Back

NewtCap: an efficient target capture approach to boost genomic studies in Salamandridae (True Salamanders and Newts)

de Visser, M. C.; France, J.; McCartney-Melstad, E.; Bucciarelli, G. M.; Theodoropoulos, A.; Shaffer, H. B.; Wielstra, B.

2024-10-25 genomics
10.1101/2024.10.25.620290 bioRxiv
Show abstract

Salamanders have large and complex genomes, hampering whole genome sequencing. However, reduced representation sequencing provides a feasible alternative to obtain genome-wide data. We present NewtCap: a sequence capture bait set that targets c.7k coding regions across the genomes of all true salamanders and newts (the family Salamandridae, also known as salamandrids). We test the efficacy of NewtCap, originally designed for the Eurasian Triturus newts, in 30 species, belonging to 17 different genera, that cover all main Salamandridae lineages. We also test NewtCap in two other salamander families. We discover that NewtCap performs well across all Salamandridae lineages (but not in the salamander families Ambystomatidae and Hynobiidae). As expected, the amount of genetic divergence from the genus Triturus correlates negatively to capture efficacy and mapping success. However, this does not impede our downstream analyses. We showcase the potential of NewtCap in the contexts of; 1) phylogenomics, by reconstructing the phylogeny of Salamandridae, 2) phylogeography, by sequencing the four closely related species comprising the genus Taricha, 3) hybrid zone analysis, by genotyping two Lissotriton species and different classes of interspecific hybrids, and 4) conservation genetics, by comparing Triturus ivanbureschi samples from several wild populations and one captive-bred population. Overall, NewtCap has the potential to boost straightforward, reproducible, and affordable genomic studies, tackling both fundamental and applied research questions across salamandrids.

Matching journals

The top 2 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.