Architecture of the human regulatory network derived from ENCODE data

By Mark Gerstein, Anshul B Kundaje, M. Hariharan, S. Landt, Koon-Kiu Yan, Chao Cheng, X. Mu, Ekta Khurana, Joel S. Rozowsky, Roger P. Alexander and 45 more. Nature, 2012

1,495 citations recorded by the source corpus.

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/440e593ab4d2cb0fd0058365ff7e71ae99d50f37

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Citations in the corpus

Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics (2013, background): “This suggests that regions where differential allelic activity is not observed may be under stronger purifying selection (29).”
Tools and best practices for data processing in allelic expression analysis (2015, methodology): “More exhaustive but computationally intensive approaches include alignment to personalized genomes [18, 32, 33], or use of a variant-aware aligner, such as GSNAP [34].”
Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data (2013, background): “Computational predictions based on correlations between gene activity and activity of distal elements across panels of cell lines also led to the prediction that genes are regulated by multiple distal elements 54–56 .”
Quantitative and logic modelling of gene and molecular networks (2015, background): “Genome-wide reconstructions of gene interactions in various cell types (7,8) mean that we can now model complete gene regulatory networks with increased accuracy.”
Accurate prediction of nucleic acid and protein-nucleic acid complexes using RoseTTAFoldNA (2022, background): “…more difficult by subsampling MSAs) should further increase model accuracy [10]; for this purpose there are databases of structured RNAs [15,16] and DNA binding profiles for thousands of proteins [17,18], and the latter should be useful for training a model fine-tuned for DNA specificity as well.”
Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features (2014, methodology): “Encouraged by the analyses of CTCF and EP300 data sets above, we systematically compared gkm-SVM to kmer-SVM using a very broad range of human data sets generated by the ENCODE project [17,18].”
The SIB Swiss Institute of Bioinformatics’ resources: focus on curated databases (2015, methodology): “These classifications are complemented with selected annotation from the ENCODE project (18) were incorporated such as the chromatin state (19), the number of transcription factors that bind to the genomic region (27), and DNase accessibility values (28).”
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA (2023, background): “Use of high-confidence predicted structures as additional training examples (made more difficult by subsampling MSAs) should further increase model accuracy 10 ; for this purpose there are databases of structured RNAs 17,18 and DNA-binding profiles for thousands of proteins 19,20 , and the latter”
Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics (2013, methodology): “A straightforward addition to our methodology would consider location specific priors for each variant, which would depend for example on the distance to the gene of interest, or the presence of functional elements in this chromosome region [26, 27].”
Landscape of transcription in human cells (2012, neutral): “…within the ENCODE consortium 20 , we carried outa comparison of theGENCODE/RNA-seqand CAGE-determined TSSs and correlated themto chromatinand DNA features characteristic of initiation of transcription, such as DNase hypersensitivity 21 , chromatin modification and DNA binding elements 22,23 .”
Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. (2013, positive): “As described before (Gerstein et al., 2012; Kagey et al., 2010), strong co-occurrence was observed between subunits of mediator and cohesin, cohesin and CTCF, and factors sharing a few GO annotations, among them ‘‘promoter binding’’ and ‘‘dimer formation.”