Composition of seed sequence is a major determinant of microRNA targeting patterns
Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in gene expression regulation. One major roadblock in functional miRNA studies is the reliable prediction of genes targeted by miRNAs, as rules defining miRNA target recognition have not been well-established to d...
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description | Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in gene expression regulation. One major roadblock in functional miRNA studies is the reliable prediction of genes targeted by miRNAs, as rules defining miRNA target recognition have not been well-established to date. Availability of high-throughput experimental data from a recent CLASH (cross linking, ligation and sequencing of hybrids) study has presented an unprecedented opportunity to characterize miRNA target recognition patterns, which may provide guidance for improved miRNA target prediction.
Results: The CLASH data were analysed to identify distinctive sequence features that characterize canonical and non-canonical miRNA target types. Most miRNA targets were of non-canonical type, i.e. without involving perfect pairing to canonical miRNA seed region. Different miRNAs have distinct targeting patterns, and this miRNA-to-miRNA variability was associated with seed sequence composition. Specifically, seed-based canonical target recognition was dependent on the GC content of the miRNA seed. For miRNAs with low GC content of the seed region, non-canonical targeting was the dominant mechanism for target recognition. In contrast to canonical targeting, non-canonical targeting did not lead to significant target downregulation at either the RNA or protein level.
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xwang@radonc.wustl.edu |
doi_str_mv | 10.1093/bioinformatics/btu045 |
format | Article |
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Results: The CLASH data were analysed to identify distinctive sequence features that characterize canonical and non-canonical miRNA target types. Most miRNA targets were of non-canonical type, i.e. without involving perfect pairing to canonical miRNA seed region. Different miRNAs have distinct targeting patterns, and this miRNA-to-miRNA variability was associated with seed sequence composition. Specifically, seed-based canonical target recognition was dependent on the GC content of the miRNA seed. For miRNAs with low GC content of the seed region, non-canonical targeting was the dominant mechanism for target recognition. In contrast to canonical targeting, non-canonical targeting did not lead to significant target downregulation at either the RNA or protein level.
Contact:
xwang@radonc.wustl.edu</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btu045</identifier><identifier>PMID: 24470575</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Base Composition ; Bioinformatics ; Cell Line ; Determinants ; Gene expression ; Gene Expression Regulation ; Genes ; High-Throughput Nucleotide Sequencing - methods ; Humans ; MicroRNAs - genetics ; MicroRNAs - metabolism ; Original Papers ; Proteins ; Ribonucleic acids ; Seeds ; Target recognition ; Transcription, Genetic</subject><ispartof>Bioinformatics, 2014-05, Vol.30 (10), p.1377-1383</ispartof><rights>The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c584t-a77585156dda47dbc0727cb9be9c38c961056b5102de2fe6cea5b7b35185a3663</citedby><cites>FETCH-LOGICAL-c584t-a77585156dda47dbc0727cb9be9c38c961056b5102de2fe6cea5b7b35185a3663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4016705/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4016705/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,1598,27901,27902,53766,53768</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bioinformatics/btu045$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24470575$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Xiaowei</creatorcontrib><title>Composition of seed sequence is a major determinant of microRNA targeting patterns</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in gene expression regulation. One major roadblock in functional miRNA studies is the reliable prediction of genes targeted by miRNAs, as rules defining miRNA target recognition have not been well-established to date. Availability of high-throughput experimental data from a recent CLASH (cross linking, ligation and sequencing of hybrids) study has presented an unprecedented opportunity to characterize miRNA target recognition patterns, which may provide guidance for improved miRNA target prediction.
Results: The CLASH data were analysed to identify distinctive sequence features that characterize canonical and non-canonical miRNA target types. Most miRNA targets were of non-canonical type, i.e. without involving perfect pairing to canonical miRNA seed region. Different miRNAs have distinct targeting patterns, and this miRNA-to-miRNA variability was associated with seed sequence composition. Specifically, seed-based canonical target recognition was dependent on the GC content of the miRNA seed. For miRNAs with low GC content of the seed region, non-canonical targeting was the dominant mechanism for target recognition. In contrast to canonical targeting, non-canonical targeting did not lead to significant target downregulation at either the RNA or protein level.
Contact:
xwang@radonc.wustl.edu</description><subject>Base Composition</subject><subject>Bioinformatics</subject><subject>Cell Line</subject><subject>Determinants</subject><subject>Gene expression</subject><subject>Gene Expression Regulation</subject><subject>Genes</subject><subject>High-Throughput Nucleotide Sequencing - methods</subject><subject>Humans</subject><subject>MicroRNAs - genetics</subject><subject>MicroRNAs - metabolism</subject><subject>Original Papers</subject><subject>Proteins</subject><subject>Ribonucleic acids</subject><subject>Seeds</subject><subject>Target recognition</subject><subject>Transcription, Genetic</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkU9LHTEUxYMoarUfQZmlm6nJ5N9kU5BHbQvSwkPXIcnceY28SaZJRui3N49npa7qJgnc3znck4PQBcGfCFb02vrowxjTZIp3-dqWBTN-gE4JE7jtMFeH9U2FbFmP6Qn6kPMjxpwwxo7RSceYxFzyU7RexWmO2RcfQxPHJgMM9fi9QHDQ-NyYZjKPMTUDFEiTDyaUHTd5l-L6x01TTNpA8WHTzKZUJORzdDSabYaPL_cZerj9cr_61t79_Pp9dXPXOt6z0hopec8JF8NgmBysw7KTzioLytHeKUEwF5YT3A3QjSAcGG6lpZz03FAh6Bn6vPedFzvB4CCUZLZ6Tn4y6Y-Oxuu3k-B_6U180gwTUeNXg6sXgxRr4Fz05LOD7dYEiEvWRPaCKKKU-D_KKes7yqR6B9oxIoWSXUX5Hq1_mXOC8XV5gvWuZf22Zb1vueou_03-qvpbawXwHojL_E7PZ3t4u0g</recordid><startdate>20140515</startdate><enddate>20140515</enddate><creator>Wang, Xiaowei</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QO</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7SC</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>5PM</scope></search><sort><creationdate>20140515</creationdate><title>Composition of seed sequence is a major determinant of microRNA targeting patterns</title><author>Wang, Xiaowei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c584t-a77585156dda47dbc0727cb9be9c38c961056b5102de2fe6cea5b7b35185a3663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Base Composition</topic><topic>Bioinformatics</topic><topic>Cell Line</topic><topic>Determinants</topic><topic>Gene expression</topic><topic>Gene Expression Regulation</topic><topic>Genes</topic><topic>High-Throughput Nucleotide Sequencing - methods</topic><topic>Humans</topic><topic>MicroRNAs - genetics</topic><topic>MicroRNAs - metabolism</topic><topic>Original Papers</topic><topic>Proteins</topic><topic>Ribonucleic acids</topic><topic>Seeds</topic><topic>Target recognition</topic><topic>Transcription, Genetic</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xiaowei</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Xiaowei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Composition of seed sequence is a major determinant of microRNA targeting patterns</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2014-05-15</date><risdate>2014</risdate><volume>30</volume><issue>10</issue><spage>1377</spage><epage>1383</epage><pages>1377-1383</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><abstract>Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in gene expression regulation. One major roadblock in functional miRNA studies is the reliable prediction of genes targeted by miRNAs, as rules defining miRNA target recognition have not been well-established to date. Availability of high-throughput experimental data from a recent CLASH (cross linking, ligation and sequencing of hybrids) study has presented an unprecedented opportunity to characterize miRNA target recognition patterns, which may provide guidance for improved miRNA target prediction.
Results: The CLASH data were analysed to identify distinctive sequence features that characterize canonical and non-canonical miRNA target types. Most miRNA targets were of non-canonical type, i.e. without involving perfect pairing to canonical miRNA seed region. Different miRNAs have distinct targeting patterns, and this miRNA-to-miRNA variability was associated with seed sequence composition. Specifically, seed-based canonical target recognition was dependent on the GC content of the miRNA seed. For miRNAs with low GC content of the seed region, non-canonical targeting was the dominant mechanism for target recognition. In contrast to canonical targeting, non-canonical targeting did not lead to significant target downregulation at either the RNA or protein level.
Contact:
xwang@radonc.wustl.edu</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>24470575</pmid><doi>10.1093/bioinformatics/btu045</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Base Composition Bioinformatics Cell Line Determinants Gene expression Gene Expression Regulation Genes High-Throughput Nucleotide Sequencing - methods Humans MicroRNAs - genetics MicroRNAs - metabolism Original Papers Proteins Ribonucleic acids Seeds Target recognition Transcription, Genetic |
title | Composition of seed sequence is a major determinant of microRNA targeting patterns |
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