RNA-Seq of Mouse Models - Environmentally-Induced T2D and Control

This is a study that is focusing on Alternative Splicing events occurring in the brain and liver tissues of Western Diet (WD) that got environmentally-induced type 2 diabetes (T2D) and a corresponding control. Total RNA is from the dissected liver and brain samples is isolated with the help of the Qiagen’s RNeasy Mini Kit. TruSeq RNA v2 is used for the RNA-Seq library preparation, including isolating mRNA and preparation for sequencing. RNA integrity number (RIN) on an Agilent 2500 BioAnalyzer is used for RNA quality validation. Samples are deep sequenced on an Illumina HiSeq 2000 using 2 lanes for each sample to achieve close to 100 million 75 bp paired-end reads per sample. The RNA sequencing analysis pipeline consists of Trimmomatic with default settings to remove the low quality reads, Tophat v2 to align on GRCm38.p5, and Cufflinks v2 to quantify and reassemble expression levels. Dataset consists of processed data along with isoform lists of highly-differentiated data (>5-fold

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology