Evidence map›Paper›PMID 41495659›Full record

ArticleBMC genomics2026

Detecting foldback artifacts in long-reads.

Jakob M Heinz, Matthew Meyerson, Heng Li

Abstract read
In one paragraph

Article in BMC genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Jakob M HeinzDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Matthew MeyersonCancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Heng LiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. hli@ds.dfci.harvard.edu.

Funding

Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
NKX2-1 Enhancer Amplification and Lineage Addiction in Lung AdenocarcinomaR35CA197568 · NCI · DANA-FARBER CANCER INST · PI Matthew L. Meyerson · 2015 to 2026
$12.4M
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
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 R35 CA197568NCI NIH HHS U24 CA294203NHGRI NIH HHS R01 HG010040NHGRI NIH HHS R01 HG014175NHGRI NIH HHS T32 HG002295U.S. National Institutes of Health R35CA197568U.S. National Institutes of Health T32HG002295U.S. National Institutes of Health U24CA294203
6 · The paper itself

Abstract

Long-read sequencing data is useful for detecting large and complex structural variations; however, technical artifacts can lead to false structural variant calls. In our analyses, we became aware of a foldback artifact in long-read data. Therefore, we developed the open-source Breakinator tool to flag putative foldback artifact reads, as well as previously known chimeric artifacts. Through an alignment-based approach, Breakinator can detect artifacts missed by existing quality control tools. We profiled the occurrences of foldbacks and chimeric reads in both Oxford Nanopore and PacBio sequences across a range of specimens, library types, sequencing chemistries, sequencing machines, and base-calling software.

Indexed as

ArtifactsHigh-Throughput Nucleotide SequencingSequence Analysis, DNAHumansSoftwareLong-read sequencingNanoporeQuality controlRNA-SequencingTechnical artifacts

Identifiers

PMID41495659
PMCPMC12870357

What OpenQuestion holds

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Registered trials

None linked

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.