hannah Boekweg |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Proteomics Software documentation CPTAC Computer science Interface (computing) data dissemination Biochemistry Article 03 medical and health sciences Disk formatting Consistency (database systems) data access Neoplasms medicine genomics cancer Humans reproducibility computer.programming_language mass spectrometry Information retrieval 030102 biochemistry & molecular biology Cancer Reproducibility of Results General Chemistry Resource (Windows) Python (programming language) medicine.disease Data science 030104 developmental biology Data access ComputingMethodologies_PATTERNRECOGNITION proteogenomics Raw data computer Software Python |
| Zdroj: | Journal of Proteome Research |
| ISSN: | 1535-3907 1535-3893 |
| Popis: | Comprehensive cancer data sets recently generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) offer great potential for advancing our understanding of how to combat cancer. These data sets include DNA, RNA, protein, and clinical characterization for tumor and normal samples from large cohorts of many different cancer types. The raw data are publicly available at various Cancer Research Data Commons. However, widespread reuse of these data sets is also facilitated by easy access to the processed quantitative data tables. We have created a data application programming interface (API) to distribute these processed tables, implemented as a Python package called cptac. We implement it such that users who prefer to work in R can easily use our package for data access and then transfer the data into R for analysis. Our package distributes the finalized processed CPTAC data sets in a consistent, up-to-date format. This consistency makes it easy to integrate the data with common graphing, statistical, and machine-learning packages for advanced analysis. Additionally, consistent formatting across all cancer types promotes the investigation of pan-cancer trends. The data API structure of directly streaming data within a programming environment enhances the reproducibility. Finally, with the accompanying tutorials, this package provides a novel resource for cancer research education. View the software documentation at https://paynelab.github.io/cptac/. View the GitHub repository at https://github.com/PayneLab/cptac. |
| Databáze: | OpenAIRE |
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