Evidence map›Paper›PMID 27895111›Full record

ArticleGenome research2017

Discovery and genotyping of structural variation from long-read haploid genome sequence data.

John Huddleston, Mark J P Chaisson, Karyn Meltz Steinberg, Wes Warren, Kendra Hoekzema, David Gordon, Tina A Graves-Lindsay, Katherine M Munson, Zev N Kronenberg, Laura Vives and 6 more

Erratum issuedAbstract read
In one paragraph

Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 232 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
232citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

232 citing papers in PubMed, 1 synthesis or guideline pooled it.

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172 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

John HuddlestonDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
Mark J P ChaissonDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
Karyn Meltz SteinbergMcDonnell Genome Institute, Department of Medicine, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
Wes WarrenMcDonnell Genome Institute, Department of Medicine, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
Kendra HoekzemaDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
David GordonDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
Tina A Graves-LindsayMcDonnell Genome Institute, Department of Medicine, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA.
Katherine M MunsonDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
Zev N KronenbergDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
Laura VivesDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.
Paul PelusoPacific Biosciences of California, Incorporated, Menlo Park, California 94025, USA.
Matthew BoitanoPacific Biosciences of California, Incorporated, Menlo Park, California 94025, USA.
Chen-Shin ChinPacific Biosciences of California, Incorporated, Menlo Park, California 94025, USA.
Jonas KorlachPacific Biosciences of California, Incorporated, Menlo Park, California 94025, USA.
Richard K WilsonDepartment of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.
Evan E EichlerDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA.

Funding

Sequence and Assembly of Segmental DuplicationsR01HG002385 · NHGRI · UNIVERSITY OF WASHINGTON · PI Evan Eichler · 2001 to 2026
$13.3M
Improving the Human Reference Genome ResourceU41HG007635 · NHGRI · WASHINGTON UNIVERSITY · PI WILSON, RICHARD K. · 2014 to 2017
$9.6M
NHGRI NIH HHS R01 HG002385NHGRI NIH HHS U41 HG007635
6 · The paper itself

Abstract

In an effort to more fully understand the full spectrum of human genetic variation, we generated deep single-molecule, real-time (SMRT) sequencing data from two haploid human genomes. By using an assembly-based approach (SMRT-SV), we systematically assessed each genome independently for structural variants (SVs) and indels resolving the sequence structure of 461,553 genetic variants from 2 bp to 28 kbp in length. We find that >89% of these variants have been missed as part of analysis of the 1000 Genomes Project even after adjusting for more common variants (MAF > 1%). We estimate that this theoretical human diploid differs by as much as ∼16 Mbp with respect to the human reference, with long-read sequencing data providing a fivefold increase in sensitivity for genetic variants ranging in size from 7 bp to 1 kbp compared with short-read sequence data. Although a large fraction of genetic variants were not detected by short-read approaches, once the alternate allele is sequence-resolved, we show that 61% of SVs can be genotyped in short-read sequence data sets with high accuracy. Uncoupling discovery from genotyping thus allows for the majority of this missed common variation to be genotyped in the human population. Interestingly, when we repeat SV detection on a pseudodiploid genome constructed in silico by merging the two haploids, we find that ∼59% of the heterozygous SVs are no longer detected by SMRT-SV. These results indicate that haploid resolution of long-read sequencing data will significantly increase sensitivity of SV detection.

Indexed as

Genome, HumanGenomic Structural VariationHaploidyContig MappingHuman Genome ProjectHumansSequence Analysis, DNA

Identifiers

PMID27895111
PMCPMC5411763

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.