A Nonnegativity and Sparsity constrained Maximum A Posteriori (NSMAP) model is proposed to estimate the expression levels of isoforms from RNA-Seq data without the annotation information.
In contrast to previous methods, NSMAP performs identification of the structures of expressed isoforms and estimation of the expression levels of those expressed isoforms simultaneously, which enables better identification of isoforms. In the simulations parameterized by two real RNA-Seq data sets, more than 77% expressed isoforms are correctly identified and quantified.
The development of techniques for sequencing the messenger RNA (RNA-Seq) enables it to study the biological mechanisms such as alternative splicing and gene expression regulation more deeply and accurately. Most existing methods employ RNA-Seq to quantify the expression levels of already annotated isoforms from the reference genome. However, the current reference genome is very incomplete due to the complexity of the transcriptome which hiders the comprehensive investigation of transcriptome using RNA-Seq. Novel study on isoform inference and estimation purely from RNA-Seq without annotation information is desirable.
The authors apply NSMAP on two RNA-Seq data sets of myelodysplastic syndromes (MDS) samples and one normal sample in order to identify differentially expressed known and novel isoforms in MDS disease.
NSMAP package is freely available at https://sites.google.com/site/nsmapforrnaseq
Xia Z, Wen J, Chang CC, Zhou Z. (2011) NSMAP: A Method for Spliced Isoforms Identification and Quantification from RNA-Seq. BMC Bioinformatics [Epub ahead of print]. [abstract]