TCGA2BED: extracting, extending, integrating, and querying The Cancer Genome Atlas

BMC Bioinformatics

Software

Emanuel Weitschek¹  Stefano Ceri²  Marco Masseroli²  Giulia Fiscon³  Fabio Cumbo⁴ 

[1] Department of Engineering, Uninettuno International University, Corso Vittorio Emanuele II 39, 00186, Rome, Italy;Institute of Systems Analysis and Computer Science “A. Ruberti”, National Research Council, Via dei Taurini 19, 00185, Rome, Italy;Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milan, Italy;Institute of Systems Analysis and Computer Science “A. Ruberti”, National Research Council, Via dei Taurini 19, 00185, Rome, Italy;Institute of Systems Analysis and Computer Science “A. Ruberti”, National Research Council, Via dei Taurini 19, 00185, Rome, Italy;Department of Engineering, Roma Tre University, Via della Vasca Navale 79, 00146, Rome, Italy;

关键词: Cancer;    Data extraction;    Data integration;    Knowledge extraction;   

DOI  :  10.1186/s12859-016-1419-5

 received in 2016-08-03, accepted in 2016-12-10,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundData 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.ResultsWe propose TCGA2BED 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© The Author(s) 2017