Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation.

Journal Article (Journal Article)

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 nonhuman assemblies built from Pacific Biosciences HiFi and continuous long reads or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce assembly quality and completeness metrics that account for the expected genomic copy numbers.

Full Text

Duke Authors

Cited Authors

  • Formenti, G; Rhie, A; Walenz, BP; Thibaud-Nissen, F; Shafin, K; Koren, S; Myers, EW; Jarvis, ED; Phillippy, AM

Published Date

  • June 2022

Published In

Volume / Issue

  • 19 / 6

Start / End Page

  • 696 - 704

PubMed ID

  • 35361932

Pubmed Central ID

  • PMC9745813

Electronic International Standard Serial Number (EISSN)

  • 1548-7105

Digital Object Identifier (DOI)

  • 10.1038/s41592-022-01445-y


  • eng

Conference Location

  • United States