Project description:The goal of the Human Microbiome Project (HMP) is to generate a comprehensive catalog of human-associated microorganisms including reference genomes representing the most common species. Toward this goal, the HMP has characterized the microbial communities at 18 body habitats in a cohort of over 200 healthy volunteers using 16S rRNA gene (16S) sequencing and has generated nearly 1,000 reference genomes from human-associated microorganisms. To determine how well current reference genome collections capture the diversity observed among the healthy microbiome and to guide isolation and future sequencing of microbiome members, we compared the HMP's 16S data sets to several reference 16S collections to create a 'most wanted' list of taxa for sequencing. Our analysis revealed that the diversity of commonly occurring taxa within the HMP cohort microbiome is relatively modest, few novel taxa are represented by these OTUs and many common taxa among HMP volunteers recur across different populations of healthy humans. Taken together, these results suggest that it should be possible to perform whole-genome sequencing on a large fraction of the human microbiome, including the 'most wanted', and that these sequences should serve to support microbiome studies across multiple cohorts. Also, in stark contrast to other taxa, the 'most wanted' organisms are poorly represented among culture collections suggesting that novel culture- and single-cell-based methods will be required to isolate these organisms for sequencing.
Project description:Understanding gut microbiome functions requires cultivated bacteria for experimental validation and reference bacterial genome sequences to interpret metagenome datasets and guide functional analyses. We present the Human Gastrointestinal Bacteria Culture Collection (HBC), a comprehensive set of 737 whole-genome-sequenced bacterial isolates, representing 273 species (105 novel species) from 31 families found in the human gastrointestinal microbiota. The HBC increases the number of bacterial genomes derived from human gastrointestinal microbiota by 37%. The resulting global Human Gastrointestinal Bacteria Genome Collection (HGG) classifies 83% of genera by abundance across 13,490 shotgun-sequenced metagenomic samples, improves taxonomic classification by 61% compared to the Human Microbiome Project (HMP) genome collection and achieves subspecies-level classification for almost 50% of sequences. The improved resource of gastrointestinal bacterial reference sequences circumvents dependence on de novo assembly of metagenomes and enables accurate and cost-effective shotgun metagenomic analyses of human gastrointestinal microbiota.
Project description:Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.
Project description:The Human Microbiome Project will establish a reference data set for analysis of the microbiome of healthy adults by surveying multiple body sites from 300 people and generating data from over 12,000 samples. To characterize these samples, the participating sequencing centers evaluated and adopted 16S rDNA community profiling protocols for ABI 3730 and 454 FLX Titanium sequencing. In the course of establishing protocols, we examined the performance and error characteristics of each technology, and the relationship of sequence error to the utility of 16S rDNA regions for classification- and OTU-based analysis of community structure. The data production protocols used for this work are those used by the participating centers to produce 16S rDNA sequence for the Human Microbiome Project. Thus, these results can be informative for interpreting the large body of clinical 16S rDNA data produced for this project.
Project description:BACKGROUND:Current methods used for annotating metagenomics shotgun sequencing (MGS) data rely on a computationally intensive and low-stringency approach of mapping each read to a generic database of proteins or reference microbial genomes. RESULTS:We developed MGS-Fast, an analysis approach for shotgun whole-genome metagenomic data utilizing Bowtie2 DNA-DNA alignment of reads that is an alternative to using the integrated catalog of reference genes database of well-annotated genes compiled from human microbiome data. This method is rapid and provides high-stringency matches (>90% DNA sequence identity) of the metagenomics reads to genes with annotated functions. We demonstrate the use of this method with data from a study of liver disease and synthetic reads, and Human Microbiome Project shotgun data, to detect differentially abundant Kyoto Encyclopedia of Genes and Genomes gene functions in these experiments. This rapid annotation method is freely available as a Galaxy workflow within a Docker image. CONCLUSIONS:MGS-Fast can confidently transfer functional annotations from gene databases to metagenomic reads, with speed and accuracy.
Project description:A simple method to analyze microbiome beta-diversity computes mean beta-diversity distances from a test sample to standard reference samples. We used reference stool and nasal samples from the Human Microbiome Project and regressed an outcome on mean distances (2 degrees-of-freedom (df) test) or additionally on squares and cross-product of mean distances (5-df test). We compared the power of 2-df and 5-df tests with the microbiome regression-based kernel association test (MiRKAT). In simulations, MiRKAT had moderately greater power than the 2-df test for discriminating skin versus saliva and skin versus nasal samples, but differences were negligible for skin versus stool and stool versus nasal samples. The 2-df test had slightly greater power than MiRKAT for Dirichlet multinomial samples. In associating body mass index with beta-diversity in stool samples from the American Gut Project, the 5-df test yielded smaller P values than MiRKAT for most taxonomic levels and beta-diversity measures. Unlike procedures like MiRKAT that are based on the beta-diversity matrix, mean distances to reference samples can be analyzed with standard statistical tools and shared or meta-analyzed without sharing primary DNA data. Our data indicate that standard reference tests have power comparable to MiRKAT's (and to permutational multivariate analysis of variance), but more simulations and applications are needed to confirm this.
Project description:There is currently no criterion to select appropriate bioinformatics tools and reference databases for analysis of 16S rRNA amplicon data in the human oral microbiome. Our study aims to determine the influence of multiple tools and reference databases on ?-diversity measurements and ?-diversity comparisons analyzing the human oral microbiome. We compared the results of taxonomical classification by Greengenes, the Human Oral Microbiome Database (HOMD), National Center for Biotechnology Information (NCBI) 16S, SILVA, and the Ribosomal Database Project (RDP) using Quantitative Insights Into Microbial Ecology (QIIME) and the Divisive Amplicon Denoising Algorithm (DADA2). There were 15 phyla present in all of the analyses, four phyla exclusive to certain databases, and different numbers of genera were identified in each database. Common genera found in the oral microbiome, such as Veillonella, Rothia, and Prevotella, are annotated by all databases; however, less common genera, such as Bulleidia and Paludibacter, are only annotated by large databases, such as Greengenes. Our results indicate that using different reference databases in 16S rRNA amplicon data analysis could lead to different taxonomic compositions, especially at genus level. There are a variety of databases available, but there are no defined criteria for data curation and validation of annotations, which can affect the accuracy and reproducibility of results, making it difficult to compare data across studies.
Project description:Experimental efforts to characterize the human microbiota often use bacterial strains that were chosen for historical rather than biological reasons. Here, we report an analysis of 380 whole-genome shotgun samples from 100 subjects from the NIH Human Microbiome Project. By mapping their reads to 1,751 reference genome sequences and analyzing the resulting relative strain abundance in each sample we present metrics and visualizations that can help identify strains of interest for experimentalists. We also show that approximately 14 strains of 10 species account for 80% of the mapped reads from a typical stool sample, indicating that the function of a community may not be irreducibly complex. Some of these strains account for >20% of the sequence reads in a subset of samples but are absent in others, a dichotomy that could underlie biological differences among subjects. These data should serve as an important strain selection resource for the community of researchers who take experimental approaches to studying the human microbiota.