MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants
MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants
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DOI：
10.1016/j.isci.2019.07.011
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发表时间：
2019-08-30
期刊：
影响因子：
5.8
通讯作者：
Bonizzoni, Paola
中科院分区：
文献类型：
--
作者：
Denti, Luca;Previtali, Marco;Bonizzoni, Paola
在人类群体中发现的遗传变异数量正在迅速增长，导致具有挑战性的计算任务，例如变异识别。解决这个问题的标准方法包括读取映射，这是一个计算昂贵的过程;因此，近年来已经提出了无映射工具。这些工具专注于分离的双等位基因SNP，为多等位基因SNP和核苷酸的短插入和缺失（indel）提供有限的支持。在这里，我们介绍MALVA，这是一种从读数样本中对个体进行基因分型的免映射方法。MALVA是第一个能够对多等位基因SNP和indel进行基因分型的免定位工具，即使在高密度的基因组区域，也能有效地处理大量的变异。MALVA需要一个数量级更少的时间来基因型的供体比基于测序的管道，提供类似的准确性。值得注意的是，在indel上，MALVA提供了比最广泛采用的变体发现工具更好的结果。
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.