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

MUMdex: MUM-based structural variation detection

Peter A. Andrews, Ivan Iossifov, Jude Kendall, Steven Marks, Lakshmi Muthuswamy, Zihua Wang, Dan Levy, Michael Wigler
doi: https://doi.org/10.1101/078261
Peter A. Andrews
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ivan Iossifov
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
2New York Genome Center, New York, NY 10013, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jude Kendall
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Steven Marks
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lakshmi Muthuswamy
2New York Genome Center, New York, NY 10013, USA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zihua Wang
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dan Levy
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michael Wigler
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA,
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Motivation Standard genome sequence alignment tools primarily designed to find one alignment per read have difficulty detecting inversion, translocation and large insertion and deletion (indel) events. Moreover, dedicated split read alignment methods that depend only upon the reference genome may misidentify or find too many potential split read alignments because of reference genome anomalies.

Methods We introduce MUMdex, a Maximal Unique Match (MUM)-based genomic analysis software package consisting of a sequence aligner to the reference genome, a storage-indexing format and analysis software. Discordant reference alignments of MUMs are especially suitable for identifying inversion, translocation and large indel differences in unique regions. Extracted population databases are used as filters for flaws in the reference genome. We describe the concepts underlying MUM-based analysis, the software implementation and its usage.

Results We demonstrate via simulation that the MUMdex aligner and alignment format are able to correctly detect and record genomic events. We characterize alignment performance and output file sizes for human whole genome data and compare to Bowtie 2 and the BAM format. Preliminary results demonstrate the practicality of the analysis approach by detecting de novo mutation candidates in human whole genome DNA sequence data from 510 families. We provide a population database of events from these families for use by others.

Availability http://mumdex.com/

Contact andrewsp{at}cshl.edu (or paa{at}drpa.us)

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-ND 4.0 International license.
Back to top
PreviousNext
Posted September 30, 2016.
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.
MUMdex: MUM-based structural variation detection
(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
MUMdex: MUM-based structural variation detection
Peter A. Andrews, Ivan Iossifov, Jude Kendall, Steven Marks, Lakshmi Muthuswamy, Zihua Wang, Dan Levy, Michael Wigler
bioRxiv 078261; doi: https://doi.org/10.1101/078261
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
MUMdex: MUM-based structural variation detection
Peter A. Andrews, Ivan Iossifov, Jude Kendall, Steven Marks, Lakshmi Muthuswamy, Zihua Wang, Dan Levy, Michael Wigler
bioRxiv 078261; doi: https://doi.org/10.1101/078261

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

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4113)
  • Biochemistry (8815)
  • Bioengineering (6518)
  • Bioinformatics (23460)
  • Biophysics (11789)
  • Cancer Biology (9207)
  • Cell Biology (13322)
  • Clinical Trials (138)
  • Developmental Biology (7436)
  • Ecology (11409)
  • Epidemiology (2066)
  • Evolutionary Biology (15150)
  • Genetics (10436)
  • Genomics (14043)
  • Immunology (9171)
  • Microbiology (22153)
  • Molecular Biology (8812)
  • Neuroscience (47567)
  • Paleontology (350)
  • Pathology (1428)
  • Pharmacology and Toxicology (2491)
  • Physiology (3730)
  • Plant Biology (8079)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6037)
  • Zoology (1253)