Back

Evaluating restriction enzyme selection for genome reduction in conservation genomics

Lopez, A.; Carreras, C.; Pascual, M.; Pegueroles, C.

2022-11-27 genomics
10.1101/2022.11.26.518029 bioRxiv
Show abstract

Conservation genomic studies in non-model organisms generally rely on genome reduction techniques based on restriction enzymes to identify population structure as well as candidate loci for local adaptation. These reduced libraries ensure a high density of SNP loci and high coverage for accurate genotyping. Despite the fraction of the genome that is sequenced is expected to be randomly located, the reduction of the genome might depend on the recognition site of the restriction enzyme used. Here, we evaluate the distribution and functional composition of loci obtained after Genotyping-by-sequencing (GBS) genome reduction with two widely used restriction enzymes (EcoT22I and ApeKI). To do so, we compared data from two endemic fish species (Symphodus ocellatus and Symphodus tinca, EcoT22I enzyme) and two ecosystem engineer sea urchins (Paracentrotus lividus and Arbacia lixula, ApeKI enzyme). In brief, we mapped the sequenced loci to the phylogenetically closest reference genome available (Labrus bergylta for fish and Strongylocentrotus purpuratus for sea urchins), classified them as exonic, intronic, and intergenic, and studied their functionality by using GO terms. We detected an enrichment towards exonic or intergenic regions depending on the restriction enzyme used, and we did not detect differences between total loci and candidate loci for adaptation. Despite most GO terms being shared between species, the analysis of their abundance showed differences between taxonomic groups, which may be attributed to differences of the targeted loci. Our results highlight the importance of restriction enzyme selection and the need for high-quality annotated genomes in conservation genomic studies.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.