Filtering de novo indels in parent-offspring trios.

Yongzhuang Liu, Jian Liu, Yadong Wang

Abstract read

In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yongzhuang LiuSchool of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China.

Jian LiuSchool of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China.

Yadong WangSchool of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China. ydwang@hit.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentification of de novo indels from whole genome or exome sequencing data of parent-offspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate.

resultsIn this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity.

conclusionsThe software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https://github.com/yongzhuang/DNMFilter_Indel .

Indexed as

ParentsChildHumansINDEL MutationPrincipal Component AnalysisSoftwareDe novo indelGradient boostingMachine learning

Identifiers

PMID33323105

DOI10.1186/s12859-020-03900-z

PMCPMC7739476

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LicenceCC BY

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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.