Hidden breakpoints in genome alignments

During the course of evolution, an organism's genome can undergo changes that affect the large-scale structure of the genome. These changes include gene gain, loss, duplication, chromosome fusion, fission, and rearrangement. When gene gain and loss occurs in addition to other types of rearrangement, breakpoints of rearrangement can exist that are only detectable by comparison of three or more genomes. An arbitrarily large number of these "hidden" breakpoints can exist among genomes that exhibit no rearrangements in pairwise comparisons. We present an extension of the multichromosomal breakpoint median problem to genomes that have undergone gene gain and loss. We then demonstrate that the median distance among three genomes can be used to calculate a lower bound on the number of hidden breakpoints present. We provide an implementation of this calculation including the median distance, along with some practical improvements on the time complexity of the underlying algorithm. We apply our approach to measure the abundance of hidden breakpoints in simulated data sets under a wide range of evolutionary scenarios. We demonstrate that in simulations the hidden breakpoint counts depend strongly on relative rates of inversion and gene gain/loss. Finally we apply current multiple genome aligners to the simulated genomes, and show that all aligners introduce a high degree of error in hidden breakpoint counts, and that this error grows with evolutionary distance in the simulation. Our results suggest that hidden breakpoint error may be pervasive in genome alignments.Comment: 13 pages, 4 figure

Ergatis: a web interface and scalable software system for bioinformatics workflows

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users

CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

Next-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software. We describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms. The CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.https://doi.org/10.1186/1471-2105-12-35

Comparative Genomics of Emerging Human Ehrlichiosis Agents

Anaplasma (formerly Ehrlichia) phagocytophilum, Ehrlichia chaffeensis, and Neorickettsia (formerly Ehrlichia) sennetsu are intracellular vector-borne pathogens that cause human ehrlichiosis, an emerging infectious disease. We present the complete genome sequences of these organisms along with comparisons to other organisms in the Rickettsiales order. Ehrlichia spp. and Anaplasma spp. display a unique large expansion of immunodominant outer membrane proteins facilitating antigenic variation. All Rickettsiales have a diminished ability to synthesize amino acids compared to their closest free-living relatives. Unlike members of the Rickettsiaceae family, these pathogenic Anaplasmataceae are capable of making all major vitamins, cofactors, and nucleotides, which could confer a beneficial role in the invertebrate vector or the vertebrate host. Further analysis identified proteins potentially involved in vacuole confinement of the Anaplasmataceae, a life cycle involving a hematophagous vector, vertebrate pathogenesis, human pathogenesis, and lack of transovarial transmission. These discoveries provide significant insights into the biology of these obligate intracellular pathogens

Bioinformatics for the human microbiome project

Microbes inhabit virtually all sites of the human body, yet we know very little about the role they play in our health. In recent years, there has been increasing interest in studying human-associated microbial communities, particularly since microbial dysbioses have now been implicated in a number of human diseases [1]–[3]. Dysbiosis, the disruption of the normal microbial community structure, however, is impossible to define without first establishing what “normal microbial community structure” means within the healthy human microbiome. Recent advances in sequencing technologies have made it feasible to perform large-scale studies of microbial communities, providing the tools necessary to begin to address this question [4], [5]. This led to the implementation of the Human Microbiome Project (HMP) in 2007, an initiative funded by the National Institutes of Health Roadmap for Biomedical Research and constructed as a large, genome-scale community research project [6]. Any such project must plan for data analysis, computational methods development, and the public availability of tools and data; here, we provide an overview of the corresponding bioinformatics organization, history, and results from the HMP (Figure 1).National Institutes of Health (U.S.) (NIH U54HG004969)National Institutes of Health (U.S.) (grant R01HG004885)National Institutes of Health (U.S.) (grant R01HG005975)National Institutes of Health (U.S.) (grant R01HG005969

Short Term Evolution of a Highly Transmissible Methicillin-Resistant Staphylococcus aureus Clone (ST228) in a Tertiary Care Hospital

Staphylococcus aureus is recognized as one of the major human pathogens and is by far one of the most common nosocomial organisms. The genetic basis for the emergence of highly epidemic strains remains mysterious. Studying the microevolution of the different clones of S. aureus is essential for identifying the forces driving pathogen emergence and spread. The aim of the present study was to determine the genetic changes characterizing a lineage belonging to the South German clone (ST228) that spread over ten years in a tertiary care hospital in Switzerland. For this reason, we compared the whole genome of eight isolates recovered between 2001 and 2008 at the Lausanne hospital. The genetic comparison of these isolates revealed that their genomes are extremely closely related. Yet, a few more important genetic changes, such as the replacement of a plasmid, the loss of large fragments of DNA, or the insertion of transposases, were observed. These transfers of mobile genetic elements shaped the evolution of the ST228 lineage that spread within the Lausanne hospital. Nevertheless, although the strains analyzed differed in their dynamics, we have not been able to link a particular genetic element with spreading success. Finally, the present study showed that new sequencing technologies improve considerably the quality and quantity of information obtained for a single strain; but this information is still difficult to interpret and important investments are required for the technology to become accessible for routine investigations

Pyrosequencing of Bacterial Symbionts within Axinella corrugata Sponges: Diversity and Seasonal Variability

Background: Marine sponge species are of significant interest to many scientific fields including marine ecology, conservation biology, genetics, host-microbe symbiosis and pharmacology. One of the most intriguing aspects of the sponge ‘‘holobiont’’ system is the unique physiology, interaction with microbes from the marine environment and the development of a complex commensal microbial community. However, intraspecific variability and temporal stability of sponge-associated bacterial symbionts remain relatively unknown. Methodology/Principal Findings: We have characterized the bacterial symbiont community biodiversity of seven different individuals of the Caribbean reef sponge Axinella corrugata, from two different Florida reef locations during variable seasons using multiplex 454 pyrosequencing of 16 S rRNA amplicons. Over 265,512 high-quality 16 S rRNA sequences were generated and analyzed. Utilizing versatile bioinformatics methods and analytical software such as the QIIME and CloVR packages, we have identified 9,444 distinct bacterial operational taxonomic units (OTUs). Approximately 65,550 rRNA sequences (24%) could not be matched to bacteria at the class level, and may therefore represent novel taxa. Differentially abundant classes between seasonal Axinella communities included Gammaproteobacteria, Flavobacteria, Alphaproteobacteria, Cyanobacteria, Acidobacter and Nitrospira. Comparisons with a proximal outgroup sponge species (Amphimedon compressa), and the growing sponge symbiont literature, indicate that this study has identified approximately 330 A. corrugata-specific symbiotic OTUs, many of which are related to the sulfur-oxidizing Ectothiorhodospiraceae. This family appeared exclusively within A. corrugata, comprising \u3e34.5% of all sequenced amplicons. Other A. corrugata symbionts such as Deltaproteobacteria, Bdellovibrio, and Thiocystis among many others are described. Conclusions/Significance: Slight shifts in several bacterial taxa were observed between communities sampled during spring and fall seasons. New 16 S rDNA sequences and concomitant identifications greatly expand the microbial community profile for this model reef sponge, and will likely be useful as a baseline for any future comparisons regarding sponge microbial community dynamics