MALVA: genotyping by Mapping-free ALlele detection of known VAriants

Bernardini, Giulia; Bonizzoni, Paola; Denti, Luca; Previtali, Marco; SchoÌˆnhuth, A.

doi:10.1101/575126

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…Alignment-free approaches are also valuable for genotyping known variants in a sequenced genome. Luca Denti described MALVA (Bernardini et al., 2019), which can genotype both SNVs and short indels efficiently and improves upon previous methods for this problem. The success of alignment-free methods suggests that approaches that combine k-mer-based analysis with reference-based mapping could maximize accuracy for variant detection and genotyping using NGS reads.…”

Section: Main Textmentioning

confidence: 99%

Sequencing Technologies and Analyses: Where Have We Been and Where Are We Going?

Bansal

Boucher

2019

iScience

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A wave of technologies transformed sequencing over a decade ago into the high-throughput era, demanding research in new computational methods to analyze these data. The applications of these sequencing technologies have continuously expanded since then. The RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMB-Seq) meeting, established in 2011, brings together leading researchers in computational genomics and genomic biology to discuss emerging frontiers in algorithm development for massively parallel sequencing data. The ninth edition of this workshop was held in Washington, DC, in George Washington University on May 3 and 4, 2019. There was an exploration of several traditional topics in sequence analysis, including genome assembly, sequence alignment, and data compression, and development of methods for new sequencing technologies, including linked reads and single-molecule long-read sequencing. Here we revisit these topics and discuss the current status and perspectives of sequencing technologies and analyses.

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Section: Main Textmentioning

confidence: 99%

Sequencing Technologies and Analyses: Where Have We Been and Where Are We Going?

Bansal

Boucher

2019

iScience

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“…COBASI (Gómez-Romero et al., 2018) performs rapid and accurate de novo SNV discovery on whole-genome sequencing of trios by computing perfect matches to unique strings in the reference genome and then identifying abrupt shifts in the coverage of the resulting alignments. Finally, mapping-free approaches such as LAVA (Shajii et al., 2016), VarGeno (Sun and Medvedev, 2018), MALVA (Bernardini et al., 2019), and Nebula (Khorsand and Hormozdiari, 2019) were recently developed for fast and accurate genotyping of common variation using whole-genome sequencing data.…”

Section: Introductionmentioning

confidence: 99%

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants

2019

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Summary De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k -mers unique to the individual of interest. Reads containing these k -mers are partitioned into disjoint sets by shared k -mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy.

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Kevlar: a mapping-free framework for accurate discovery ofde novovariants

Standage¹,

Brown²,

Hormozdiari³

2019

Preprint

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Motivation: Discovery of genetic variants by whole genome sequencing has proven a powerful approach to study the etiology of complex genetic disorders. Elucidation of all variants is a necessary step in identifying causative variants and disease genes. In particular, there is an increased interest in detection of de novo variation and investigation of its role in various disorders. State-of-the-art methods for variant discovery rely on mapping reads from each individual to a reference genome and predicting variants from difference observed between the mapped reads and the reference genome. This process typically results in millions of variant predictions, most of which are inherited and irrelevant to the phenotype of interest. To distinguish between inherited variation and novel variation resulting from de novo germline mutation, whole-genome sequencing of close relatives (especially parents and siblings) is commonly used. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction, which in many cases arises from problems with read mapping. This is a particular challenge in predicting de novo indels and structural variants. Results: We have developed a mapping-free method, Kevlar, for de novo variant discovery based on direct comparison of sequence content between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest and retrieves the reads containing these k-mers. These reads are easily partitioned into disjoint sets by shared k-mer content for subsequent locus-by-locus processing and variant calling. Kevlar also utilizes a novel probabilistic approach to score and rank the variant predictions to identify the most likely de novo variants. We evaluated Kevlar on simulated and real pedigrees, and demonstrate its ability to detect both de novo SNVs and indels with high sensitivity and specificity. Availability: https://github.com/dib-lab/kevlar

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MALVA: genotyping by Mapping-free ALlele detection of known VAriants

Cited by 3 publications

References 41 publications

Sequencing Technologies and Analyses: Where Have We Been and Where Are We Going?

Sequencing Technologies and Analyses: Where Have We Been and Where Are We Going?

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants

Kevlar: a mapping-free framework for accurate discovery ofde novovariants

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