High throughput sequencing of mRNA (RNA-Seq) has led to tremendous improvements in the detection of expressed genes and reconstruction of RNA transcripts. However, the extensive dynamic range of gene expression, technical limitations and biases, as well as the observed complexity of the transcriptional landscape pose profound computational challenges for transcriptome reconstruction.
Researchers at the Sloan-Kettering Institute present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. They define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data, and show how to find the optimal solution using Mixed Integer Programming. MITIE can
a) take advantage of known transcripts,
b) reconstruct and quantify transcripts simultaneously in multiple samples, as well as
c) resolve the location of multi-mapping reads.
It is designed for genome- and assembly-based transcriptome reconstruction. The researchers present an extensive study based on realistic, simulated RNA-Seq data. When compared to state-of-the-art approaches, MITIE proves to be significantly more sensitive and overall more accurate. Moreover, MITIE yields substantial performance gains when used with multiple samples. They applied our system to 38 D. melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Their results corroborate that a well-motivated objective paired with appropriate optimization techniques lead to significant improvements over the state-of-the-art in transcriptome reconstruction.
AVAILABILITY: MITIE is implemented mostly in C++ and is available from http://bioweb.me/mitie under the GPL license.
CONTACT: Jonas_Behr@web.de and email@example.com.
- Behr J, Kahles A, Zhong Y, Sreedharan VT, Drewe P, Rätsch G. (2013) MITIE: Simultaneous RNA-Seq-based Transcript Identification and Quantification in Multiple Samples. Bioinformatics [Epub ahead of print]. [article]