RECOMB-CG 2015

Hierarchical Orthologous Groups: a unifying and scalable framework for large-scale gene evolution

Sequencing data are rapidly piling up. Because many genes are highly conserved in sequence and function across different species—in some cases despite billions of years of intervening evolution—knowledge painstakingly gleaned through experiments can often be propagated across evolutionarily related genes. In theory, the more we know about the sequence universe, the easier elucidating these evolutionary relationships should get. Frustratingly however, the opposite seems true in practice: dealing with multiple species is conceptually and practically challenging, and as a result many evolutionary analyses remain stuck in a “two-species at a time” paradigm or only consider single-copy genes across multiple species. To overcome this impasse, I’ll introduce the concept of Hierarchical Orthologous Groups (HOGs)—nested groups of genes descending from a single ancestral gene within clades of interest. I’ll present an algorithm to accurately and efficiently infer HOGs. I will show how HOGs can be used to reconstruct ancestral genomes and to propagate functional knowledge from model species to non-model species.

Strain level microbial comparative genomics using shotgun metagenomics

Microbial comparative genomics is now routinely performed by sequencing the genome of target microorganisms cultivated in vitro. Advances in genome assembly and analysis approaches are providing very accurate tools for detecting genomic features (genes, SNPs, motifs, repeats) associated with the conditions of interest and with epidemiology patterns. This approach is however time consuming and generally applicable to only a fraction of human-associated microbes (typically pathogens) for which a known cultivation protocol is available. Shotgun metagenomics, on the other hand, provides an untargeted snapshot of the whole microbial diversity populating an environment, but accurate taxonomic profiling is still a challenging task. I will present here new methods we developed that are able to profile microbes from metagenomes with strain level resolution. This enables cultivation-free microbial population genomics and epidemiology studies using the several thousands of publicly available metagenomes. I will describe the novel computational framework for “meta-epidemiology” and discuss several results on human-associated commensals and opportunistic pathogens.

Investigating meiosis in allohexaploid Brassica

The Brassica genus contains many agriculturally significant crop species with interesting genomic relationships. Three diploid species (cabbage, turnip and black mustard; 2n = CC, AA and BB respectively) gave rise to allotetraploid species Indian mustard, Ethiopian mustard and oilseed rape (2n = AABB, BBCC and AACC). Although allohexaploid Brassica (2n = AABBCC) does not exist in nature, combining the three genomes may allow production of a new, vigorous hybrid crop species, as well as allowing investigation of polyploid speciation processes. Genome stability and meiotic behaviour were investigated in different allohexaploid Brassica populations using a high-throughput molecular genotyping approach. A, B and C-genome allele inheritance and meiotic interactions between the three genomes were quantified. Identification of the genetic and genomic factors underlying control of meiosis in Brassica will assist in production of a new, stable allohexaploid crop species.