Robust and efficient software for reference-free genomic diversity analysis of GBS data on diploid and polyploid species
Genotype-by-sequencing (GBS) is a widely used cost-effective technique to obtain large numbers of genetic markers from populations. Although a standard reference-based pipeline can be followed to analyze these reads, a reference genome is still not available for a large number of species. Hence,...
| Main Authors: | , , , , |
|---|---|
| Format: | article |
| Language: | Inglés |
| Published: |
Cold Sprimg Harbor Laboratory (CSH)
2024
|
| Subjects: | |
| Online Access: | https://www.biorxiv.org/content/10.1101/2020.11.28.402131v1 http://hdl.handle.net/20.500.12324/39793 https://doi.org/10.1101/2020.11.28.402131 |
| Summary: | Genotype-by-sequencing (GBS) is a widely used cost-effective technique
to obtain large numbers of genetic markers from populations. Although
a standard reference-based pipeline can be followed to analyze these reads, a
reference genome is still not available for a large number of species. Hence, several
research groups require reference-free approaches to generate the genetic
variability information that can be obtained from a GBS experiment. Unfortunately,
tools to perform de-novo analysis of GBS reads are scarce and some of
the existing solutions are difficult to operate under different settings generated by
the existing GBS protocols. In this manuscript we describe a novel algorithm to
perform reference-free variants detection and genotyping from GBS reads. Nonexact
searches on a dynamic hash table of consensus sequences allow to perform
efficient read clustering and sorting. This algorithm was integrated in the Next
Generation Sequencing Experience Platform (NGSEP) to integrate the state-ofthe-
art variants detector already implemented in this tool. We performed benchmark
experiments with three different real populations of plants and animals with
different structures and ploidies, and sequenced with different GBS protocols at
different read depths. These experiments show that NGSEP has comparable and
in some cases better accuracy and always better computational efficiency compared
to existing solutions. We expect that this new development will be useful
for several research groups conducting population genetic studies in a wide variety
of species. |
|---|