Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples

Christopher Wilks, Phani Gaddipati, Abhinav Nellore, View ORCID ProfileBen Langmead
doi: https://doi.org/10.1101/097881
Christopher Wilks
1Department of Computer Science, Johns Hopkins University
2Center for Computational Biology, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: chris.wilks@jhu.edu langmea@cs.jhu.edu
Phani Gaddipati
3Department of Biomedical Engineering, Johns Hopkins University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Abhinav Nellore
4Department of Biomedical Engineering, Oregon Health & Science University
5Department of Surgery, Oregon Health & Science University
6Computational Biology Program, Oregon Health & Science University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ben Langmead
1Department of Computer Science, Johns Hopkins University
2Center for Computational Biology, Johns Hopkins University
7Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ben Langmead
  • For correspondence: chris.wilks@jhu.edu langmea@cs.jhu.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

As more and larger genomics studies appear, there is a growing need for comprehensive and queryable cross-study summaries. 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 also rank and score junctions according to tissue specificity or other criteria. Further, Snaptron can rank and score samples according to the relative frequency of different splicing patterns. We outline biological questions that can be explored with Snaptron queries, including a study of novel exons in annotated genes, of exonization of repetitive element loci, and of a recently discovered alternative transcription start site for the ALK gene. Web app and documentation are at http://snaptron.cs.jhu.edu. Source code is at https://github.com/ChristopherWilks/snaptron under the MIT license.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted January 09, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples
Christopher Wilks, Phani Gaddipati, Abhinav Nellore, Ben Langmead
bioRxiv 097881; doi: https://doi.org/10.1101/097881
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples
Christopher Wilks, Phani Gaddipati, Abhinav Nellore, Ben Langmead
bioRxiv 097881; doi: https://doi.org/10.1101/097881

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3505)
  • Biochemistry (7346)
  • Bioengineering (5323)
  • Bioinformatics (20263)
  • Biophysics (10016)
  • Cancer Biology (7743)
  • Cell Biology (11300)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9951)
  • Epidemiology (2065)
  • Evolutionary Biology (13322)
  • Genetics (9361)
  • Genomics (12583)
  • Immunology (7701)
  • Microbiology (19021)
  • Molecular Biology (7441)
  • Neuroscience (41036)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2137)
  • Physiology (3160)
  • Plant Biology (6860)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1896)
  • Systems Biology (5311)
  • Zoology (1089)