Evidence map›Paper›PMID 41279411›Full record

ArticlebioRxiv : the preprint server for biology2025

Improving long-read somatic structural variant calling with pangenome and de novo personal genome assembly.

Qian Qin, Jakob Heinz, Heng Li

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

3 authors.

Qian QinDepartment of Data Science, Dana-Farber Cancer Institute.ORCID 0000-0002-2119-6263
Jakob HeinzDepartment of Data Science, Dana-Farber Cancer Institute.
Heng LiDepartment of Data Science, Dana-Farber Cancer Institute.ORCID 0000-0003-4874-2874

Funding

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
NCI NIH HHS U24 CA294203NHGRI NIH HHS R01 HG010040NHGRI NIH HHS U01 HG013748
6 · The paper itself

Abstract

Accurate detection of mosaic and somatic structural variants (SVs) provides early diagnostic and therapeutic evidence for cancers. While long-read whole-genome sequencing leads to more accurate SV detection than short read sequencing, existing long-read SV callers only look at alignment against a single reference genome and are susceptible to systematic false discovery caused by germline differences between the individual genome and the reference genome. Here we develop a new SV calling method that jointly considers the alignment against a pangenome and the de novo assembly of the germline genome. It dramatically reduces false positive mosaic SVs in normal samples and somatic SVs in cancer cell lines with little loss in sensitivity. Our study highlights the essential need for pangenome or personal genome assembly to integrate SV calls for both SV discoveries and clinical diagnostics.

Identifiers

PMID41279411
PMCPMC12636368

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Registered trials

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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.