000010219 001__ 10219
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000010219 02470 $$ahttps://doi.org/10.1371/journal.pcbi.1000516$$2doi
000010219 037__ $$aTEXTUAL
000010219 037__ $$bArticle
000010219 041__ $$aeng
000010219 245__ $$aStatistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification
000010219 269__ $$a2009-09-25
000010219 336__ $$aArticle
000010219 520__ $$a&lt;p&gt;MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in &lt;em&gt;homo sapiens&lt;/em&gt; from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies.&lt;/p&gt;
000010219 536__ $$oNational Cancer Institute$$cT32 CA 09565
000010219 536__ $$oNational Eye Institute$$cT32 EY007119
000010219 536__ $$oAmgen Inc.$$cMEAM06-33571
000010219 536__ $$oNational Library of Medicine$$cNLM 2T15LM007359
000010219 536__ $$oNational Cancer Institute$$cR01 CA64364
000010219 540__ $$a&lt;p&gt;© 2009 Stanhope et al.&lt;/p&gt; &lt;p&gt;This is an open access article distributed under the terms of the &lt;a href="http://creativecommons.org/licenses/by/4.0/" target="_blank"&gt;Creative Commons Attribution License&lt;/a&gt;, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.&lt;/p&gt;
000010219 542__ $$fCC BY
000010219 690__ $$aThe College
000010219 691__ $$aBiological Sciences
000010219 7001_ $$aStanhope, Stephen A.$$uUniversity of Chicago
000010219 7001_ $$aSengupta, Srikumar$$uThe Wicell Research Institute
000010219 7001_ $$aden Boon, Johan$$uUniversity of Wisconsin-Madison
000010219 7001_ $$aAhlquist, Paul$$uUniversity of Wisconsin-Madison
000010219 7001_ $$aNewton, Michael A.$$uUniversity of Wisconsin-Madison
000010219 773__ $$tPLOS Computational Biology
000010219 8564_ $$ySupporting information$$9ce89342c-f581-43ff-941b-6a3d32bdd757$$s6828122$$uhttps://knowledge.uchicago.edu/record/10219/files/journal.pcbi.1000516.zip$$ePublic
000010219 8564_ $$yArticle$$978beb646-32bc-4bb4-8f04-3120537c06d9$$s622813$$uhttps://knowledge.uchicago.edu/record/10219/files/journal.pcbi.1000516.pdf$$ePublic
000010219 908__ $$aI agree
000010219 909CO $$ooai:uchicago.tind.io:10219$$pGLOBAL_SET
000010219 983__ $$aArticle