Journal
BIOINFORMATICS
Volume 31, Issue 12, Pages 1881-1888Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv048
Keywords
-
Categories
Funding
- 'Data-Driven Genomic Computing [GenData 2020]' PRIN project - Italian Ministry of the University and Research (MIUR)
Ask authors/readers for more resources
Motivation: Improvement of sequencing technologies and data processing pipelines is rapidly providing sequencing data, with associated high-level features, of many individual genomes in multiple biological and clinical conditions. They allow for data-driven genomic, transcriptomic and epigenomic characterizations, but require state-of-the-art 'big data' computing strategies, with abstraction levels beyond available tool capabilities. Results: We propose a high-level, declarative GenoMetric Query Language (GMQL) and a toolkit for its use. GMQL operates downstream of raw data preprocessing pipelines and supports queries over thousands of heterogeneous datasets and samples; as such it is key to genomic 'big data' analysis. GMQL leverages a simple data model that provides both abstractions of genomic region data and associated experimental, biological and clinical metadata and interoperability between many data formats. Based on Hadoop framework and Apache Pig platform, GMQL ensures high scalability, expressivity, flexibility and simplicity of use, as demonstrated by several biological query examples on ENCODE and TCGA datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available