School of Mathematics and Statistics - Research Publications
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ItemDifferential expression analysis of multifactor RNA-Seq experiments with respect to biological variation(OXFORD UNIV PRESS, 2012-05)A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
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ItemMOZ and BMI1 play opposing roles during Hox gene activation in ES cells and in body segment identity specification in vivo(NATL ACAD SCIENCES, 2015-04-28)Hox genes underlie the specification of body segment identity in the anterior-posterior axis. They are activated during gastrulation and undergo a dynamic shift from a transcriptionally repressed to an active chromatin state in a sequence that reflects their chromosomal location. Nevertheless, the precise role of chromatin modifying complexes during the initial activation phase remains unclear. In the current study, we examined the role of chromatin regulators during Hox gene activation. Using embryonic stem cell lines lacking the transcriptional activator MOZ and the polycomb-family repressor BMI1, we showed that MOZ and BMI1, respectively, promoted and repressed Hox genes during the shift from the transcriptionally repressed to the active state. Strikingly however, MOZ but not BMI1 was required to regulate Hox mRNA levels after the initial activation phase. To determine the interaction of MOZ and BMI1 in vivo, we interrogated their role in regulating Hox genes and body segment identity using Moz;Bmi1 double deficient mice. We found that the homeotic transformations and shifts in Hox gene expression boundaries observed in single Moz and Bmi1 mutant mice were rescued to a wild type identity in Moz;Bmi1 double knockout animals. Together, our findings establish that MOZ and BMI1 play opposing roles during the onset of Hox gene expression in the ES cell model and during body segment identity specification in vivo. We propose that chromatin-modifying complexes have a previously unappreciated role during the initiation phase of Hox gene expression, which is critical for the correct specification of body segment identity.