Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

Background  

Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data.     

A survey of motif discovery methods in an integrated framework

Background  

There has been a growing interest in computational discovery of regulatory elements, and a multitude of motif discovery methods have been proposed. Computational motif discovery has been used with some success in simple organisms like yeast. However, as we move to higher organisms with more complex genomes, more sensitive methods are needed. Several recent methods try to integrate additional sources of information, including microarray experiments (gene expression and ChlP-chip). There is also a growing awareness that regulatory elements work in combination, and that this combinatorial behavior must be modeled for successful motif discovery. However, the multitude of methods and approaches makes it difficult to get a good understanding of the current status of the field.     

Learning gene regulatory networks from only positive and unlabeled data

Background  

Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.     

MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes

Background  

Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity.     

Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Background  

Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene sets can provide clues on which functional themes or regulatory sequence motifs are associated with individual gene clusters. In spite of the potential utility, gene clustering and enrichment analysis have been used in separate platforms, thus, the development of integrative algorithm linking both methods is highly challenging.     

SPABBATS: A pathway-discovery method based on Boolean satisfiability that facilitates the characterization of suppressor mutants

Background  

Several computational methods exist to suggest rational genetic interventions that improve the productivity of industrial strains. Nonetheless, these methods are less effective to predict possible genetic responses of the strain after the intervention. This problem requires a better understanding of potential alternative metabolic and regulatory pathways able to counteract the targeted intervention.     

Prediction of regulatory elements in mammalian genomes using chromatin signatures

Background  

Recent genomic scale survey of epigenetic states in the mammalian genomes has shown that promoters and enhancers are correlated with distinct chromatin signatures, providing a pragmatic way for systematic mapping of these regulatory elements in the genome. With rapid accumulation of chromatin modification profiles in the genome of various organisms and cell types, this chromatin based approach promises to uncover many new regulatory elements, but computational methods to effectively extract information from these datasets are still limited.