Pangenomes: Developing algorithms and linking to phenotypes

• PI: Brendan Mumey (MSU); Joann Mudge (NCGR)
• Co-PIs: Thiru Ramaraj (DePaul University); Indika Kahanda (MSU)

The current trajectory of next generation sequencing improvements, including falling costs and increased read lengths and throughput, ensures that multiple genomes per species will be routine within the next decade. This project initiates work on a next generation of bioinformatics software that can exploit the increased information content available from multiple accessions and intelligently use the data for unbiased, species-wide analyses.

We develop pangenomic software algorithms and tools that can scale to complex eukaryotic organisms. These tools allow researchers to study large numbers of genome sequences from a single species to understand the genomic regions responsible for phenotypic adaptions such as the ability to adapt to different environments. Each individual's genomic sequence corresponds to path in a graph data structure called a De Bruijn graph, which are large and tangled and can have millions of nodes and edges. Our tool finds hotspots or frequented regions (FRs) in De Bruijn graphs representing regions shared across individuals, as well as regions that aren't frequented (unique to an individual).

We are developing software and machine learning techniques that can automatically filter shared and unique regions in a pangenome to identify the most interesting candidate regions. These tools will help researchers to discover regions that are conserved across evolutionary space, regions that are novel, regions that have diverged due to positive selection, and regions coding for phenotypic differences across the population.

Algorithms and software tools are available at github.com/abi-pangenomics.

Publications

• S. Hokin, A. Cleary and J. Mudge.
  Disease association with frequented regions of genotype graphs
  medRxiv, 2020
  Pan-genomic analysis of complex human diseases  Pangenomic Algorithms 
• S. Hokin and A. Cleary.
  Disease Classification with Pan-Genome Frequented Regions and Machine Learning
  Gordon Research Conference, 2019
  Pan-genomic analysis of complex human diseases  Pangenomic Algorithms 
• A. Cleary, T. Ramaraj, I. Kahanda, J. Mudge and B. Mumey.
  Exploring frequented regions in pan-genomic graphs
  IEEE/ACM transactions on computational biology and bioinformatics, 2018, DOI 10.1109/tcbb.2018.2864564
  Pan-genomic analysis of complex human diseases  Pangenomic Algorithms