CGAT: computational genomics analysis toolkit

• DOI: 10.1093/bioinformatics/btt756
• Publication Date: 2014-01-07T02:44:52Z
• Authors: David Sims; Nicholas E. Ilott; Stephen N. Sansom; Ian M. Sudbery; Jethro S. Johnson; Katherine A. Fawcett; Antonio J. Berlan...; Sebastian Luna-Va...; Chris P. Ponting; Andreas Heger
• Keywords: 0301 basic medicine; 570; Technology; Biochemistry & Molecular Biology; Bioinformatics; Statistics & Probability; Biochemical Research Methods; Workflow; Databases; 03 medical and health sciences; Genetic; Databases, Genetic; Interdisciplinary Applications; 01 Mathematical Sciences; 08 Information And Computing Sciences; Science & Technology; High-Throughput Nucleotide Sequencing; BIOTECHNOLOGY & APPLIED MICROBIOLOGY; Genomics; 06 Biological Sciences; Applications Notes; 004; MATHEMATICAL & COMPUTATIONAL BIOLOGY; Biotechnology & Applied Microbiology; Physical Sciences; Computer Science; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Life Sciences & Biomedicine; Mathematics; BIOCHEMICAL RESEARCH METHODS; Software

Abstract Summary: Computational genomics seeks to draw biological inferences from genomic datasets, often by integrating and contextualizing next-generation sequencing data. CGAT provides an extensive suite of tools designed to assist in the analysis of genome scale data from a range of standard file formats. The toolkit enables filtering, comparison, conversion, summarization and annotation of genomic intervals, gene sets and sequences. The tools can both be run from the Unix command line and installed into visual workflow builders, such as Galaxy. Availability: The toolkit is freely available from http://github.com/CGATOxford/cgat Contact: [e-mail]