Identification of human short introns
Canonical pre-mRNA splicing requires snRNPs and associated splicing factors to excise conserved intronic sequences, with a minimum intron length required for efficient splicing. Non-canonical splicing-intron excision without the spliceosome-has been documented; most notably, some tRNAs and the XBP1...
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          | Published in | PloS one Vol. 12; no. 5; p. e0175393 | 
|---|---|
| Main Authors | , , , , , , , , , , , , , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        United States
          Public Library of Science
    
        17.05.2017
     Public Library of Science (PLoS)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1932-6203 1932-6203  | 
| DOI | 10.1371/journal.pone.0175393 | 
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| Abstract | Canonical pre-mRNA splicing requires snRNPs and associated splicing factors to excise conserved intronic sequences, with a minimum intron length required for efficient splicing. Non-canonical splicing-intron excision without the spliceosome-has been documented; most notably, some tRNAs and the XBP1 mRNA contain short introns that are not removed by the spliceosome. There have been some efforts to identify additional short introns, but little is known about how many short introns are processed from mRNAs. Here, we report an approach to identify RNA short introns from RNA-Seq data, discriminating against small genomic deletions. We identify hundreds of short introns conserved among multiple human cell lines. These short introns are often alternatively spliced and are found in a variety of RNAs-both mRNAs and lncRNAs. Short intron splicing efficiency is increased by secondary structure, and we detect both canonical and non-canonical short introns. In many cases, splicing of these short introns from mRNAs is predicted to alter the reading frame and change protein output. Our findings imply that standard gene prediction models which often assume a lower limit for intron size fail to predict short introns effectively. We conclude that short introns are abundant in the human transcriptome, and short intron splicing represents an added layer to mRNA regulation. | 
    
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| AbstractList | Canonical pre-mRNA splicing requires snRNPs and associated splicing factors to excise conserved intronic sequences, with a minimum intron length required for efficient splicing. Non-canonical splicing-intron excision without the spliceosome-has been documented; most notably, some tRNAs and the XBP1 mRNA contain short introns that are not removed by the spliceosome. There have been some efforts to identify additional short introns, but little is known about how many short introns are processed from mRNAs. Here, we report an approach to identify RNA short introns from RNA-Seq data, discriminating against small genomic deletions. We identify hundreds of short introns conserved among multiple human cell lines. These short introns are often alternatively spliced and are found in a variety of RNAs-both mRNAs and lncRNAs. Short intron splicing efficiency is increased by secondary structure, and we detect both canonical and non-canonical short introns. In many cases, splicing of these short introns from mRNAs is predicted to alter the reading frame and change protein output. Our findings imply that standard gene prediction models which often assume a lower limit for intron size fail to predict short introns effectively. We conclude that short introns are abundant in the human transcriptome, and short intron splicing represents an added layer to mRNA regulation. Canonical pre-mRNA splicing requires snRNPs and associated splicing factors to excise conserved intronic sequences, with a minimum intron length required for efficient splicing. Non-canonical splicing-intron excision without the spliceosome-has been documented; most notably, some tRNAs and the XBP1 mRNA contain short introns that are not removed by the spliceosome. There have been some efforts to identify additional short introns, but little is known about how many short introns are processed from mRNAs. Here, we report an approach to identify RNA short introns from RNA-Seq data, discriminating against small genomic deletions. We identify hundreds of short introns conserved among multiple human cell lines. These short introns are often alternatively spliced and are found in a variety of RNAs-both mRNAs and lncRNAs. Short intron splicing efficiency is increased by secondary structure, and we detect both canonical and non-canonical short introns. In many cases, splicing of these short introns from mRNAs is predicted to alter the reading frame and change protein output. Our findings imply that standard gene prediction models which often assume a lower limit for intron size fail to predict short introns effectively. We conclude that short introns are abundant in the human transcriptome, and short intron splicing represents an added layer to mRNA regulation.Canonical pre-mRNA splicing requires snRNPs and associated splicing factors to excise conserved intronic sequences, with a minimum intron length required for efficient splicing. Non-canonical splicing-intron excision without the spliceosome-has been documented; most notably, some tRNAs and the XBP1 mRNA contain short introns that are not removed by the spliceosome. There have been some efforts to identify additional short introns, but little is known about how many short introns are processed from mRNAs. Here, we report an approach to identify RNA short introns from RNA-Seq data, discriminating against small genomic deletions. We identify hundreds of short introns conserved among multiple human cell lines. These short introns are often alternatively spliced and are found in a variety of RNAs-both mRNAs and lncRNAs. Short intron splicing efficiency is increased by secondary structure, and we detect both canonical and non-canonical short introns. In many cases, splicing of these short introns from mRNAs is predicted to alter the reading frame and change protein output. Our findings imply that standard gene prediction models which often assume a lower limit for intron size fail to predict short introns effectively. We conclude that short introns are abundant in the human transcriptome, and short intron splicing represents an added layer to mRNA regulation.  | 
    
| Audience | Academic | 
    
| Author | Arnold, Zachary R. Day, R. Thomas Abebrese, Emmanuel L. Armstrong, Katharine Hsu, Daniel G. Friend, Kyle Andrews, Victoria M. Jarrell, Katherine Ali, Syed H. Raza, Zain Lee, Grace Crowder, Hannah R. Burns, Lindsay Mugayo, Daphine Luo, Yi  | 
    
| AuthorAffiliation | International Centre for Genetic Engineering and Biotechnology, ITALY Department of Chemistry and Biochemistry, Washington and Lee University, Lexington, Virginia, United States of America  | 
    
| AuthorAffiliation_xml | – name: International Centre for Genetic Engineering and Biotechnology, ITALY – name: Department of Chemistry and Biochemistry, Washington and Lee University, Lexington, Virginia, United States of America  | 
    
| Author_xml | – sequence: 1 givenname: Emmanuel L. surname: Abebrese fullname: Abebrese, Emmanuel L. – sequence: 2 givenname: Syed H. orcidid: 0000-0003-0524-5376 surname: Ali fullname: Ali, Syed H. – sequence: 3 givenname: Zachary R. surname: Arnold fullname: Arnold, Zachary R. – sequence: 4 givenname: Victoria M. orcidid: 0000-0003-0606-9431 surname: Andrews fullname: Andrews, Victoria M. – sequence: 5 givenname: Katharine surname: Armstrong fullname: Armstrong, Katharine – sequence: 6 givenname: Lindsay surname: Burns fullname: Burns, Lindsay – sequence: 7 givenname: Hannah R. orcidid: 0000-0002-0329-1048 surname: Crowder fullname: Crowder, Hannah R. – sequence: 8 givenname: R. Thomas surname: Day fullname: Day, R. Thomas – sequence: 9 givenname: Daniel G. surname: Hsu fullname: Hsu, Daniel G. – sequence: 10 givenname: Katherine surname: Jarrell fullname: Jarrell, Katherine – sequence: 11 givenname: Grace orcidid: 0000-0001-6793-7269 surname: Lee fullname: Lee, Grace – sequence: 12 givenname: Yi surname: Luo fullname: Luo, Yi – sequence: 13 givenname: Daphine surname: Mugayo fullname: Mugayo, Daphine – sequence: 14 givenname: Zain orcidid: 0000-0002-7244-2356 surname: Raza fullname: Raza, Zain – sequence: 15 givenname: Kyle orcidid: 0000-0003-4045-7104 surname: Friend fullname: Friend, Kyle  | 
    
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28520720$$D View this record in MEDLINE/PubMed | 
    
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| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Abebrese et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 Abebrese et al 2017 Abebrese et al  | 
    
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceptualization: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.Data curation: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.Formal analysis: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.Funding acquisition: KF.Investigation: KF.Methodology: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.Project administration: KF.Resources: KF.Software: KF.Supervision: KF.Validation: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.Visualization: KF.Writing – original draft: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.Writing – review & editing: ELA SHA VMA KA ZRA LB HRC RTD DGH KJ GL YL DM ZR KF.  | 
    
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| SubjectTerms | Activating transcription factor 1 Algorithms Alignment Alternative splicing Biochemistry Biology and Life Sciences Burns Cell culture Cell fate Cell Line Chemistry Chromatin Circular RNA Circularity Coding Conserved sequence Contamination Cyclic AMP response element-binding protein Cytoplasm Decay Deoxyribonucleic acid DNA Endonuclease Enzymes Exons Gene sequencing Genes Genetic regulation Genetic research Genome, Human Genomes Gold Homology Humans Introns Iron Mammals Messenger RNA Nuclei Nucleic acids Nucleotides Open Reading Frames Phosphates Position (location) Prediction models Properties Protein structure Proteins Research and Analysis Methods Ribonucleic acid RNA RNA Splicing RNA, Messenger - chemistry RNA, Messenger - genetics RNA, Transfer - chemistry RNA, Transfer - genetics Sequence Analysis, RNA - methods Sequence Deletion Splicing factors Tissue culture Translation X-Box Binding Protein 1 - genetics Yeast  | 
    
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| Title | Identification of human short introns | 
    
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