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Background: Cumulative sequencing efforts have yielded enough genomes to construct pangenomes for dozens of bacterial species and elucidate intraspecies gene conservation. Given the diversity of organisms for which this is achievable, similar analyses for ancestral species are feasible through the integration of pangenomics and phylogenetics, promising deeper insights into the nature of ancient life., Results: We construct pangenomes for 183 bacterial species from 54,085 genomes and identify their core genomes using a novel statistical model to estimate genome-specific error rates and underlying gene frequencies. The core genomes are then integrated into a phylogenetic tree to reconstruct the core genome of the last bacterial common ancestor (LBCA), yielding three main results: First, the gene content of modern and ancestral core genomes are diverse at the level of individual genes but are similarly distributed by functional category and share several poorly characterized genes. Second, the LBCA core genome is distinct from any individual modern core genome but has many fundamental biological systems intact, especially those involving translation machinery and biosynthetic pathways to all major nucleotides and amino acids. Third, despite this metabolic versatility, the LBCA core genome likely requires additional non-core genes for viability, based on comparisons with the minimal organism, JCVI-Syn3A., Conclusions: These results suggest that many cellular systems commonly conserved in modern bacteria were not just present in ancient bacteria but were nearly immutable with respect to short-term intraspecies variation. Extending this analysis to other domains of life will likely provide similar insights into more distant ancestral species., (© 2023. BioMed Central Ltd., part of Springer Nature.)

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.0 TB in FASTA format, within only 34 min on a 128-core workstation. Our results further identify 1269 redundant genomes, with identical nucleotide content, in the RefSeq bacterial genomes database., (© 2023. The Author(s).)

Compared with conventional comparative genomics, the recent studies in pan-genomics have provided further insights into species genomic dynamics, taxonomy and identification, pathogenicity and environmental adaptation. To better understand genome characteristics of species of interest and to fully excavate key metabolic and resistant genes and their conservations and variations, here we present ProPan (https://ngdc.cncb.ac.cn/propan), a public database covering 23 archaeal species and 1,481 bacterial species (in a total of 51,882 strains) for comprehensively profiling prokaryotic pan-genome dynamics. By analyzing and integrating these massive datasets, ProPan offers three major aspects for the pan-genome dynamics of the species of interest: 1) the evaluations of various species' characteristics and composition in pan-genome dynamics; 2) the visualization of map association, the functional annotation and presence/absence variation for all contained species' gene clusters; 3) the typical characteristics of the environmental adaptation, including resistance genes prediction of 126 substances (biocide, antimicrobial drug and metal) and evaluation of 31 metabolic cycle processes. Besides, ProPan develops a very user-friendly interface, flexible retrieval and multi-level real-time statistical visualization. Taken together, ProPan will serve as a weighty resource for the studies of prokaryotic pan-genome dynamics, taxonomy and identification as well as environmental adaptation., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Due to sequencing bias, sequencing error, and repeat problems, the genome assemblies usually contain misarrangements and gaps. When tackling these problems, current assemblers commonly consider the read libraries as a whole and adopt the same strategy to deal with them. However, if we can divide reads into different categories and take different assembly strategies for different read categories, we expect to reduce the mutual effects on problems in genome assembly and facilitate to produce satisfactory assemblies. In this paper, we present a new pipeline for genome assembly based on read classification (ARC). ARC classifies reads into three categories according to the frequencies of k-mers they contain. The three categories refer to (1) low depth reads, which contain a certain low frequency k-mers and are often caused by sequencing errors or bias; (2) high depth reads, which contain a certain high frequency k-mers and usually come from repetitive regions; and (3) normal depth reads, which are the rest of reads. After read classification, an existing assembler is used to assemble different read categories separately, which is beneficial to resolve problems in the genome assembly. ARC adopts loose assembly parameters for low depth reads, and strict assembly parameters for normal depth and high depth reads. We test ARC using five datasets. The experimental results show that, assemblers combining with ARC can generate better assemblies in terms of NA50, NGA50, and genome fraction.

Motivation: Non-B DNA conformations play an important role in genomic rearrangements, structural three-dimensional organization and gene regulation. Many non-B DNA structures show symmetrical properties as palindromes and mirrors that can form hairpins, cruciform structures or triplexes. A comprehensive tool, capable to perform a fast genome wide search for exact and degenerate symmetrical patterns, is needed for further investigating nucleotide tracts potentially forming non-B DNA structures., Results: We developed NeSSie, an easily customizable C/C++ 64-bit library and tool, based on dynamic programming, to quickly scan for perfect and degenerate DNA palindromes, mirrors and potential triplex forming patterns. In addition, the tool computes linguistic complexity and Shannon entropy measures to verify the repetitive nature of the DNA regions enriched in these motifs. As a case study, the analysis of the Mycobacterium bovis genome is presented., Availability and Implementation: http://www.medcomp.medicina.unipd.it/main_site/doku.php? id=nessie and https://github.com/B3rse/nessie., Supplementary Information: Supplementary data are available at Bioinformatics online.

We evaluated the performance of the MinION DNA sequencer in-flight on the International Space Station (ISS), and benchmarked its performance off-Earth against the MinION, Illumina MiSeq, and PacBio RS II sequencing platforms in terrestrial laboratories. Samples contained equimolar mixtures of genomic DNA from lambda bacteriophage, Escherichia coli (strain K12, MG1655) and Mus musculus (female BALB/c mouse). Nine sequencing runs were performed aboard the ISS over a 6-month period, yielding a total of 276,882 reads with no apparent decrease in performance over time. From sequence data collected aboard the ISS, we constructed directed assemblies of the ~4.6 Mb E. coli genome, ~48.5 kb lambda genome, and a representative M. musculus sequence (the ~16.3 kb mitochondrial genome), at 100%, 100%, and 96.7% consensus pairwise identity, respectively; de novo assembly of the E. coli genome from raw reads yielded a single contig comprising 99.9% of the genome at 98.6% consensus pairwise identity. Simulated real-time analyses of in-flight sequence data using an automated bioinformatic pipeline and laptop-based genomic assembly demonstrated the feasibility of sequencing analysis and microbial identification aboard the ISS. These findings illustrate the potential for sequencing applications including disease diagnosis, environmental monitoring, and elucidating the molecular basis for how organisms respond to spaceflight.

Background: DNA sequences contain repetitive motifs which have various functions in the physiology of the organism. A number of methods have been developed for discovery of such sequence motifs with a primary focus on detection of regulatory motifs and particularly transcription factor binding sites. Most motif-finding methods apply probabilistic models to detect motifs characterized by unusually high number of copies of the motif in the analyzed sequences., Results: We present a novel method for detection of pairs of motifs separated by spacers of variable nucleotide sequence but conserved length. Unlike existing methods for motif discovery, the motifs themselves are not required to occur at unusually high frequency but only to exhibit a significant preference to occur at a specific distance from each other. In the present implementation of the method, motifs are represented by pentamers and all pairs of pentamers are evaluated for statistically significant preference for a specific distance. An important step of the algorithm eliminates motif pairs where the spacers separating the two motifs exhibit a high degree of sequence similarity; such motif pairs likely arise from duplications of the whole segment including the motifs and the spacer rather than due to selective constraints indicative of a functional importance of the motif pair. The method was used to scan 569 complete prokaryotic genomes for novel sequence motifs. Some motifs detected were previously known but other motifs found in the search appear to be novel. Selected motif pairs were subjected to further investigation and in some cases their possible biological functions were proposed., Conclusions: We present a new motif-finding technique that is applicable to scanning complete genomes for sequence motifs. The results from analysis of 569 genomes suggest that the method detects previously known motifs that are expected to be found as well as new motifs that are unlikely to be discovered by traditional motif-finding methods. We conclude that our approach to detection of significant motif pairs can complement existing motif-finding techniques in discovery of novel functional sequence motifs in complete genomes.

Sequence comparison has become an essential tool in bioinformatics, because highly homologous sequences usually imply significant functional or structural similarity. Traditional sequence analysis techniques are based on preprocessing and alignment, which facilitate measuring and quantitative characterization of genetic differences, variability and complexity. However, recent developments of next generation and whole genome sequencing technologies give rise to new challenges that are related to measuring similarity and capturing rearrangements of large segments contained in the genome. This work is devoted to illustrating different methods recently introduced for quantifying sequence distances and variability. Most of the alignment-free methods rely on counting words, which are small contiguous fragments of the genome. Our approach considers the locations of nucleotides in the sequences and relies more on appropriate statistical distributions. The results of this technique for comparing sequences, by extracting information and comparing matching fidelity and location regularization information, are very encouraging, specifically to classify mutation sequences., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Motivation: Next-generation sequencing technology is generating a wealth of highly similar genome sequences for many species, paving the way for a transition from single-genome to pan-genome analyses. Accordingly, genomics research is going to switch from reference-centric to pan-genomic approaches. We define the pan-genome as a comprehensive representation of multiple annotated genomes, facilitating analyses on the similarity and divergence of the constituent genomes at the nucleotide, gene and genome structure level. Current pan-genomic approaches do not thoroughly address scalability, functionality and usability., Results: We introduce a generalized De Bruijn graph as a pan-genome representation, as well as an online algorithm to construct it. This representation is stored in a Neo4j graph database, which makes our approach scalable to large eukaryotic genomes. Besides the construction algorithm, our software package, called PanTools, currently provides functionality for annotating pan-genomes, adding sequences, grouping genes, retrieving gene sequences or genomic regions, reconstructing genomes and comparing and querying pan-genomes. We demonstrate the performance of the tool using datasets of 62 E. coli genomes, 93 yeast genomes and 19 Arabidopsis thaliana genomes., Availability and Implementation: The Java implementation of PanTools is publicly available at http://www.bif.wur.nl, Contact: sandra.smit@wur.nl., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: Phylogenetic footprinting is an important computational technique for identifying cis-regulatory motifs in orthologous regulatory regions from multiple genomes, as motifs tend to evolve slower than their surrounding non-functional sequences. Its application, however, has several difficulties for optimizing the selection of orthologous data and reducing the false positives in motif prediction., Results: Here we present an integrative phylogenetic footprinting framework for accurate motif predictions in prokaryotic genomes (MP(3)). The framework includes a new orthologous data preparation procedure, an additional promoter scoring and pruning method and an integration of six existing motif finding algorithms as basic motif search engines. Specifically, we collected orthologous genes from available prokaryotic genomes and built the orthologous regulatory regions based on sequence similarity of promoter regions. This procedure made full use of the large-scale genomic data and taxonomy information and filtered out the promoters with limited contribution to produce a high quality orthologous promoter set. The promoter scoring and pruning is implemented through motif voting by a set of complementary predicting tools that mine as many motif candidates as possible and simultaneously eliminate the effect of random noise. We have applied the framework to Escherichia coli k12 genome and evaluated the prediction performance through comparison with seven existing programs. This evaluation was systematically carried out at the nucleotide and binding site level, and the results showed that MP(3) consistently outperformed other popular motif finding tools. We have integrated MP(3) into our motif identification and analysis server DMINDA, allowing users to efficiently identify and analyze motifs in 2,072 completely sequenced prokaryotic genomes., Conclusion: The performance evaluation indicated that MP(3) is effective for predicting regulatory motifs in prokaryotic genomes. Its application may enhance progress in elucidating transcription regulation mechanism, thus provide benefit to the genomic research community and prokaryotic genome researchers in particular.

Genome-Scale Metabolic Reconstructions (GSMRs), along with optimization-based methods, predominantly Flux Balance Analysis (FBA) and its derivatives, are widely applied for assessing and predicting the behavior of metabolic networks upon perturbation, thereby enabling identification of potential novel drug targets and biotechnologically relevant pathways. The abundance of alternate flux profiles has led to the evolution of methods to explore the complete solution space aiming to increase the accuracy of predictions. Herein we present a novel, generic algorithm to characterize the entire flux space of GSMR upon application of FBA, leading to the optimal value of the objective (the optimal flux space). Our method employs Modified Latin-Hypercube Sampling (LHS) to effectively border the optimal space, followed by Principal Component Analysis (PCA) to identify and explain the major sources of variability within it. The approach was validated with the elementary mode analysis of a smaller network of Saccharomyces cerevisiae and applied to the GSMR of Pseudomonas aeruginosa PAO1 (iMO1086). It is shown to surpass the commonly used Monte Carlo Sampling (MCS) in providing a more uniform coverage for a much larger network in less number of samples. Results show that although many fluxes are identified as variable upon fixing the objective value, majority of the variability can be reduced to several main patterns arising from a few alternative pathways. In iMO1086, initial variability of 211 reactions could almost entirely be explained by 7 alternative pathway groups. These findings imply that the possibilities to reroute greater portions of flux may be limited within metabolic networks of bacteria. Furthermore, the optimal flux space is subject to change with environmental conditions. Our method may be a useful device to validate the predictions made by FBA-based tools, by describing the optimal flux space associated with these predictions, thus to improve them.

Next-generation sequencing technologies for whole-genome sequencing of mycobacteria are rapidly becoming an attractive alternative to more traditional sequencing methods. In particular this technology is proving useful for genome-wide identification of mutations in mycobacteria (comparative genomics) as well as for de novo assembly of whole genomes. Next-generation sequencing however generates a vast quantity of data that can only be transformed into a usable and comprehensible form using bioinformatics. Here we describe the methodology one would use to prepare libraries for whole-genome sequencing, and the basic bioinformatics to identify mutations in a genome following Illumina HiSeq or MiSeq sequencing, as well as de novo genome assembly following sequencing using Pacific Biosciences (PacBio).

There is an acute need for better tools to extract knowledge from the growing flood of sequence data. For example, thousands of complete genomes have been sequenced, and their metabolic networks inferred. Such data should enable a better understanding of evolution. However, most existing network analysis methods are based on pair-wise comparisons, and these do not scale to thousands of genomes. Here we propose the use of weighted graphs as a data structure to enable large-scale phylogenetic analysis of networks. We have developed three types of weighted graph for enzymes: taxonomic (these summarize phylogenetic importance), isoenzymatic (these summarize enzymatic variety/redundancy), and sequence-similarity (these summarize sequence conservation); and we applied these types of weighted graph to survey prokaryotic metabolism. To demonstrate the utility of this approach we have compared and contrasted the large-scale evolution of metabolism in Archaea and Eubacteria. Our results provide evidence for limits to the contingency of evolution.

A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity within species that is now being revealed by genome sequencing on a daily basis. To address this lack of a general intra-species classification and naming system adequate for today's speed of discovery of new diversity, we propose a classification and naming system that is exclusively based on genome similarity and that is suitable for automatic assignment of codes to any genome-sequenced organism without requiring any phenotypic or phylogenetic analysis. We provide examples demonstrating that genome similarity-based codes largely align with current taxonomic groups at many different levels in bacteria, animals, humans, plants, and viruses. Importantly, the proposed approach is only slightly affected by the order of code assignment and can thus provide codes that reflect similarity between organisms and that do not need to be revised upon discovery of new diversity. We envision genome similarity-based codes to complement current biological nomenclature and to provide a universal means to communicate unambiguously about any genome-sequenced organism in fields as diverse as biodiversity research, infectious disease control, human and microbial forensics, animal breed and plant cultivar certification, and human ancestry research.

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species. The project exploits and extends technologies for genome annotation, analysis and dissemination, developed in the context of the vertebrate-focused Ensembl project, and provides a complementary set of resources for non-vertebrate species through a consistent set of programmatic and interactive interfaces. These provide access to data including reference sequence, gene models, transcriptional data, polymorphisms and comparative analysis. This article provides an update to the previous publications about the resource, with a focus on recent developments. These include the addition of important new genomes (and related data sets) including crop plants, vectors of human disease and eukaryotic pathogens. In addition, the resource has scaled up its representation of bacterial genomes, and now includes the genomes of over 9000 bacteria. Specific extensions to the web and programmatic interfaces have been developed to support users in navigating these large data sets. Looking forward, analytic tools to allow targeted selection of data for visualization and download are likely to become increasingly important in future as the number of available genomes increases within all domains of life, and some of the challenges faced in representing bacterial data are likely to become commonplace for eukaryotes in future.

We propose an extension to alignment-free approaches that can produce reasonably accurate phylogenetic groupings starting from unaligned genomes, for example, as fast as 1 min on a standard desktop computer for 25 bacterial genomes. A 6-fold speed-up and 11-fold reduction in memory requirements compared to previous alignment-free methods is achieved by reducing the comparison space to a representative sample of k-mers of optimal length and with specific tag motifs. This approach was applied to the test case of fitting the enterohemorrhagic O104:H4 E.coli strain from the 2011 outbreak in Germany into the phylogenetic network of previously known E.coli-related strains and extend the method to allow assigning any new strain to the correct phylogenetic group even directly from unassembled short sequence reads from next generation sequencing data. Hence, this approach is also useful to quickly identify the most suitable reference genome for subsequent assembly steps.

Background: No attention has been paid on comparing a set of genome sequences crossing genetic components and biological categories with far divergence over large size range. We define it as the systematic comparative genomics and aim to develop the methodology., Results: First, we create a method, GenomeFingerprinter, to unambiguously produce a set of three-dimensional coordinates from a sequence, followed by one three-dimensional plot and six two-dimensional trajectory projections, to illustrate the genome fingerprint of a given genome sequence. Second, we develop a set of concepts and tools, and thereby establish a method called the universal genome fingerprint analysis (UGFA). Particularly, we define the total genetic component configuration (TGCC) (including chromosome, plasmid, and phage) for describing a strain as a systematic unit, the universal genome fingerprint map (UGFM) of TGCC for differentiating strains as a universal system, and the systematic comparative genomics (SCG) for comparing a set of genomes crossing genetic components and biological categories. Third, we construct a method of quantitative analysis to compare two genomes by using the outcome dataset of genome fingerprint analysis. Specifically, we define the geometric center and its geometric mean for a given genome fingerprint map, followed by the Euclidean distance, the differentiate rate, and the weighted differentiate rate to quantitatively describe the difference between two genomes of comparison. Moreover, we demonstrate the applications through case studies on various genome sequences, giving tremendous insights into the critical issues in microbial genomics and taxonomy., Conclusions: We have created a method, GenomeFingerprinter, for rapidly computing, geometrically visualizing, intuitively comparing a set of genomes at genome fingerprint level, and hence established a method called the universal genome fingerprint analysis, as well as developed a method of quantitative analysis of the outcome dataset. These have set up the methodology of systematic comparative genomics based on the genome fingerprint analysis.

RNA-guided Cas9 nucleases derived from clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems have dramatically transformed our ability to edit the genomes of diverse organisms. We believe tools and techniques based on Cas9, a single unifying factor capable of colocalizing RNA, DNA and protein, will grant unprecedented control over cellular organization, regulation and behavior. Here we describe the Cas9 targeting methodology, detail current and prospective engineering advances and suggest potential applications ranging from basic science to the clinic.

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The microbial genome database for comparative analysis (MBGD, available at http://mbgd.genome.ad.jp/) is a platform for microbial genome comparison based on orthology analysis. As its unique feature, MBGD allows users to conduct orthology analysis among any specified set of organisms; this flexibility allows MBGD to adapt to a variety of microbial genomic study. Reflecting the huge diversity of microbial world, the number of microbial genome projects now becomes several thousands. To efficiently explore the diversity of the entire microbial genomic data, MBGD now provides summary pages for pre-calculated ortholog tables among various taxonomic groups. For some closely related taxa, MBGD also provides the conserved synteny information (core genome alignment) pre-calculated using the CoreAligner program. In addition, efficient incremental updating procedure can create extended ortholog table by adding additional genomes to the default ortholog table generated from the representative set of genomes. Combining with the functionalities of the dynamic orthology calculation of any specified set of organisms, MBGD is an efficient and flexible tool for exploring the microbial genome diversity.

A major goal in evolutionary biology is to understand the origins and fates of adaptive mutations. Natural selection may act to increase the frequency of de novo beneficial mutations, or those already present in the population as standing genetic variation. These beneficial mutations may ultimately reach fixation in a population, or they may stop increasing in frequency once a particular phenotypic state has been achieved. It is not yet well understood how different features of population biology, and/or different environmental circumstances affect these adaptive processes. Experimental evolution is a promising technique for studying the dynamics of beneficial alleles, as populations evolving in the laboratory experience natural selection in a replicated, controlled manner. Whole-genome sequencing, regularly obtained over the course of sustained laboratory selection, could potentially reveal insights into the mutational dynamics that most likely occur in natural populations under similar circumstances. To date, only a few evolution experiments for which whole-genome data are available exist. This review describes results from these resequenced laboratory-selected populations, in systems with and without sexual recombination. In asexual systems, adaptation from new mutations can be studied, and results to date suggest that the complete, unimpeded fixation of these mutations is not always observed. In sexual systems, adaptation from standing genetic variation can be studied, and in the admittedly few examples we have, the complete fixation of standing variants is not always observed. To date, the relative frequency of adaptation from new mutations versus standing variation has not been tested using a single experimental system, but recent studies using Caenorhabditis elegans and Saccharomyces cerevisiae suggest that this a realistic future goal.

This Short Communication highlights the diversity of 'secondary' genome data (like mitochondrial and plastid genomes) that can be gleaned from next-generation sequencing projects, and encourages researchers to be mindful that these data are often as informative and useful as the 'primary' genome data.

Genome-wide mutagenesis studies indicate that up to about 90% of genes in bacteria and 80% in eukaryotes can be inactivated individually leaving an organism viable, often seemingly unaffected. Several strategies are used to learn what these apparently dispensable genes contribute to fitness. Assays of growth under hundreds of physical and chemical stresses are among the most effective experimental approaches. Comparative studies of genomic DNA sequences continue to be valuable in discriminating between the core bacterial genome and the more variable niche-specific genes. The concept of the core genome appears currently unfeasible for eukaryotes but progress has been made in understanding why they contain numerous gene duplicates., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Recent advances in DNA sequencing technology and their focal role in Genome Wide Association Studies (GWAS) have rekindled a growing interest in the whole-genome sequence assembly (WGSA) problem, thereby, inundating the field with a plethora of new formalizations, algorithms, heuristics and implementations. And yet, scant attention has been paid to comparative assessments of these assemblers' quality and accuracy. No commonly accepted and standardized method for comparison exists yet. Even worse, widely used metrics to compare the assembled sequences emphasize only size, poorly capturing the contig quality and accuracy. This paper addresses these concerns: it highlights common anomalies in assembly accuracy through a rigorous study of several assemblers, compared under both standard metrics (N50, coverage, contig sizes, etc.) as well as a more comprehensive metric (Feature-Response Curves, FRC) that is introduced here; FRC transparently captures the trade-offs between contigs' quality against their sizes. For this purpose, most of the publicly available major sequence assemblers--both for low-coverage long (Sanger) and high-coverage short (Illumina) reads technologies--are compared. These assemblers are applied to microbial (Escherichia coli, Brucella, Wolbachia, Staphylococcus, Helicobacter) and partial human genome sequences (Chr. Y), using sequence reads of various read-lengths, coverages, accuracies, and with and without mate-pairs. It is hoped that, based on these evaluations, computational biologists will identify innovative sequence assembly paradigms, bioinformaticists will determine promising approaches for developing "next-generation" assemblers, and biotechnologists will formulate more meaningful design desiderata for sequencing technology platforms. A new software tool for computing the FRC metric has been developed and is available through the AMOS open-source consortium.

A very simple new program is presented (G-SQUARES). It is useful in order to visualize the composition and basic structural features of whole genomes and selected chromosome regions. The frequency of all dimer and tetramer sequences is reported. Overall structural features are calculated, such as the tendency for alternation. A direct visual comparison among different sequences is easily available. Furthermore, the features which are visualized indicate further studies which should be carried out. Examples are presented on Alu sequences, CpG islands, whole eukaryotic and bacterial genomes., (Copyright © 2010 Elsevier B.V. All rights reserved.)

In the past decade, over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms. These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism, leading to greater understanding of observed phenotypes. These methods have been sparsely applied to comparisons between multiple organisms, however, due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared. To circumvent this obstacle, we developed a novel process, termed metabolic network reconciliation, whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based. This process was applied to two organisms of great importance to disease and biotechnological applications, Pseudomonas aeruginosa and Pseudomonas putida, respectively. The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences (to the degree that is currently known), rather than artifacts of the reconstruction process. The reconstructions were re-validated with various experimental data after reconciliation. With the reconciled and validated reconstructions, we performed a genome-wide comparison of metabolic flexibility between P. aeruginosa and P. putida that generated significant new insight into the underlying biology of these important organisms. Through this work, we provide a novel methodology for reconciling models, present new genome-scale reconstructions of P. aeruginosa and P. putida that can be directly compared at a network level, and perform a network-wide comparison of the two species. These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads, and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species.

Background: The genus Cronobacter (formerly called Enterobacter sakazakii) is composed of five species; C. sakazakii, C. malonaticus, C. turicensis, C. muytjensii, and C. dublinensis. The genus includes opportunistic human pathogens, and the first three species have been associated with neonatal infections. The most severe diseases are caused in neonates and include fatal necrotizing enterocolitis and meningitis. The genetic basis of the diversity within the genus is unknown, and few virulence traits have been identified., Methodology/principal Findings: We report here the first sequence of a member of this genus, C. sakazakii strain BAA-894. The genome of Cronobacter sakazakii strain BAA-894 comprises a 4.4 Mb chromosome (57% GC content) and two plasmids; 31 kb (51% GC) and 131 kb (56% GC). The genome was used to construct a 387,000 probe oligonucleotide tiling DNA microarray covering the whole genome. Comparative genomic hybridization (CGH) was undertaken on five other C. sakazakii strains, and representatives of the four other Cronobacter species. Among 4,382 annotated genes inspected in this study, about 55% of genes were common to all C. sakazakii strains and 43% were common to all Cronobacter strains, with 10-17% absence of genes., Conclusions/significance: CGH highlighted 15 clusters of genes in C. sakazakii BAA-894 that were divergent or absent in more than half of the tested strains; six of these are of probable prophage origin. Putative virulence factors were identified in these prophage and in other variable regions. A number of genes unique to Cronobacter species associated with neonatal infections (C. sakazakii, C. malonaticus and C. turicensis) were identified. These included a copper and silver resistance system known to be linked to invasion of the blood-brain barrier by neonatal meningitic strains of Escherichia coli. In addition, genes encoding for multidrug efflux pumps and adhesins were identified that were unique to C. sakazakii strains from outbreaks in neonatal intensive care units.

The Genomes On Line Database (GOLD) is a comprehensive resource for centralized monitoring of genome and metagenome projects worldwide. Both complete and ongoing projects, along with their associated metadata, can be accessed in GOLD through precomputed tables and a search page. As of September 2009, GOLD contains information for more than 5800 sequencing projects, of which 1100 have been completed and their sequence data deposited in a public repository. GOLD continues to expand, moving toward the goal of providing the most comprehensive repository of metadata information related to the projects and their organisms/environments in accordance with the Minimum Information about a (Meta)Genome Sequence (MIGS/MIMS) specification. GOLD is available at: http://www.genomesonline.org and has a mirror site at the Institute of Molecular Biology and Biotechnology, Crete, Greece, at: http://gold.imbb.forth.gr/

During the last decade, models have been developed to characterize cellular metabolism at the level of an entire metabolic network. The main concept that underlies whole-network metabolic modeling is the identification and mathematical definition of constraints. Here, we review large-scale metabolic network modeling, in particular, stoichiometric- and constraint-based approaches. Although many such models have been reconstructed, few networks have been extensively validated and tested experimentally, and we focus on these. We describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints on metabolic fluxes. We then discuss relatively successful approaches, including flux balance analysis (FBA), pathway analysis, and common extensions or modifications to these approaches. Finally, we describe techniques for integrating these approaches with models of other biological processes.

Unlabelled: Array-based comparative genomic hybridization (CGH) technology is used to discover and validate genomic structural variation, including copy number variants, insertions, deletions and other structural variants (SVs). The visualization and summarization of the array CGH data outputs, potentially across many samples, is an important process in the identification and analysis of SVs. We have developed a software tool for SV analysis using data from array CGH technologies, which is also amenable to short-read sequence data., Availability and Implementation: SnoopCGH is written in java and is available from http://snoopcgh.sourceforge.net/

Acquired immunity in prokaryotes is achieved by integrating short fragments of foreign nucleic acids into clustered regularly interspaced short palindromic repeats (CRISPRs). This nucleic acid-based immune system is mediated by a variable cassette of up to 45 protein families that represent distinct immune system subtypes. CRISPR-associated gene 1 (cas1) encodes the only universally conserved protein component of CRISPR immune systems, yet its function is unknown. Here we show that the Cas1 protein is a metal-dependent DNA-specific endonuclease that produces double-stranded DNA fragments of approximately 80 base pairs in length. The 2.2 A crystal structure of the Cas1 protein reveals a distinct fold and a conserved divalent metal ion-binding site. Mutation of metal ion-binding residues, chelation of metal ions, or metal-ion substitution inhibits Cas1-catalyzed DNA degradation. These results provide a foundation for understanding how Cas1 contributes to CRISPR function, perhaps as part of the machinery for processing foreign nucleic acids.

Deletion rates are thought to be important factors in determining the genome size of organisms in nature. Although it is indisputable that deletions, and thus deletion rates, affect genome size, it is unclear how, or indeed if, genome size is regulated via the deletion rate. Here, we employ a mathematical model to determine the evolutionary fate of deletion rate mutants. Simulations are employed to explore the interactions between deletions, deletion rate mutants, and genome size. The results show that, in this model, the fate of deletion rate mutants will depend on the fraction of essential genomic material, on the frequency of sexual recombination, as well as on the population size of the organism. We find that there is no optimal deletion rate in any state. However, at one critical coding density, all changes in deletion rate are neutral and the rate may drift either up or down. As a consequence, the coding density of the genome is expected to fluctuate around this critical density. Characteristic differences in the impact of deletion rate mutations on prokaryote and eukaryote genomes are described.

Motivation: Spatial clusters of genes conserved across multiple genomes provide important clues to gene functions and evolution of genome organization. Existing methods of identifying these clusters often made restrictive assumptions, such as exact conservation of gene order, and relied on heuristic algorithms., Results: We developed a very efficient algorithm based on a 'gene teams' model that allows genes in the clusters to appear in different orders. This allows us to detect conserved gene clusters under flexible evolutionary constraints in a large number of genomes. Our statistical evaluation incorporates the evolutionary relationship among genomes, a key aspect that has been missing in most previous studies. We conducted a large-scale analysis of 133 bacterial genomes. Our results confirm that our approach is an effective way of uncovering functionally related genes. The comparison with known operons and the analysis of the structural properties of our predicted clusters suggest that operons are an important source of constraint, but there are also other forces that determine evolution of gene order and arrangement. Using our method, we predicted functions of many poorly characterized genes in bacterial. The combined algorithmic and statistical methods we present here provide a rigorous framework for systematically studying evolutionary constraints of genomic contexts., Availability: The software, data and the full results of this article are available online at http://www.ews.uiuc.edu/~xuling/mcmusec.

Bacterial infectious diseases remain a major cause of deaths and disabilities in the world. Although conventional vaccinology approaches were successful in conferring protection against several diseases, they failed in providing efficient vaccines against many others. Together to the sequencing of the first genome, a new chapter in the vaccinology history started to be written. Reverse vaccinology changed the way to think about vaccine development, using the information provided by the microorganisms' genome against themselves. Since then, reverse vaccinology has evolved and helped researchers to overcome the limits of the conventional vaccinology approaches and led to the discovery and development of novel vaccines concerning emerging diseases, like Neisseria meningitidis B and Streptococcus agalactiae. A lot of work must be done, but deciphering the information provided by genome sequences and using it to better understand the host-pathogen interactions has proved to be the key for protection.

Analysis of 100 complete sets of the cytoplasmic elongator tRNA genes from Bacteria, Archaea, and Eukarya pointed to correspondences between types of anticodon and composition of the rest of the tRNA body. The number of the hydrogen bonds formed between the complementary nucleotides in the anticodon-codon duplex appeared as a major quantitative parameter determining covariations in all three domains of life. Our analysis has supported and advanced the "extended anticodon" concept that is based on the argument that the decoding performance of the anticodon is enhanced by selection of a matching anticodon stem-loop sequence, as reported by Yarus in 1982. In addition to the anticodon stem-loop, we have found covariations between the anticodon nucleotides and the composition of the distant regions of their respective tRNAs that include dihydrouridine (D) and thymidyl (T) stem-loops. The majority of the covariable tRNA positions were found at the regions with the increased dynamic potential--such as stem-loop and stem-stem junctions. The consistent occurrences of the covariations on the multigenomic level suggest that the number and pattern of the hydrogen bonds in the anticodon-codon duplex constitute a major factor in the course of translation that is reflected in the fine-tuning of the tRNA composition and structure.

While genome sequencing efforts reveal the basic building blocks of life, a genome sequence alone is insufficient for elucidating biological function. Genome annotation--the process of identifying genes and assigning function to each gene in a genome sequence--provides the means to elucidate biological function from sequence. Current state-of-the-art high-throughput genome annotation uses a combination of comparative (sequence similarity data) and non-comparative (ab initio gene prediction algorithms) methods to identify protein-coding genes in genome sequences. Because approaches used to validate the presence of predicted protein-coding genes are typically based on expressed RNA sequences, they cannot independently and unequivocally determine whether a predicted protein-coding gene is translated into a protein. With the ability to directly measure peptides arising from expressed proteins, high-throughput liquid chromatography-tandem mass spectrometry-based proteomics approaches can be used to verify coding regions of a genomic sequence. Here, we highlight several ways in which high-throughput tandem mass spectrometry-based proteomics can improve the quality of genome annotations and suggest that it could be efficiently applied during the gene calling process so that the improvements are propagated through the subsequent functional annotation process.

Comparative genometrics of microorganisms is a relatively new area, in which genome properties are translated into numerical indexes. Such indexes can be used for a comprehensive and comparative analysis of microbial genomes, contributing to the understanding of their evolution. This work presents a new method for quantitative determination of gene strand bias in prokaryotic chromosomes, in which data transformation of gene position skew leads to a numerical index that can be applied to quantitative comparisons of genome organization. It was applied in the comparative analysis of 49 completely sequenced Firmicutes genomes, allowing the distinction of groups defined according to their patterns of gene strand preference. The resulting groups revealed that, regarding gene strand bias, reduced genomes are, in general, the more disordered among Firmicutes, while genomes of extremophile organisms comprehend those with the highest degree of genome organization in this phylum.

To date, the complete genome sequences of more than 250 organisms have been determined. This information can now be used to determine whether there exist any invariant sequences that are conserved among all organisms, from bacteria to plants, animals, and humans. The existence of invariant sequences would strongly suggest that these sequences have been inherited unchanged from the last common ancestor of all life, and that they have essential functions. We have developed a new software program to identify invariant sequences conserved among the currently sequenced genomes and applied this analysis to the complete genome sequences of 266 organisms. We have identified 3 invariant DNA sequences longer than or equal to 11 bp and 6 invariant amino acid sequences longer than or equal to 6 aa. The longest invariant DNA sequence, AAGTCGTACAAGGT (15 bp), was found in the 16S/18S rRNA gene. Two 8 aa sequences, GHVDHGKT in IF2 and EF-Tu and DTPGHVDF in EF-G, were the longest invariant amino acid sequences detected. These sequences could be essential elements from the genome of the last common ancestor and may have remained unchanged throughout evolution.

We perform an exhaustive, taxon by taxon, comparison of the branchings in the composition vector trees (CVTrees) inferred from 432 prokaryotic genomes available on 31 December 2006, with the bacteriologists' taxonomy--primarily the latest online Outline of the Bergey's Manual of Systematic Bacteriology. The CVTree phylogeny agrees very well with the Bergey's taxonomy in majority of fine branchings and overall structures. At the same time most of the differences between the trees and the Manual have been known to biologists to some extent and may hint at taxonomic revisions. Instead of demonstrating the overwhelming agreement this paper puts emphasis on the biological implications of the differences.

To assess the potential of protein function prediction in environmental genomics data, we analyzed shotgun sequences from four diverse and complex habitats. Using homology searches as well as customized gene neighborhood methods that incorporate intergenic and evolutionary distances, we inferred specific functions for 76% of the 1.4 million predicted ORFs in these samples (83% when nonspecific functions are considered). Surprisingly, these fractions are only slightly smaller than the corresponding ones in completely sequenced genomes (83% and 86%, respectively, by using the same methodology) and considerably higher than previously thought. For as many as 75,448 ORFs (5% of the total), only neighborhood methods can assign functions, illustrated here by a previously undescribed gene associated with the well characterized heme biosynthesis operon and a potential transcription factor that might regulate a coupling between fatty acid biosynthesis and degradation. Our results further suggest that, although functions can be inferred for most proteins on earth, many functions remain to be discovered in numerous small, rare protein families.

Unlabelled: Synonymous codon usage biases are associated with various biological factors, such as gene expression level, gene length, gene translation initiation signal, protein amino acid composition, protein structure, tRNA abundance, mutation frequency and patterns, and GC compositions. Quantification of codon usage bias helps understand evolution of living organisms. A codon usage bias pipeline is demanding for codon usage bias analyses within and across genomes. Here we present a CodonO webserver service as a user-friendly tool for codon usage bias analyses across and within genomes in real time. The webserver is available at http//www.sysbiology.org/CodonO., Contact: wanhenry@yahoo.com.

We analyze publicly available data on Affymetrix microarray spike-in experiments on the human HGU133 chipset in which sequences are added in solution at known concentrations. The spike-in set contains sequences of bacterial, human, and artificial origin. Our analysis is based on a recently introduced molecular-based model (Carlon, E.; Heim, T. Physica A 2006, 362, 433) that takes into account both probe-target hybridization and target-target partial hybridization in solution. The hybridization free energies are obtained from the nearest-neighbor model with experimentally determined parameters. The molecular-based model suggests a rescaling that should result in a "collapse" of the data at different concentrations into a single universal curve. We indeed find such a collapse, with the same parameters as obtained previously for the older HGU95 chip set. The quality of the collapse varies according to the probe set considered. Artificial sequences, chosen by Affymetrix to be as different as possible from any other human genome sequence, generally show a much better collapse and thus a better agreement with the model than all other sequences. This suggests that the observed deviations from the predicted collapse are related to the choice of probes or have a biological origin rather than being a problem with the proposed model.

Background: Systematic genome comparisons are an important tool to reveal gene functions, pathogenic features, metabolic pathways and genome evolution in the era of post-genomics. Furthermore, such comparisons provide important clues for vaccines and drug development. Existing genome comparison software often lacks accurate information on orthologs, the function of similar genes identified and genome-wide reports and lists on specific functions. All these features and further analyses are provided here in the context of a modular software tool "inGeno" written in Java with Biojava subroutines., Results: InGeno provides a user-friendly interactive visualization platform for sequence comparisons (comprehensive reciprocal protein--protein comparisons) between complete genome sequences and all associated annotations and features. The comparison data can be acquired from several different sequence analysis programs in flexible formats. Automatic dot-plot analysis includes output reduction, filtering, ortholog testing and linear regression, followed by smart clustering (local collinear blocks; LCBs) to reveal similar genome regions. Further, the system provides genome alignment and visualization editor, collinear relationships and strain-specific islands. Specific annotations and functions are parsed, recognized, clustered, logically concatenated and visualized and summarized in reports., Conclusion: As shown in this study, inGeno can be applied to study and compare in particular prokaryotic genomes against each other (gram positive and negative as well as close and more distantly related species) and has been proven to be sensitive and accurate. This modular software is user-friendly and easily accommodates new routines to meet specific user-defined requirements.

Summary: We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data., Availability: http://rodeo.med.harvard.edu/tools/roundup.

Motivation: Establishment of intra-cellular life involved a profound re-configuration of the genetic characteristics of bacteria, including genome reduction and rearrangements. Understanding the mechanisms underlying these phenomena will shed light on the genome rearrangements essential for the development of an intra-cellular lifestyle. Comparison of genomes with differences in their sizes poses statistical as well as computational problems. Little efforts have been made to develop flexible computational tools with which to analyse genome reduction and rearrangements., Results: Investigation of genome reduction and rearrangements in endosymbionts using a novel computational tool (GRAST) identified gathering of genes with similar functions. Conserved clusters of functionally related genes (CGSCs) were detected. Heterogeneous gene and gene cluster non-functionalization/loss are identified between genome regions, functional gene categories and during evolution. Results show that gene non-functionalisation has accelerated during the last 50 MY of Buchnera's evolution while CGSCs have been static.

The selective mechanisms operating in regulatory regions of bacterial genomes are poorly understood. We have previously shown that, in most bacterial genomes, regulatory regions contain high densities of sigma70 promoter-like signals that are significantly above the densities detected in nonregulatory genomic regions. In order to investigate the molecular evolutionary forces that operate in bacterial regulatory regions and how they affect the observed redundancy of promoter-like signals, we have undertaken a comparative analysis across the completely sequenced genomes of enteric gamma-proteobacteria. This analysis detects significant positional conservation of promoter-like signal clusters across enterics, some times in spite of strong primary sequence divergence. This suggests that the conservation of the nature and exact position of specific nucleotides is not necessarily the priority of selection for maintaining the transcriptional function in these bacteria. We have further characterized the structural conservation of the regulatory regions of dnaQ and crp across all enterics. These two regions differ in essentiality and mode of regulation, the regulation of crp being more complex and involving interactions with several transcription factors. This results in substantially different modes of evolution, with the dnaQ region appearing to evolve under stronger purifying selection and the crp region showing the likely effects of stabilizing selection for a complex pattern of gene expression. The higher flexibility of the crp region is consistent with the observed less conservation of global regulators in evolution. Patterns of regulatory evolution are also found to be markedly different in endosymbiotic bacteria, in a manner consistent with regulatory regions suffering some level of degradation, as has been observed for many other characters in these genomes. Therefore, the mode of evolution of bacterial regulatory regions appears to be highly dependent on both the lifestyle of the bacterium and the specific regulatory requirements of different genes. In fact, in many bacteria, the mode of evolution of genes requiring significant physiological adaptability in expression levels may follow patterns similar to those operating in the more complex regulatory regions of eukaryotic genomes.