Experiment level curation identifies high confidence transcriptional regulatory interactions in neurodevelopment
Eric Ching-Pan ChuiD, Alexander M. MoriniD, Tak Hou Calvin ChangiD, Tue Gia NguyeniD, Yi-Cheng Tsai, Aman SharmaiD, Chao Chun LiuiD, Paul PavlidisiD
Abstract
To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we hypothesize that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965, 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high confidence DTRI resource in order to support the development of large-scale TRNs. Author SummaryThe capacity to computationally reconstruct gene regulatory networks using large-scale biological data is currently limited by the absence of a high confidence set of one-to-one regulatory interactions. Given the lengthy history of using small scale experimental assays to investigate individual interactions, we hypothesize that a reliable collection of gene regulatory interactions could be compiled by systematically inspecting the published literature. To this end, we developed a curation protocol to examine and record evidence of regulatory interactions at the individual experiment level. Focusing on the area of brain development, we applied our pipeline to 1,310 publications. We identified 3,601 individual experiments, providing detailed information about 1,499 regulatory interactions. Many of these interactions have verified activity specifically in the embryonic brain. By capturing reports of regulatory interactions at this level of granularity, we present a resource that is more interpretable than other similar resources.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 6
- FWCI
- 0.24
- Field percentile
- 44th percentile
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Human transcriptional gene regulatory network compiled from 14 data resources
Vijaykumar Yogesh Muley, Rainer König·BiochimiePeer-reviewed·Feb 1, 2022
- Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities
Sophia Müller‐Dott, Eirini Tsirvouli, … Julio Sáez-Rodríguez·Nucleic Acids ResearchPeer-reviewed·Sep 22, 2023
- Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets
Alexander M. Morin, Eric Ching-Pan Chu, … Paul Pavlidis·Genome ResearchPeer-reviewed·May 1, 2023
- Genome-scale transcriptional regulatory network models of psychiatric and neurodegenerative disorders
Jocelynn R. Pearl, Carlo Colantuoni, … Seth A. Ament·Cell SystemsPeer-reviewed·Feb 1, 2019
- Systematic analysis of YFP gene traps reveals common discordance between mRNA and protein across the nervous system
J. S. Titlow, M. Kiourlappou, … I. Davis·bioRxiv·Mar 21, 2022
- Decoding yeast transcriptional regulation via a data- and mechanism-driven distributed large-scale network model
Xingcun Fan, Guangming Xiang, … Xuefeng Yan·Synthetic and Systems BiotechnologyPeer-reviewed·Jun 14, 2025