000197008 001__ 197008
000197008 005__ 20181203023433.0
000197008 0247_ $$2doi$$a10.1101/gr.157420.113
000197008 022__ $$a1549-5469
000197008 037__ $$aARTICLE
000197008 245__ $$aInferring gene expression from ribosomal promoter sequences, a crowdsourcing approach
000197008 260__ $$c2013
000197008 269__ $$a2013
000197008 336__ $$aJournal Articles
000197008 520__ $$aThe Gene Promoter Expression Prediction challenge consisted of predicting gene expression from promoter sequences in a previously unknown experimentally generated data set. The challenge was presented to the community in the framework of the sixth Dialogue for Reverse Engineering Assessments and Methods (DREAM6), a community effort to evaluate the status of systems biology modeling methodologies. Nucleotide-specific promoter activity was obtained by measuring fluorescence from promoter sequences fused upstream of a gene for yellow fluorescence protein and inserted in the same genomic site of yeast Saccharomyces cerevisiae. Twenty-one teams submitted results predicting the expression levels of 53 different promoters from yeast ribosomal protein genes. Analysis of participant predictions shows that accurate values for low-expressed and mutated promoters were difficult to obtain, although in the latter case, only when the mutation induced a large change in promoter activity compared to the wild-type sequence. As in previous DREAM challenges, we found that aggregation of participant predictions provided robust results, but did not fare better than the three best algorithms. Finally, this study not only provides a benchmark for the assessment of methods predicting activity of a specific set of promoters from their sequence, but it also shows that the top performing algorithm, which used machine-learning approaches, can be improved by the addition of biological features such as transcription factor binding sites.
000197008 700__ $$aMeyer, Pablo
000197008 700__ $$aSiwo, Geoffrey
000197008 700__ $$aZeevi, Danny
000197008 700__ $$aSharon, Eilon
000197008 700__ $$aNorel, Raquel
000197008 700__ $$aSegal, Eran
000197008 700__ $$aStolovitzky, Gustavo
000197008 700__ $$aDREAM6, Promoter Prediction Consortium
000197008 773__ $$j23$$tGenome research$$k11$$q1928-37
000197008 909C0 $$xU11780$$0252244$$pGR-BUCHER
000197008 909CO $$pSV$$particle$$ooai:infoscience.tind.io:197008
000197008 917Z8 $$x182396
000197008 937__ $$aEPFL-ARTICLE-197008
000197008 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000197008 980__ $$aARTICLE