Sequence information gain based motif analysis

Background: The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions. Results: This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70 % of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions. Conclusions: Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at http://b2slab.upc.edu.Postprint (published version

Sequence information gain based motif analysis

Background: The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions. Results: This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70 % of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions. Conclusions: Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at http://​b2slab.​upc.​edu

Computational analysis of the evolutionarily conserved Missing In Metastasis/Metastasis Suppressor 1 gene predicts novel interactions, regulatory regions and transcriptional control

Missing in Metastasis (MIM), or Metastasis Suppressor 1 (MTSS1), is a highly conserved protein, which links the plasma membrane to the actin cytoskeleton. MIM has been implicated in various cancers, however, its modes of action remain largely enigmatic. Here, we performed an extensive in silico characterisation of MIM to gain better understanding of its function. We detected previously unappreciated functional motifs including adaptor protein (AP) complex interaction site and a C-helix, pointing to a role in endocytosis and regulation of actin dynamics, respectively. We also identified new functional regions, characterised with phosphorylation sites or distinct hydrophilic properties. Strong negative selection during evolution, yielding high conservation of MIM, has been combined with positive selection at key sites. Interestingly, our analysis of intra-molecular co-evolution revealed potential regulatory hotspots that coincided with reduced potentially\ua0pathogenic polymorphisms. We explored databases for the mutations and expression levels of MIM in cancer. Experimentally, we focused on chronic lymphocytic leukaemia (CLL), where MIM showed high overall expression, however, downregulation on poor prognosis samples. Finally, we propose strong conservation of MTSS1 also on the transcriptional level and predict novel transcriptional regulators. Our data highlight important targets for future studies on the role of MIM in different tissues and cancers

Computational analysis of the evolutionarily conserved Missing In Metastasis/Metastasis Suppressor 1 gene predicts novel interactions, regulatory regions and transcriptional control

Missing in Metastasis (MIM), or Metastasis Suppressor 1 (MTSS1), is a highly conserved protein, which links the plasma membrane to the actin cytoskeleton. MIM has been implicated in various cancers, however, its modes of action remain largely enigmatic. Here, we performed an extensive in silico characterisation of MIM to gain better understanding of its function. We detected previously unappreciated functional motifs including adaptor protein (AP) complex interaction site and a C-helix, pointing to a role in endocytosis and regulation of actin dynamics, respectively. We also identified new functional regions, characterised with phosphorylation sites or distinct hydrophilic properties. Strong negative selection during evolution, yielding high conservation of MIM, has been combined with positive selection at key sites. Interestingly, our analysis of intra-molecular co-evolution revealed potential regulatory hotspots that coincided with reduced potentially pathogenic polymorphisms. We explored databases for the mutations and expression levels of MIM in cancer. Experimentally, we focused on chronic lymphocytic leukaemia (CLL), where MIM showed high overall expression, however, downregulation on poor prognosis samples. Finally, we propose strong conservation of MTSS1 also on the transcriptional level and predict novel transcriptional regulators. Our data highlight important targets for future studies on the role of MIM in different tissues and cancers