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PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules
Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158750/ https://www.ncbi.nlm.nih.gov/pubmed/30007348 http://dx.doi.org/10.1093/nar/gky609 |
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author | Thody, Joshua Folkes, Leighton Medina-Calzada, Zahara Xu, Ping Dalmay, Tamas Moulton, Vincent |
author_facet | Thody, Joshua Folkes, Leighton Medina-Calzada, Zahara Xu, Ping Dalmay, Tamas Moulton, Vincent |
author_sort | Thody, Joshua |
collection | PubMed |
description | Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods. |
format | Online Article Text |
id | pubmed-6158750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-61587502018-10-02 PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules Thody, Joshua Folkes, Leighton Medina-Calzada, Zahara Xu, Ping Dalmay, Tamas Moulton, Vincent Nucleic Acids Res Computational Biology Small RNAs (sRNAs) are short, non-coding RNAs that play critical roles in many important biological pathways. They suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex to their sequence-specific mRNA target(s). In plants, this typically results in mRNA cleavage and subsequent degradation of the mRNA. The resulting mRNA fragments, or degradome, provide evidence for these interactions, and thus degradome analysis has become an important tool for sRNA target prediction. Even so, with the continuing advances in sequencing technologies, not only are larger and more complex genomes being sequenced, but also degradome and associated datasets are growing both in number and read count. As a result, existing degradome analysis tools are unable to process the volume of data being produced without imposing huge resource and time requirements. Moreover, these tools use stringent, non-configurable targeting rules, which reduces their flexibility. Here, we present a new and user configurable software tool for degradome analysis, which employs a novel search algorithm and sequence encoding technique to reduce the search space during analysis. The tool significantly reduces the time and resources required to perform degradome analysis, in some cases providing more than two orders of magnitude speed-up over current methods. Oxford University Press 2018-09-28 2018-07-11 /pmc/articles/PMC6158750/ /pubmed/30007348 http://dx.doi.org/10.1093/nar/gky609 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Thody, Joshua Folkes, Leighton Medina-Calzada, Zahara Xu, Ping Dalmay, Tamas Moulton, Vincent PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules |
title | PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules |
title_full | PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules |
title_fullStr | PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules |
title_full_unstemmed | PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules |
title_short | PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules |
title_sort | paresnip2: a tool for high-throughput prediction of small rna targets from degradome sequencing data using configurable targeting rules |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158750/ https://www.ncbi.nlm.nih.gov/pubmed/30007348 http://dx.doi.org/10.1093/nar/gky609 |
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