TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Cumbo, Fabio; Fiscon, Giulia; Ceri, Stefano; Masseroli, Marco; Weitschek, Emanuel
• Is Part Of: BMC bioinformatics. Volume 18:Issue 1(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-12
• Subjects: Cancer -- Data extraction -- Data integration -- Knowledge extraction; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1419-5
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Data extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types. Results We proposeTCGA2BED a software tool to search and retrieve TCGA data, and convert them in the structured BED format for their seamless use and integration. Additionally, it supports the conversion in CSV, GTF, JSON, and XML standard formats. Furthermore, TCGA2BED extends TCGA data with information extracted from other genomic databases (i.e., NCBI Entrez Gene, HGNC, UCSC, and miRBase). We also provide and maintain an automatically updated data repository with publicly available Copy Number Variation, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq (V1, V2) experimental data of TCGA converted into the BED format, and their associated clinical and biospecimen meta data in attribute-value text format. Conclusions The availability of the valuable TCGA data in BED format reduces the time spent in taking advantage of them: it is possible to efficiently and effectively deal with huge amounts of cancer genomic data integratively, and to search, retrieve and extend them with additional information. The BED format facilitates the investigators allowing several knowledge discovery analyses on all tumor types in TCGA with the final aim of understanding pathological mechanisms and aiding cancer treatments.