Constructing Transcriptional Regulatory Networks Through Gene Functional Similarity.

The methods used nowadays to build transcriptional regulatory networks (differential cloning, microarray analysis, promoter analysis...etc) are expensive and time consuming. We devise a method that uses Gene Ontology to augment our training set of human transcriptional regulatory network.

Local Renyi entropic profiles of DNA sequences.

In a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM).

The present work extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the pdf estimations obtained previously. These profiles are applied to the study of the same dataset and a new, fractal, kernel function more adjusted to the estimation is explored, instead of normal distributions.

This work achieves two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each DNA conserved region, which can have future applications on the recognition of foreign genomic material and inference of motif structures. This ability to detect local conservation is particularly relevant for biological applications of motif/pattern recognition.