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

Nature Methods volume 19, pages 696–704 (2022)Cite this article

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Abstract

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.

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Fig. 1: Algorithms and results used in Merfin.
Fig. 2: CHM13 evaluation and polishing.
Fig. 3: HG002 human trio polishing and evaluation.
Fig. 4: Polishing and evaluation of VGP pseudo-haploid assemblies.
Fig. 5: Merfin results against quality scores.

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Data availability

HG002 variant-call data were downloaded from https://data.nist.gov/od/id/mds2-2336 (SEX9X, NFT0L, 23O09 and QUE7Q). Sequencing data and assemblies for CHM13, HG002 and VGP genomes are available at https://github.com/marbl/CHM13, https://github.com/human-pangenomics/HG002_Data_Freeze_v1.0 and https://vgp.github.io/.

Source data used for generating all figures in this manuscript are available at https://github.com/gf777/misc/tree/master/merfin/paper/figures.

The K* tracks for HiFi and Illumina of the CHM13 are browsable in the associated UCSC browser (http://genome.ucsc.edu/cgi-bin/hgTracks?db=hub_2395475_t2t-chm13-v1.1). All variant calls used in the genotyping benchmarks, k-mer databases, fitted histogram tables and K* tracks are available to download at https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html?prefix=publications/MERFIN_2021/ with a step-by-step guideline available at https://github.com/arangrhie/merfin/wiki/Best-practices-for-Merfin. All data are publicly open for download with no restrictions.

Code availability

A stable release and the source code for Merfin and examples from this work are available under Apache License 2.0 at GitHub (https://github.com/arangrhie/merfin) and Zenodo (https://doi.org/10.5281/zenodo.5527270)52. The only dependency is the k-mer counter Meryl, which comes with the release. Merfin can be run in five modes (1) the -filter mode scores each variant or variants within distance k and their combinations by error k-mers for improved genotyping; (2) the -completeness mode generates completeness metrics; (3) the -dump mode computes KC, KR, K* for each base in the assembly along with QV and QV* for each sequence; (4) the -hist mode provides a K* histogram and genome-wide QV and QV* averages; and (5) the -polish mode scores each variant or variants within distance k and their combinations by the K* for polishing. Merfin is fully parallelized using OpenMP. A combination of bash and Rscript used for data analysis and visualization is available at https://github.com/gf777/misc/tree/master/merfin/paper/figures. A Code Ocean capsule of the package is provided (https://doi.org/10.24433/CO.2292349.v1)53.

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Acknowledgements

We thank T. Rhyker Ranallo-Benavidez and M.C. Schatz for the useful discussion on adapting Genomescope2 models. We also thank the communities of the T2T, HPRC and VGP consortia for their constant support. G.F. and E.D.J. were supported by Rockefeller University and HHMI funds. A.R., B.P.W., S.K. and A.M.P. were supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health (NIH) (1ZIAHG200398). The work of F.T.-N. was supported by the Intramural Research Program of the National Library of Medicine, NIH. K.S. was supported by NIH/NHGRI (R01HG010485, U41HG010972, U01HG010961, U24HG011853 and OT2OD026682). E.W.M. was partially supported by the German Federal Ministry of Education and Research (01IS18026C). Part of this work used the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov).

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Author notes

  1. These authors contributed equally: Giulio Formenti, Arang Rhie.

Authors and Affiliations

  1. Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA

    Giulio Formenti & Erich D. Jarvis

  2. Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA

    Giulio Formenti & Erich D. Jarvis

  3. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Giulio Formenti & Erich D. Jarvis

  4. Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA

    Arang Rhie, Brian P. Walenz, Sergey Koren & Adam M. Phillippy

  5. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA

    Françoise Thibaud-Nissen

  6. UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA

    Kishwar Shafin

  7. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Eugene W. Myers

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Contributions

A.R., G.F., B.P.W. and E.W.M. implemented Merfin. G.F. and A.R. performed the validation analyses. K.S. performed the GIAB variant-calling analysis on HG002. F.T. generated the gene annotations for the VGP genomes. S.K. contributed to the conceptual development. G.F. and A.R. wrote the manuscript. G.F., A.R., E.D.J. and A.M.P. conceived the study. All authors reviewed, edited and approved the manuscript.

Corresponding authors

Correspondence to Giulio Formenti or Arang Rhie.

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Competing interests

S.K. has received travel funds to speak at symposia organized by Oxford Nanopore. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Chuan-Le Xiao and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Flowchart diagram of each mode in Merfin.

Text inside gray boxes on the top represents input files required (solid) or optional (dashed) for Merfin. a, genotyping (-filter) and polishing (-polish) modes. b, K* histogram (-hist) and K* completeness (-completeness) modes. Steps listed in bullet points are marked in gray if it is only applicable in -polish (a) or -completeness (b) mode.

Extended Data Fig. 2 Genome-wide density distribution of the K* using Illumina k-mers.

When the assembly is in agreement with the raw data, the K* is normally distributed with mean 0 and the smaller the standard deviation the higher the agreement. T2T-CHM13v1.0 shows a less dispersed distribution of the K* compared to v0.7.

Extended Data Fig. 3 A region of negative K* highlighting sequencing bias.

An example of low coverage in both HiFi and Illumina reads associated with high guanine content, and specifically a GA-rich repeat (heatmap). GA bias has been reported in PacBio HiFi data, and results in gaps in the assembly that in CHM13 were filled with Nanopore data22. The K* both from HiFi and Illumina k-mers (top tracks) recapitulate the coverage drop. Nanopore coverage appears less affected. Position Chr. 12:~129,862,000 bp.

Extended Data Fig. 4 The K* can identify issues in the assembly at the base level.

a, 40 bp window with K* close to 0, highlighting perfect agreement of the assembly with the raw reads. Position Chr18:~7,000,000 bp. b, A region of negative K* in coincidence with two heterozygous indels. Position Chr1:~105,008,350 bp.

Extended Data Fig. 5 Coverage titration experiment and impact on QV*.

The QV* is only marginally influenced by the coverage of the dataset being considered.

Extended Data Fig. 6 Haplotype phasing before and after polishing with Merfin.

In both parental assemblies, the haplotypes remained fully phased, and the size of the blocks substantially increased compared to the unpolished version (a,b) after polishing with Merfin (c,d). A theoretical human genome size of 3.1 Gbp was used to normalize NG* values.

Extended Data Fig. 7 VGP assembly pipeline.

Compared to the previous v1.6, the introduction of Merfin in v1.7 (green) resulted in a minimal change of the workflow, but in a generalized improvement in QV scores and gene annotations. Pipeline available at https://github.com/VGP/vgp-assembly.

Extended Data Fig. 8 Phase block analysis of zebra finch pseudo-haploid assembly.

a, Phase blocks in the primary assembly after mapping the reads to both the primary and alternate assemblies. b, Phase blocks in the primary assembly after mapping the reads to the primary only. c, Phase blocks in the alternate assembly after mapping the reads to both the primary and alternate assemblies. d, Phase blocks in the alternate assembly. In all cases, the application of Merfin filtering minor heterozygous variants (green) leads to block sizes better or comparable to prior polishing methods alone (blue). Unpolished assembly in gray. Results of Merfin without filtering in red. A genome size of ~1.03 Gbp derived from Genomescope2 was used to normalize NG* values.

Extended Data Fig. 9 Effect of merfin correction on the kinetochore scaffold 1 (KNL1) annotation.

a, Deleterious presence of an extra A around position 1,321,620 of scaffold_7 (red box) in the polished, non-merfin-corrected sequence is indicated by a 1-base gap in the alignments of zebra finch PacBio IsoSeq SRR8695295.20794.1 and KNL1 transcripts from three other Passeriformes songbirds. This insertion causes a disruption in the frame and a premature stop codon in the translated sequence (see amino acid sequence in red). b, Corresponding span in the merfin-corrected assembly, with gapless alignments of the IsoSeq read and Passeriformes transcripts, and uninterrupted translation.

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Formenti, G., Rhie, A., Walenz, B.P. et al. Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation. Nat Methods 19, 696–704 (2022). https://doi.org/10.1038/s41592-022-01445-y

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