Data for: Meta gene regulatory networks in maize highlight functionally relevant regulatory interactions
2020-03-12T15:21:01Z
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2019-07-10
2020-01-31
2020-01-31
Date completed
2020-03-10
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Title
Data for: Meta gene regulatory networks in maize highlight functionally relevant regulatory interactions
Published Date
2020-03-12T15:21:01Z
Authors
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Zhou, Peng
zhoux379@umn.edu
zhoux379@umn.edu
Type
Dataset
Genomics Data
Genomics Data
Abstract
Regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent a map of potential transcriptional regulation. A consistent analysis of a large number of public maize transcriptome datasets including >6000 RNA-Seq samples was used to generate 45 co- expression based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, tissue-and-genotype, etc). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes.
Description
These are the processed datasets used to create networks (raw and filtered expression tables) and predicted interactions
Funding Information
This study was funded by grants from the National Science Foundation (IOS-1546899 and IOS- 1733633). This work is supported in part by Michigan State University and the National Science Foundation Research Traineeship Program (DGE-1828149) to FGC. No conflict of interest declared.
Referenced by
Peng Zhou, Zhi Li, Erika Magnusson, Fabio A. Gomez Cano, Peter Alexander Crisp, Jaclyn Noshay, Erich Grotewold, Candice Hirsch, Steven Paul Briggs, Nathan M. Springer. (2020). Exploring Gene Regulatory Networks in Maize. The Plant Cell, tpc.00080.2020
https://doi.org/10.1105/tpc.20.00080
https://doi.org/10.1105/tpc.20.00080
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Funding information
This study was funded by grants from the National Science Foundation (IOS-1546899 and IOS- 1733633). This work is supported in part by Michigan State University and the National Science Foundation Research Traineeship Program (DGE-1828149) to FGC. No conflict of interest declared.
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| File View/Open | Description | Size |
|---|---|---|
| Zhou_MaizeGRNs_2020_ReadMe.txt | Description of data | 5.75 KB |
| studies.xlsx | Details of 45 public maize RNA-Seq studies and the IDs used in this study | 10.94 KB |
| cpm_tables_raw.tar.gz | Raw CPM Tables of 45 Maize RNA-Seq Studies | 2.15 GB |
| cpm_tables_filtered.tar.gz | Filtered CPM Tables of 45 Maize RNA-Seq Studies | 1.4 GB |
| rf_1m.tar.gz | Top 1 million edges predicted in each of the 45 studies (using Random Forest) | 617.49 MB |
| rf_100k.tar.gz | Top 100k edges predicted in each of the 45 studies (using Random Forest) | 62.85 MB |
| et_1m.tar.gz | Top 1 million edges predicted in each of the 45 studies (using Extra Trees) | 614.16 MB |
| et_100k.tar.gz | Top 100k edges predicted in each of the 45 studies (using Extra Trees) | 62.19 MB |
| xgb_1m.tar.gz | Top 1 million edges predicted in each of the 45 studies (using XGB) | 605.71 MB |
| xgb_100k.tar.gz | Top 100k edges predicted in each of the 45 studies (using XGB) | 61.93 MB |
| studies_archivalcopy.zip | Archival copy of the "studies" spreadsheet | 7.55 KB |
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