J. Comput. Biol. - IsoLasso: a LASSO regression approach to RNA-Seq based transcriptome assembly.

Tópicos

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Resumo

The new second generation sequencing technology revolutionizes many biology-related research fields and poses various computational biology challenges. One of them is transcriptome assembly based on RNA-Seq data, which aims at reconstructing all full-length mRNA transcripts simultaneously from millions of short reads. In this article, we consider three objectives in transcriptome assembly: the maximization of prediction accuracy, minimization of interpretation, and maximization of completeness. The first objective, the maximization of prediction accuracy, requires that the estimated expression levels based on assembled transcripts should be as close as possible to the observed ones for every expressed region of the genome. The minimization of interpretation follows the parsimony principle to seek as few transcripts in the prediction as possible. The third objective, the maximization of completeness, requires that the maximum number of mapped reads (or ?expressed segments? in gene models) be explained by (i.e., contained in) the predicted transcripts in the solution. Based on the above three objectives, we present IsoLasso, a new RNA-Seq based transcriptome assembly tool. IsoLasso is based on the well-known LASSO algorithm, a multivariate regression method designated to seek a balance between the maximization of prediction accuracy and the minimization of interpretation. By including some additional constraints in the quadratic program involved in LASSO, IsoLasso is able to make the set of assembled transcripts as complete as possible. Experiments on simulated and real RNA-Seq datasets show that IsoLasso achieves, simultaneously, higher sensitivity and precision than the state-of-art transcript assembly tools.

Resumo Limpo

new second generat sequenc technolog revolution mani biologyrel research field pose various comput biolog challeng one transcriptom assembl base rnaseq data aim reconstruct fulllength mrna transcript simultan million short read articl consid three object transcriptom assembl maxim predict accuraci minim interpret maxim complet first object maxim predict accuraci requir estim express level base assembl transcript close possibl observ one everi express region genom minim interpret follow parsimoni principl seek transcript predict possibl third object maxim complet requir maximum number map read express segment gene model explain ie contain predict transcript solut base three object present isolasso new rnaseq base transcriptom assembl tool isolasso base wellknown lasso algorithm multivari regress method design seek balanc maxim predict accuraci minim interpret includ addit constraint quadrat program involv lasso isolasso abl make set assembl transcript complet possibl experi simul real rnaseq dataset show isolasso achiev simultan higher sensit precis stateofart transcript assembl tool

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