EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns

Bayesian machine learning methods for predicting protein-peptide interactions and detecting mosaic structures in DNA sequences alignments

Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. High-throughput experiments like yeast two-hybrid and phage display are expensive and intrinsically noisy, therefore it would be desirable to target informative interactions and pursue in silico approaches. We propose a probabilistic discriminative approach for predicting PRM-mediated protein-protein interactions from sequence data. The model suffered from over-fitting, so Laplacian regularisation was found to be important in achieving a reasonable generalisation performance. A hybrid approach yielded the best performance, where the binding site motifs were initialised with the predictions of a generative model. We also propose another discriminative model which can be applied to all sequences present in the organism at a significantly lower computational cost. This is due to its additional assumption that the underlying binding sites tend to be similar.It is difficult to distinguish between the binding site motifs of the PRM due to the small number of instances of each binding site motif. However, closely related species are expected to share similar binding sites, which would be expected to be highly conserved. We investigated rate variation along DNA sequence alignments, modelling confounding effects such as recombination. Traditional approaches to phylogenetic inference assume that a single phylogenetic tree can represent the relationships and divergences between the taxa. However, taxa sequences exhibit varying levels of conservation, e.g. due to regulatory elements and active binding sites, and certain bacteria and viruses undergo interspecific recombination. We propose a phylogenetic factorial hidden Markov model to infer recombination and rate variation. We examined the performance of our model and inference scheme on various synthetic alignments, and compared it to state of the art breakpoint models. We investigated three DNA sequence alignments: one of maize actin genes, one bacterial (Neisseria), and the other of HIV-1. Inference is carried out in the Bayesian framework, using Reversible Jump Markov Chain Monte Carlo

Characterization and gene expression analysis of the cir multi-gene family of plasmodium chabaudi chabaudi (AS)

Background: The pir genes comprise the largest multi-gene family in Plasmodium, with members found in P. vivax, P. knowlesi and the rodent malaria species. Despite comprising up to 5% of the genome, little is known about the functions of the proteins encoded by pir genes. P. chabaudi causes chronic infection in mice, which may be due to antigenic variation. In this model, pir genes are called cir s and may be involved in this mechanism, allowing evasion of host immune responses. In order to fully understand the role(s) of CIR proteins during P. chabaudi infection, a detailed characterization of the cir gene family was required. Results: The cir repertoire was annotated and a detailed bioinformatic characterization of the encoded CIR proteins was performed. Two major sub-families were identified, which have been named A and B. Members of each sub-family displayed different amino acid motifs, and were thus predicted to have undergone functional divergence. In addition, the expression of the entire cir repertoire was analyzed via RNA sequencing and microarray. Up to 40% of the cir gene repertoire was expressed in the parasite population during infection, and dominant cir transcripts could be identified. In addition, some differences were observed in the pattern of expression between the cir subgroups at the peak of P. chabaudi infection. Finally, specific cir genes were expressed at different time points during asexual blood stages. Conclusions: In conclusion, the large number of cir genes and their expression throughout the intraerythrocytic cycle of development indicates that CIR proteins are likely to be important for parasite survival. In particular, the detection of dominant cir transcripts at the peak of P. chabaudi infection supports the idea that CIR proteins are expressed, and could perform important functions in the biology of this parasite. Further application of the methodologies described here may allow the elucidation of CIR sub-family A and B protein functions, including their contribution to antigenic variation and immune evasion

Identifying statistical dependence in genomic sequences via mutual information estimates

Questions of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural dependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region in zmSRp32 and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from the FBI's Combined DNA Index System (CODIS), we demonstrate that our approach is particularly well suited for the problem of discovering short tandem repeats, an application of importance in genetic profiling.Comment: Preliminary version. Final version in EURASIP Journal on Bioinformatics and Systems Biology. See http://www.hindawi.com/journals/bsb