Biology subjects

Lozano-Arce, D.

Publications and source records attributed to Lozano-Arce, D..

2 recordsLinked to original sources

bioRxiv2022

New algorithms for accurate and efficient de-novo genome assembly from long DNA sequencing reads

Producing de-novo genome assemblies for complex genomes is possible thanks to long-read DNA sequencing technologies. However, maximizing the quality of assemblies based on long reads is a challenging task that requires the development of specialized data analysis techniques. In this paper, we present new algorithms for assembling long-DNA sequencing reads from haploid and diploid organisms. The assembly algorithm builds an undirected graph with two vertices for each read based on minimizers selected by a hash function derived from the k-mers distribution. Statistics collected during the graph construction are used as features to build layout paths by selecting edges, ranked by a likelihood function that is calculated from the inferred distributions of features on a subset of safe edges. For diploid samples, we integrated a reimplementation of the ReFHap algorithm to perform molecular phasing. The phasing procedure is used to remove edges connecting reads assigned to different haplotypes and to obtain a phased assembly by running the layout algorithm on the filtered graph. We ran the implemented algorithms on PacBio HiFi and Nanopore sequencing data taken from bacteria, yeast, Drosophila, rice, maize, and human samples. Our algorithms showed competitive efficiency and contiguity of assemblies, as well as superior accuracy in some cases, as compared to other currently used software. We expect that this new development will be useful for researchers building genome assemblies for different species.

bioinformatics↗

bioRxiv2020

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, 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. Non-exact 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-of- the-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.

bioinformatics↗