Evidence map›Paper›PMID 29713083›Full record

ArticleNature methods2018

Accurate detection of complex structural variations using single-molecule sequencing.

Fritz J Sedlazeck, Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler, Michael C Schatz

Abstract read
In one paragraph

Article in Nature methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 995 papers.

0numbers the graph read from it
0cells of the map it votes in
995citing 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

995 citing papers in PubMed.

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  16. [Applications and Challenges of Deep Learning in Human Genome Research].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026
    Review
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935 more citing papers are in PubMed but not listed here.

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

7 authors.

Fritz J SedlazeckHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. fritz.sedlazeck@bcm.edu.ORCID http://orcid.org/0000-0001-6040-2691
Philipp ReschenederCenter for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna, Austria.
Moritz SmolkaCenter for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna, Austria.ORCID http://orcid.org/0000-0002-8621-600X
Han FangSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Maria NattestadSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.ORCID http://orcid.org/0000-0002-4796-2894
Arndt von HaeselerCenter for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Vienna, Austria.
Michael C SchatzSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA. mschatz@cs.jhu.edu.ORCID http://orcid.org/0000-0002-4118-4446

Funding

Genomic Architecture of Common Disease in Diverse Populations: WGS of Ongoing Hemorrhagic Stroke Study SupplementUM1HG008898 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI GIBBS, RICHARD A · 2016 to 2020
$77.0M
Computational Methods for Genome Assembly, Transcript Assembly, and Variant DiscoveryR01HG006677 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI SALZBERG, STEVEN L. · 2011 to 2025
$10.7M
NHGRI NIH HHS R01 HG006677NHGRI NIH HHS UM1 HG008898
6 · The paper itself

Abstract

Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr ) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles ) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

Indexed as

DNA Mutational AnalysisGenome, HumanGenomicsHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNA

Identifiers

PMID29713083
PMCPMC5990442

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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.