Author Archives: Tom

Findr preprint

We have posted a preprint “Efficient causal inference with hidden confounders from genome-transcriptome variation data”. In this paper we introduce a new method for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors. The method has been implemented in the Findr software, and validated using the DREAM5 Systems Genetics Challenge and GEUVADIS datasets.

Welcome Pau!

A warm welcome to Pau Erola who joins us as a postdoctoral Research Fellow, having completed a PhD in Computer Engineering from the Universitat Rovira i Virgili and an MSc in Biomedicine from the Universitat de Barcelona.

Cell Systems publication

Update: Our paper has been selected for a Highlight presentation at ECCB 2016.

Update: Cell Systems has published an Editorial Preview of our paper.

Husain’s paper “Cross-Tissue Regulatory Gene Networks in Coronary Artery Disease” has been published in Cell Systems, the new flagship systems biology journal from Cell Press. In this paper we reconstructed and analyzed regulatory gene networks across 7 vascular and metabolic tissues using genotype and gene expression data sampled from more than 100 individuals. Here’s the graphical abstract:

CAD-RGN-Cell-Syst-graphabstract.

Congrats Husain!

Preprint ChIP-RNA correlation

A preprint of Chris’s paper on “Functional transcription factor target discovery via compendia of binding and expression profiles” is available from the arXiv. In this paper we demonstrated that prediction of functional target genes responding to the silencing of a transcription factor (TF) can be improved by correlating a gene’s TF-binding and expression profiles across multiple experimental conditions.

Preprint eQTL review paper

A preprint written by Lingfei on “Detection of regulator genes and eQTLs in gene networks” is available from the arXiv. This is a review article/book chapter reviewing the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.