Multiple insert size paired-end sequencing for deconvolution of complex transcriptomes

Integrating heterogeneous genomic data to accurately identify disease subtypes

Background

High-throughput biotechnologies have been widely used to characterize clinical samples from various perspectives e.g., epigenomics, genomics and transcriptomics. However, because of the heterogeneity of these technologies and their outputs, individual analysis of the various types of data is hard to create a comprehensive view of disease subtypes. Integrative methods are of pressing need.

Methods

In this study, we evaluated the possible issues that hamper integrative analysis of the heterogeneous disease data types, and proposed iBFE, an effective and efficient computational method to subvert those issues from a feature extraction perspective.

Results

Strict experiments on both simulated and real datasets demonstrated that iBFE can easily overcome issues caused by scale conflicts, noise conflicts, incompleteness of patient relationships, and conflicts between patient relationships, and that iBFE can effectively combine the merits of DNA methylation, mRNA expression and microRNA (miRNA) expression datasets to accurately identify disease subtypes of significantly different prognosis.

Conclusions

iBFE is an effective and efficient method for integrative analysis of heterogeneous genomic data to accurately identify disease subtypes. The Matlab code of iBFE is freely available from http://zhangroup.aporc.org/iBFE.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0154-5) contains supplementary material, which is available to authorized users.

MITIE: Simultaneous RNA-Seq-based transcript identification and quantification in multiple samples

MOTIVATION

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.

RESULTS

We present the novel framework MITIE (Mixed Integer Transcript IdEntification) for simultaneous transcript reconstruction and quantification. We 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 (i) take advantage of known transcripts, (ii) reconstruct and quantify transcripts simultaneously in multiple samples, and (iii) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction. We present an extensive study based on realistic simulated RNA-Seq data. When compared with 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. We applied our system to 38 Drosophila melanogaster modENCODE RNA-Seq libraries and estimated the sensitivity of reconstructing omitted transcript annotations and the specificity with respect to annotated transcripts. Our 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 in C++ and is available from http://bioweb.me/mitie under the GPL license.