Disease correlation network: a computational package for identifying temporal correlations between disease states from Large-Scale longitudinal medical records.

OBJECTIVE: To provide an open-source software package for determining temporal correlations between disease states using longitudinal electronic medical records (EMR). MATERIALS AND METHODS: We have developed an R-based package, Disease Correlation Network (DCN), which builds retrospective matched cohorts from longitudinal medical records to assess for significant temporal correlations between diseases using two independent methodologies: Cox proportional hazards regression and random forest survival analysis. This optimizable package has the potential to control for relevant confounding factors such as age, gender, and other demographic and medical characteristics. Output is presented as a DCN which may be analyzed using a JavaScript-based interactive visualization tool for users to explore statistically significant correlations between disease states of interest using graph-theory-based network topology. RESULTS: We have applied this package to a longitudinal dataset at Loyola University Chicago Medical Center with 654 084 distinct initial diagnoses of 51 conditions in 175 539 patients. Over 90% of disease correlations identified are supported by literature review. DCN is available for download at https://github.com/qunfengdong/DCN. CONCLUSIONS: DCN allows screening of EMR data to identify potential relationships between chronic disease states. This data may then be used to formulate novel research hypotheses for further characterization of these relationships.

A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy.

BACKGROUND: Species-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers, because existing taxonomic classification tools for 16S rRNA gene sequences either do not provide species-level classification, or their classification results are unreliable. The unreliable results are due to the limitations in the existing methods which either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, or use nucleotide k-mer frequency as the proxy for sequence similarity measurement. RESULTS: We have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our method does not need any training datasets specific for different taxonomic groups. Instead only a reference database is required for aligning to the query sequences, making our method easily applicable for different regions of the 16S rRNA gene or other phylogenetic marker genes. CONCLUSIONS: Reliable species-level classification for 16S rRNA or other phylogenetic marker genes is critical for microbiome research. Our software shows significantly higher classification accuracy than the existing tools and we provide probabilistic-based confidence scores to evaluate the reliability of our taxonomic classification assignments based on multiple database matches to query sequences. Despite its higher computational costs, our method is still suitable for analyzing large-scale microbiome datasets for practical purposes. Furthermore, our method can be applied for taxonomic classification of any phylogenetic marker gene sequences. Our software, called BLCA, is freely available at https://github.com/qunfengdong/BLCA .

A web-based multi-genome synteny viewer for customized data.

BACKGROUND: Web-based synteny visualization tools are important for sharing data and revealing patterns of complicated genome conservation and rearrangements. Such tools should allow biologists to upload genomic data for their own analysis. This requirement is critical because individual biologists are generating large amounts of genomic sequences that quickly overwhelm any centralized web resources to collect and display all those data. Recently, we published a web-based synteny viewer, GSV, which was designed to satisfy the above requirement. However, GSV can only compare two genomes at a given time. Extending the functionality of GSV to visualize multiple genomes is important to meet the increasing demand of the research community. RESULTS: We have developed a multi-Genome Synteny Viewer (mGSV). Similar to GSV, mGSV is a web-based tool that allows users to upload their own genomic data files for visualization. Multiple genomes can be presented in a single integrated view with an enhanced user interface. Users can navigate through all the selected genomes in either pairwise or multiple viewing mode to examine conserved genomic regions as well as the accompanying genome annotations. Besides serving users who manually interact with the web server, mGSV also provides Web Services for machine-to-machine communication to accept data sent by other remote resources. The entire mGSV package can also be downloaded for easy local installation. CONCLUSIONS: mGSV significantly enhances the original functionalities of GSV. A web server hosting mGSV is provided at http://cas-bioinfo.cas.unt.edu/mgsv.

GSV: a web-based genome synteny viewer for customized data.

BACKGROUND: The analysis of genome synteny is a common practice in comparative genomics. With the advent of DNA sequencing technologies, individual biologists can rapidly produce their genomic sequences of interest. Although web-based synteny visualization tools are convenient for biologists to use, none of the existing ones allow biologists to upload their own data for analysis. RESULTS: We have developed the web-based Genome Synteny Viewer (GSV) that allows users to upload two data files for synteny visualization, the mandatory synteny file for specifying genomic positions of conserved regions and the optional genome annotation file. GSV presents two selected genomes in a single integrated view while still retaining the browsing flexibility necessary for exploring individual genomes. Users can browse and filter for genomic regions of interest, change the color or shape of each annotation track as well as re-order, hide or show the tracks dynamically. Additional features include downloadable images, immediate email notification and tracking of usage history. The entire GSV package is also light-weighted which enables easy local installation. CONCLUSIONS: GSV provides a unique option for biologists to analyze genome synteny by uploading their own data set to a web-based comparative genome browser. A web server hosting GSV is provided at http://cas-bioinfo.cas.unt.edu/gsv, and the software is also freely available for local installations.

Diversity of bacteria at healthy human conjunctiva.

PURPOSE: Ocular surface (OS) microbiota contributes to infectious and autoimmune diseases of the eye. Comprehensive analysis of microbial diversity at the OS has been impossible because of the limitations of conventional cultivation techniques. This pilot study aimed to explore true diversity of human OS microbiota using DNA sequencing-based detection and identification of bacteria. METHODS: Composition of the bacterial community was characterized using deep sequencing of the 16S rRNA gene amplicon libraries generated from total conjunctival swab DNA. The DNA sequences were classified and the diversity parameters measured using bioinformatics software ESPRIT and MOTHUR and tools available through the Ribosomal Database Project-II (RDP-II). RESULTS: Deep sequencing of conjunctival rDNA from four subjects yielded a total of 115,003 quality DNA reads, corresponding to 221 species-level phylotypes per subject. The combined bacterial community classified into 5 phyla and 59 distinct genera. However, 31% of all DNA reads belonged to unclassified or novel bacteria. The intersubject variability of individual OS microbiomes was very significant. Regardless, 12 genera-Pseudomonas, Propionibacterium, Bradyrhizobium, Corynebacterium, Acinetobacter, Brevundimonas, Staphylococci, Aquabacterium, Sphingomonas, Streptococcus, Streptophyta, and Methylobacterium-were ubiquitous among the analyzed cohort and represented the putative "core" of conjunctival microbiota. The other 47 genera accounted for <4% of the classified portion of this microbiome. Unexpectedly, healthy conjunctiva contained many genera that are commonly identified as ocular surface pathogens. CONCLUSIONS: The first DNA sequencing-based survey of bacterial population at the conjunctiva have revealed an unexpectedly diverse microbial community. All analyzed samples contained ubiquitous (core) genera that included commensal, environmental, and opportunistic pathogenic bacteria.

Characteristic male urine microbiomes associate with asymptomatic sexually transmitted infection.

BACKGROUND: The microbiome of the male urogenital tract is poorly described but it has been suggested that bacterial colonization of the male urethra might impact risk of sexually transmitted infection (STI). Previous cultivation-dependent studies showed that a variety of non-pathogenic bacteria colonize the urethra but did not thoroughly characterize these microbiomes or establish links between the compositions of urethral microbiomes and STI. METHODOLOGY/FINDINGS: Here, we used 16S rRNA PCR and sequencing to identify bacteria in urine specimens collected from men who lacked symptoms of urethral inflammation but who differed in status for STI. All of the urine samples contained multiple bacterial genera and many contained taxa that colonize the human vagina. Uncultivated bacteria associated with female genital tract pathology were abundant in specimens from men who had STI. CONCLUSIONS: Urine microbiomes from men with STI were dominated by fastidious, anaerobic and uncultivated bacteria. The same taxa were rare in STI negative individuals. Our findings suggest that the composition of male urine microbiomes is related to STI.

An Ergatis-based prokaryotic genome annotation web server.

SUMMARY: Ergatis is a flexible workflow management system for designing and executing complex bioinformatics pipelines. However, its complexity restricts its usage to only highly skilled bioinformaticians. We have developed a web-based prokaryotic genome annotation server, Integrative Services for Genomics Analysis (ISGA), which builds upon the Ergatis workflow system, integrates other dynamic analysis tools and provides intuitive web interfaces for biologists to customize and execute their own annotation pipelines. ISGA is designed to be installed at genomics core facilities and be used directly by biologists. AVAILABILITY: ISGA is accessible at http://isga.cgb.indiana.edu/ and the system is also freely available for local installation.

A web-based software system for dynamic gene cluster comparison across multiple genomes.

SUMMARY: Investigating the conservation of gene clusters across multiple genomes has become a standard practice in the era of comparative genomics. However, all existing software and databases rely heavily on pre-computation to identify homologous genes by genome-wide comparisons. Such pre-computing strategies lack accuracy and updating the data is computationally intensive. Since most molecular biologists are often interested only in a small cluster of genes, catering to this need, we have developed a web-based software system that allows users to upload a list of genes, perform dynamic search against the genomes of their choices and interactively visualize the gene cluster conservation using a novel multi-genome browser. Our approach avoids expensive genome-wide pre-computing and allows users to dynamically change the search criteria to fit their genes of interest. Our system can be customized for any genome sequences. We have applied it to both prokaryotic and eukaryotic genomes to illustrate its usability. AVAILABILITY: Our software is freely available at http://cgcv.cgb.indiana.edu/cgi-bin/index.cgi.

BOV--a web-based BLAST output visualization tool.

BACKGROUND: The BLAST program is one of the most widely used sequence similarity search tools for genomic research, even by those biologists lacking extensive bioinformatics training. As the availability of sequence data increases, more researchers are downloading the BLAST program for local installation and performing larger and more complex tasks, including batch queries. In order to manage and interpret the results of batch queries, a host of software packages have been developed to assist with data management and post-processing. Among these programs, there is almost a complete lack of visualization tools to provide graphic representation of complex BLAST pair-wise alignments. We have developed a web-based program, BLAST Output Visualization Tool (BOV), that allows users to interactively visualize the matching regions of query and database hit sequences, thereby allowing the user to quickly and easily dissect complex matching patterns. RESULTS: Users can upload the standard BLAST output in pair-wise alignment format as input to the web server (including batch queries generated installing and running the stand-alone BLAST program on a local server). The program extracts the alignment coordinates of matching regions between the query and the corresponding database hit sequence. The coordinates are used to plot each matching region as colored lines or trapezoids. Using the straightforward control panels throughout the web site, each plotted matching region can be easily explored in detail by, for example, highlighting the region of interest or examining the raw pair-wise sequence alignment. Tutorials are provided at the website to guide users step-by-step through the functional features of BOV. CONCLUSION: BOV provides a user-friendly web interface to visualize the standard BLAST output for investigating wide-ranging genomic problems, including single query and batch query datasets. In particular, this software is valuable to users interested in identifying regions of co-linearity, duplication, translocation, and inversion among sequences. A web server hosting BOV is accessible via http://bioportal.cgb.indiana.edu/cgi-bin/BOV/index.cgi and the software is freely available for local installations.