CRISPR detection from short reads using partial overlap graphs

Ilan Ben-Bassat*, Benny Chor

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Clustered regularly interspaced short palindromic repeats (CRISPR) are structured regions in bacterial and archaeal genomes, which are part of an adaptive immune system against phages. Most of the automated tools that detect CRISPR loci rely on assembled genomes. However, many assemblers do not successfully handle repetitive regions. The first tool to work directly on raw sequence data is Crass, which requires that reads are long enough to contain two copies of the same repeat. We developed a method to identify CRISPR repeats from a raw sequence data of short reads. The algorithm is based on an observation differentiating CRISPR repeats from other types of repeats, and it involves a series of partial constructions of the overlap graph. A preliminary implementation of the algorithm shows good results and detects CRISPR repeats in cases where other tools fail to do so.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 19th Annual International Conference, RECOMB 2015, Proceedings
EditorsTeresa M. Przytycka
PublisherSpringer Verlag
Pages16-27
Number of pages12
ISBN (Electronic)9783319167053
DOIs
StatePublished - 2015
Event19th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2015 - Warsaw, Poland
Duration: 12 Apr 201515 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9029
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2015
Country/TerritoryPoland
CityWarsaw
Period12/04/1515/04/15

Keywords

  • CRISPR Detection
  • Filtering
  • K-mer counting
  • Overlap graph
  • Partial overlap graph
  • Sampling

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