Project description:16S rRNA deep sequencing analysis, targeting V3 region was performed using Illumina bar coded sequencing. Sediment samples from two hot springs (Atri and Taptapani) were collected. Atri and Taptapani metagenomes were classified into 50 and 51 bacterial phyla. Proteobacteria (45.17%) dominated the Taptapani sample metagenome followed by Bacteriodetes (23.43%) and Cyanobacteria (10.48%) while in the Atri sample, Chloroflexi (52.39%), Nitrospirae (10.93%) and Proteobacteria (9.98%) dominated. A large number of sequences remained taxonomically unresolved in both hot springs, indicating the presence of potentially novel microbes in these two unique habitats thus unraveling the importance of the current study. Metagenome sequence information is now available at NCBI, SRA database accession no. SRP057428.
Project description:A taxonomic description of bacteria was deduced from 5.78 Mb metagenomic sequence retrieved from Tulsi Shyam hot spring, India using bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). Metagenome contained 10,893 16S rDNA sequences that were analyzed by MG-RAST server to generate the comprehensive profile of bacteria. Metagenomic data are available at EBI under EBI Metagenomics database with accession no. ERP009559. Metagenome sequences represented the 98.2% bacteria origin, 1.5% of eukaryotic and 0.3% were unidentified. A total of 16 bacterial phyla demonstrating 97 families and 287 species were revealed in the hot spring metagenome. Most abundant phyla were Firmicutes (65.38%), Proteobacteria (21.21%) and unclassified bacteria (10.69%). Whereas, Peptostreptococcaceae (37.33%), Clostridiaceae (23.36%), and Enterobacteriaceae (16.37%) were highest reported families in metagenome. Ubiquitous species were Clostridium bifermentans (17.47%), Clostridium lituseburense (13.93%) and uncultured bacterium (10.15%). Our data provide new information on hot spring bacteria and shed light on their abundance, diversity, distribution and coexisting organisms.
Project description:Reconstructing the genomes of microbial community members is key to the interpretation of shotgun metagenome samples. Genome binning programs deconvolute reads or assembled contigs of such samples into individual bins. However, assessing their quality is difficult due to the lack of evaluation software and standardized metrics. Here, we present Assessment of Metagenome BinnERs (AMBER), an evaluation package for the comparative assessment of genome reconstructions from metagenome benchmark datasets. It calculates the performance metrics and comparative visualizations used in the first benchmarking challenge of the initiative for the Critical Assessment of Metagenome Interpretation (CAMI). As an application, we show the outputs of AMBER for 11 binning programs on two CAMI benchmark datasets. AMBER is implemented in Python and available under the Apache 2.0 license on GitHub.
Project description:Urine culture and microscopy techniques are used to profile the bacterial species present in urinary tract infections. To gain insight into the urinary flora, we analyzed clinical laboratory features and the microbial metagenome of 121 clean-catch urine samples. 16S rDNA gene signatures were successfully obtained for 116 participants, while metagenome sequencing data was successfully generated for samples from 49 participants. Although 16S rDNA sequencing was more sensitive, metagenome sequencing allowed for a more comprehensive and unbiased representation of the microbial flora, including eukarya and viral pathogens, and of bacterial virulence factors. Urine samples positive by metagenome sequencing contained a plethora of bacterial (median 41 genera/sample), eukarya (median 2 species/sample) and viral sequences (median 3 viruses/sample). Genomic analyses suggested cases of infection with potential pathogens that are often missed during routine urine culture due to species specific growth requirements. While conventional microbiological methods are inadequate to identify a large diversity of microbial species that are present in urine, genomic approaches appear to more comprehensively and quantitatively describe the urinary microbiome.
Project description:BACKGROUND: Variation of microorganism communities in the rumen of cattle (Bos taurus) is of great interest because of possible links to economically or environmentally important traits, such as feed conversion efficiency or methane emission levels. The resolution of studies investigating this variation may be improved by utilizing untargeted massively parallel sequencing (MPS), that is, sequencing without targeted amplification of genes. The objective of this study was to develop a method which used MPS to generate "rumen metagenome profiles", and to investigate if these profiles were repeatable among samples taken from the same cow. Given faecal samples are much easier to obtain than rumen fluid samples; we also investigated whether rumen metagenome profiles were predictive of faecal metagenome profiles. RESULTS: Rather than focusing on individual organisms within the rumen, our method used MPS data to generate quantitative rumen micro-biome profiles, regardless of taxonomic classifications. The method requires a previously assembled reference metagenome. A number of such reference metagenomes were considered, including two rumen derived metagenomes, a human faecal microflora metagenome and a reference metagenome made up of publically available prokaryote sequences. Sequence reads from each test sample were aligned to these references. The "rumen metagenome profile" was generated from the number of the reads that aligned to each contig in the database. We used this method to test the hypothesis that rumen fluid microbial community profiles vary more between cows than within multiple samples from the same cow. Rumen fluid samples were taken from three cows, at three locations within the rumen. DNA from the samples was sequenced on the Illumina GAIIx. When the reads were aligned to a rumen metagenome reference, the rumen metagenome profiles were repeatable (P?<?0.00001) by cow regardless of location of sampling rumen fluid. The repeatability was estimated at 9%, albeit with a high standard error, reflecting the small number of animals in the study. Finally, we compared rumen microbial profiles to faecal microbial profiles. Our hypothesis, that there would be a stronger correlation between faeces and rumen fluid from the same cow than between faeces and rumen fluid from different cows, was not supported by our data (with much greater significance of rumen versus faeces effect than animal effect in mixed linear model). CONCLUSIONS: We have presented a simple and high throughput method of metagenome profiling to assess the similarity of whole metagenomes, and illustrated its use on two novel datasets. This method utilises widely used freeware. The method should be useful in the exploration and comparison of metagenomes.