Prediction of potent shRNAs with a sequential classification algorithm
The most effective shRNAs to silence a gene are calculated by a machine learning algorithm. We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably pre...
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| Veröffentlicht in: | Nature biotechnology 2017-04, Vol.35 (4), p.350-353 |
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| creator | Pelossof, Raphael Fairchild, Lauren Huang, Chun-Hao Widmer, Christian Sreedharan, Vipin T Sinha, Nishi Lai, Dan-Yu Guan, Yuanzhe Premsrirut, Prem K Tschaharganeh, Darjus F Hoffmann, Thomas Thapar, Vishal Xiang, Qing Garippa, Ralph J Rätsch, Gunnar Zuber, Johannes Lowe, Scott W Leslie, Christina S Fellmann, Christof |
| description | The most effective shRNAs to silence a gene are calculated by a machine learning algorithm.
We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries. |
| doi_str_mv | 10.1038/nbt.3807 |
| format | Article |
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We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.</description><identifier>ISSN: 1087-0156</identifier><identifier>EISSN: 1546-1696</identifier><identifier>DOI: 10.1038/nbt.3807</identifier><identifier>PMID: 28263295</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>13/44 ; 13/89 ; 631/114/1305 ; 631/114/2401 ; 631/337/505 ; 631/61/191/505 ; 631/67/68 ; Agriculture ; Algorithms ; Analysis ; Bioinformatics ; Biology ; Biomedical Engineering/Biotechnology ; Biomedicine ; Biotechnology ; brief-communication ; Cancer ; Chromosome Mapping - methods ; Classification ; Clustered Regularly Interspaced Short Palindromic Repeats - genetics ; Computer science ; CRISPR-Cas Systems - genetics ; Datasets ; Gene Silencing ; Genetics ; Life Sciences ; Machine Learning ; Methods ; MicroRNA ; MicroRNAs ; Physiological aspects ; RNA sequencing ; RNA, Small Interfering - genetics ; Sequence Analysis, RNA - methods ; Software ; Support vector machines</subject><ispartof>Nature biotechnology, 2017-04, Vol.35 (4), p.350-353</ispartof><rights>Springer Nature America, Inc. 2017</rights><rights>COPYRIGHT 2017 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Apr 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c584t-b8d9b6fc8e428de3055bb89370cba74964b4e1437498425c24af404fb8497ab53</citedby><cites>FETCH-LOGICAL-c584t-b8d9b6fc8e428de3055bb89370cba74964b4e1437498425c24af404fb8497ab53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nbt.3807$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nbt.3807$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28263295$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pelossof, Raphael</creatorcontrib><creatorcontrib>Fairchild, Lauren</creatorcontrib><creatorcontrib>Huang, Chun-Hao</creatorcontrib><creatorcontrib>Widmer, Christian</creatorcontrib><creatorcontrib>Sreedharan, Vipin T</creatorcontrib><creatorcontrib>Sinha, Nishi</creatorcontrib><creatorcontrib>Lai, Dan-Yu</creatorcontrib><creatorcontrib>Guan, Yuanzhe</creatorcontrib><creatorcontrib>Premsrirut, Prem K</creatorcontrib><creatorcontrib>Tschaharganeh, Darjus F</creatorcontrib><creatorcontrib>Hoffmann, Thomas</creatorcontrib><creatorcontrib>Thapar, Vishal</creatorcontrib><creatorcontrib>Xiang, Qing</creatorcontrib><creatorcontrib>Garippa, Ralph J</creatorcontrib><creatorcontrib>Rätsch, Gunnar</creatorcontrib><creatorcontrib>Zuber, Johannes</creatorcontrib><creatorcontrib>Lowe, Scott W</creatorcontrib><creatorcontrib>Leslie, Christina S</creatorcontrib><creatorcontrib>Fellmann, Christof</creatorcontrib><title>Prediction of potent shRNAs with a sequential classification algorithm</title><title>Nature biotechnology</title><addtitle>Nat Biotechnol</addtitle><addtitle>Nat Biotechnol</addtitle><description>The most effective shRNAs to silence a gene are calculated by a machine learning algorithm.
We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.</description><subject>13/44</subject><subject>13/89</subject><subject>631/114/1305</subject><subject>631/114/2401</subject><subject>631/337/505</subject><subject>631/61/191/505</subject><subject>631/67/68</subject><subject>Agriculture</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Bioinformatics</subject><subject>Biology</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>brief-communication</subject><subject>Cancer</subject><subject>Chromosome Mapping - methods</subject><subject>Classification</subject><subject>Clustered Regularly Interspaced Short Palindromic Repeats - genetics</subject><subject>Computer science</subject><subject>CRISPR-Cas Systems - genetics</subject><subject>Datasets</subject><subject>Gene 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We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>28263295</pmid><doi>10.1038/nbt.3807</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 13/44 13/89 631/114/1305 631/114/2401 631/337/505 631/61/191/505 631/67/68 Agriculture Algorithms Analysis Bioinformatics Biology Biomedical Engineering/Biotechnology Biomedicine Biotechnology brief-communication Cancer Chromosome Mapping - methods Classification Clustered Regularly Interspaced Short Palindromic Repeats - genetics Computer science CRISPR-Cas Systems - genetics Datasets Gene Silencing Genetics Life Sciences Machine Learning Methods MicroRNA MicroRNAs Physiological aspects RNA sequencing RNA, Small Interfering - genetics Sequence Analysis, RNA - methods Software Support vector machines |
| title | Prediction of potent shRNAs with a sequential classification algorithm |
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