SUPR
De novo assembly of Acoelomorpha genomes for phylogenomics and analysis of cryptic diversity
Dnr:

NAISS 2024/22-1092

Type:

NAISS Small Compute

Principal Investigator:

Ulf Jondelius

Affiliation:

Naturhistoriska riksmuseet, Stockholms universitet

Start Date:

2024-09-02

End Date:

2025-10-01

Primary Classification:

10612: Biological Systematics

Allocation

Abstract

This project is supported by the Swedish Research Council VR grant to UJ (2018-05191, Urmaskarnas evolution) and The Swedish Taxonomy Initiative through a PhD grant to UJ. We plan to analyse about 70 genomes from microscopic marine animals belonging to the group Acoelomorpha. About half of the genomes have been sequenced to-date and initial bioinformatics is on-going. The aim is to generate robust phylogenetic hypotheses, analyse cryptic biodiversity and test hypotheses of genome duplication. We will also conduct some additional analyses of eDNA data where the initial bioinformatics have been carried out elsewhere. Acoelomorpha are an abundant and diverse group of microscopic marine animals that are poorly known from a taxonomic and phylogenetic perspective. Our goal here is to sort out some problematic taxa where there are signs of introgression at the family level, analyse the extent of cryptic diversity indicated by our previous Sanger-based studies, and to evaluate the efficacy of our sampling of these animals through analysis of eDNA. Nemertoderma westbladi is part of an ancient animal group, Xenacoelomorpha, that forms the sister clade of all bilaterian animals. We have previously within the project sequenced and assembled the reference genome for one species within Acoelomorpha (Nemertoderma westbladi). After initial assembly steps, we will perform the phylogenomic analyses, tests for introgression, phylogenetic placement of eDNA data etc. We foresee a relatively high demand for processor hours as the parameter rich nucleotide substitution models implemented in e.g. PhyloBayes are computationally very demanding. Furthermore we will need to perform a set of sensitivity analyses where the dataset is modified in various ways and then reanalysed, which multiplies the resource requirements.