Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation.
Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation.
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DOI：
10.1038/s41592-022-01445-y
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发表时间：
2022-06
期刊：
影响因子：
48
通讯作者：
Phillippy, Adam M.
中科院分区：
文献类型：
--
作者：
Formenti, Giulio;Rhie, Arang;Walenz, Brian P.;Thibaud-Nissen, Francoise;Shafin, Kishwar;Koren, Sergey;Myers, Eugene W.;Jarvis, Erich D.;Phillippy, Adam M.
变异召唤已广泛用于基因分型和提高长读序列的一致性准确性。变体调用通常使用用户定义的截止值进行硬过滤。然而，不可能定义一组最佳截止，因为调用在很大程度上取决于读取的质量、选择的变量调用者和未抛光程序集的质量。在这里，我们介绍Merfin，一种基于k-mer的变异过滤算法，用于提高基因分型和基因组组装抛光的准确性。Merfin根据读取中预期的k-mer多样性评估每个变体，独立于读取对齐的质量和变体调用者的内部评分。Merfin在几个基准中提高了基因分型呼叫的精度，提高了共识准确性，并减少了移码错误，这些移码错误是由Pacific Biosciences HiFi和CLR reads或Oxford Nanopore reads构建的人类和非人类组装，包括第一个完整的人类基因组。此外，我们引入了新的装配质量和完整性指标，说明预期的基因组拷贝数。
Variant calling has been widely used for genotyping and for improving the consensus accuracy of long-read assemblies. Variant calls are commonly hard-filtered with user-defined cutoffs. However, it is impossible to define a single set of optimal cutoffs, as the calls heavily depend on the quality of the reads, the variant caller of choice, and the quality of the unpolished assembly. Here, we introduce Merfin, a k-mer based variant filtering algorithm for improved accuracy in genotyping and genome assembly polishing. Merfin evaluates each variant based on the expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller’s internal score. Merfin increased the precision of genotyped calls in several benchmarks, improved consensus accuracy and reduced frameshift errors when applied to human and non-human assemblies built from Pacific Biosciences HiFi and CLR reads, or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce novel assembly quality and completeness metrics that account for the expected genomic copy numbers.
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影响因子：
7
作者：
Huddleston J;Chaisson MJP;Steinberg KM;Warren W;Hoekzema K;Gordon D;Graves-Lindsay TA;Munson KM;Kronenberg ZN;Vives L;Peluso P;Boitano M;Chin CS;Korlach J;Wilson RK;Eichler EE
通讯作者：
Eichler EE
DOI：
10.1093/bioinformatics/btw663
发表时间：
2017-02-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Mapleson D;Garcia Accinelli G;Kettleborough G;Wright J;Clavijo BJ
通讯作者：
Clavijo BJ
影响因子：
48
作者：
Cheng H;Concepcion GT;Feng X;Zhang H;Li H
通讯作者：
Li H
影响因子：
5.8
作者：
Li, Heng
通讯作者：
Li, Heng
影响因子：
48
作者：
Chin, Chen-Shan;Peluso, Paul;Schatz, Michael C.
通讯作者：
Schatz, Michael C.