Application of regulatory sequence analysis and metabolic network analysis to the interpretation of gene expression data

We present two complementary approaches for the interpretation of clusters of co-regulated genes, such as those obtained from DNA chips and related methods. Starting from a cluster of genes with similar expression profiles, two basic questions can be asked: 1. Which mechanism is responsible for the coordinated transcriptional response of the genes? This question is approached by extracting motifs that are shared between the upstream sequences of these genes. The motifs extracted are putative cis-acting regulatory elements. 2. What is the physiological meaning for the cell to express together these genes? One way to answer the question is to search for potential metabolic pathways that could be catalyzed by the products of the genes. This can be done by selecting the genes from the cluster that code for enzymes, and trying to assemble the catalyzed reactions to form metabolic pathways. We present tools to answer these two questions, and we illustrate their use with selected examples in the yeast Saccharomyces cerevisiae. The tools are available on the web (http://ucmb.ulb.ac.be/bioinformatics/rsa-tools/; http://www.ebi.ac.uk/research/pfbp/; http://www.soi.city.ac.uk/~msch/)

Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences

Oyster RNA-seq data support the development of Malacoherpesviridae genomics

The family of double-stranded DNA (dsDNA) Malacoherpesviridae includes viruses able to infect marine mollusks and detrimental for worldwide aquaculture production. Due to fast-occurring mortality and a lack of permissive cell lines, the available data on the few known Malacoherpesviridae provide only partial support for the study of molecular virus features, life cycle, and evolutionary history. Following thorough data mining of bivalve and gastropod RNA-seq experiments, we used more than five million Malacoherpesviridae reads to improve the annotation of viral genomes and to characterize viral InDels, nucleotide stretches, and SNPs. Both genome and protein domain analyses confirmed the evolutionary diversification and gene uniqueness of known Malacoherpesviridae. However, the presence of Malacoherpesviridae-like sequences integrated within genomes of phylogenetically distant invertebrates indicates broad diffusion of these viruses and indicates the need for confirmatory investigations. The manifest co-occurrence of OsHV-1 genotype variants in single RNA-seq samples of Crassostrea gigas provide further support for the Malacoherpesviridae diversification. In addition to simple sequence motifs inter-punctuating viral ORFs, recombination-inducing sequences were found to be enriched in the OsHV-1 and AbHV1-AUS genomes. Finally, the highly correlated expression of most viral ORFs in multiple oyster samples is consistent with the burst of viral proteins during the lytic phase

Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.Comment: 1 Tabl

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs

Background: Transcription factors (TFs) control transcription by binding to specific regions of DNA called transcription factor binding sites (TFBSs). The identification of TFBSs is a crucial problem in computational biology and includes the subtask of predicting the location of known TFBS motifs in a given DNA sequence. It has previously been shown that, when scoring matches to known TFBS motifs, interdependencies between positions within a motif should be taken into account. However, this remains a challenging task owing to the fact that sequences similar to those of known TFBSs can occur by chance with a relatively high frequency. Here we present a new method for matching sequences to TFBS motifs based on intuitionistic fuzzy sets (IFS) theory, an approach that has been shown to be particularly appropriate for tackling problems that embody a high degree of uncertainty. Results: We propose SCintuit, a new scoring method for measuring sequence-motif affinity based on IFS theory. Unlike existing methods that consider dependencies between positions, SCintuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SCintuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position. We used SCintuit to identify known TFBSs in DNA sequences. Our method provides excellent results when dealing with both synthetic and real data, outperforming the sensitivity and the specificity of two existing methods in all the experiments we performed. Conclusions: The results show that SCintuit improves the prediction quality for TFs of the existing approaches without compromising sensitivity. In addition, we show how SCintuit can be successfully applied to real research problems. In this study the reliability of the IFS theory for motif discovery tasks is proven

Integrative omics analysis of Pseudomonas aeruginosa virus PA5oct highlights the molecular complexity of jumbo phages

Pseudomonas virus vB_PaeM_PA5oct is proposed as a model jumbo bacteriophage to investigate phage-bacteria interactions and is a candidate for phage therapy applications. Combining hybrid sequencing, RNA-Seq and mass spectrometry allowed us to accurately annotate its 286,783 bp genome with 461 coding regions including four non-coding RNAs (ncRNAs) and 93 virion-associated proteins. PA5oct relies on the host RNA polymerase for the infection cycle and RNA-Seq revealed a gradual take-over of the total cell transcriptome from 21% in early infection to 93% in late infection. PA5oct is not organized into strictly contiguous regions of temporal transcription, but some genomic regions transcribed in early, middle and late phases of infection can be discriminated. Interestingly, we observe regions showing limited transcription activity throughout the infection cycle. We show that PA5oct upregulates specific bacterial operons during infection including operons pncA-pncB1-nadE involved in NAD biosynthesis, psl for exopolysaccharide biosynthesis and nap for periplasmic nitrate reductase production. We also observe a downregulation of T4P gene products suggesting mechanisms of superinfection exclusion. We used the proteome of PA5oct to position our isolate amongst other phages using a gene-sharing network. This integrative omics study illustrates the molecular diversity of jumbo viruses and raises new questions towards cellular regulation and phage-encoded hijacking mechanisms

Multigenome DNA sequence conservation identifies Hox cis-regulatory elements

To learn how well ungapped sequence comparisons of multiple species can predict cis-regulatory elements in Caenorhabditis elegans, we made such predictions across the large, complex ceh-13/lin-39 locus and tested them transgenically. We also examined how prediction quality varied with different genomes and parameters in our comparisons. Specifically, we sequenced ∼0.5% of the C. brenneri and C. sp. 3 PS1010 genomes, and compared five Caenorhabditis genomes (C. elegans, C. briggsae, C. brenneri, C. remanei, and C. sp. 3 PS1010) to find regulatory elements in 22.8 kb of noncoding sequence from the ceh-13/lin-39 Hox subcluster. We developed the MUSSA program to find ungapped DNA sequences with N-way transitive conservation, applied it to the ceh-13/lin-39 locus, and transgenically assayed 21 regions with both high and low degrees of conservation. This identified 10 functional regulatory elements whose activities matched known ceh-13/lin-39 expression, with 100% specificity and a 77% recovery rate. One element was so well conserved that a similar mouse Hox cluster sequence recapitulated the native nematode expression pattern when tested in worms. Our findings suggest that ungapped sequence comparisons can predict regulatory elements genome-wide