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Abid D, Brent MR. NetProphet 3: a machine learning framework for transcription factor network mapping and multi-omics integration. Bioinformatics 2023; 39:7000334. [PMID: 36692138 PMCID: PMC9912366 DOI: 10.1093/bioinformatics/btad038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/11/2023] [Accepted: 01/18/2023] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Many methods have been proposed for mapping the targets of transcription factors (TFs) from gene expression data. It is known that combining outputs from multiple methods can improve performance. To date, outputs have been combined by using either simplistic formulae, such as geometric mean, or carefully hand-tuned formulae that may not generalize well to new inputs. Finally, the evaluation of accuracy has been challenging due to the lack of genome-scale, ground-truth networks. RESULTS We developed NetProphet3, which combines scores from multiple analyses automatically, using a tree boosting algorithm trained on TF binding location data. We also developed three independent, genome-scale evaluation metrics. By these metrics, NetProphet3 is more accurate than other commonly used packages, including NetProphet 2.0, when gene expression data from direct TF perturbations are available. Furthermore, its integration mode can forge a consensus network from gene expression data and TF binding location data. AVAILABILITY AND IMPLEMENTATION All data and code are available at https://zenodo.org/record/7504131#.Y7Wu3i-B2x8. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dhoha Abid
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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Ma CZ, Brent MR. Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data. Bioinformatics 2021; 37:1234-1245. [PMID: 33135076 PMCID: PMC8189679 DOI: 10.1093/bioinformatics/btaa947] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/26/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cynthia Z Ma
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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Kang Y, Patel NR, Shively C, Recio PS, Chen X, Wranik BJ, Kim G, McIsaac RS, Mitra R, Brent MR. Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses. Genome Res 2020; 30:459-471. [PMID: 32060051 PMCID: PMC7111528 DOI: 10.1101/gr.259655.119] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 02/11/2020] [Indexed: 12/22/2022]
Abstract
A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.
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Affiliation(s)
- Yiming Kang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Nikhil R Patel
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
| | - Christian Shively
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Pamela Samantha Recio
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Xuhua Chen
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Bernd J Wranik
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - Griffin Kim
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - R Scott McIsaac
- Calico Life Sciences LLC, South San Francisco, California 94080, USA
| | - Robi Mitra
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, Missouri 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, Missouri 63130, USA
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Brent MR. Past Roadblocks and New Opportunities in Transcription Factor Network Mapping. Trends Genet 2016; 32:736-750. [PMID: 27720190 DOI: 10.1016/j.tig.2016.08.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Revised: 08/12/2016] [Accepted: 08/16/2016] [Indexed: 12/11/2022]
Abstract
One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.
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Affiliation(s)
- Michael R Brent
- Departments of Computer Science and Genetics and Center for Genome Sciences and Systems Biology, Washington University, , Saint Louis, MO, USA.
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