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      Characterization of CRISPR RNA transcription by exploiting stranded metatranscriptomic data

      research-article
      ,
      RNA
      Cold Spring Harbor Laboratory Press
      CRISPR–Cas systems, CRISPR RNA (crRNA), metatranscriptomics

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          Abstract

          CRISPR–Cas systems are bacterial adaptive immune systems, each typically composed of a locus of cas genes and a CRISPR array of spacers flanked by repeats. Processed transcripts of CRISPR arrays (crRNAs) play important roles in the interference process mediated by these systems, guiding targeted immunity. Here we developed computational approaches that allow us to characterize the expression of many CRISPRs in their natural environments, using community RNA-seq (metatranscriptomic) data. By exploiting public human gut metatranscriptomic data sets, we studied the expression of 56 repeat-sequence types of CRISPRs, revealing that most CRISPRs are transcribed in one direction (producing crRNAs). In rarer cases, including a type II system associated with Bacteroides fragilis, CRISPRs are transcribed in both directions. Type III CRISPR–Cas systems were found in the microbiomes, but metatranscriptomic reads were barely found for their CRISPRs. We observed individual-level variation of the crRNA transcription, and an even greater transcription of a CRISPR from the antisense strand than the crRNA strand in one sample. The orientations of CRISPR expression implicated by metatranscriptomic data are largely in agreement with prior predictions for CRISPRs, with exceptions. Our study shows the promise of exploiting community RNA-seq data for investigating the transcription of CRISPR–Cas systems.

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          Most cited references52

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

            In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.
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              CRISPR provides acquired resistance against viruses in prokaryotes.

              Clustered regularly interspaced short palindromic repeats (CRISPR) are a distinctive feature of the genomes of most Bacteria and Archaea and are thought to be involved in resistance to bacteriophages. We found that, after viral challenge, bacteria integrated new spacers derived from phage genomic sequences. Removal or addition of particular spacers modified the phage-resistance phenotype of the cell. Thus, CRISPR, together with associated cas genes, provided resistance against phages, and resistance specificity is determined by spacer-phage sequence similarity.
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                Author and article information

                Journal
                RNA
                RNA
                RNA
                RNA
                Cold Spring Harbor Laboratory Press
                1355-8382
                1469-9001
                July 2016
                July 2016
                : 22
                : 7
                : 945-956
                Affiliations
                School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405, USA
                Author notes
                Corresponding author: yye@ 123456indiana.edu
                Article
                9509184 RA
                10.1261/rna.055988.116
                4911918
                27190232
                e724ee95-8c07-4c17-9419-7632888d77b1
                © 2016 Ye and Zhang; Published by Cold Spring Harbor Laboratory Press for the RNA Society

                This article, published in RNA, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 14 January 2016
                : 15 April 2016
                Funding
                Funded by: National Institutes of Health http://dx.doi.org/10.13039/100000002
                Award ID: 1R01AI108888
                Categories
                Bioinformatics

                crispr–cas systems,crispr rna (crrna),metatranscriptomics

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