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TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis...

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Autores principales: Conard, Ashley Mae, Goodman, Nathaniel, Hu, Yanhui, Perrimon, Norbert, Singh, Ritambhara, Lawrence, Charles, Larschan, Erica
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262710/
https://www.ncbi.nlm.nih.gov/pubmed/34125906
http://dx.doi.org/10.1093/nar/gkab384
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author Conard, Ashley Mae
Goodman, Nathaniel
Hu, Yanhui
Perrimon, Norbert
Singh, Ritambhara
Lawrence, Charles
Larschan, Erica
author_facet Conard, Ashley Mae
Goodman, Nathaniel
Hu, Yanhui
Perrimon, Norbert
Singh, Ritambhara
Lawrence, Charles
Larschan, Erica
author_sort Conard, Ashley Mae
collection PubMed
description Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.
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spelling pubmed-82627102021-07-08 TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data Conard, Ashley Mae Goodman, Nathaniel Hu, Yanhui Perrimon, Norbert Singh, Ritambhara Lawrence, Charles Larschan, Erica Nucleic Acids Res Web Server Issue Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu. Oxford University Press 2021-06-14 /pmc/articles/PMC8262710/ /pubmed/34125906 http://dx.doi.org/10.1093/nar/gkab384 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Conard, Ashley Mae
Goodman, Nathaniel
Hu, Yanhui
Perrimon, Norbert
Singh, Ritambhara
Lawrence, Charles
Larschan, Erica
TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
title TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
title_full TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
title_fullStr TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
title_full_unstemmed TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
title_short TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
title_sort timeor: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262710/
https://www.ncbi.nlm.nih.gov/pubmed/34125906
http://dx.doi.org/10.1093/nar/gkab384
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