The role of structural variants in the rapid evolution of biodiversity
Our recent work suggests that large genomic inversions, a type of structural genetic variants, segregate in the Lake Malawi cichlid fish adaptive radiation, were transferred across species by gene flow, and were at least in some cases under positive selection. Furthermore, recent work by other groups suggests that another type of structural variants (selfish genetic elements; transposons) contributes to rapid adaptation in Malawi cichlid species. The below two positions will (1) establish novel molecular and computational techniques to identify such structural variants and (2) develop machine learning and population genomic approaches to model the evolutionary and adaptive history of these structural variants and understand their role in adaptive radiation.The actual projects can be a combination of aspects from the two advertised projects below.
The projects will allow you do do field work in Africa (if desired) and to work with and visit great international collaborators such as Yingguang Frank Chan (Friedrich Miescher Laboratory), Stephan Peischl (Uni Bern), Richard Durbin (Uni Cambridge and Wellcome Sanger Institute), Domino Joyce (Uni Hull), Kris Laukens (Adrem Data Lab), Bosco Rusuwa (University of Malawi), etc.
Click to apply for a PhD or a Postdoc position. Position is open until filled. Next review of applications 02/04/2021. Remote work possible.
Postdoc positions are for up to 3 years with an intitial contract of 1 year. PhD fellowhips are for 4 years condtional on positive evaluation after 1 year. For more information on how to apply contact jobs@uantwerpen.be. For more info on the projects contact hannes.svardal@uantwerpen.be.
Phd/postdoc project 1
Use innovative sequencing and computational techniques to identify structural variants across hundreds of closely related species of the Lake Malawi cichlid adaptive radiation.
Keywords: haplotagging, real-time, field-based ONT sequencing, genomic inversions, transposons, cichlid adaptive radiation
In this project you will produce and analyse haplotagging (a novel linked-read sequencing method) and oxford nanopore (ONT) sequencing data of wild specimens of the 100s of species of the Lake Malawi cichlid adaptive radiation as well as from unique crosses of highly divergent species available in our fish facilities. You will use existing and develop new computational methods to identify and type structural variants (in particular, inversions and transposons) in these samples. In particular, you will use haplotagging in divergent crosses to identify regions of suppressed recombination consistent with heterozygous inversions. You will use these data as a truth set to gauge an ONT based inversion detection algorithm in population samples. You will establish a field-based inversion typing protocol/algorithm making use of the real-time sequencing feature of the MinION platform directly in the field in Malawi. You will generalise these tools/this pipeline so that it can be applied to other biological model systems.
Candidates should match several (but not necessarily all) of the following criteria
Quantitative background in bioinformatics, computational biology or a related field.
Experience with and interest in analysis of whole-genome sequencing linked or long read data.
Experience with and interest in developing computational algorithms to genomic data and developing bioinformatic tools for the scientific community.
Understanding of molecular biology and population genetic principles.
Experience with ONT sequencing data analysis.
Wet lab skills related to (ONT) genome sequencing are not required but a plus.
Phd/postdoc project 2
Using population genomics and machine learning to infer the evolutionary and adaptive history of structural variants across the Lake Malawi cichlid adaptive radiation
Keywords: machine learning, population genetics, evolutionary simulations, genomic inversions, transposons, genetic adaptation, cichlid adaptive radiation, speciation
In this project you will develop computational approaches to study the evolutionary history of genomic inversions and/or selfish genetic elements across the Lake Malawi adaptive radiation. For this you will apply a combination of supervised machine learning, population genetic modelling, and evolutionary simulations to existing and above produced data of inversion occurrence and inversion haplotypes from 100s of Lake Malawi cichlid species. You will test for introgression of structural alleles across species, test for positive selection on the corresponding haplotypes and identify genes involved and putative phenotypes.
Candidates should match several (but not necessarily all) of the following criteria
Quantitative background in bioinformatics, computational biology or a related field.
Experience with and interest in population genetic/genomic inference.
Experience with and/or interest in machine learning.
Good understanding of population genetic principles and coalescent theory.
Experience with and interest in analysis of whole-genome sequencing data.