DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios

Yongzhuang Liu; Jian Liu; Yadong Wang

doi:10.1109/BIBM47256.2019.8983407

DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Identification 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. In 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. The 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.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1758-1761
Number of pages	4
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983407
State	Published - Nov 2019
Externally published	Yes
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

de novo indel
gradient boosting
machine learning

Access to Document

10.1109/BIBM47256.2019.8983407

Cite this

Liu, Y., Liu, J., & Wang, Y. (2019). DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 1758-1761). Article 8983407 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983407

Liu, Yongzhuang ; Liu, Jian ; Wang, Yadong. / DNMFilter-Indel : Filtering de novo Indels in Parent-Offspring Trios. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1758-1761 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@inproceedings{7cc79e08ee2a49d6b5bb027087121106,

title = "DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios",

abstract = "Identification 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. In 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. The 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.",

keywords = "de novo indel, gradient boosting, machine learning",

author = "Yongzhuang Liu and Jian Liu and Yadong Wang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

month = nov,

doi = "10.1109/BIBM47256.2019.8983407",

language = "英语",

series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1758--1761",

editor = "Illhoi Yoo and Jinbo Bi and Hu, \{Xiaohua Tony\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

address = "美国",

}

Liu, Y, Liu, J & Wang, Y 2019, DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983407, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1758-1761, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983407

DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios. / Liu, Yongzhuang; Liu, Jian; Wang, Yadong.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1758-1761 8983407 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - DNMFilter-Indel

T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

AU - Liu, Yongzhuang

AU - Liu, Jian

AU - Wang, Yadong

PY - 2019/11

Y1 - 2019/11

N2 - Identification 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. In 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. The 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.

AB - Identification 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. In 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. The 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.

KW - de novo indel

KW - gradient boosting

KW - machine learning

UR - https://www.scopus.com/pages/publications/85084339695

U2 - 10.1109/BIBM47256.2019.8983407

DO - 10.1109/BIBM47256.2019.8983407

M3 - 会议稿件

AN - SCOPUS:85084339695

T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

SP - 1758

EP - 1761

BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

A2 - Yoo, Illhoi

A2 - Bi, Jinbo

A2 - Hu, Xiaohua Tony

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 November 2019 through 21 November 2019

ER -

Liu Y, Liu J, Wang Y. DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1758-1761. 8983407. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983407

DNMFilter-Indel: Filtering de novo Indels in Parent-Offspring Trios

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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