Motivation Thanks to the great advances in biomedical technologies, we are faced with huge amounts of genomic and clinical data. A striking example is The Cancer Genome Atlas (TCGA), one of the largest public repositories of genomic and clinical data about cancer. TCGA contains more than 15 TB of genomic and clinical data, whose analysis and interpretation are posing great challenges to the bioinformatics community. In this work, we focus on data retrieval, conversion, integration and querying of Next Generation Sequencing (NGS) data and their clinical information extracted from TCGA. In particular, we focus on all publicly available Copy Number Variation (CNV), DNA-methylation, DNA-sequencing (DNA-seq), Gene Expression (RNA-seq V1 and V2), microRNA sequencing (miRNA-seq), and meta (clinical and biospecimen) data. Methods We propose TCGA2BED (http://bioinf.iasi.cnr.it/tcga2bed/), a software tool able to retrieve genomic and clinical data from TCGA and convert them into the tab-delimited BED format. Additionally, it integrates them with external data (e.g., gene coordinates) from other state-of-the-art biological databases and services such as UCSC Genome Browser, HUGO Gene Nomenclature Committee (HGNC), NCBI Gene, and miRBase. TCGA2BED is available with a graphic user interface and includes three different main components: • the controller, that reads and executes the user’s requests (i.e., data download and conversion) through the graphic user interface or an XML configuration file • the retrieval system, which handles the search and retrieval of the public genomic and clinical data available from TCGA by building ad-hoc queries and send them to the REST service of TCGA • the BioParser, which converts all TCGA genomic data types (i.e., CNV, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq V1 and V2) into the tab-delimited BED format, and all their related clinical metadata into a tab-delimited attribute-value text format. Results Using TCGA2BED, we downloaded and converted all publicly available CNV, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq V1 and V2 experimental and meta data from TCGA. For each patient sample, cancer type and experiment type in TCGA, we create (i) a .bed file, containing the genomic data of the sample converted in BED format, and (ii) a .meta file, including the clinical data of the sample; additionally, (iii) a header.schema file in XML format that describes the structure of the .bed data files, and (iv) a .txt metadata dictionary file that contains all metadata attributes with all the values that each attribute assumes in the metadata. The TCGA converted data can be easily processed and analysed with wide-spread bioinformatics tools, including the GenoMetric Query Language (GMQL) available at http://www.bioinformatics.deib.polimi.it/GMQL/, a key instrument for the integrative querying of genomic and clinical big data from heterogeneous sources. Here we report an example GMQL query that integrates DNA-seq and RNA-seq data; for each tumor sample of each patient, it searches and returns the DNA mutations that are the closest to expressed genes: DNA = SELECT(*) DNAseq; RNA = SELECT(*) RNAseq; JoinDnaToRna = JOIN(left->bcr_sample_barcode == right->bcr_sample_barcode, MINDISTANCE(1), left) DNA RNA; MATERIALIZE JoinDnaToRna; The use of the BED format reduces the time spent in managing and analyzing the valuable TCGA data: it is possible to efficiently deal with huge amounts of cancer data, and to easily integrate and query them using GMQL. The BED format facilitates the investigators in easily performing knowledge discovery analyses aiming at aiding cancer treatments. For example, the TCGA data in BED format can be straightforwardly analyzed with CAMUR, a tool using a supervised approach able to elicit a high amount of knowledge by computing many rule-based classification models, and therefore able to identify most of the clinical and genomic features related to the predicted cancer type.
|Titolo:||TCGA2BED: converting and querying The Cancer Genome Atlas.|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||04.1 Contributo in Atti di convegno|