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Title: ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks

Abstract

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate acrossmore » different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.« less

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1597250
Alternate Identifier(s):
OSTI ID: 1591857
Report Number(s):
BNL-213599-2020-JAAM
Journal ID: ISSN 1471-2164; ark:/13030/qt9cj705pg
Grant/Contract Number:  
AC02-05CH11231; SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
BMC Genomics
Additional Journal Information:
Journal Volume: 20; Journal Issue: S12; Journal ID: ISSN 1471-2164
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Manifold learning; Manifold regularization; Clustering; Multiview learning; Functional genomics; Comparative network analysis; Comparative genomics; Biofuel

Citation Formats

Nguyen, Nam D., Blaby, Ian K., and Wang, Daifeng. ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks. United States: N. p., 2019. Web. doi:10.1186/s12864-019-6329-2.
Nguyen, Nam D., Blaby, Ian K., & Wang, Daifeng. ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks. United States. doi:10.1186/s12864-019-6329-2.
Nguyen, Nam D., Blaby, Ian K., and Wang, Daifeng. Sun . "ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks". United States. doi:10.1186/s12864-019-6329-2. https://www.osti.gov/servlets/purl/1597250.
@article{osti_1597250,
title = {ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks},
author = {Nguyen, Nam D. and Blaby, Ian K. and Wang, Daifeng},
abstractNote = {BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.},
doi = {10.1186/s12864-019-6329-2},
journal = {BMC Genomics},
number = S12,
volume = 20,
place = {United States},
year = {2019},
month = {12}
}

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