Network Methods for Pathway Analysis of Genomic Data (Review)

Rosemary Braun, Sahil Shah
(Submitted on 7 Nov 2014)

Rapid advances in high-throughput technologies have led to considerable interest in analyzing genome-scale data in the context of biological pathways, with the goal of identifying functional systems that are involved in a given phenotype. In the most common approaches, biological pathways are modeled as simple sets of genes, neglecting the network of interactions comprising the pathway and treating all genes as equally important to the pathway’s function. Recently, a number of new methods have been proposed to integrate pathway topology in the analyses, harnessing existing knowledge and enabling more nuanced models of complex biological systems. However, there is little guidance available to researches choosing between these methods. In this review, we discuss eight topology-based methods, comparing their methodological approaches and appropriate use cases. In addition, we present the results of the application of these methods to a curated set of ten gene expression profiling studies using a common set of pathway annotations. We report the computational efficiency of the methods and the consistency of the results across methods and studies to help guide users in choosing a method. We also discuss the challenges and future outlook for improved network analysis methodologies.

WASP: allele-specific software for robust discovery of molecular quantitative trait loci

Bryce van de Geijn, Graham McVicker, Yoav Gilad, Jonathan Pritchard
doi: http://dx.doi.org/10.1101/011221

Allele-specific sequencing reads provide a powerful signal for identifying molecular quantitative trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that our approach has a low error rate and is far more powerful than existing QTL mapping approaches.

Differential gene co-expression networks via Bayesian biclustering models

Chuan Gao, Shiwen Zhao, Ian C. McDowell, Christopher D. Brown, Barbara E. Engelhardt
(Submitted on 7 Nov 2014)

Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes whose covariation may be observed in only a subset of the samples. Our biclustering method, BicMix, has desirable properties, including allowing overcomplete representations of the data, computational tractability, and jointly modeling unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios. Further, we develop a method to recover gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and recover a gene co-expression network that is differential across ER+ and ER- samples.

Annotating RNA motifs in sequences and alignments
Paul P Gardner, Hisham Eldai
doi: http://dx.doi.org/10.1101/011197

RNA performs a diverse array of important functions across all cellular life. These functions include important roles in translation, building translational machinery and maturing messenger RNA. More recent discoveries include the miRNAs and bacterial sRNAs that regulate gene expression, the thermosensors, riboswitches and other cis-regulatory elements that help prokaryotes sense their environment and eukaryotic piRNAs that suppress transposition. However, there can be a long period between the initial discovery of a RNA and determining its function. We present a bioinformatic approach to characterise RNA motifs, which are the central building blocks of RNA structure. These motifs can, in some instances, provide researchers with functional hypotheses for uncharacterised RNAs. Moreover, we introduce a new profile-based database of RNA motifs – RMfam – and illustrate its application for investigating the evolution and functional characterisation of RNA. All the data and scripts associated with this work is available from: https://github.com/ppgardne/RMfam