What can we learn from noncoding regions of similarity between genomes?

Computational approach towards promoter sequence comparison via TF mapping using a new distance measure

We propose a method for identifying transcription factor binding sites (TFBS) in the given promoter sequence and mapping the transcription factors (TFs). The proposed algorithm searches the +1 transcription start site (TSS) for eukaryotic and prokaryotic sequences individually. The algorithm was tested with sequences from both eukaryotes and prokaryotes for at least 9 experimentally verified and validated functional TFs in promoter sequences. The order and type of TF binding to the promoter of genes encoding central metabolic pathway (CMP) enzyme was tabulated. A new similarity measure was devised for scoring the similarity between a pair of promoter sequences based on the number and order of motifs. Further, these were grouped in clusters considering the scores between them. The distance between each of the clusters in individual pathway was calculated and a phylogenetic tree was developed. This method is further applied to other pathways such as lipid and amino acid biosynthesis to retrieve and compare experimentally verified and conserved TFBS.

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

MOTIVATION

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.

RESULTS

The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.

CONTACT

dargenio@bmsr.usc.edu

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Prediction of physical protein-protein interactions

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

A machine learning strategy to identify candidate binding sites in human protein-coding sequence

Background

The splicing of RNA transcripts is thought to be partly promoted and regulated by sequences embedded within exons. Known sequences include binding sites for SR proteins, which are thought to mediate interactions between splicing factors bound to the 5' and 3' splice sites. It would be useful to identify further candidate sequences, however identifying them computationally is hard since exon sequences are also constrained by their functional role in coding for proteins.

Results

This strategy identified a collection of motifs including several previously reported splice enhancer elements. Although only trained on coding exons, the model discriminates both coding and non-coding exons from intragenic sequence.

Conclusion

We have trained a computational model able to detect signals in coding exons which seem to be orthogonal to the sequences' primary function of coding for proteins. We believe that many of the motifs detected here represent binding sites for both previously unrecognized proteins which influence RNA splicing as well as other regulatory elements.