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Cited by 116Genomics and Phylogenetic StudiesChromosomal and Genetic VariationsGenomic variations and chromosomal abnormalities

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Computational methods for chromosome-scale haplotype reconstruction

Open access
Garg, Shilpa·2021·Genome Biology
DOI 10.1186/s13059-021-02328-9PMC PMC8040228
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Abstract

High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.

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Fig. 1 Third-generation sequencing technologies and their characteristics (read length, error rate and scale of information). The read length and scale of information (local versus chromosome-scale) together determine the haplotype range that can be achieved; moving down the schematic this range increases (orange arrow). Sequencing costs per sample increase moving from short-read sequencing down to nanopore sequencing, and then decrease again for BioNano and Hi-C (yellow arrows). Similarly, read length and error rate first increase moving down to nanopore sequencing, and then decrease again for BioNano and Hi-C (green arrows)
Fig. 1 Third-generation sequencing technologies and their characteristics (read length, error rate and scale of information). The read length and scale of information (local versus chromosome-scale) together determine the haplotype range that can be achieved; moving down the schematic this range increases (orange arrow). Sequencing costs per sample increase moving from short-read sequencing down to nanopore sequencing, and then decrease again for BioNano and Hi-C (yellow arrows). Similarly, read length and error rate first increase moving down to nanopore sequencing, and then decrease again for BioNano and Hi-C (green arrows)CC BY · PubMed Central
Fig. 2 Molecular haplotyping techniques in reference-based phasing. Individual haplotypes are derived directly from sequencing data of the target sample based on read alignments to the reference genome. Local and chromosome-scale haplotype phasing make use of short- and long-range sequencing data, respectively; hybrid haplotype phasing combines the two data types
Fig. 2 Molecular haplotyping techniques in reference-based phasing. Individual haplotypes are derived directly from sequencing data of the target sample based on read alignments to the reference genome. Local and chromosome-scale haplotype phasing make use of short- and long-range sequencing data, respectively; hybrid haplotype phasing combines the two data typesCC BY · PubMed Central
Fig. 3 Haplotype-aware de novo assembly. Collapsed assembly approaches identify sequence variants on a consensus assembly and subsequently phase these variants into haplotypes using chromosome-scale data (Hi-C). Semi-collapsed approaches follow a similar approach, but after phasing, variants in the initial assembly graph are updated and final contigs are produced based on this updated graph. Uncollapsed approaches directly determine haplotype-specific overlaps in local sequencing reads by retaining SNPs and repeat variation in all possible overlaps and construct haplotypes based on the selected overlaps
Fig. 3 Haplotype-aware de novo assembly. Collapsed assembly approaches identify sequence variants on a consensus assembly and subsequently phase these variants into haplotypes using chromosome-scale data (Hi-C). Semi-collapsed approaches follow a similar approach, but after phasing, variants in the initial assembly graph are updated and final contigs are produced based on this updated graph. Uncollapsed approaches directly determine haplotype-specific overlaps in local sequencing reads by retaining SNPs and repeat variation in all possible overlaps and construct haplotypes based on the selected overlapsCC BY · PubMed Central
Fig. 4 Strain-resolved metagenome assembly. a Given a pooled sequencing sample, the goal of strain-resolved metagenome assembly is to reconstruct all individual microbial strains. b A typical workflow consists of four steps: de novo assembly, contig binning, bin-wise re-assembly, and assembly curation. Each step can be performed at the species-level or at the strain-level, as illustrated in the left and middle column, respectively. Some workflows skip the initial de novo assembly step and perform strain-resolved binning directly on the sequencing reads, which can be reference-guided (right column)
Fig. 4 Strain-resolved metagenome assembly. a Given a pooled sequencing sample, the goal of strain-resolved metagenome assembly is to reconstruct all individual microbial strains. b A typical workflow consists of four steps: de novo assembly, contig binning, bin-wise re-assembly, and assembly curation. Each step can be performed at the species-level or at the strain-level, as illustrated in the left and middle column, respectively. Some workflows skip the initial de novo assembly step and perform strain-resolved binning directly on the sequencing reads, which can be reference-guided (right column)CC BY · PubMed Central

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Citations

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Research works printed but not linked: 3

  1. Zaccaria S, Raphael BJ. Characterizing the allele- and haplotype-specific copy number landscape of cancer genomes at single-cell resolution with CHISEL. bioRxiv. 2019:837195. https://doi.org/10.1101/837195. · DOI
  2. Kronenberg ZN, et al. FALCON-Phase: Integrating PacBio and Hi-C data for phased diploid genomes. bioRxiv. 2018:327064. https://doi.org/10.1101/327064. · DOI
  3. Bzikadze, A. V. & Pevzner, P. A. centroFlye: assembling centromeres with long error-prone reads. 2019; 772103 doi:https://doi.org/10.1101/772103. · DOI

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Metadata

Authors
Garg, S.
Affiliations
University of Copenhagen (DK)
Corresponding author
Garg, Shilpa
ORCID
Garg, Shilpa
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2021-04-12
Volume
22
Issue
1
Pages
101-101
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-021-02328-9
PMC
PMC8040228
OpenAlex
W3156261638
Cited by
116
References
154
Citations by year
2026: 11 · 2025: 26 · 2024: 25 · 2023: 20 · 2022: 28 · 2021: 6
FWCI
2.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Chromosomal and Genetic Variations
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
Keywords
haplotype reconstruction, haplotype assembly, diploid genome, polyploid genomes, metagenome, long-read sequencing, repetitive regions, genetic variation
MeSH terms
Chromosomes, Diploidy, Haplotypes, Humans, Polyploidy, Sequence Analysis, DNA, Computational Biology, Genomics, Metagenome, Metagenomics, High-Throughput Nucleotide Sequencing

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