The amount of genetic variation discovered in human populations is growing rapidly leading to challenging computational tasks, such as variant calling. Standard methods for addressing this problem include read mapping, a computationally expensive procedure; thus, mapping-free tools have been proposed in recent years. These tools focus on isolated, biallelic SNPs, providing limited support for multi-allelic SNPs and short insertions and deletions of nucleotides (indels). Here we introduce MALVA, a mapping-free method to genotype an individual from a sample of reads. MALVA is the first mapping-free tool able to genotype multi-allelic SNPs and indels, even in high-density genomic regions, and to effectively handle a huge number of variants. MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools. Biological Sciences; Genetics; Genomics; Bioinformatics

Denti, L., Previtali, M., Bernardini, G., Schönhuth, A., Bonizzoni, P. (2019). MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants. ISCIENCE, 18, 20-27 [10.1016/j.isci.2019.07.011].

MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants

Denti, Luca;Previtali, Marco
;
Bernardini, Giulia;Bonizzoni, Paola
2019

Abstract

The amount of genetic variation discovered in human populations is growing rapidly leading to challenging computational tasks, such as variant calling. Standard methods for addressing this problem include read mapping, a computationally expensive procedure; thus, mapping-free tools have been proposed in recent years. These tools focus on isolated, biallelic SNPs, providing limited support for multi-allelic SNPs and short insertions and deletions of nucleotides (indels). Here we introduce MALVA, a mapping-free method to genotype an individual from a sample of reads. MALVA is the first mapping-free tool able to genotype multi-allelic SNPs and indels, even in high-density genomic regions, and to effectively handle a huge number of variants. MALVA requires one order of magnitude less time to genotype a donor than alignment-based pipelines, providing similar accuracy. Remarkably, on indels, MALVA provides even better results than the most widely adopted variant discovery tools. Biological Sciences; Genetics; Genomics; Bioinformatics
Articolo in rivista - Articolo scientifico
Bioinformatics; Biological Sciences; Genetics; Genomics;
Mapping-free; Genotyping; Next Generation Sequencing; Multi-Allelic SNPs; Indels
English
20
27
8
Available online 12 July 2019
Denti, L., Previtali, M., Bernardini, G., Schönhuth, A., Bonizzoni, P. (2019). MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants. ISCIENCE, 18, 20-27 [10.1016/j.isci.2019.07.011].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/236169
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