About the Lunch Talks

The Biomina Lunch Talks are an initiative of a number of young researchers in the biomina network and is sponsored by the Flemish Government. We aim to stimulate the interaction between researchers from different disciplines who encounter bioinformatics and computational biology, and consequently we focus on a broad and multidisciplinary public. With this informal medium we would like to provide a platform where knowledge and experience can be presented and exchanged, across partners from both academia and industry. In this manner we have had the pleasure to welcome speakers from various institutes such as the University of Antwerp, the Institute of Tropical Medicine, Janssen Pharmaceutica, the Antwerp University Hospital and Open Analytics. Last, but not least, these sessions can provide a great opportunity for young researchers to acquaint themselves with new ideas and methods in the field of bioinformatics and medical informatics.

Speakers

Senne Heeren

Parasite hybridization promotes spreading of endosymbiotic viruses

Leishmania braziliensis is a vector-borne zoonotic parasite inflicting (muco-)cutaneous leishmaniasis (CL) in Central and South America. The parasite is frequently associated with the double-stranded RNA virus Leishmaniavirus (LRV) 1, forming a unique ‘matryoshka’ infection in mammals. Phylogenetic studies have shown that LRV was most likely present in the common ancestor of Leishmania and suggest a long-term co-evolutionary history of the LeishmaniaLRV symbiosis. However, no study has ever investigated their population genomic structure and diversity on ecological timescales. Here, we studied the epidemiology of CL in Peru and Bolivia through a joint evolutionary analysis of 79 L. braziliensis and 31 LRV1 genomes. Population and landscape genomic analyses based on 410,918 SNPs revealed (i) three genetically distinct and spatially structured ancestral parasite populations circulating within patches of tropical rainforest that were enclosed by tropical monsoon forest, along with (ii) admixed parasite groups that were geographically dispersed across various ecological regions. In parallel, we identified nine divergent LRV1 lineages of which the majority occurred in single localities, suggesting viral dispersal was historically confined, and two lineages with a more widespread distribution. Remarkably, viral prevalence and lineage diversity was much lower in the ancestral parasite populations compared to the admixed parasite groups that contained multiple divergent LRV1 lineages. Our results show that – while the main L braziliensis populations and their unique LRV1 lineages circulate in isolated pockets of suitable habitat – frequent secondary contacts resulted in a more widespread distribution of hybrid parasites and LRV1 lineages. Given the implications of LRV1 in clinical outcome, the successful dispersal of LRV1 lineages due to hybridization in the Leishmania host population may have important consequences towards the epidemiology of CL in the region.

Valeriya Malysheva

Detection and analysis of chromosomal interactions in Capture Hi-C data for prioritisation of disease-associated genes

DNA regulatory elements such as enhancers can be up to megabases away from the genes they control, but come into their physical proximity through 3D chromosomal contacts. Therefore, understanding how DNA is folded in the nucleus is required for deciphering the mechanisms of gene regulation and their aberrations in disease. Capture Hi-C (CHi-C) is a powerful biochemical method that detects fine-scale 3D chromosomal topology, such as promoter-enhancer interactions. The asymmetric nature of CHi-C confers unique statistical properties to the resulting data, making most statistical tools developed for chromosome conformation capture data normalisation and signal detection generally unsuitable for the analysis of this data type. To address these challenges and detect statistically significant long-range interactions we have developed a computational pipeline, which implements a statistical model accounting for biological and technical background components, as well as bespoke normalisation and multiple testing procedures for this data type. To further increase the sensitivity of detecting functional chromosomal interactions from CHi-C data, we adapted the Activity-by-Contact approach for this assay. Combining these two approaches has enabled us to cover the “blind spots” of each of them and identify both short- and long-range regulatory elements, which can then be used to assign risk-variants to their target genes and prioritise disease-related genes. To do so we built on the foundations of our CHiC-informed GWAS gene prioritisation pipeline COGS and developed multiCOGS. This new pipeline leverages multivariate statistical fine-mapping to relax the unnecessarily rigid assumption of at most a single causal variant per LD block that is used in many GWAS analysis techniques, particularly for summary data. We show that doing away with this assumption significantly increases the sensitivity of GWAS prioritisation based on CHi-C and allows the discovery of novel disease-associated candidates.