Evidence map›Paper›PMID 40799803›Full record

ArticleArXiv2025

Finding easy regions for short-read variant calling from pangenome data.

Heng Li

Abstract readPreprint
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Heng LiDepartment of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02215, USA.ORCID 0000-0003-4874-2874

Funding

The WashU-UCSC-EBI Human Genome Reference Center."U41HG010972 · NHGRI · WASHINGTON UNIVERSITY · PI Ting Wang · 2019 to 2026
$24.9M
ELSI Administrative Supplement - Center for Human Reference Genome DiversityU01HG010971 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI EICHLER, EVAN, JARVIS, ERICH D · 2019 to 2023
$18.4M
Telomere-to-telomere assemblies of human genomesR01HG011274 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI Karen Hayden Miga · 2020 to 2026
$4.5M
Advanced computational methods in analyzing high-throughput sequencing dataR01HG010040 · NHGRI · DANA-FARBER CANCER INST · PI Heng Li · 2018 to 2026
$3.7M
Tools for comprehensive variant characterization using the pangenomeU01HG013748 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI LI, HENG, MARSCHALL, TOBIAS · 2024 to 2024
$1.7M
Building Tools and Community to Make Pangenomes AccessibleU01HG013760 · NHGRI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI GARRISON, ERIK · 2024 to 2024
$1.6M
Tooling for accurately studying the epigenome along the human pangenome referenceU01HG013744 · NHGRI · UNIVERSITY OF WASHINGTON · PI STERGACHIS, ANDREW BEN · 2024 to 2024
$1.4M
Integrating the reference pangenome with biobank-scale data for complex trait analysisU01HG013755 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GYMREK, MELISSA · 2024 to 2024
$1.3M
NHGRI NIH HHS R01 HG010040NHGRI NIH HHS R01 HG011274NHGRI NIH HHS U01 HG010971NHGRI NIH HHS U01 HG013744NHGRI NIH HHS U01 HG013748NHGRI NIH HHS U01 HG013755NHGRI NIH HHS U01 HG013760NHGRI NIH HHS U41 HG010972
6 · The paper itself

Abstract

Background: While benchmarks on short-read variant calling suggest low error rate below 0.5%, they are only applicable to predefined confident regions. For a human sample without such regions, the error rate could be 10 times higher. Although multiple sets of easy regions have been identified to alleviate the issue, they fail to consider non-reference samples or are biased towards existing short-read data or aligners. Results: Here, using hundreds of high-quality human assemblies, we derived a set of sample-agnostic easy regions where short-read variant calling reaches high accuracy. These regions cover 88.2% of GRCh38, 92.2% of coding regions and 96.3% of ClinVar pathogenic variants. They achieve a good balance between coverage and easiness and can be generated for other human assemblies or species with multiple well assembled genomes. Conclusion: This resource provides a convient and powerful way to filter spurious variant calls for clinical or research human samples.

Indexed as

assemblypangenomevariant calling

Identifiers

PMID40799803
PMCPMC12340882

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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.