scan_tcga tools for integrated epigenomic and transcriptomic analysis of tumor subgroups

The Cancer Genome Atlas contains multiple levels of genomic data (mutation, gene expression, DNA methylation, copy number variation) for 33 cancer types for almost 11,000 patients. However, a dearth of appropriate software tools makes it difficult for bench scientists to use these data effectively....

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Bibliographic Details
Published in:Epigenomics 2016-10, Vol.8 (10), p.1315-1330
Main Authors: Chatterjee, Aniruddha, Stockwell, Peter A, Rodger, Euan J, Parry, Matthew F, Eccles, Michael R
Format: Article
Language:English
Subjects:
Cancer
cancer genome atlas
Cell growth
Copy number
Datasets
Deoxyribonucleic acid
Deregulation
DNA
DNA Methylation
Epigenetics
Epigenomics
Gene expression
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Genomes
Humans
Melanoma
Melanoma - genetics
Metastases
Metastasis
MicroRNAs
MicroRNAs - metabolism
miRNA
Mutation
Patients
Proteins
Ribonucleic acid
RNA
RNA, Messenger - genetics
Senescence
Signatures
Skin Neoplasms - genetics
Software
Software development tools
Subgroups
Tumors
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Summary:The Cancer Genome Atlas contains multiple levels of genomic data (mutation, gene expression, DNA methylation, copy number variation) for 33 cancer types for almost 11,000 patients. However, a dearth of appropriate software tools makes it difficult for bench scientists to use these data effectively. Here, we present a suite of flexible, fast and command line-based scripts that will allow retrieval and analysis of DNA methylation (tool: scan_tcga_methylation.awk), mRNA (tool: scan_tcga_mRNA.awk) and miRNA expression (tool: scan_tcga_miRNAs.awk) from cancer genome atlas network level 3 data. We demonstrate the utility of these tools by analyzing DNA methylation and mRNA expression signatures of 60 frequently deregulated cancer genes and also of 30 miRNAs in primary (n = 102) and metastatic melanoma patients (n = 367). Our analysis illustrates the validity of the scan_tcga tools and reveals the epigenomic signatures and importance of identifying smaller patient subgroups with distinct molecular profiles.
ISSN:1750-1911
1750-192X
DOI:10.2217/epi-2016-0063