Evidence map›Paper›PMID 41384802›Full record

ArticleGigaScience2025

Challenges in structural variant calling in low-complexity regions.

Qian Qin, Heng Li

Abstract read
In one paragraph

Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Short-Read Sequencing Benchmarking with Donor-Specific Assemblies.bioRxiv : the preprint server for biology · 2026
    Article
  4. Finding low-complexity DNA sequences with longdust.Bioinformatics (Oxford, England) · 2026
    Article
  5. Efficient near telomere-to-telomere assembly of Nanopore Simplex reads.bioRxiv : the preprint server for biology · 2025
    Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qian QinDivision of Rheumatology, Inflammation and Immunity, Brigham Women's Hospital, Boston, MA 02115, USA.ORCID 0000-0002-2119-6263
Heng LiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02215, USA.ORCID 0000-0003-4874-2874

Funding

The WashU-UCSC-EBI Human Genome Reference Center."U41HG010972 · NHGRI · WASHINGTON UNIVERSITY · PI Ira M Hall, Heng Li · 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
Enhancement and further development of informatics methods for long-read cancer sequencingU24CA294203 · NCI · DANA-FARBER CANCER INST · PI Catarina D. Campbell, Heng Li · 2024 to 2026
$2.6M
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
Bioinformatics Technology to Characterize Tumor Infiltrating Immune RepertoiresU01CA226196 · NCI · DANA-FARBER CANCER INST · PI LI, HENG · 2018 to 2020
$1.3M
Calling germline and mosaic variants from long genomic and RNA-seq readsR01HG014175 · NHGRI · DANA-FARBER CANCER INST · PI Heng Li · 2025 to 2026
$1.0M
NCI NIH HHS U01 CA226196NCI NIH HHS U24 CA294203NHGRI NIH HHS R01 HG010040NHGRI NIH HHS R01 HG011274NHGRI NIH HHS R01 HG014175NHGRI NIH HHS U01 HG010971NHGRI NIH HHS U01 HG013744NHGRI NIH HHS U01 HG013748NHGRI NIH HHS U01 HG013755NHGRI NIH HHS U01 HG013760NHGRI NIH HHS U41 HG010972NIH HHS R01HG010040NIH HHS R01HG014175NIH HHS U01HG013748NIH HHS U24CA294203NIH HHS U41HG010972
6 · The paper itself

Abstract

backgroundStructural variants (SVs) are genomic differences $\ge$50 bp in length. They remain challenging to detect, even with long-sequence reads, and the sources of these difficulties are not well quantified.

resultsWe identified 35.4 Mb of low-complexity regions (LCRs) in GRCh38. Although these regions cover only 1.2% of the genome, they contain 69.1% of confident SVs in sample HG002. Across long-read SV callers, 77.3-91.3% of erroneous SV calls occur within LCRs, with error rates increasing with LCR length.

conclusionSVs are enriched and difficult to call in LCRs. Special care needs to be taken for calling and analyzing these variants.

Indexed as

Genome, HumanGenomicsGenomic Structural VariationHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNAevaluationlow-complexity regionsstructural variant

Identifiers

PMID41384802
PMCPMC12758381

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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.