Abstract

CRISPR arrays and CRISPR-associated (Cas) proteins comprise a widespread adaptive immune system in bacteria and archaea. These systems function as a defense against exogenous parasitic mobile genetic elements that include bacteriophages, plasmids and foreign nucleic acids. With the continuous spread of antibiotic resistance, knowledge of pathogen susceptibility to bacteriophage therapy is becoming more critical. Additionally, gene-editing applications would benefit from the discovery of new cas genes with favorable properties. While next-generation sequencing has produced staggering quantities of data, transitioning from raw sequencing reads to the identification of CRISPR/Cas systems has remained challenging. This is especially true for metagenomic data, which has the highest potential for identifying novel cas genes. We report a comprehensive computational pipeline, CasCollect, for the targeted assembly and annotation of cas genes and CRISPR arrays—even isolated arrays—from raw sequencing reads. Benchmarking our targeted assembly pipeline demonstrates significantly improved timing by almost two orders of magnitude compared with conventional assembly and annotation, while retaining the ability to detect CRISPR arrays and cas genes. CasCollect is a highly versatile pipeline and can be used for targeted assembly of any specialty gene set, reconfigurable for user provided Hidden Markov Models and/or reference nucleotide sequences.

Details

Title
CasCollect: targeted assembly of CRISPR-associated operons from high-throughput sequencing data
Author
Podlevsky, Joshua D 1   VIAFID ORCID Logo  ; Hudson, Corey M 2 ; Timlin, Jerilyn A 2 ; Williams, Kelly P 3   VIAFID ORCID Logo 

 Molecular and Microbiology, Sandia National Laboratories , Albuquerque, NM 87185, USA 
 Computational Biology and Biophysics, Sandia National Laboratories , Albuquerque, NM 87185, USA 
 Systems Biology, Sandia National Laboratories , Livermore, CA 94550, USA 
Publication year
2020
Publication date
Sep 2020
Publisher
Oxford University Press
e-ISSN
26319268
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3170915154
Copyright
© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.