I was in Lille on thursday and friday for an intense conference on Statistical Models for Post-Genomic Data. There were two main themes that emerged: genetics of bacteria and viruses and change point detection. I’ll just talk about the first one and an unrelated talk on miRNA.
Viral evolutionary inference
Phillipe Lemey showed us how sequencing of virus genome could be used to retrace the spatio-temporal evolution of diseases. By sequencing viruses, you can reconstruct the phylogeny of viruses and therefore you can find where the virus came from. This allows to understand the dynamic of the epidemy in a much more precise way. See for example the spread of H1N1. He also showed us his results on ebola which is the first epidemic to be sequenced as it unfolds. This showed how the disease went from district to district. His work was retrospective as he pooled the data of different teams. He stressed the importance of efficient data sharing. His work allows to see how the epidemic is propagated and therefore allows to understand what public health measures are efficient.
GWAS for bacteria
Genome wide-association studies can help discover the genetic determinant of traits. But this idea is not limited to humans. One of the main trait of interest of bacteria is resistance to antibiotics. However, bacterial genome are very challenging in several ways :