Snaptron

Querying splicing patterns across tens of thousands of RNA-seq samples

Christopher Wilks, Phani Gaddipati, Abhinav Nellore, Ben Langmead

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Motivation: As more and larger genomics studies appear, there is a growing need for comprehensive and queryable cross-study summaries. These enable researchers to leverage vast datasets that would otherwise be difficult to obtain. Results: Snaptron is a search engine for summarized RNA sequencing data with a query planner that leverages R-tree, B-tree and inverted indexing strategies to rapidly execute queries over 146 million exon-exon splice junctions from over 70 000 human RNA-seq samples. Queries can be tailored by constraining which junctions and samples to consider. Snaptron can score junctions according to tissue specificity or other criteria, and can score samples according to the relative frequency of different splicing patterns. We describe the software and outline biological questions that can be explored with Snaptron queries.

Original languageEnglish (US)
Pages (from-to)114-116
Number of pages3
JournalBioinformatics
Volume34
Issue number1
DOIs
StatePublished - Jan 1 2018

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RNA
Exons
Query
RNA Sequence Analysis
Search Engine
Organ Specificity
Search engines
Genomics
Leverage
Software
Research Personnel
Tissue
B-tree
R-tree
Indexing
Sequencing
Specificity
Datasets

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

Snaptron : Querying splicing patterns across tens of thousands of RNA-seq samples. / Wilks, Christopher; Gaddipati, Phani; Nellore, Abhinav; Langmead, Ben.

In: Bioinformatics, Vol. 34, No. 1, 01.01.2018, p. 114-116.

Research output: Contribution to journalArticle

Wilks, Christopher ; Gaddipati, Phani ; Nellore, Abhinav ; Langmead, Ben. / Snaptron : Querying splicing patterns across tens of thousands of RNA-seq samples. In: Bioinformatics. 2018 ; Vol. 34, No. 1. pp. 114-116.
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