TFClass: expanding the classification of human transcription factors to their mammalian orthologs

Wingender, Edgar; Schoeps, Torsten; Haubrock, Martin; Krull, Mathias; Dönitz, Jürgen

doi:10.1093/nar/gkx987

Cited by 116 publications

(149 citation statements)

References 13 publications

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“…For each comparison, the TF subfamilies were inferred from the TFs which had the most significant anti-correlation scores. The candidate MRTFs were next identified within the TF subfamily with FDR < 0.05 using the classification from TFClass[53].…”

Section: Methodsmentioning

confidence: 99%

Lineage-Specific Epigenomic and Genomic Activation of the Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas

Pan

Silva

Gull

et al. 2019

Preprint

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AbstractBackgroundsGastrointestinal adenocarcinomas (GIACs) of the tubular GI tract including esophagus, stomach, colon and rectum comprise most GI cancers and share a spectrum of genomic features. However, the unified epigenomic changes specific to GIACs are less well-characterized.We applied mathematical algorithms to large-scale DNA methylome and transcriptome profiles to reconstruct transcription factor (TF) networks using 907 GIAC samples from The Cancer Genome Atlas (TCGA). Complementary epigenomic technologies were performed to investigate HNF4A activation, including Circularized Chromosome Conformation Capture (4C), Chromatin immunoprecipitation (ChIP) sequencing, Whole Genome Bisulfite Sequencing (WGBS), and Assay for Transposase-Accessible Chromatin (ATAC) sequencing. In vitro and in vivo cellular phenotypical assays were conducted to study HNF4A functions.ResultsWe identified a list of functionally hyperactive master regulator (MR)TFs shared across different GIACs. As the top candidate, HNF4A exhibited prominent genomic and epigenomic activation in a GIAC-specific manner. We further characterized a complex interplay between HNF4A promoter and three distal enhancer elements, which was coordinated by GIAC-specific MRTFs including ELF3, GATA4, GATA6 and KLF5. HNF4A also self-regulated its own promoter and enhancers. Functionally, HNF4A promoted cancer proliferation and survival by transcriptionally activating many downstream targets including HNF1A and factors of Interleukin signaling in a lineage-specific manner.ConclusionWe use a large cohort of patient samples and an unbiased mathematical approach to highlight lineage-specific oncogenic MRTFs, which provide new insights into the GIAC-specific gene regulatory networks, and identify potential therapeutic strategies against these common cancers.

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Section: Methodsmentioning

confidence: 99%

Lineage-Specific Epigenomic and Genomic Activation of the Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas

Pan

Silva

Gull

et al. 2019

Preprint

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“…Human transcription factors were annotated according to their Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) identification number using the using the multi-symbol checker tool. Discovered transcription factors were classified by shared DNA binding domains according to the hierarchical classification of human transcription factors (TFClass) database 46 . Transcription factor binding and promoter sites were annotated utilizing transcription factor databases 30,47 .…”

Section: Network Analysis and Transcription Factor Target Enrichmentmentioning

confidence: 99%

A network of epigenomic and transcriptional cooperation encompassing an epigenomic master regulator in cancer

Wilson

Filipp

2018

Preprint

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Coordinatedexperiments focused on transcriptional responses and chromatin states are wellequipped to capture different epigenomic and transcriptomic levels governing the circuitry of a regulatory network. We propose a workflow for the genome-wide identification of epigenomic and transcriptional cooperation to elucidate transcriptional networks in cancer. Gene promoter annotation in combination with network analysis and sequence-resolution of enriched transcriptional motifs in epigenomic data reveals transcription factor families that act synergistically with epigenomic master regulators. A close teamwork of the transcriptional and epigenomic machinery was discovered. The network is tightly connected and includes the histone lysine demethylase KDM3A, basic helix-loop-helix factors MYC, HIF1A, and SREBF1, as well as differentiation factors AP1, MYOD1, SP1, MEIS1, ZEB1 and ELK1. In such a cooperation network, one component opens the chromatin, another one recognizes gene-specific DNA motifs, others scaffold between histones, cofactors, and the transcriptional complex. In cancer, due to the ability to team up with transcription factors, epigenetic factors concert mitogenic and metabolic gene networks, claiming the role of a cancer master regulators or epioncogenes.Specific histone modification patterns are commonly associated with open or closed chromatin states, and are linked to distinct biological outcomes by transcriptional activation or repression. Disruption of patterns of histone modifications is associated with loss of proliferative control and cancer. There is tremendous therapeutic potential in understanding and targeting histone modification pathways. Thus, investigating cooperation of chromatin remodelers and the transcriptional machinery is not only important for elucidating fundamental mechanisms of chromatin regulation, but also necessary for the design of targeted therapeutics.

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“…The structural classes of human TFs or TF complexes that recognize the 435 known motifs identified by DESSO were analyzed with TFClass [56]. Twenty-six structure classes, including five dimerization structures and 21 monomer structures were represented in the identified motifs (Fig.…”

Section: Analysis Of 683 Representative Sequence Motifs Identified Bymentioning

confidence: 99%

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework

Yang

B³

et al. 2018

Preprint

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Identification of transcription factor binding sites (TFBSs) and cis-regulatory motifs (motifs for short) from genomics datasets, provides a powerful view of the rules governing the interactions between TFs and DNA. Existing motif prediction methods however, are limited by high false positive rates in TFBSs identification, contributions from non-sequence-specific binding, and complex and indirect binding mechanisms. High throughput next-generation sequencing data provides unprecedented opportunities to overcome these difficulties, as it provides multiple whole-genome scale measurements of TF binding information. Uncovering this information brings new computational and modeling challenges in high-dimensional data mining and heterogeneous data integration. To improve TFBS identification and novel motifs prediction accuracy in the human genome, we developed an advanced computational technique based on deep learning (DL) and high-performance computing, named DESSO. DESSO utilizes deep neural network and binomial distribution to optimize the motif prediction. Our results showed that DESSO outperformed existing tools in predicting distinct motifs from the 690 in vivo ENCODE ChIP-Sequencing (ChIP-Seq) datasets for 161 human TFs in 91 cell lines. We also found that protein-protein interactions (PPIs) are prevalent among human TFs, and a total of 61 potential tethering binding were identified among the 100 TFs in the K562 cell line. To further 2 expand DESSO's deep-learning capabilities, we included DNA shape features and found that (i) shape information has a strong predictive power for TF-DNA binding specificity; and (ii) it aided in identification of the shape motifs recognized by human TFs which in turn contributed to the interpretation of TF-DNA binding in the absence of sequence recognition. DESSO and the analyses it enabled will continue to improve our understanding of how gene expression is controlled by TFs and the complexities of DNA binding. The source code and the predicted motifs and TFBSs from the 690 ENCODE TF ChIP-Seq datasets are freely available at the DESSO web server: http://bmbl.sdstate.edu/DESSO. KEYWORDSTF-DNA interactions, ChIP-Seq, Motif prediction, Deep learning, DNA shape motif.DNA shape of the target sequences. Thus, features in DNA sequences in combination with shapes determine the TF binding in a more sophisticated way than was originally thought [27][28][29][30].ChIP-Seq provides the genome-wide interactions between DNA and DNA-associated proteins and large-scale ChIP-Seq efforts enable new insights into gene regulation analyses. A considerable amount of ChIP-Seq data has been generated in the public domain, including approximately 6,000 datasets of human from the ENCODE database [31]. These datasets provide an unprecedented opportunity to predict motifs, identify TFBSs, and capture more features affecting TF binding [32]. ChIP-Seq data mining and modeling have many challenges in computation, facing high-dimensional and heterogeneous data properties, and a variety of popular methods have bee...

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TFClass: expanding the classification of human transcription factors to their mammalian orthologs

Cited by 116 publications

References 13 publications

Lineage-Specific Epigenomic and Genomic Activation of the Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas

Lineage-Specific Epigenomic and Genomic Activation of the Oncogene HNF4A Promotes Gastrointestinal Adenocarcinomas

A network of epigenomic and transcriptional cooperation encompassing an epigenomic master regulator in cancer

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework

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