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DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models
BACKGROUND: Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2963605/ https://www.ncbi.nlm.nih.gov/pubmed/21049040 http://dx.doi.org/10.1371/journal.pone.0013397 |
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author | Greenfield, Alex Madar, Aviv Ostrer, Harry Bonneau, Richard |
author_facet | Greenfield, Alex Madar, Aviv Ostrer, Harry Bonneau, Richard |
author_sort | Greenfield, Alex |
collection | PubMed |
description | BACKGROUND: Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge. METHODOLOGY: We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test–based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations. CONCLUSION/SIGNIFICANCE: Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of [Image: see text] methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/. |
format | Text |
id | pubmed-2963605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-29636052010-11-03 DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models Greenfield, Alex Madar, Aviv Ostrer, Harry Bonneau, Richard PLoS One Research Article BACKGROUND: Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge. METHODOLOGY: We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test–based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations. CONCLUSION/SIGNIFICANCE: Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of [Image: see text] methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/. Public Library of Science 2010-10-25 /pmc/articles/PMC2963605/ /pubmed/21049040 http://dx.doi.org/10.1371/journal.pone.0013397 Text en Greenfield et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Greenfield, Alex Madar, Aviv Ostrer, Harry Bonneau, Richard DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models |
title | DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models |
title_full | DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models |
title_fullStr | DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models |
title_full_unstemmed | DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models |
title_short | DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models |
title_sort | dream4: combining genetic and dynamic information to identify biological networks and dynamical models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2963605/ https://www.ncbi.nlm.nih.gov/pubmed/21049040 http://dx.doi.org/10.1371/journal.pone.0013397 |
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