Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Aritro Nath; Paul Geeleher; R. Stephanie Huang

doi:10.1093/bib/bby129

Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Aritro Nath, Paul Geeleher, R. Stephanie Huang

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

Original language	English (US)
Pages (from-to)	637-648
Number of pages	12
Journal	Briefings in Bioinformatics
Volume	21
Issue number	2
DOIs	https://doi.org/10.1093/bib/bby129
State	Published - Mar 23 2020

Bibliographical note

Funding Information:
NIH/NCI (grant 1R01CA204856-01A1 and grant R21 CA139278 to R.S.H.),Avon Foundation forWomen (to R.S.H.),NIH/NIGMS (grant K08GM089941 and grant UO1GM61393 to R.S.H.), Circle of Service Foundation Early Career Investigator award (to R.S.H.) and Chicago Biomedical Consortium (grant PDR-020 to P.G.). The funding agencies did not participate in the design of the study, nor do they have any influence on the collection, analysis, and interpretation of data or in writing the manuscript.

Funding Information:
NIH/NCI (grant 1R01CA204856-01A1 and grant R21 CA139278 to R.S.H.), Avon Foundation for Women (to R.S.H.), NIH/NIGMS (grant K08GM089941 and grant UO1GM61393 to R.S.H.), Circle of Service Foundation Early Career Investigator award (to R.S.H.) and Chicago Biomedical Consortium (grant PDR-020 to P.G.). The funding agencies did not participate in the design of the study, nor do they have any influence on the collection, analysis, and interpretation of data or in writing the manuscript.

Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.

Keywords

GTEX
TCGA
expression
imputation
lncRNA
machine learning

UN SDGs

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

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10.1093/bib/bby129

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title = "Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes",

abstract = "Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.",

keywords = "GTEX, TCGA, expression, imputation, lncRNA, machine learning",

author = "Aritro Nath and Paul Geeleher and Huang, {R. Stephanie}",

note = "Funding Information: NIH/NCI (grant 1R01CA204856-01A1 and grant R21 CA139278 to R.S.H.),Avon Foundation forWomen (to R.S.H.),NIH/NIGMS (grant K08GM089941 and grant UO1GM61393 to R.S.H.), Circle of Service Foundation Early Career Investigator award (to R.S.H.) and Chicago Biomedical Consortium (grant PDR-020 to P.G.). The funding agencies did not participate in the design of the study, nor do they have any influence on the collection, analysis, and interpretation of data or in writing the manuscript. Funding Information: NIH/NCI (grant 1R01CA204856-01A1 and grant R21 CA139278 to R.S.H.), Avon Foundation for Women (to R.S.H.), NIH/NIGMS (grant K08GM089941 and grant UO1GM61393 to R.S.H.), Circle of Service Foundation Early Career Investigator award (to R.S.H.) and Chicago Biomedical Consortium (grant PDR-020 to P.G.). The funding agencies did not participate in the design of the study, nor do they have any influence on the collection, analysis, and interpretation of data or in writing the manuscript. Publisher Copyright: {\textcopyright} 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.",

year = "2020",

month = mar,

day = "23",

doi = "10.1093/bib/bby129",

language = "English (US)",

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journal = "Briefings in Bioinformatics",

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AU - Geeleher, Paul

AU - Huang, R. Stephanie

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PY - 2020/3/23

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N2 - Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

AB - Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

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Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes

Abstract

Bibliographical note

Keywords

UN SDGs

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