IsoformEx: Isoform level gene expression estimation using weighted non-negative least squares from mRNA-Seq data

Hyunsoo Kim, Yingtao Bi, Sharmistha Pal, Ravi Gupta, Ramana V. Davuluri

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Background: mRNA-Seq technology has revolutionized the field of transcriptomics for identification and quantification of gene transcripts not only at gene level but also at isoform level. Estimating the expression levels of transcript isoforms from mRNA-Seq data is a challenging problem due to the presence of constitutive exons.Results: We propose a novel algorithm (IsoformEx) that employs weighted non-negative least squares estimation method to estimate the expression levels of transcript isoforms. Validations based on in silico simulation of mRNA-Seq and qRT-PCR experiments with real mRNA-Seq data showed that IsoformEx could accurately estimate transcript expression levels. In comparisons with published methods, the transcript expression levels estimated by IsoformEx showed higher correlation with known transcript expression levels from simulated mRNA-Seq data, and higher agreement with qRT-PCR measurements of specific transcripts for real mRNA-Seq data.Conclusions: IsoformEx is a fast and accurate algorithm to estimate transcript expression levels and gene expression levels, which takes into account short exons and alternative exons with a weighting scheme. The software is available at

Original languageEnglish (US)
Article number305
JournalBMC bioinformatics
StatePublished - Jul 27 2011
Externally publishedYes

Bibliographical note

Funding Information:
We thank members of the high throughput sequencing (HTS) data analysis meeting at Penn for helpful discussion. This work was supported by RSG-07-097-01-MGO from American Cancer Society. The use of resources in the Bioinformatics Shared Facilities of Wistar Cancer Centre (grant # P30 CA010815) are gratefully acknowledged.


Dive into the research topics of 'IsoformEx: Isoform level gene expression estimation using weighted non-negative least squares from mRNA-Seq data'. Together they form a unique fingerprint.

Cite this