A bioinformatics method for predicting long noncoding RNAs associated with vascular disease
Long noncoding RNAs（lncRNAs）play important roles in human diseases including vascular disease.Given the large number of lncRNAs,however,whether the majority of them are associated with vascular disease remains unknown.For this purpose,here we present a genomic location based bioinformatics method to...
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| Veröffentlicht in: | Science China. Life sciences 2014-08, Vol.57 (8), p.852-857 |
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| container_title | Science China. Life sciences |
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| creator | Li, JianWei Gao, Cheng Wang, YuChen Ma, Wei Tu, Jian Wang, JunPei Chen, ZhenZhen Kong, Wei Cui, QingHua |
| description | Long noncoding RNAs（lncRNAs）play important roles in human diseases including vascular disease.Given the large number of lncRNAs,however,whether the majority of them are associated with vascular disease remains unknown.For this purpose,here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease.We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease.As a result,we predicted 3043 putative vascular disease associated lncRNAs.To test the accuracy of the method,we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells（VSMCs）for further experimental validation.The results confirmed that eight of the 10 lncRNAs（80%）are validated.This result suggests that the presented method has a reliable prediction performance.Finally,the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease. |
| doi_str_mv | 10.1007/s11427-014-4692-4 |
| format | Article |
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Life sciences, 2014-08, Vol.57 (8), p.852-857</ispartof><rights>The Author(s) 2014</rights><rights>Science in China Press and Springer-Verlag GmbH 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c508t-1ce716ee2ecfa280ee974c9865340a97664d5aa791dacf02aedff43c4e4c48b93</citedby><cites>FETCH-LOGICAL-c508t-1ce716ee2ecfa280ee974c9865340a97664d5aa791dacf02aedff43c4e4c48b93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/60112X/60112X.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11427-014-4692-4$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11427-014-4692-4$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25104459$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, JianWei</creatorcontrib><creatorcontrib>Gao, Cheng</creatorcontrib><creatorcontrib>Wang, YuChen</creatorcontrib><creatorcontrib>Ma, Wei</creatorcontrib><creatorcontrib>Tu, Jian</creatorcontrib><creatorcontrib>Wang, JunPei</creatorcontrib><creatorcontrib>Chen, ZhenZhen</creatorcontrib><creatorcontrib>Kong, Wei</creatorcontrib><creatorcontrib>Cui, QingHua</creatorcontrib><title>A bioinformatics method for predicting long noncoding RNAs associated with vascular disease</title><title>Science China. Life sciences</title><addtitle>Sci. China Life Sci</addtitle><addtitle>Sci China Life Sci</addtitle><description>Long noncoding RNAs（lncRNAs）play important roles in human diseases including vascular disease.Given the large number of lncRNAs,however,whether the majority of them are associated with vascular disease remains unknown.For this purpose,here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease.We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease.As a result,we predicted 3043 putative vascular disease associated lncRNAs.To test the accuracy of the method,we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells（VSMCs）for further experimental validation.The results confirmed that eight of the 10 lncRNAs（80%）are validated.This result suggests that the presented method has a reliable prediction performance.Finally,the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease.</description><subject>Base Sequence</subject><subject>Bioinformatics</subject><subject>Biomedical and Life Sciences</subject><subject>Cell Movement</subject><subject>Cell Proliferation</subject><subject>China</subject><subject>Computational Biology</subject><subject>Diagnosis</subject><subject>DNA Primers</subject><subject>Human</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Migration</subject><subject>Muscle, Smooth, Vascular - cytology</subject><subject>Muscles</subject><subject>Real-Time Polymerase Chain Reaction</subject><subject>Research Paper</subject><subject>Ribonucleic acids</subject><subject>RNA, Long Noncoding - genetics</subject><subject>RNA, Long Noncoding - physiology</subject><subject>Vascular Diseases - genetics</subject><subject>Vascular Diseases - pathology</subject><subject>人类疾病</subject><subject>全球范围</subject><subject>心血管疾病</subject><subject>生物信息学</subject><subject>血管平滑肌细胞</subject><subject>血管性疾病</subject><subject>非编码RNA</subject><subject>预测性能</subject><issn>1674-7305</issn><issn>1869-1889</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkTtvFDEUhS1ERKIkP4AGWdDQDPj9KFcRBKQIJAQVheW172QdzYw39gxR_n287BIhCuLCz-8cX_sg9JKSd5QQ_b5SKpjuCBWdUJZ14hk6oUbZjhpjn7e50qLTnMhjdF7rDWmNc8K0foGOmaRECGlP0M8VXqecpj6X0c8pVDzCvMkRtw28LRBTmNN0jYfcuilPIcfd8tuXVcW-1hySnyHiuzRv8C9fwzL4gmOq4CucoaPeDxXOD-Mp-vHxw_eLT93V18vPF6urLkhi5o4G0FQBMAi9Z4YAWC2CNUpyQbzVSokovdeWRh96wjzEvhc8CBBBmLXlp-jt3ndb8u0CdXZjqgGGwU-Ql-qoVowKqrh6GlWs_S0ngjyNSsm4VsTsCnjzD3qTlzK1N_-mKJHc8EbRPRVKrrVA77Yljb7cO0rcLlK3j9S1SN0uUiea5tXBeVmPEB8VfwJsANsDtR1N11D-uvo_rq8PlWxarLdN92isFDVCc2P4A6ugthc</recordid><startdate>20140801</startdate><enddate>20140801</enddate><creator>Li, JianWei</creator><creator>Gao, Cheng</creator><creator>Wang, YuChen</creator><creator>Ma, Wei</creator><creator>Tu, Jian</creator><creator>Wang, JunPei</creator><creator>Chen, ZhenZhen</creator><creator>Kong, Wei</creator><creator>Cui, QingHua</creator><general>Science China Press</general><general>Springer Nature B.V</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>~WA</scope><scope>C6C</scope><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>3V.</scope><scope>7QP</scope><scope>7TK</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>8AO</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20140801</creationdate><title>A bioinformatics method for predicting long noncoding RNAs associated with vascular disease</title><author>Li, JianWei ; 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Life sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, JianWei</au><au>Gao, Cheng</au><au>Wang, YuChen</au><au>Ma, Wei</au><au>Tu, Jian</au><au>Wang, JunPei</au><au>Chen, ZhenZhen</au><au>Kong, Wei</au><au>Cui, QingHua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A bioinformatics method for predicting long noncoding RNAs associated with vascular disease</atitle><jtitle>Science China. Life sciences</jtitle><stitle>Sci. China Life Sci</stitle><addtitle>Sci China Life Sci</addtitle><date>2014-08-01</date><risdate>2014</risdate><volume>57</volume><issue>8</issue><spage>852</spage><epage>857</epage><pages>852-857</pages><issn>1674-7305</issn><eissn>1869-1889</eissn><abstract>Long noncoding RNAs（lncRNAs）play important roles in human diseases including vascular disease.Given the large number of lncRNAs,however,whether the majority of them are associated with vascular disease remains unknown.For this purpose,here we present a genomic location based bioinformatics method to predict the lncRNAs associated with vascular disease.We applied the presented method to globally screen the human lncRNAs potentially involved in vascular disease.As a result,we predicted 3043 putative vascular disease associated lncRNAs.To test the accuracy of the method,we selected 10 lncRNAs predicted to be implicated in proliferation and migration of vascular smooth muscle cells（VSMCs）for further experimental validation.The results confirmed that eight of the 10 lncRNAs（80%）are validated.This result suggests that the presented method has a reliable prediction performance.Finally,the presented bioinformatics method and the predicted vascular disease associated lncRNAs together may provide helps for not only better understanding of the roles of lncRNAs in vascular disease but also the identification of novel molecules for the diagnosis and therapy of vascular disease.</abstract><cop>Beijing</cop><pub>Science China Press</pub><pmid>25104459</pmid><doi>10.1007/s11427-014-4692-4</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Base Sequence Bioinformatics Biomedical and Life Sciences Cell Movement Cell Proliferation China Computational Biology Diagnosis DNA Primers Human Humans Life Sciences Migration Muscle, Smooth, Vascular - cytology Muscles Real-Time Polymerase Chain Reaction Research Paper Ribonucleic acids RNA, Long Noncoding - genetics RNA, Long Noncoding - physiology Vascular Diseases - genetics Vascular Diseases - pathology 人类疾病 全球范围 心血管疾病 生物信息学 血管平滑肌细胞 血管性疾病 非编码RNA 预测性能 |
| title | A bioinformatics method for predicting long noncoding RNAs associated with vascular disease |
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