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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. [ABSTRACT FROM AUTHOR]

Background: Domestic combustion of biomass fuels, such as wood, charcoal, crop residue and dung causes Household Air Pollution (HAP). These inhaled particulates affect more than half of the world's population, causing respiratory problems such as infection and inflammatory lung disease. We examined whether the presence of black carbon in alveolar macrophages was associated with alterations in the lung microbiome in a Malawi population. Methods: Bronchoalveolar lavage samples from 44 healthy adults were sequenced using 16S rDNA amplification to assess microbial diversity, richness and relative taxa abundance. Individuals were classified as high or low particulate exposure as determined by questionnaire and the percentage of black carbon within their alveolar macrophages. Results: Subjects in the low and high particulate groups did not differ in terms of source of fuels used for cooking or lighting. There was no difference in alpha or beta diversity by particulate group. Neisseria and Streptococcus were significantly more abundant in samples from high particulate exposed individuals, and Tropheryma was found less abundant. Petrobacter abundance was higher in people using biomass fuel for household cooking and lighting, compared with exclusive use of electricity. Conclusions: Healthy adults in Malawi exposed to higher levels of particulates have higher abundances of potentially pathogenic bacteria (Streptococcus, Neisseria) within their lung microbiome. Domestic biomass fuel use was associated with an uncommon environmental bacterium (Petrobacter) associated with oil-rich niches. [ABSTRACT FROM AUTHOR]

Background: Modern pyrosequencing techniques make it possible to study complex bacterial populations, such as 16S rRNA, directly from environmental or clinical samples without the need for laboratory purification. Alignment of sequences across the resultant large data sets (100,000+ sequences) is of particular interest for the purpose of identifying potential gene clusters and families, but such analysis represents a daunting computational task. The aim of this work is the development of an efficient pipeline for the clustering of large sequence read sets. Methods: Pairwise alignment techniques are used here to calculate genetic distances between sequence pairs. These methods are pleasingly parallel and have been shown to more accurately reflect accurate genetic distances in highly variable regions of rRNA genes than do traditional multiple sequence alignment (MSA) approaches. By utilizing Needleman-Wunsch (NW) pairwise alignment in conjunction with novel implementations of interpolative multidimensional scaling (MDS), we have developed an effective method for visualizing massive biosequence data sets and quickly identifying potential gene clusters. Results: This study demonstrates the use of interpolative MDS to obtain clustering results that are qualitatively similar to those obtained through full MDS, but with substantial cost savings. In particular, the wall clock time required to cluster a set of 100,000 sequences has been reduced from seven hours to less than one hour through the use of interpolative MDS. Conclusions: Although work remains to be done in selecting the optimal training set size for interpolative MDS, substantial computational cost savings will allow us to cluster much larger sequence sets in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]