Evidence map›Paper›PMID 30824715›Full record

ArticleScientific reports2019

Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings.

Kyu-Baek Hwang, In-Hee Lee, Honglan Li, Dhong-Geon Won, Carles Hernandez-Ferrer, Jose Alberto Negron, Sek Won Kong

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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

50 citing papers in PubMed.

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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.

Kyu-Baek HwangSchool of Computer Science and Engineering, Soongsil University, Seoul, 06978, Korea.
In-Hee LeeComputational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA.ORCID 0000-0002-8857-1355
Honglan LiSchool of Computer Science and Engineering, Soongsil University, Seoul, 06978, Korea.
Dhong-Geon WonSchool of Computer Science and Engineering, Soongsil University, Seoul, 06978, Korea.
Carles Hernandez-FerrerComputational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA.
Jose Alberto NegronComputational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA.
Sek Won KongComputational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA. sekwon.kong@childrens.harvard.edu.ORCID 0000-0003-4877-7567

Funding

Coordinating Center for the Undiagnosed Diseases NetworkU01HG007530 · NHGRI · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S. · 2014 to 2022
$30.2M
Biorepository of iPSC lines from diverse rat strains for regenerative medicine researchR24OD024622 · OD · MCLEAN HOSPITAL · PI KIM, KWANG-SOO, KONG, SEK WON · 2018 to 2021
$3.4M
An environment-wide association study in autism spectrum disorders using novel bioinformatics methods and metabolomics via mass spectrometryR01MH107205 · NIMH · BOSTON CHILDREN'S HOSPITAL · PI KONG, SEK WON · 2015 to 2019
$2.1M
NHGRI NIH HHS U01 HG007530NIH HHS R24 OD024622NIMH NIH HHS R01 MH107205
6 · The paper itself

Abstract

Comprehensive and accurate detection of variants from whole-genome sequencing (WGS) is a strong prerequisite for translational genomic medicine; however, low concordance between analytic pipelines is an outstanding challenge. We processed a European and an African WGS samples with 70 analytic pipelines comprising the combination of 7 short-read aligners and 10 variant calling algorithms (VCAs), and observed remarkable differences in the number of variants called by different pipelines (max/min ratio: 1.3~3.4). The similarity between variant call sets was more closely determined by VCAs rather than by short-read aligners. Remarkably, reported minor allele frequency had a substantial effect on concordance between pipelines (concordance rate ratio: 0.11~0.92; Wald tests, P < 0.001), entailing more discordant results for rare and novel variants. We compared the performance of analytic pipelines and pipeline ensembles using gold-standard variant call sets and the catalog of variants from the 1000 Genomes Project. Notably, a single pipeline using BWA-MEM and GATK-HaplotypeCaller performed comparable to the pipeline ensembles for 'callable' regions (~97%) of the human reference genome. While a single pipeline is capable of analyzing common variants in most genomic regions, our findings demonstrated the limitations and challenges in analyzing rare or novel variants, especially for non-European genomes.

Indexed as

Genetic VariationAlgorithmsAllelesBlack PeopleGene FrequencyGenome, HumanGenomicsGenotypeHaplotypesHigh-Throughput Nucleotide SequencingHumansPolymorphism, Single NucleotideWhite PeopleWhole Genome Sequencing

Identifiers

PMID30824715
PMCPMC6397176

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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.