ICDS database: interrupted CoDing sequences in prokaryotic genomes

Unrecognized frameshifts, in-frame stop codons and sequencing errors lead to Interrupted CoDing Sequence (ICDS) that can seriously affect all subsequent steps of functional characterization, from in silico analysis to high-throughput proteomic projects. Here, we describe the Interrupted CoDing Sequence database containing ICDS detected by a similarity-based approach in 80 complete prokaryotic genomes. ICDS can be retrieved by species browsing or similarity searches via a web interface (). The definition of each interrupted gene is provided as well as the ICDS genomic localization with the surrounding sequence. Furthermore, to facilitate the experimental characterization of ICDS, we propose optimized primers for re-sequencing purposes. The database will be regularly updated with additional data from ongoing sequenced genomes. Our strategy has been validated by three independent tests: (i) ICDS prediction on a benchmark of artificially created frameshifts, (ii) comparison of predicted ICDS and results obtained from the comparison of the two genomic sequences of Bacillus licheniformis strain ATCC 14580 and (iii) re-sequencing of 25 predicted ICDS of the recently sequenced genome of Mycobacterium smegmatis. This allows us to estimate the specificity and sensitivity (95 and 82%, respectively) of our program and the efficiency of primer determination

Od sekvencije DNA do kemijske strukture – pretraživanje mikrobnih genomskih i metagenomskih skupova podataka radi pronalaženja novih prirodnih spojeva

Rapid mining of large genomic and metagenomic data sets for modular polyketide synthases, non-ribosomal peptide synthetases and hybrid polyketide synthase/non-ribosomal peptide synthetase biosynthetic gene clusters has been achieved using the generic computer program packages ClustScan and CompGen. These program packages perform the annotation with the hierarchical structuring into polypeptides, modules and domains, as well as storage and graphical presentations of the data. This aims to achieve the most accurate predictions of the activities and specificities of catalytically active domains that can be made with present knowledge, leading to a prediction of the most likely chemical structures produced by these enzymes. The program packages also allow generation of novel clusters by homologous recombination of the annotated genes in silico. ClustScan and CompGen were used to construct a custom database of known compounds (CSDB) and of predicted entirely novel recombinant products (r-CSDB) that can be used for in silico screening with computer aided drug design technology. The use of these programs has been exemplified by analysing genomic sequences from terrestrial prokaryotes and eukaryotic microorganisms, a marine metagenomic data set and a newly discovered example of a \u27shared metabolic pathway\u27 in marine-microbial endosymbiosis.Brzo pretraživanje genomskih i metagenomskih skupova podataka, modularnih biosintetskih genskih nakupina poliketid sintaza i sintetaza neribosomalno sintetiziranih peptida, postignuto je primjenom generičkih računalnih programskih paketa ClustScan i CompGen. Ti programski paketi provode anotaciju hijerarhijskim strukturiranjem podataka na polipeptide, module i domene, te pohranu i grafičku prezentaciju tih podataka. Na temelju dosadašnjih spoznaja, nastoji se postići najtočnije moguće predviđanje aktivnosti i specifičnosti katalitički aktivnih domena, što vodi prema predviđanju najvjerojatnijih kemijskih struktura koje ti enzimi mogu sintetizirati. Programski paketi ClustScan i CompGen omogućuju generiranje novih genskih nakupina homolognom rekombinacijom anotiranih gena u uvjetima in silico, a upotrijebljeni su i za konstrukciju vlastitih baza podataka poznatih poliketidnih i peptidnih supstancija (CSDB) te potpuno novih poliketidnih i peptidnih supstancija produkata rekombinacije (r-CSDB). Ti će se produkti rekombinacije moći upotrijebiti za izbor supstancija s potencijalnom biološkom aktivnošću pomoću računalom vođenog dizajna lijekova u uvjetima in silico. Primjenjivost programskih paketa ClustScan i CompGen dokazana je u analizi genomskih sekvencija prokariotskih i eukariotskih mikroorganizama što žive u tlu, analizi metagenomske skupine podataka u uzorku iz morske vode, a i na nedavno opisanom primjeru \u27zajedničkog metaboličkoga puta\u27 u mikrobnog endosimbionta morske životinje

VIGOR, an annotation program for small viral genomes

<p>Abstract</p> <p>Background</p> <p>The decrease in cost for sequencing and improvement in technologies has made it easier and more common for the re-sequencing of large genomes as well as parallel sequencing of small genomes. It is possible to completely sequence a small genome within days and this increases the number of publicly available genomes. Among the types of genomes being rapidly sequenced are those of microbial and viral genomes responsible for infectious diseases. However, accurate gene prediction is a challenge that persists for decoding a newly sequenced genome. Therefore, accurate and efficient gene prediction programs are highly desired for rapid and cost effective surveillance of RNA viruses through full genome sequencing.</p> <p>Results</p> <p>We have developed VIGOR (Viral Genome ORF Reader), a web application tool for gene prediction in influenza virus, rotavirus, rhinovirus and coronavirus subtypes. VIGOR detects protein coding regions based on sequence similarity searches and can accurately detect genome specific features such as frame shifts, overlapping genes, embedded genes, and can predict mature peptides within the context of a single polypeptide open reading frame. Genotyping capability for influenza and rotavirus is built into the program. We compared VIGOR to previously described gene prediction programs, ZCURVE_V, GeneMarkS and FLAN. The specificity and sensitivity of VIGOR are greater than 99% for the RNA viral genomes tested.</p> <p>Conclusions</p> <p>VIGOR is a user friendly web-based genome annotation program for five different viral agents, influenza, rotavirus, rhinovirus, coronavirus and SARS coronavirus. This is the first gene prediction program for rotavirus and rhinovirus for public access. VIGOR is able to accurately predict protein coding genes for the above five viral types and has the capability to assign function to the predicted open reading frames and genotype influenza virus. The prediction software was designed for performing high throughput annotation and closure validation in a post-sequencing production pipeline.</p

Galaxy and Apollo as a biologist-friendly interface for high-quality cooperative phage genome annotation

In the modern genomic era, scientists without extensive bioinformatic training need to apply high-power computational analyses to critical tasks like phage genome annotation. At the Center for Phage Technology (CPT), we developed a suite of phage-oriented tools housed in open, user-friendly web-based interfaces. A Galaxy platform conducts computationally intensive analyses and Apollo, a collaborative genome annotation editor, visualizes the results of these analyses. The collection includes open source applications such as the BLAST+ suite, InterProScan, and several gene callers, as well as unique tools developed at the CPT that allow maximum user flexibility. We describe in detail programs for finding Shine-Dalgarno sequences, resources used for confident identification of lysis genes such as spanins, and methods used for identifying interrupted genes that contain frameshifts or introns. At the CPT, genome annotation is separated into two robust segments that are facilitated through the automated execution of many tools chained together in an operatio

From DNA sequences to chemical structures – methods for mining microbial genomic and metagenomic data sets for new natural products

Rapid mining of large genomic and metagenomic data sets for modular polyketide synthases, non-ribosomal peptide synthetases and hybrid polyketide synthase/non-ribosomal peptide synthetase biosynthetic gene clusters has been achieved using the generic computer program packages ClustScan and CompGen. These program packages perform the annotation with the hierarchical structuring into polypeptides, modules and domains, as well as storage and graphical presentations of the data. This aims to achieve the most accurate predictions of the activities and specificities of catalytically active domains that can be made with present knowledge, leading to a prediction of the most likely chemical structures produced by these enzymes. The program packages also allow generation of novel clusters by homologous recombination of the annotated genes in silico. ClustScan and CompGen were used to construct a custom database of known compounds (CSDB) and of predicted entirely novel recombinant products (r-CSDB) that can be used for in silico screening with computer-aided drug design technology. The use of these programs has been exemplified by analysing genomic sequences from terrestrial prokaryotes and eukaryotic microorganisms, a marine metagenomic data set and a newly discovered example of a 'shared metabolic pathway' in marine-microbial endosymbiosis

Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

Gene annotation and transcriptome delineation on a de novo genome assembly for the reference Leishmania major friedlin strain

Leishmania major is the main causative agent of cutaneous leishmaniasis in humans. The Friedlin strain of this species (LmjF) was chosen when a multi-laboratory consortium undertook the objective of deciphering the first genome sequence for a parasite of the genus Leishmania. The objective was successfully attained in 2005, and this represented a milestone for Leishmania molecular biology studies around the world. Although the LmjF genome sequence was done following a shotgun strategy and using classical Sanger sequencing, the results were excellent, and this genome assembly served as the reference for subsequent genome assemblies in other Leishmania species. Here, we present a new assembly for the genome of this strain (named LMJFC for clarity), generated by the combination of two high throughput sequencing platforms, Illumina short-read sequencing and PacBio Single Molecular Real-Time (SMRT) sequencing, which provides long-read sequences. Apart from resolving uncertain nucleotide positions, several genomic regions were reorganized and a more precise composition of tandemly repeated gene loci was attained. Additionally, the genome annotation was improved by adding 542 genes and more accurate coding-sequences defined for around two hundred genes, based on the transcriptome delimitation also carried out in this work. As a result, we are providing gene models (including untranslated regions and introns) for 11,238 genes. Genomic information ultimately determines the biology of every organism; therefore, our understanding of molecular mechanisms will depend on the availability of precise genome sequences and accurate gene annotations. In this regard, this work is providing an improved genome sequence and updated transcriptome annotations for the reference L. major Friedlin strai

Recherche d'éléments structurés dans les génomes par modèles logiques

Le recodage est un processus biologique qui permet une lecture alternative du message génétique. Ainsi, il permet la production de deux protéines distinctes en proportions différentes à partir d'une unique séquence d'ARN messager. Les travaux décrits ici concernent le ''décalage de cadre de lecture en -1'', dit ''frameshift -1'', qui est une des situations de recodage. Dans ce type d'événements, le décalage s'opère au sein d'une zone particulière de la séquence d'ARN en train d'être traduite, qu'on appelle ''fenêtre glissante'', et il est provoqué par une structure secondaire stable de l'ARN, de type pseudo-noeud, présente en aval de la fenêtre glissante. Le ribosome, en train d'effectuer la traduction, vient buter sur la structure, et recule alors d'un nucléotide. Nous avons élaboré une méthode de détection des événements de frameshift-1 basée sur l'utilisation de Logol, un outil de reconnaissance de motifs (pattern matching) développé par notre équipe Dyliss en collaboration avec la plateforme bioinformatique GenOuest. Des modèles Logol ont été élaborés et affinés afin de fournir une modélisation la plus fidèle possible des différents compartiments qui composent le ''motif de frameshift-1'', ce qui a permis au final de détecter avec une bonne précision les événements de décalage de phase en -1 présents dans un jeu de référence. L'approche est originale et se démarque par le fait qu'il n'y ait pas de véritable calcul de stabilité énergétique des structures secondaires recherchées (les pseudo-noeuds). Ces travaux ont amené à utiliser Logol de façon approfondie. Cela a contribué à le faire évoluer, par l'ajout de nouveaux éléments de langage utiles à la modélisation de séquences biologiques. Cela a également contribué à valider l'utilisation de cet outil générique au service de la recherche de motifs biologiques complexes

METHODS FOR HIGH-THROUGHPUT COMPARATIVE GENOMICS AND DISTRIBUTED SEQUENCE ANALYSIS

High-throughput sequencing has accelerated applications of genomics throughout the world. The increased production and decentralization of sequencing has also created bottlenecks in computational analysis. In this dissertation, I provide novel computational methods to improve analysis throughput in three areas: whole genome multiple alignment, pan-genome annotation, and bioinformatics workflows. To aid in the study of populations, tools are needed that can quickly compare multiple genome sequences, millions of nucleotides in length. I present a new multiple alignment tool for whole genomes, named Mugsy, that implements a novel method for identifying syntenic regions. Mugsy is computationally efficient, does not require a reference genome, and is robust in identifying a rich complement of genetic variation including duplications, rearrangements, and large-scale gain and loss of sequence in mixtures of draft and completed genome data. Mugsy is evaluated on the alignment of several dozen bacterial chromosomes on a single computer and was the fastest program evaluated for the alignment of assembled human chromosome sequences from four individuals. A distributed version of the algorithm is also described and provides increased processing throughput using multiple CPUs. Numerous individual genomes are sequenced to study diversity, evolution and classify pan-genomes. Pan-genome annotations contain inconsistencies and errors that hinder comparative analysis, even within a single species. I introduce a new tool, Mugsy-Annotator, that identifies orthologs and anomalous gene structure across a pan-genome using whole genome multiple alignments. Identified anomalies include inconsistently located translation initiation sites and disrupted genes due to draft genome sequencing or pseudogenes. An evaluation of pan-genomes indicates that such anomalies are common and alternative annotations suggested by the tool can improve annotation consistency and quality. Finally, I describe the Cloud Virtual Resource, CloVR, a desktop application for automated sequence analysis that improves usability and accessibility of bioinformatics software and cloud computing resources. CloVR is installed on a personal computer as a virtual machine and requires minimal installation, addressing challenges in deploying bioinformatics workflows. CloVR also seamlessly accesses remote cloud computing resources for improved processing throughput. In a case study, I demonstrate the portability and scalability of CloVR and evaluate the costs and resources for microbial sequence analysis

Comparative analysis of plant genomes through data integration

When we started our research in 2008, several online resources for genomics existed, each with a different focus. TAIR (The Arabidopsis Information Resource) has a focus on the plant model species Arabidopsis thaliana, with (at that time) little or no support for evolutionary or comparative genomics. Ensemble provided some basic tools and functions as a data warehouse, but it would only start incorporating plant genomes in 2010. There was no online resource at that time however, that provided the necessary data content and tools for plant comparative and evolutionary genomics that we required. As such, the plant community was missing an essential component to get their research at the same level as the biomedicine oriented research communities. We started to work on PLAZA in order to provide such a data resource that could be accessed by the plant community, and which also contained the necessary data content to help our research group’s focus on evolutionary genomics. The platform for comparative and evolutionary genomics, which we named PLAZA, was developed from scratch (i.e. not based on an existing database scheme, such as Ensemble). Gathering the data for all species, parsing this data into a common format and then uploading it into the database was the next step. We developed a processing pipeline, based on sequence similarity measurements, to group genes into gene families and sub families. Functional annotation was gathered through both the original data providers and through InterPro scans, combined with Interpro2GO. This primary data information was then ready to be used in every subsequent analysis. Building such a database was good enough for research within our bioinformatics group, but the target goal was to provide a comprehensive resource for all plant biologists with an interest in comparative and evolutionary genomics. Designing and creating a user-friendly, visually appealing web interface, connected to our database, was the next step. While the most detailed information is commonly presented in data tables, aesthetically pleasing graphics, images and charts are often used to visualize trends, general statistics and also used in specific tools. Design and development of these tools and visualizations is thus one of the core elements within my PhD. The PLAZA platform was designed as a gene-centric data resource, which is easily navigated when a biologist wants to study a relative small number of genes. However, using the default PLAZA website to retrieve information for dozens of genes quickly becomes very tedious. Therefore a ’gene set’-centric extra layer was developed where user-defined gene sets could be quickly analyzed. This extra layer, called the PLAZA workbench, functions on top of the normal PLAZA website, implicating that only gene sets from species present within the PLAZA database can be directly analyzed. The PLAZA resource for comparative and evolutionary genomics was a major success, but it still had several issues. We tried to solve at least two of these problems at the same time by creating a new platform. The first issue was the building procedure of PLAZA: adding a single species, or updating the structural annotation of an existing one, requires the total re-computation of the database content. The second issue was the restrictiveness of the PLAZA workbench: through a mapping procedure gene sets could be entered for species not present in the PLAZA database, but for species without a phylogenetic close relative this approach did not always yield satisfying results. Furthermore, the research in question might just focus on the difference between a species present in PLAZA and a close relative not present in PLAZA (e.g. to study adaptation to a different ecological niche). In such a case, the mapping procedure is in itself useless. With the advent of NGS transcriptome data sets for a growing number of species, it was clear that a next challenge had presented itself. We designed and developed a new platform, named TRAPID, which could automatically process entire transcriptome data sets, using a reference database. The target goal was to have the processing done quickly with the results containing both gene family oriented data (such as multiple sequence alignments and phylogenetic trees) and functional characterization of the transcripts. Major efforts went into designing the processing pipeline so it could be reliable, fast and accurate