Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Segal, Eran; Shapira, Michael Y.; Regev, Aviv; Pe’er, Dana; Botstein, David; Koller, Daphne; Friedman, Nir

doi:10.1038/ng1165

Cited by 1,496 publications

(1,373 citation statements)

References 39 publications

Supporting

Mentioning

1,348

Contrasting

Unclassified

Order By: Relevance

“…This is similar to the use of transcription factor enrichment to rationalize clustering results, 29 or the prediction of regulatory models from a series of experiments. 37 However, while studies have shown the predilection of transcription factors within groups of co-expressed genes, we will show that if the co-expressed genes have a correlation coefficient above a certain threshold, then a great majority of genes (>90%) will contain a small subset of transcription factor binding sites in common. This will eliminate many of the false positives and yield a set experimentally consistent transcription factors.…”

Section: Introductionmentioning

confidence: 67%

Context Specific Transcription Factor Prediction

et al. 2007

View full text Add to dashboard Cite

Abstract-One of the goals of systems biology is the identification of regulatory mechanisms that govern an organism's response to external stimuli. Transcription factors have been hypothesized as a major contributor to an organism's response to various outside stimuli, and a great deal of work has been done to predict the set of transcription factors which regulate a given gene. Most of the current methods seek to identify possible binding sites from genomic sequence. Initial attempts at predicting transcription factors from genomic sequences suffered from the problem of false positives. Making the problem more difficult, it has also been shown that while predicted binding sites might be false positives, they can be shown to bind to their corresponding sequences in vitro. One method for rectifying this is through the use of phylogenetic analysis in which only regions which show high evolutionary conservation are analyzed. However such an approach may be too stringent because of the level of degeneracy shown in transcription factor binding site position weight matrices. Due to the degeneracy, there may be only a few bases that need to be conserved across species. Therefore, while a sequence may not show a high level of evolutionary conservation, these sequences may still show high affinity for the same transcription factor. In predicting transcription factor binding we explore the notion that ''Coexpression implies co-regulation'' [Allocco et al. BMC Bioinformatics 5:18, 2004]. With multiple genes requiring similar transcription factors binding sites, there exists a basis for eliminating false positives. This method allows for the selection of transcription factors binding sites that are active under a given experimental paradigm, thereby allowing us to indirectly incorporate the effects of chromosome and recognition site presentation upon transcription factor binding prediction. Rather than having to rationalize that a few transcription factors binding sites are over-represented in a cluster of genes, one can show that a few transcription factors are active in the cluster of genes that have been grouped together. Although the method focuses on predicting experiment-specific transcription factor binding sites, it is possible that if such a methodology were used in an iterative process where different experiments were analyzed, one could obtain a comprehensive set of transcription factors binding sites which regulate the various dynamic responses shown by biological systems under a variety of conditions hence building a more comprehensive model of transcriptional regulation.

show abstract

Section: Introductionmentioning

confidence: 67%

Context Specific Transcription Factor Prediction

et al. 2007

View full text Add to dashboard Cite

show abstract

“…In many organisms, in-depth transcriptome analysis has revealed the modular architecture of gene expression [22]. A regulatory module is a self-consistent regulatory unit R ( TF , G , I ) representing a set of co-expressed genes G = { g 1 , g 2 , ..., g n } regulated in concert by a group of TFs in TF = { tf 1 , tf 2 , ..., tf m } that govern the target genes' behaviors via regulatory interaction I [5]. An example of the regulatory module is shown in Figure 1b.…”

Section: Methodsmentioning

confidence: 99%

“…A regulatory interaction includes target genes and all the TFs that control their transcriptional activities. An individual-TF regulatory interaction has been defined in terms of two properties: the TF's functional role as an activator or a repressor, and its logical role as being necessary or sufficient (see Figure 1a) [3,5]. The categories in the TF's functional and logical roles are combinable; they can be activator necessary (AN), activator sufficient (AS), or activator necessary and sufficient (ANS).…”

Section: Introductionmentioning

confidence: 99%

Inferring transcription factor collaborations in gene regulatory networks

Awad

Chen

2014

BMC Systems Biology

View full text Add to dashboard Cite

BackgroundLiving cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation.ResultsWe model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones.ConclusionsmTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.

show abstract

“…Computational clustering of genes according to their connectivities [39][40][41][42][43] shows that these features may correspond to known cellular machines (e.g., the ribosome, the proteasome, etc.) 44,45 but may also be totally unexplored [46][47][48][49] .…”

Section: Low High Mediummentioning

confidence: 99%

“…Processes can thus be defined at different levels of complexity, which results directly in a hierarchical organization of subnetworks, networks, supernetworks. The networks thus implicitly capture the hierarchical organization of biological processes in the cell, and this hierarchy can be explored by clustering the genes according to their network connections, the genes' precise functions being reflected in their memberships in different clusters [39][40][41][42][43][44][45][46][47][48][49] . Third, however one defines a process in the full network, these processes are highly interlinked.…”

Section: Properties and Limitations Of Networkmentioning

confidence: 99%

A probabilistic view of gene function

2004

View full text Add to dashboard Cite

Cells are controlled by the complex and dynamic actions of thousands of genes. With the sequencing of many genomes, the key problem has shifted from identifying genes to knowing what the genes do; we need a framework for expressing that knowledge. Even the most rigorous attempts to construct ontological frameworks describing gene function (e.g., the Gene Ontology project) ultimately rely on manual curation and are thus labor-intensive and subjective. But an alternative exists: the field of functional genomics is piecing together networks of gene interactions, and although these data are currently incomplete and error-prone, they provide a glimpse of a new, probabilistic view of gene function. We outline such a framework, which revolves around a statistical description of gene interactions derived from large, systematically compiled data sets. In this probabilistic view, pleiotropy is implicit, all data have errors and the definition of gene function is an iterative process that ultimately converges on the correct functions. The relationships between the genes are defined by the data, not by hand. Even this comprehensive view fails to capture key aspects of gene function, not least their dynamics in time and space, showing that there are limitations to the model that must ultimately be addressed.The necessity for a logical framework A primary issue in biology is describing the global organization of genes into systems and understanding the coordination of these systems in the cell. The sequencing of complete genomes has yielded the full parts lists of several organisms, and we now have powerful techniques to sample different aspects of gene function at a genome-wide level. Together, these advances hold great promise in leading us closer to a complete description of the molecular biology of the cell. For us to reach this goal, however, we need some coherent framework in which to express what we learn about gene function through the accumulation of data. What do we mean by 'gene function'? More importantly, what should we mean by this? What are the key properties of genes that we should measure to adequately describe their function in the cell? If we have all the measurements, how should we integrate them into a complete description of the molecular machineries that are the basis of a cell? Our framework for thinking about gene function inevitably colors the questions we ask and the conclusions we draw; therefore, defining a rational framework is not merely an exercise in abstraction but a key tool in understanding how an organism works.Great effort (e.g., refs. 1-8) has already gone into defining precisely what we mean by the functions of genes, as well as how we should record this information. These summaries help us by identifying common features of genes with similar functions and giving clues to the organization and control of processes in the cell. They may also help us to see whether this organization can explain observed biological properties and guide us toward new processes. The functional framework itsel...

show abstract

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Cited by 1,496 publications

References 39 publications

Context Specific Transcription Factor Prediction

Context Specific Transcription Factor Prediction

Inferring transcription factor collaborations in gene regulatory networks

A probabilistic view of gene function

Contact Info

Product

Resources

About