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PubMed2005

SNPsFinder--a web-based application for genome-wide discovery of single nucleotide polymorphisms in microbial genomes.

UNLABELLED: Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations in closely related microbial species, strains or isolates. Some SNPs confer selective advantages for microbial pathogens during infection and many others are powerful genetic markers for distinguishing closely related strains or isolates that could not be distinguished otherwise. To facilitate SNP discovery in microbial genomes, we have developed a web-based application, SNPsFinder, for genome-wide identification of SNPs. SNPsFinder takes multiple genome sequences as input to identify SNPs within homologous regions. It can also take contig sequences and sequence quality scores from ongoing sequencing projects for SNP prediction. SNPsFinder will use genome sequence annotation if available and map the predicted SNP regions to known genes or regions to assist further evaluation of the predicted SNPs for their functional significance. SNPsFinder can generate PCR primers for all predicted SNP regions according to user's input parameters to facilitate experimental validation. The results from SNPsFinder analysis are accessible through the World Wide Web. AVAILABILITY: The SNPsFinder program is available at http://snpsfinder.lanl.gov/. SUPPLEMENTARY INFORMATION: The user's manual is available at http://snpsfinder.lanl.gov/UsersManual/

Algorithms↗
PubMed2000

From microbial genome sequence to applications.

Whole genome sequences of microbial pathogens present new opportunities for clinical applications. Chief among these are development of antimicrobials, diagnostics, and vaccines. While antimicrobial development is a more difficult, long-term prospect, new diagnostics and vaccines are likely to be the first products of microbial genomics. To take advantage of whole genome sequences, methods for production of gene products in surrogate hosts (heterologous expression) are required that will work for large-scale, high-throughput gene expression. This will allow genomic information from even the most experimentally difficult pathogens to be mined for applications. In addition, screening methods to test gene products for their potential as vaccine candidates are needed for large-scale screening. These areas for technological development should be stimulated by the potential for converting genomic sequence information into applications.

Animals↗
PubMed2002

Genome Information Broker (GIB): data retrieval and comparative analysis system for completed microbial genomes and more.

Genome Information Broker (GIB) is a powerful tool for the study of comparative genomics. GIB allows users to retrieve and display partial and/or whole genome sequences together with the relevant biological annotation. GIB has accumulated all the completed microbial genome and has recently been expanded to include Arabidopsis thaliana genome data from DDBJ/EMBL/GenBank. In the near future, hundreds of genome sequences will be determined. In order to handle such huge data, we have enhanced the GIB architecture by using XML, CORBA and distributed RDBs. We introduce the new GIB here. GIB is freely accessible at http://gib.genes.nig.ac.jp/.

Arabidopsis↗
PubMed2005

Four basic symmetry types in the universal 7-cluster structure of microbial genomic sequences.

Coding information is the main source of heterogeneity (non-randomness) in the sequences of microbial genomes. The heterogeneity corresponds to a cluster structure in triplet distributions of relatively short genomic fragments (200-400 bp). We found a universal 7-cluster structure in microbial genomic sequences and explained its properties. We show that codon usage of bacterial genomes is a multi-linear function of their genomic G+C-content with high accuracy. Based on the analysis of 143 completely sequenced bacterial genomes available in Genbank in August 2004, we show that there are four "pure" types of the 7-cluster structure observed. All 143 cluster animated 3D-scatters are collected in a database which is made available on our web-site (http://www.ihes.fr/~zinovyev/7clusters). The findings can be readily introduced into software for gene prediction, sequence alignment or microbial genomes classification.

Codon↗
PubMed2000

EMGLib: the enhanced microbial genomes library (update 2000).

As the number of complete microbial genomes publicly available is still growing, the problem of annotation quality in these very large sequences remains unsolved. Indeed, the number of annotations associated with complete genomes is usually lower than those of the shorter entries encountered in the repository collections. Moreover, classical sequence database management systems have difficulties in handling entries of such size. In this context, the Enhanced Microbial Genomes Library (EMGLib) was developed to try to alleviate these problems. This library contains all the complete genomes from prokaryotes (bacteria and archaea) already sequenced and the yeast genome in GenBank format. The annotations are improved by the introduction of data on codon usage, gene orientation on the chromosome and gene families. It is possible to access EMGLib through two database systems set up on WWW servers: the PBIL server at http://pbil.univ-lyon1.fr/emglib.html and the MICADO server at http://locus.jouy.inra.fr/micado

Base Sequence↗
PubMed2025

De novo discovery of conserved gene clusters in microbial genomes with Spacedust.

Metagenomics has revolutionized environmental and human-associated microbiome studies. However, the limited fraction of proteins with known biological processes and molecular functions presents a major bottleneck. In prokaryotes and viruses, evolution favors keeping genes participating in the same biological processes colocalized as conserved gene clusters. Conversely, conservation of gene neighborhood indicates functional association. Here we present Spacedust, a tool for systematic, de novo discovery of conserved gene clusters. To find homologous protein matches, Spacedust uses fast and sensitive structure comparison with Foldseek. Partially conserved clusters are detected using novel clustering and order conservation P values. We demonstrate Spacedust's sensitivity with an all-versus-all analysis of 1,308 bacterial genomes, identifying 72,843 conserved gene clusters containing 58% of the 4.2 million genes. It recovered 95% of antiviral defense system clusters annotated by the specialized tool PADLOC. Spacedust's high sensitivity and speed will facilitate the annotation of large numbers of sequenced bacterial, archaeal and viral genomes.

Metagenomics↗
PubMed2005

Defining genes in the genome of the hyperthermophilic archaeon Pyrococcus furiosus: implications for all microbial genomes.

The original genome annotation of the hyperthermophilic archaeon Pyrococcus furiosus contained 2,065 open reading frames (ORFs). The genome was subsequently automatically annotated in two public databases by the Institute for Genomic Research (TIGR) and the National Center for Biotechnology Information (NCBI). Remarkably, more than 500 of the originally annotated ORFs differ in size in the two databases, many very significantly. For example, more than 170 of the predicted proteins differ at their N termini by more than 25 amino acids. Similar discrepancies were observed in the TIGR and NCBI databases with the other archaeal and bacterial genomes examined. In addition, the two databases contain 60 (NCBI) and 221 (TIGR) ORFs not present in the original annotation of P. furiosus. In the present study we have experimentally assessed the validity of 88 previously unannotated ORFs. Transcriptional analyses showed that 11 of 61 ORFs examined were expressed in P. furiosus when grown at either 95 or 72 degrees C. In addition, 7 of 54 ORFs examined yielded heat-stable recombinant proteins when they were expressed in Escherichia coli, although only one of the seven ORFs was expressed in P. furiosus under the growth conditions tested. It is concluded that the P. furiosus genome contains at least 17 ORFs not previously recognized in the original annotation. This study serves to highlight the discrepancies in the public databases and the problems of accurately defining the number and sizes of ORFs within any microbial genome.

Archaeal Proteins↗
PubMed2000

Predictions of gene family distributions in microbial genomes: evolution by gene duplication and modification.

A universal property of microbial genomes is the considerable fraction of genes that are homologous to other genes within the same genome. The process by which these homologues are generated is not well understood, but sequence analysis of 20 microbial genomes unveils a recurrent distribution of gene family sizes. We show that a simple evolutionary model based on random gene duplication and point mutations fully accounts for these distributions and permits predictions for the number of gene families in genomes not yet complete. Our findings are consistent with the notion that a genome evolves from a set of precursor genes to a mature size by gene duplications and increasing modifications.

Bacillus subtilis↗
PubMed2005

Application of microbial genomic science to advanced therapeutics.

Since the publication of the first complete microbial genome sequence of Haemophilus influenzae in 1995, more than 200 additional microbial genome sequences have become available in the public domain. Approximately 40% of these represent important human pathogens. Comparative in silico methods, along with large-scale approaches such as transcriptomics and proteomics, are beginning to reveal insights into new virulence genes, pathogen-host interactions, and the molecular basis of host specificity. Sequence data are also starting to accumulate from multiple isolates or strains of a single pathogen, and this type of data has proven to be quite valuable in providing new insights into the genetic variability that is present in a particular species as well as in facilitating correlations between genotype and phenotype. Ultimately, a major goal of genome-enabled infectious disease research is the development of novel diagnostics, therapeutics, and vaccines.

Animals↗
PubMed1999

Functional genomics and enzyme evolution. Homologous and analogous enzymes encoded in microbial genomes.

Computational analysis of complete genomes, followed by experimental testing of emerging hypotheses--the area of research often referred to as 'functional genomics'--aims at deciphering the wealth of information contained in genome sequences and at using it to improve our understanding of the mechanisms of cell function. This review centers on the recent progress in the genome analysis with special emphasis on the new insights in enzyme evolution. Standard methods of predicting functions for new proteins are listed and the common errors in their application are discussed. A new method of improving the functional predictions is introduced, based on a phylogenetic approach to functional prediction, as implemented in the recently constructed Clusters of Orthologous Groups (COG) database (available at http:@www.ncbi.nlm.nih.gov/COG). This approach provides a convenient way to characterize the protein families (and metabolic pathways) that are present or absent in any given organism. Comparative analysis of microbial genomes based on this approach shows that metabolic diversity generally correlates with the genome size-parasitic bacteria code for fewer enzymes and lesser number of metabolic pathways than their free-living relatives. Comparison of different genomes reveals another evolutionary trend, the non-orthologous gene displacement of some enzymes by unrelated proteins with the same cellular function. An examination of the phylogenetic distribution of such cases provides new clues to the problems of biochemical evolution, including evolution of glycolysis and the TCA cycle.

Databases, Factual↗
PubMed2026

The role of microbial genomics in delivering the UK's national action plan for confronting antimicrobial resistance 2024-29.

Antimicrobial resistance (AMR) is a major threat to human and animal health, in addition to environmental resilience. Countries set the agenda on their national action against AMR in the form of National Action Plans (NAPs), with the UK's latest NAP released in May, 2024. Advances in genomics have strengthened our ability to work towards NAP priorities; however, to date, no mapping of the role genomics plays in contributing to specific goals within the NAP has been undertaken. The UK Research and Innovation-funded Transdisciplinary Antimicrobial Resistance Genomics Network brought together a range of stakeholders to discuss the role of genomics for action on AMR and to deliver policy priority-led research, as outlined in the UK NAP 2024-29. We report our discussions in this Personal View, with key roles for genomics, including informing targeted stewardship in health-care settings, supporting AMR literacy, and supporting effective antimicrobial innovation. However, changes in infrastructure, communication, and cross-sector coordination are needed to support implementation.

United Kingdom↗
PubMed2007

Bacillus minimum genome factory: effective utilization of microbial genome information.

In 1997, the complete genomic DNA sequence of Bacillus subtilis (4.2 Mbp) was determined and 4100 genes were identified [Kunst, Ogasawara, Moszer, Albertini, Alloni, Azevedo, Bertero, Bessieres, Bolotin, Borchert, S. et al. (1997) Nature 90, 249-256]. In addition, B. subtilis, which shows an excellent ability to secrete proteins (enzymes) and antibiotics in large quantities outside the cell, plays an important role in industrial and medical fields. It is necessary to clarify the genes involved in the production of compounds by understanding the network of these 4100 genes and the proceeding analysis of genes of unknown functions. In promoting such a study, it is expected that the regulatory system of B. subtilis can be simplified by the creation of a Bacillus strain with a reduced genome by discriminating genes unnecessary for the production of proteins from essential genes, and deleting as many of these unnecessary genes as possible, which may help to understand this complex network of genes. We have previously distinguished essential and non-essential genes by evaluating the growth and enzyme-producing properties of strains of B. subtilis in which about 3000 genes (except 271 essential genes) have been disrupted or deleted singly, and have successfully utilized the findings from these studies in creating the MG1M strain with an approx. 1 Mbp deletion by serially deleting 17 unnecessary regions from the genome. This strain showed slightly reduced growth in enzyme-production medium, but no marked morphological changes. Moreover, we confirmed that the MG1M strain had cellulase and protease productivity comparable with that of the B. subtilis 168 strain, thus demonstrating that genome reduction does not contribute to a negative influence on enzyme productivity.

Bacillus subtilis↗
PubMed2003

MGView: an alignment and visualization tool to enhance gap closure of microbial genomes.

Gap closure is a challenging phase in microbial random shotgun genome sequencing projects, particularly since genome assemblies are often complicated by the presence of repeat elements, insertion sequences and other similar factors that contribute to sequence misassemblies. While it is well recognized that the conservation of genetic information between microbial genomes, combined with the exponential increase in available microbial sequences, can be exploited to increase the efficiency of gap closure, we lack the computational tools to aid in this process. We describe here a new tool, MGView, which was developed to create a graphical depiction of the alignment of a set of microbial contigs against a completed microbial genome. The results of our assembly of the Staphylococcus aureus RF122 genome show that MGView enables a considerable reduction in time and economic cost associated with closure. Together, the results also show that the application of MGView not only enables a reduction in fold-coverage requirements of the random shotgun sequence phase, but also provides interesting insights into differences in gene content and organization between finished and unfinished microbial genomes.

Base Sequence↗
PubMed1999

An automated comparative analysis of 17 complete microbial genomes.

MOTIVATION: As sequenced genomes become larger and sequencing becomes faster, there is a need to develop accurate automated genome comparison techniques and databases to facilitate derivation of genome functionality; identification of enzymes, putative operons and metabolic pathways; and to derive phylogenetic classification of microbes. RESULTS: This paper extends an automated pair-wise genome comparison technique (Bansal et al., Math. Model. Sci. Comput., 9, 1-23, 1998, Bansal and Bork, in First International Workshop of Declarative Languages, Springer, pp. 275-289, 1999) used to identify orthologs and gene groups to derive orthologous genes in a group of genomes and to identify genes with conserved functionality. Seventeen microbial genomes archived at ftp://ncbi.nlm.nih.gov/genbank/genomes have been compared using the automated technique. Data related to orthologs, gene groups, gene duplication, gene fusion, orthologs with conserved functionality, and genes specifically orthologous to Escherichia coli and pathogens has been presented and analyzed. AVAILABILITY: A prototype database is available at ftp://www.mcs.kent.edu/arvind/intellibio / orthos.html. The software is free for academic research under an academic license. The detailed database for every microbial genome in NCBI is commercially available through intellibio software and consultancy corporation (Web site: http://www.mcs.kent.edu/årvind/intellibio . html). CONTACT: arvind@mcs.kent.edu.

Algorithms↗
PubMed1999

Microbial genomes and "missing" enzymes: redefining biochemical pathways.

A biochemical pathway is the representation of a defined set of substrates, enzyme reactions and products linked together to generate an outcome beneficial to a living cell. Microbial genome sequence data are unparalleled resources for understanding cellular metabolism without the prior definitions imposed by classical biochemistry. Simple analysis of three well-studied biochemical pathways (the tricarboxylic acid cycle, pentose phosphate pathway and glycolysis) from the 17 publicly available microbial genomes has shown that these pathways may rarely occur as previously defined. Therefore, following whole-genome sequencing it has become necessary to redefine the "classical" biochemical steps leading from substrate to end-product for each pathway. Often, unique or alternative reactions appear to be required in order to maintain pathway functionality where expected enzyme reactions (as defined by the presence or absence of the corresponding genes) are "missing". Conversely, such enzymes may be accounted for by: (1) the presence of low sequence similarity or novel genes encoding enzymes performing the same or similar functions, (2) the presence of multienzyme proteins, (3) incorrectly assigned gene identities in genome databases, and (4) known enzyme functions that have yet to be correlated with a gene sequence. Most importantly, the presence of a gene sequence does not necessarily ensure that its corresponding enzyme is actually functional. This may be due to the presence of nonactive remnant genes, evolutionary pressures leading to loss of function, inactivating mutations, the lack of transcription/translation, and post-translational processing. Modifications at the gene and/or functional levels, as well as the possible use of alternative enzymes, must be considered when reconstructing biochemical pathways for fully sequenced microbial genomes.

Bacteria↗
PubMed2005

Prediction of functional modules based on gene distributions in microbial genomes.

We present a computational method for prediction of functional modules that can be directly applied to the newly sequenced microbial genomes for predicting gene functions and the component genes of biological pathways. We first quantify the functional relatedness among genes based on their distribution (i.e., their existences and orders) across multiple microbial genomes, and obtain a gene network in which every pair of genes is associated with a score representing their functional relatedness. We then apply a threshold-based clustering algorithm to this gene network, and obtain modules for each of which the number of genes is bounded from above by a pre-specified value and the component genes are more strongly functionally related to each other than genes across the predicted modules. Particularly, when the module size is bounded by 130, we obtain 167 functional modules covering 813 genes for Escherichia coli K12, and 138 functional modules covering 731 genes for Bacillus subtilis subsp. subtilis str. 168. We have used the gene ontology (GO) information to assess the prediction results. The GO similarities among the genes of the same functional module are compared with the GO similarities among the genes that are randomly clustered together. This comparison reveals that our predicted functional modules are statistically and biologically significant, and the genes of the same functional module share more commonality in terms of biological process than in terms of molecular function or cellular component. We have also examined the predicted functional modules that are common to both Escherichia coli K12 and Bacillus subtilis subsp. subtilis str. 168, and provide explanations for some functional modules.

Cluster Analysis↗