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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...

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Autores principales: Thody, Joshua, Folkes, Leighton, Medina-Calzada, Zahara, Xu, Ping, Dalmay, Tamas, Moulton, Vincent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
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.
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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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