A new preprint, “High-dimensional Bayesian network inference from systems genetics data using genetic node ordering”, is available on bioRxiv. Bayesian networks are statistical models for gene regulatory networks, and their inference from large-scale omics data is a major problem in systems genetics. In this paper we present an algorithm to solve this problem that uses causal inference, topological sorting and variable selection, and that is much more efficient than traditional Markov chain Monte Carlo algorithms. The algorithm is implemented in the Findr and lassopv software packages.
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!