Pangenomics enables genotyping of known structural variants in 5202 diverse genomes
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Abstract
Giraffe pangenomes: Genomes within a species often have a core, conserved component, as well as a variable set of genetic material among individuals or populations that is referred to as a “pangenome.” Inference of the relationships between pangenomes sequenced with short-read technology is often done computationally by mapping the sequences to a reference genome. The computational method affects genome assembly and comparisons, especially in cases of structural variants that are longer than an average sequenced region, for highly polymorphic loci, and for cross-species analyses. Siren et al . present a bioinformatic method called Giraffe, which improves mapping pangenomes in polymorphic regions of the genome containing single nucleotide polymorphisms and structural variants with standard computational resources, making large-scale genomic analyses more accessible. —LMZ
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vg map aligns read kmers against all kmers in the reference graph, including those not in constituent haplotypes, leading to exponential growth in novel kmers during indexing and storage/complexity issues.
“The subcommand vg map aligns read kmers against all kmers in the reference graph, including those that do not exist in any constituent haplotypes, which can lead to exponential growth in novel kmers during indexing of the graph and therefore significant storage and complexity issues.”
From <tt>SAFARI</tt> : pangenome alignment of ancient DNA using purine/pyrimidine encodings (2025) · cites this paper as [21]
The Results section, “Giraffe: Fast, Haplotype Aware Pangenome Mapping” (page unavailable), says graph references create a combinatorially large alignment search space, including paths representing nonexistent sequences, and that this slowed earlier graph mappers. The Introduction identifies VG-MAP as an earlier mapper and describes its prohibitive mapping cost. Our copy does not say that VG-MAP indexes all graph k-mers, that novel k-mers grow exponentially during indexing, or that this causes storage issues. (LLM, conf=0.86)
Experimental — accuracy not yet measuredvg giraffe maps only to kmers traversed by the graph's embedded walks with a GBWT or GBZ index, reducing false positives.
“A faster alternative, vg giraffe, was developed to map only to kmers traversed by the graph’s embedded walks with the help of a GBWT or GBZ index, thus reducing false positives [20–22].”
From <tt>SAFARI</tt> : pangenome alignment of ancient DNA using purine/pyrimidine encodings (2025) · cites this paper as [21]
The Results section “Giraffe: Fast, Haplotype Aware Pangenome Mapping” (page unavailable) says Giraffe uses a GBWT index to consider haplotype paths, limiting the alignment search space; “Giraffe improves pangenome mapping speed” reports that it is faster than VG-MAP. “Haplotype sampling improves read mapping” discusses opportunities for false-positive mappings and improved accuracy with sampled haplotypes. Our copy does not establish the more specific claim that Giraffe maps only to k-mers traversed by embedded walks, or that it uses a GBZ index. (LLM, conf=0.85)
Experimental — accuracy not yet measuredvg giraffe indexes the reference pangenome using minimizers.
“Unlike vg map, vg giraffe indexes the reference pangenome using minimizers.”
From <tt>SAFARI</tt> : pangenome alignment of ancient DNA using purine/pyrimidine encodings (2025) · cites this paper as [21]
The Results section, “Giraffe: Fast, Haplotype Aware Pangenome Mapping” (page unavailable), says Giraffe uses a GBWT index to store and query graph haplotypes. Our copy does not say it indexes the reference using minimizers or establish that this distinguishes it from VG-MAP (searched: “minimizer” and “minimizers index Giraffe reference pangenome”). (LLM, conf=0.82)
Experimental — accuracy not yet measuredGraph genome approaches improve SV genotyping and mapping of short reads.
“Recent improvements, including graph genome approaches, have been shown to improve SV genotyping and the mapping of short reads15.”
From Comprehensive genome analysis and variant detection at scale using DRAGEN (2025) · cites this paper as [15]
Making the switch to using pangenome mapping is not significantly more computationally expensive and resulted in an average 34% reduction in false-positive and false-negative errors compared with using standard reference methods.
“Making the switch to using pangenome mapping is not significantly more computationally expensive49 and resulted in an average 34% reduction in false-positive and false-negative errors compared with using the standard reference methods (Supplementary Fig. 38).”
From A draft human pangenome reference. (2023) · cites this paper as [49]
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Self-citationMilia, S., Leonard, A. S., Mapel, X. M., Bernal Ulloa, S. M., Drögemüller, C., & Pausch, H. (2025) · Genome Research · doi:10.1101/gr.279064.124 · Cited by 22 · see citing works
A stepwise guide for pangenome development in crop plants: an alfalfa (Medicago sativa) case study
Kaur, H., Shannon, L. M., & Samac, D. A. (2024) · BMC Genomics · doi:10.1186/s12864-024-10931-w · Cited by 25 · see citing works
Yano, R., Li, F., Hiraga, S., Takeshima, R., Kobayashi, M., Toda, K., Umehara, Y., Kajiya‐Kanegae, H., Iwata, H., Kaga, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02113-5 · Cited by 22 · see citing works
Plant graph-based pangenomics: techniques, applications, and challenges
Du, Z., He, J.-B., & Jiao, W. (2025) · aBIOTECH · doi:10.1007/s42994-025-00206-7 · Cited by 25 · see citing works
Cohen, Z., Schoville, S. D., & Hawthorne, D. J. (2023) · Molecular Ecology · doi:10.1111/mec.16838 · Cited by 20 · see citing works
Envisioning a new era: Complete genetic information from routine, telomere-to-telomere genomes
Miga, K. H., & Eichler, E. E. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.09.011 · Cited by 23 · see citing works
Barbitoff, Y. A., Ushakov, M. O., Lazareva, T. E., Nasykhova, Y. A., Glotov, A. S., & Predeus, A. V. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad508 · Cited by 26 · see citing works
VeChat: correcting errors in long reads using variation graphs
Luo, X. L., Kang, X., & Schönhuth, A. (2022) · Nature Communications · doi:10.1038/s41467-022-34381-8 · Cited by 20 · see citing works
A survey of sequence-to-graph mapping algorithms in the pangenome era
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Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research
Ahalt, S. C., Avillach, P., Boyles, R., Bradford, K., Cox, S., Davis‐Dusenbery, B. N., Grossman, R. L., Krishnamurthy, A. K., Manning, A. K., Paten, B., et al. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad048 · Cited by 20 · see citing works
Self-citationPan-human consensus genome significantly improves the accuracy of RNA-seq analysesPreprint
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Methods and Developments in Graphical Pangenomics
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Chromosome-scale haplotype-resolved pangenomics
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- Authors
- Sirén, J., Monlong, J., Chang, X., Novak, A. M., Eizenga, J. M., Markello, C., Sibbesen, J. A., Hickey, G., Chang, P.-C., Carroll, A., Gupta, N., Gabriel, S., Blackwell, T. W., Ratan, A., Taylor, K. D., Rich, S. S., Rotter, J. I., Haussler, D., Garrison, E., & Paten, B.
- Affiliations
- University of California, Santa Cruz (US)
- Google (United States) (US)
- Broad Institute (US)
- Washington University in St. Louis (US)
- University of Virginia (US)
- The Lundquist Institute (US)
- Howard Hughes Medical Institute (US)
- University of Tennessee Health Science Center (US)
- Corresponding author
- Sirén, Jouni
- Monlong, Jean
- Chang, Xian
- Novak, Adam M.
- Eizenga, Jordan M.
- Paten, Benedict
- ORCID
- Sirén, Jouni
- Monlong, Jean
- Chang, Xian
- Novak, Adam M.
- Eizenga, Jordan M.
- Markello, Charles
- Sibbesen, Jonas A.
- Hickey, Glenn
- Chang, Pi-Chuan
- Carroll, Andrew
- Gupta, Namrata
- Gabriel, Stacey
- Blackwell, Thomas W.
- Ratan, Aakrosh
- Taylor, Kent D.
- Rich, Stephen S.
- Rotter, Jerome I.
- Haussler, David
- Garrison, Erik
- Paten, Benedict
- Venue
- Science
- Published
- 2021-12-17
- Volume
- 374
- Issue
- 6574
- Pages
- abg8871-abg8871
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC9365333
- OpenAlex
- W4200597270
- Cited by
- 400
- References
- 75
- Citations by year
- 2026: 77 · 2025: 106 · 2024: 90 · 2023: 76 · 2022: 40 · 2021: 10 · 2020: 1
- FWCI
- 18.35
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health and Human Services (75N92020D00005, UL1-TR-000040, N01-HC-95159, UL1TR001079, N01-HC-95161, DK063491, 75N92020D00003, N01-HC-95168, UL1TR001420, 75N92020D00001, R01HL-120393, UL1-TR-001881, U01HG010961, N01-HC-95160, 3R01HL-117626-02S1, 75N92020D00002, N01HC95169, 75N92020D00007, N01-HC-95164, N01-HC-95162, HHSN268201800001I, U41HG010972, N01-HC-95167, HHSN268201500003I, N01-HC-95165, 75N92020D00004, 3U54HG003067-13S1, 75N92020D00006, U01HL-120393, 2U41HG007234, N02-HL-64278, N01-HC-95163, N01HC95166, HHSN268201800002I, 3UM1HG008901-03S1)
- Broad Institute (3U54HG003067‐13S1, HHSN268201500014C)
- New York Genome Center
- National Institutes of Health (75N92020D00005, n01hc095162-003, 75N92020D00003, UL1TR001420, n01hc095159-004, 1r01hl120393-01a1, HHSN268201800001I, N01-HC- 95166, n01hc095160-004, 75N92020D00006, N01-HC9-5168, 75N92020D00001 ,, n01hc095167-003, HHSN268201800002I, 5r01hl064278-04, n01hc095165-005, N01-HC- 95159, n01hc095161-004, n01hc095166-004, UL1TR001079, n02hl64278-6-0-0, HHSN268201500003I, 75N92020D00004, n01hc095168-003, n01hc095163-003, 75N92020D00007, R01HL, n01hc095169-004, UL1TR000040, OT2OD026682, 75N92020D00002, n01hc095164-003, UL1-TR-, N01‐HC‐95161, N01- HC-95163, N01- HC-95165, N01‐HC‐95160, N01 HC95164, N01-HC-95167, N02-HL-64278, N01-HC95162, N01‐HC‐95169, DK-063491, UL1TR001881)
- University of North Carolina at Chapel Hill (R01HL-120393, HHSN268201800001I, U01HL-120393, HHSN268201800002I)
- National Heart, Lung, and Blood Institute (OT3HL142481, DK-063491, 117626, HHSN268201500014C, N01 HC-95159, N01-HC-95160, N01-HC095169, 75N92020D00001, N01 HC-95168, N01-HC-095165, 75N92020D00006, HHSN268201800002I, N01 HC95167, (N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-, N01 HC095163, R01-HL120393, 75N92020D00007, N01 HC-95161, N01 HC95165, N01HC95164, N01-HC-95167, N01-HC-95168, N01-HC-95169, HHSN268201800001C, N02 HL064278, N01-HC-95167, N01-HC-95168, 75N92020D00002, UL1-TR-, N01-HC-095159, N01-HC, N01 HC095168, N02- HL-64278, N01-HC-095162, U01HL120393, HC-95168, N01-HC-095161, 75N92020D00005, 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, U01HL137183, HHSN268201500003C, HHSN26820150 0003I, 75N92020D00003, N01 HC095167, OT3HL147154, N01-HC-095160, R01 HL, N01 HC095166, N01 HC095164, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, HHSN268201800001I, N01‐HC‐95166, N01 HC-95162, HC95159, R01HL117626, 75N92020D00004, HL64278, N01 HC95169)
- National Human Genome Research Institute (U54HG003067, UM1HG008901, R01HG010485, R01HG008742, U41HG007234, U01HG010961, U24HG007234, U41HG010972)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063491)
- National Center for Advancing Translational Sciences (N01-HC-95161, 75N92020D00006, N01-HC-95168, UL1-TR-000040, UL1-TR-001079, UL1-TR, N01-HC-95162, 75N92020D00005, N01-HC-95159, DK063491, N01-HC-95160, 75N92020D00002, 75N92020D00001, HHSN268201500003I, N01-HC-95166, N01-HC-95169, N01-HC-95163, 75N92020D00007, 75N92020D00003, N01-HC-95167, N01-HC-95165, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, N01-HC-95164, 75N92020D00004)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- pangenomics, giraffe, short read mapping, sequence graphs, haplotypes, structural variants, genotyping, human genome, variant calling
- MeSH terms
- Algorithms, Alleles, Genotype, Haplotypes, Humans, Saccharomyces, Saccharomyces cerevisiae, Genetic Variation, Genome, Human, Genome, Fungal, Sequence Analysis, DNA, Computational Biology, Polymorphism, Single Nucleotide, Genomics, Quantitative Trait Loci, High-Throughput Nucleotide Sequencing, Genotyping Techniques
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