Evidence map›Paper›PMID 25535243›Full record

ArticleBioinformatics (Oxford, England)2015

A Bayesian framework for de novo mutation calling in parents-offspring trios.

Qiang Wei, Xiaowei Zhan, Xue Zhong, Yongzhuang Liu, Yujun Han, Wei Chen, Bingshan Li

Open access · bronzeAbstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

74 citing papers in PubMed, 106 citations in OpenAlex.

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14 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 at 4 institutions in 2 countries.

Qiang WeiDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Xiaowei ZhanDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Xue ZhongDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Yongzhuang LiuDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Yujun HanDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Wei ChenDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Bingshan LiDepartment of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA,Center for Human Genetic Variation, Duke University, Durham, NC, USA, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
Duke University · USHarbin Institute of Technology · CNThe University of Texas Southwestern Medical Center · USVanderbilt University · US

Funding

Statistical methods for population and family-based whole-genome sequence dataR01HG007358 · NHGRI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CHEN, WEI · 2014 to 2018
$1.7M
Design and analysis of sequencing studies for gene mapping in familiesR01HG006857 · NHGRI · VANDERBILT UNIVERSITY · PI LI, BINGSHAN · 2013 to 2016
$1.5M
NHGRI NIH HHS 1R01HG006857NHGRI NIH HHS 1R01HG007358NHGRI NIH HHS R01 HG006857NHGRI NIH HHS R01 HG007358
6 · The paper itself

Abstract

motivationSpontaneous (de novo) mutations play an important role in the disease etiology of a range of complex diseases. Identifying de novo mutations (DNMs) in sporadic cases provides an effective strategy to find genes or genomic regions implicated in the genetics of disease. High-throughput next-generation sequencing enables genome- or exome-wide detection of DNMs by sequencing parents-proband trios. It is challenging to sift true mutations through massive amount of noise due to sequencing error and alignment artifacts. One of the critical limitations of existing methods is that for all genomic regions the same pre-specified mutation rate is assumed, which has a significant impact on the DNM calling accuracy.

resultsIn this study, we developed and implemented a novel Bayesian framework for DNM calling in trios (TrioDeNovo), which overcomes these limitations by disentangling prior mutation rates from evaluation of the likelihood of the data so that flexible priors can be adjusted post-hoc at different genomic sites. Through extensively simulations and application to real data we showed that this new method has improved sensitivity and specificity over existing methods, and provides a flexible framework to further improve the efficiency by incorporating proper priors. The accuracy is further improved using effective filtering based on sequence alignment characteristics. AVAILABILITY AND IMPLEMENTATION: The C++ source code implementing TrioDeNovo is freely available at https://medschool.vanderbilt.edu/cgg. CONTACT: bingshan.li@vanderbilt.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsBayes TheoremDNA Mutational AnalysisGenomicsHigh-Throughput Nucleotide SequencingHumansSequence Alignment

Identifiers

PMID25535243
PMCPMC4410659
OpenAlexW2025707469

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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.