TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions

Han, Heonjong; Cho, Jae Won; Lee, Sangyoung; Yun, Ayoung; Kim, Hyojin; Bae, Dasom; Yang, Sunmo; Kim, Chan Yeong; Lee, Muyoung; Kim, Eunbeen; Lee, Sungho; Kang, Byung Ha; Jeong, Dabin; Kim, Yaeji; Jeon, Hyeon-Nae; Jung, Haein; Nam, Sunhwee; Chung, Michael; Kim, Jong Hoon; Lee, Insuk

doi:10.1093/nar/gkx1013

Cited by 1,377 publications

(1,295 citation statements)

References 22 publications

Supporting

Mentioning

1,277

Contrasting

Order By: Relevance

“…Regulatory interactions between transcription factors (TFs) and miRNAs (ie. TFs regulating miRNAs) were retrieved from TransmiR v1.2 (116), GTRD (117) and TRRUST v2 (118,119). Co-expression based inferences were ignored from all the above resources.…”

Section: Reconstruction Of Molecular Networkmentioning

confidence: 99%

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

Treveil

Sudhakar

Matthews

et al. 2019

Preprint

View full text Add to dashboard Cite

The epithelial lining of the small intestine consists of multiple cell types, including Paneth cells and goblet cells, that work in cohort to maintain gut health. 3D in vitro cultures of human primary epithelial cells, called organoids, have become a key model to study the functions of Paneth cells and goblet cells in normal and diseased conditions. Advances in these models include the ability to skew differentiation to particular lineages, providing a useful tool to study cell type specific function/dysfunction in the context of the epithelium. Here, we use comprehensive profiling of mRNA, microRNA and long noncoding RNA expression to confirm that Paneth cell and goblet cell enrichment of murine small intestinal organoids (enteroids) establishes a physiologically accurate model. We employ network analysis to infer the regulatory landscape altered by skewing differentiation, and using knowledge of cell type specific markers, we predict key regulators of cell type specific functions: Cebpa, Jun, Nr1d1 and Rxra specific to Paneth cells, Gfi1b and Myc specific for goblet cells and Ets1, Nr3c1 and Vdr shared between them. Links identified between these regulators and cellular phenotypes of inflammatory bowel disease (IBD) suggest that global regulatory rewiring during or after differentiation of Paneth cells and goblet cells could contribute to IBD aetiology. Future application of cell type enriched enteroids combined with the presented computational workflow can be used to disentangle multifactorial mechanisms of these cell types and propose regulators whose pharmacological targeting could be advantageous in treating IBD patients with Crohn's disease or ulcerative colitis.

show abstract

Section: Reconstruction Of Molecular Networkmentioning

confidence: 99%

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

Treveil

Sudhakar

Matthews

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…PubMed takes a query including keywords from user, and returns a list of citations that match input query. Finally, TRRUST regulatory network [20] was utilized as gold standard to evaluate the performance of ModEx. TR-RUST is a manually curated database of human transcriptional regulatory network with partial information on mode of regulation.…”

Section: Datasetsmentioning

confidence: 99%

“…There are also other sources of transcriptional regulatory network including JASPAR [10], the Open Regulatory Annotation database (ORegAnno) [11], SwissRegulon [12], the Transcriptional Regulatory Element Database (TRED) [13], the Transcription Regulatory Regions Database (TRRD) [14], TFactS [15], TRRUST [16]. These databases have been assembled with a variety of approaches, including reverse engineering approaches based on high-throughput gene expression experiments [17,18], text mining approaches [19], and manual curation [20].…”

Section: Introductionmentioning

confidence: 99%

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

Farahmand

Riley

Zarringhalam

2019

Preprint

View full text Add to dashboard Cite

A B S T R A C T Background: Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TFgene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. Methods: In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. Results: Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https:

show abstract

“…Another type of validation is comparison to known links in public network resources. The links in the best GRN were searched for in the databases TRRUST [30] , FunCoup [31] , HumanNet [32] , and STRING [33] as well as in our prior network from data mining. Where these reference networks contained undirected links, we compared them to an undirected version of our GRN.…”

Section: Validation Of the Best Grnmentioning

confidence: 99%

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Morgan

Studham

Tjärnberg

et al. 2019

Preprint

View full text Add to dashboard Cite

The gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research. Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

show abstract

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions

Cited by 1,377 publications

References 22 publications

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

Regulatory network analysis of Paneth cell and goblet cell enriched gut organoids using transcriptomics approaches

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

Contact Info

Product

Resources

About