Psiscan: a computational approach to identify H/ACA-like and AGA-like non-coding RNA in trypanosomatid genomes

Background  

Detection of non coding RNA (ncRNA) molecules is a major bioinformatics challenge. This challenge is particularly difficult when attempting to detect H/ACA molecules which are involved in converting uridine to pseudouridine on rRNA in trypanosomes, because these organisms have unique H/ACA molecules (termed H/ACA-like) that lack several of the features that characterize H/ACA molecules in most other organisms.     

Towards an efficient computational mining approach to identify EST-SSR markers

Microsatellites are the markers of choice due to their high abundance reproducibility, degree of polymorphism and co-dominant nature. These are mainly used for studying the genetic variability in different species and Marker assisted selection. Expressed Sequence Tags (ESTs) serve as the main resource for Simple Sequence Repeats (SSRs). The computational approach for detecting SSRs and developing SSR markers from EST-SSRs is preferred over the conventional methods as it reduces time and cost to a great extent. The available EST sequence databases, various web interfaces and standalone tools provide the platform for an easy analysis of the EST sequences leading to the development of potential EST-SSR Markers. This paper is an overview of in silico approach to develop SSR Markers from the EST sequence using some of the most efficient tools that are available freely for academic purpose.     

Bridging experiments, models and simulations: an integrative approach to validation in computational cardiac electrophysiology

Computational models in physiology often integrate functional and structural information from a large range of spatiotemporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and skepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace, and refine animal experiments. A fundamental requirement to fulfill these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated. The present study aims at informing strategies for validation by elucidating the complex interrelations among experiments, models, and simulations in cardiac electrophysiology. We describe the processes, data, and knowledge involved in the construction of whole ventricular multiscale models of cardiac electrophysiology. Our analysis reveals that models, simulations, and experiments are intertwined, in an assemblage that is a system itself, namely the model-simulation-experiment (MSE) system. We argue that validation is part of the whole MSE system and is contingent upon 1) understanding and coping with sources of biovariability; 2) testing and developing robust techniques and tools as a prerequisite to conducting physiological investigations; 3) defining and adopting standards to facilitate the interoperability of experiments, models, and simulations; 4) and understanding physiological validation as an iterative process that contributes to defining the specific aspects of cardiac electrophysiology the MSE system targets, rather than being only an external test, and that this is driven by advances in experimental and computational methods and the combination of both.     

A computational approach to the functional screening of genomes

Comparative genomics usually involves managing the functional aspects of genomes, by simply comparing gene-by-gene functions. Following this approach, Mushegian and Koonin proposed a hypothetical minimal genome, Minimal Gene Set (MGS), aiming for a possible oldest ancestor genome. They obtained MGS by comparing the genomes of two simple bacteria and eliminating duplicated or functionally identical genes. The authors raised the fundamental question of whether a hypothetical organism possessing MGS is able to live or not. We attacked this viability problem specifying in silico the metabolic pathways of the MGS-based prokaryote. We then performed a dynamic simulation of cellular metabolic activities in order to check whether the MGS-prokaryote reaches some equilibrium state and produces the necessary biomass. We assumed these two conditions to be necessary for a living organism. Our simulations clearly show that the MGS does not express an organism that is able to live. We then iteratively proceeded with functional replacements in order to obtain a genome composition that gives rise to equilibrium. We ruled out 76 of the original 254 genes in the MGS, because they resulted in duplication from a functional point of view. We also added seven genes not present in the MGS. These genes encode for enzymes involved in critical nodes of the metabolic network. These modifications led to a genome composed of 187 elements expressing a virtually living organism, Virtual Cell (ViCe), that exhibits homeostatic capabilities and produces biomass. Moreover, the steady-state distribution of the concentrations of virtual metabolites that resulted was similar to that experimentally measured in bacteria. We conclude then that ViCe is able to “live in silico.”     

phiGENOME: an integrative navigation throughout bacteriophage genomes

phiGENOME is a web-based genome browser generating dynamic and interactive graphical representation of phage genomes stored in the phiSITE, database of gene regulation in bacteriophages. phiGENOME is an integral part of the phiSITE web portal (http://www.phisite.org/phigenome) and it was optimised for visualisation of phage genomes with the emphasis on the gene regulatory elements. phiGENOME consists of three components: (i) genome map viewer built using Adobe Flash technology, providing dynamic and interactive graphical display of phage genomes; (ii) sequence browser based on precisely formatted HTML tags, providing detailed exploration of genome features on the sequence level and (iii) regulation illustrator, based on Scalable Vector Graphics (SVG) and designed for graphical representation of gene regulations. Bringing 542 complete genome sequences accompanied with their rich annotations and references, makes phiGENOME a unique information resource in the field of phage genomics.     

A computational approach to identify CRISPR-Cas loci in the complete genomes of the lichen-associated Burkholderia sp. PAMC28687 and PAMC26561

The genus Burkholderia and its strains PAMC28687 and PAMC26561 are lichen-associated bacteria isolated from the Antarctic region. Our study is the first to provide the genome sequence of the Burkholderia sp. PAMC26561 strain. The genus Burkholderia includes bacteria that are pathogenic to plants, animals, and humans. Computational analysis of complete genomes of strains from the uncategorized Burkholderia group was performed using the NCBI databank and PATRIC (for genome sequence information) and CRISPRCasFinder (online and offline versions) software in order to predict the CRISPR loci and Cas genes. The RNAfold Webserver online software was used to predict RNA secondary structures. Our study showed that strain MSMB0852 (plasmid) possesses CRISPR-Cas system Class 2, and two lichen-associated strains, PAMC28687 (chromosome I) and PAMC26561 (chromosome I), possess CRISPR-Cas system Class 1. Additionally, only the two lichen-associated strains possess a variety of Cas genes.     

A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

A computational approach for identifying pathogenicity islands in prokaryotic genomes

Background  

Pathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs.     

Developing robust protein analysis profiles to identify bacterial acid phosphatases in genomes and metagenomic libraries

Phylogenetic analysis of more than 4000 annotated bacterial acid phosphatases was carried out. Our analysis enabled us to sort these enzymes into the following three types: (1) class B acid phosphatases, which were distantly related to the other types, (2) class C acid phosphatases and (3) generic acid phosphatases (GAP). Although class B phosphatases are found in a limited number of bacterial families, which include known pathogens, class C acid phosphatases and GAP proteins are found in a variety of microbes that inhabit soil, fresh water and marine environments. As part of our analysis, we developed three profiles, named Pfr-B-Phos, Pfr-C-Phos and Pfr-GAP, to describe the three groups of acid phosphatases. These sequence-based profiles were then used to scan genomes and metagenomes to identify a large number of formerly unknown acid phosphatases. A number of proteins in databases annotated as hypothetical proteins were also identified by these profiles as putative acid phosphatases. To validate these in silico results, we cloned genes encoding candidate acid phosphatases from genomic DNA or recovered from metagenomic libraries or genes synthesized in vitro based on protein sequences recovered from metagenomic data. Expression of a number of these genes, followed by enzymatic analysis of the proteins, further confirmed that sequence similarity searches using our profiles could successfully identify previously unknown acid phosphatases.     

MultiMSOAR 2.0: an accurate tool to identify ortholog groups among multiple genomes

The identification of orthologous genes shared by multiple genomes plays an important role in evolutionary studies and gene functional analyses. Based on a recently developed accurate tool, called MSOAR 2.0, for ortholog assignment between a pair of closely related genomes based on genome rearrangement, we present a new system MultiMSOAR 2.0, to identify ortholog groups among multiple genomes in this paper. In the system, we construct gene families for all the genomes using sequence similarity search and clustering, run MSOAR 2.0 for all pairs of genomes to obtain the pairwise orthology relationship, and partition each gene family into a set of disjoint sets of orthologous genes (called super ortholog groups or SOGs) such that each SOG contains at most one gene from each genome. For each such SOG, we label the leaves of the species tree using 1 or 0 to indicate if the SOG contains a gene from the corresponding species or not. The resulting tree is called a tree of ortholog groups (or TOGs). We then label the internal nodes of each TOG based on the parsimony principle and some biological constraints. Ortholog groups are finally identified from each fully labeled TOG. In comparison with a popular tool MultiParanoid on simulated data, MultiMSOAR 2.0 shows significantly higher prediction accuracy. It also outperforms MultiParanoid, the Roundup multi-ortholog repository and the Ensembl ortholog database in real data experiments using gene symbols as a validation tool. In addition to ortholog group identification, MultiMSOAR 2.0 also provides information about gene births, duplications and losses in evolution, which may be of independent biological interest. Our experiments on simulated data demonstrate that MultiMSOAR 2.0 is able to infer these evolutionary events much more accurately than a well-known software tool Notung. The software MultiMSOAR 2.0 is available to the public for free.     

Junker: an intergenic explorer for bacterial genomes

In the past few decades, scientists from all over the world have taken a keen interest in novel functional units such as small regulatory RNAs, small open reading frames, pseudogenes, transposons, integrase binding attB/attP sites, repeat elements within the bacterial intergenic regions (IGRs) and in the analysis of those "junk" regions for genomic complexity. Here we have developed a web server, named Junker, to facilitate the in-depth analysis of IGRs for examining their length distribution, four-quadrant plots, GC percentage and repeat details. Upon selection of a particular bacterial genome, the physical genome map is displayed as a multiple loci with options to view any loci of interest in detail. In addition, an IGR statistics module has been created and implemented in the web server to analyze the length distribution of the IGRs and to understand the disordered grouping of IGRs across the genome by generating the four-quadrant plots. The proposed web server is freely available at the URL http://pranag.physics.iisc.ernet.in/junker/.     

Comparative approach to correction of annotated gene starts in complete bacterial genomes

A method for refining the beginnings of genes and a search for shifts of the reading frame is proposed. The method is based on a comparison of nucleotide and amino acid sequences of homologous genes of related organisms. The algorithm is based on the fact that the rate of changes in the protein-coding regions of the genome is substantially lower than that of noncoding regions. A modification of the Smith-Waterman algorithm is proposed, which makes it possible to align the amino acid sequences obtained by formal translation of the starting nucleotide sequences by taking into account a possible shift of the reading frame. The algorithm has been implemented in the package of ORTOLOGATOR-GeneCorrector programs. Testing the program showed that the approach enables one to detect a wrong annotation of the beginnings in 1% of genes (even in well-studied organisms such as Escherichia coli) and identify several (approximately 10) shifts of the open reading frame. Thus, the algorithm can be used at both the initial and final stages of analysis of the genome.     

Advances in Ecological Speciation: an integrative approach

The role of natural selection in promoting reproductive isolation has received substantial renewed interest within the last two decades. As a consequence, the study of ecological speciation has become an extremely productive research area in modern evolutionary biology. Recent innovations in sequencing technologies offer an unprecedented opportunity to study the mechanisms involved in ecological speciation. Genome scans provide significant insights but have some important limitations; efforts are needed to integrate them with other approaches to make full use of the sequencing data deluge. An international conference ‘Advances in Ecological Speciation’ organized by the University of Porto (Portugal) aimed to review current progress in ecological speciation. Using some of the examples presented at the conference, we highlight the benefits of integrating ecological and genomic data and discuss different mechanisms of parallel evolution. Finally, future avenues of research are suggested to advance our knowledge concerning the role of natural selection in the establishment of reproductive isolation during ecological speciation.     

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background

Bacterial interactions with the environment- and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity.

Results

In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred.

Conclusions

We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-349) contains supplementary material, which is available to authorized users.     

Comparative analysis of bacterial genomes: identification of divergent regions in mycobacterial strains using an anchor-based approach

Comparative genomic approaches are useful in identifying molecular differences between organisms. Currently available methods fail to identify small changes in genomes, such as expansion of short repetitive motifs and to analyse divergent sequences. In this report, we describe an anchor-based whole genome comparison (ABWGC) method. ABWGC is based on random sampling of anchor sequences from one genome, followed by analysis of sampled and homologous regions from the target genome. The method was applied to compare two strains of Mycobacterium tuberculosis CDC1551 and H37Rv. ABWGC was able to identify a total of 104 indels including 20 expansion of short repetitive sequences and five recombination events. It included 18 new unidentified genomic differences. ABWGC also identified 188 SNPs including eight new ones. The method was also used to compare M. tuberculosis H37Rv and M. avium genomes. ABWGC was able to correctly pick 1002 additional indels (size>100nt) between the two organisms in contrast to MUMmer, a popular tool for comparative genomics. ABWGC was able to identify correctly repeat expansion and indels in a set of simulated sequences. The study also revealed important role of small repeat expansion in the evolution of M. tuberculosis strains.