MetaCRAST: Reference-guided extraction of CRISPR spacers from unassembled metagenomes
- Published
- Accepted
- Subject Areas
- Bioinformatics, Genomics, Microbiology, Computational Science
- Keywords
- Metagenomics, repetitive sequences, CRISPR, microbial ecology, virus-host interactions
- Copyright
- © 2017 Moller et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. MetaCRAST: Reference-guided extraction of CRISPR spacers from unassembled metagenomes. PeerJ Preprints 5:e2278v4 https://doi.org/10.7287/peerj.preprints.2278v4
Abstract
Clustered regularly interspaced short palindromic repeat (CRISPR) systems are the adaptive immune systems of bacteria and archaea against viral infection. While CRISPRs have been exploited as a tool for genetic engineering, their spacer sequences can also provide valuable insights into microbial ecology by linking environmental viruses to their microbial hosts. Despite this importance, metagenomic CRISPR detection remains a major challenge. Here we present a reference-guided CRISPR spacer detection tool (Metagenomic CRISPR Reference-Aided Search Tool - MetaCRAST) that constrains searches based on user-specified direct repeats (DRs). These DRs could be expected from assembly or taxonomic profiles of metagenomes. We compared the performance of MetaCRAST to those of two existing metagenomic CRISPR detection tools – Crass and MinCED – using both real and simulated acid mine drainage (AMD) and enhanced biological phosphorus removal (EBPR) metagenomes. Our evaluation shows MetaCRAST improves CRISPR spacer detection in real metagenomes compared to the de novo CRISPR detection methods Crass and MinCED. Evaluation on simulated metagenomes show it performs better than de novo tools for Illumina metagenomes and comparably for 454 metagenomes. It also has comparable performance dependence on read length and community composition, run time, and accuracy to these tools MetaCRAST is implemented in Perl, parallelizable through the Many Core Engine (MCE), and takes metagenomic sequence reads and direct repeat queries (FASTA or FASTQ) as input. It is freely available for download at https://github.com/molleraj/MetaCRAST.
Author Comment
This version of the preprint includes an updated version of the abstract. Please combine this into the previous preprint version (v3) if necessary.