TCGA2BED and CAMUR for cancer NGS data processing

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. 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. 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. The TCGA converted data can be easily processed and analysed with wide-spread bioinformatics tools, including the GenoMetric Query Language (GMQL), a key instrument for the integrative querying of genomic and clinical big data from heterogeneous sources. 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.