SeQuiLa: an elastic, fast and scalable SQL-oriented solution for processing and querying genomic intervals

Marek Wiewiórka; Anna Leśniewska; Agnieszka Szmurło; Kacper Stępień; Mateusz Borowiak; Michał Okoniewski; Tomasz Gambin

doi:10.1093/bioinformatics/bty940

SeQuiLa: an elastic, fast and scalable SQL-oriented solution for processing and querying genomic intervals

Bioinformatics. 2019 Jun 1;35(12):2156-2158. doi: 10.1093/bioinformatics/bty940.

Authors

Marek Wiewiórka¹, Anna Leśniewska², Agnieszka Szmurło¹, Kacper Stępień², Mateusz Borowiak², Michał Okoniewski³, Tomasz Gambin¹

Affiliations

¹ Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland.
² Department of Computer Science, Poznan University of Technology, Poznań, Poland.
³ Scientific IT Services, ETH Zurich, Zürich, Switzerland.

PMID: 30428005
DOI: 10.1093/bioinformatics/bty940

Abstract

Summary: Efficient processing of large-scale genomic datasets has recently become possible due to the application of 'big data' technologies in bioinformatics pipelines. We present SeQuiLa-a distributed, ANSI SQL-compliant solution for speedy querying and processing of genomic intervals that is available as an Apache Spark package. Proposed range join strategy is significantly (∼22×) faster than the default Apache Spark implementation and outperforms other state-of-the-art tools for genomic intervals processing.

Availability and implementation: The project is available at http://biodatageeks.org/sequila/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genome
Genomics*
Software*