We contributed two chapters to a Methods in Molecular Biology book on Gene Regulatory Networks: a chapter by Lingfei about the use of Findr for the inference of transcriptome-wide causal networks, and a chapter by Pau about the use of lemon-tree for the inference of differential module networks.
Four PhD positions in computer science are available at the Department of Informatics. These positions can be held in any of the department’s research areas (algorithms, bioinformatics, machine learning, optimization, programming theory, security, and visualization). Applicants with an interest in computational biology and machine learning are welcome to contact me prior to submitting their application.
For more details and application instructions, see here.
I will give a talk at the VIB training event
Challenges within and between omics data integration.
My paper “Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net” has been accepted to NeurIPS. A great excuse to visit the largest conference in machine learning and AI!
In this collaboration with Liam Morrison, we applied long read sequencing (PacBio) to VSG amplicons generated from blood extracted from mice infected with T. brucei. We found that long read sequencing is reliable for resolving allelic differences between VSGs, that there is significant expressed diversity (449 VSGs detected across 20 mice) and that there is a striking semi-reproducible pattern of expressed diversity across the timeframe of study.
Well done Sid and everyone else who contributed to this study!
There is a lot of research showing that genetic information in combination with gene expression data can be used to predict causal interactions between genes, on the basis that genetic variation among individuals causes gene expression variation but not vice versa (this PLOS CompBio article is a contribution to the field from our group and has links to earlier work). Anagha Joshi’s group asked if this principle could be extended to other contexts, and in a joint preprint “Causal gene regulatory network inference using enhancer activity as a causal anchor” an affirmative answer is given: variation of epigenetic activity at enhancer elements across multiple cell types or experimental treatments together with gene expression data also predicts causal interactions. The accompanying statistical methods have been implemented in our Findr software.