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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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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This bias can take the form of unmappable reads in the presence of diverse samples 37 or, more subtly, variant calls being skewed toward the reference allele 3,6 .”
From Pangenome graph construction from genome alignments with Minigraph-Cactus (2024) · cites this paper as [6]
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“To this end, we have ensured that graphs produced with Minigraph-Cactus are compatible with most state-of-the art pangenome tools (re-engineering the tools as necessary) such as vg 3,6 , Giraffe 3,5,6 , PanGenie 30 and GraphAligner 27 .”
From Pangenome graph construction from genome alignments with Minigraph-Cactus (2024) · cites this paper as [6]
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“Either the graphs needed to be constructed from variant calls against a reference 3,6,32 and, therefore, unable to properly represent nested variation while still suffering from some reference bias, or they were limited to only SVs 16 and unable to effectively be used for short-read mapping with current tools 6 .”
From Pangenome graph construction from genome alignments with Minigraph-Cactus (2024) · cites this paper as [6]
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“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]
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“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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Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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