Biology subjects

Best, A.

Publications and source records attributed to Best, A..

2 recordsLinked to original sources

bioRxiv2017

IMPROVEMENTS TO BAYESIAN GENE ACTIVITY STATE ESTIMATION FROM GENOME-WIDE TRANSCRIPTOMICS DATA

An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.

genomics↗

bioRxiv2017

Whippet: an efficient method for the detection and quantification of alternative splicing reveals extensive transcriptomic complexity

Alternative splicing (AS) is a widespread process underlying the generation of transcriptomic and proteomic diversity in metazoans. Major challenges in comprehensively detecting and quantifying patterns of AS are that RNA-seq datasets are expanding near exponentially, while existing analysis tools are computationally inefficient and ineffective at handling complex splicing patterns. Here, we describe Whippet, a method that rapidly, and with minimal hardware requirements, models and quantifies splicing events of any complexity without significant loss of accuracy. Using an entropic measure of splicing complexity, Whippet reveals that approximately 33% of human protein coding genes contain complex AS events that result in substantial expression of multiple splice isoforms. These events frequently affect tandem arrays of folded protein domains. Remarkably, high-entropy AS events are more prevalent in tumour relative to matched normal tissues, and these differences correlate with increased expression of proto-oncogenic splicing factors. Whippet thus affords the rapid and accurate analysis of AS events of any complexity, and as such will facilitate biomedical research.

genomics↗