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. 2012;13 Suppl 13(Suppl 13):S11.
doi: 10.1186/1471-2105-13-S13-S11. Epub 2012 Aug 24.

"V体育ios版" The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences

Kevin Riehle  1 , Cristian Coarfa, Andrew Jackson, Jun Ma, Arpit Tandon, Sameer Paithankar, Sriram Raghuraman, Toni-Ann Mistretta, Delphine Saulnier, Sabeen Raza, Maria Alejandra Diaz, Robert Shulman, Kjersti Aagaard, James Versalovic, Aleksandar Milosavljevic
Affiliations

V体育安卓版 - The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences

"VSports" Kevin Riehle et al. BMC Bioinformatics. 2012.

Abstract

Background: Microbial metagenomic analyses rely on an increasing number of publicly available tools VSports手机版. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings. .

Methods: To address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection. V体育安卓版.

Results: We validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome V体育ios版. .

Conclusions: By lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries VSports最新版本. .

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Figures

Figure 1
Figure 1
Genboree Microbiome Toolset Dataflow. The flow begins by producing quality filtered sequences from the 16S rRNA sequences and the sample metadata. These can be passed to the taxonomic classification pipeline for taxonomic abundance reports or to the multi-step OTU picking pipeline for alpha diversity, beta diversity, classification using supervised machine learning, and feature selection.
Figure 2
Figure 2
Genboree Workbench interface. (A) A summary of interactions between the researchers using the web-based Workbench UI, which both exposes accessible data and permits configuration of Toolset analyses to be run on a compute cluster. The dashed lines indicate communication via the Genboree API. (B) Within the Workbench UI, a folder system in the left pane (i) lists sequencing results, tool results, and all other data types organized by Groups at the top level, and by Databases and Project pages at the second level. The three right panes indicate: attributes of selected data objects (ii), tool input data (iii), and target destinations for tool outputs (iv).
Figure 3
Figure 3
Species richness analysis for lean twin and obese twin samples. Species richness comparison between lean twin (n=49) and obese twin (n=45) samples. The lean twin cohort contains a higher degree of species richness as compared to the obese twin cohort.
Figure 4
Figure 4
Renyi diversity profiles for lean twin and obese twin samples. Renyi diversity profiles for lean twin (n=50) and obese twin (n=50) samples. A non-intersecting line indicates that the obese twin cohort has a lower diversity as compared to the lean twin cohort.
Figure 5
Figure 5
Taxonomic abundance comparison between children with IBS and healthy children. The pediatric gut microbiomes of children with IBS are characterized by greater abundance of Pasteurellales. Taxonomic classification was made using RDP classifier (Order) with 454 sequencing data. A) Percentage of all bacterial Orders represented. B) Percentage of bacterial taxa found in lower abundance (< 8% of total bacteria). Healthy children include 29 samples from 22 subjects, IBS patients include 42 samples from 22 patients (V1-V3 region). #: Significantly different between IBS and healthy children (P <.05).
Figure 6
Figure 6
Global phylogenetic tree comparing the intestinal microbiomes of healthy children, children with IBS-C, and children with IBS-U. The phylogenetic tree was generated using QIIME and drawn with iTOL. Data comprised of the V1-V3 region for 22 healthy children (69 samples), 9 children with IBS-U (23 samples), and 13 children with IBS-C (42 samples). The map is colored by Phyla (exterior text), Patient Status (IBS-U - Light Red; IBS-C - Purple; Healthy - Light Green) and Family.
Figure 7
Figure 7
Beta diversity analysis results for intestinal microbiomes of children with IBS-C and IBS-U. The Principal Coordinates Analysis plot, using beta diversity analysis results and utilizing the Hellinger distance metric, shows that distal gut microbiomes of children segregate the IBS with constipation (IBS-C (blue), n=41 samples) and unsubtyped IBS (IBS-U (green), n=22 samples).
Figure 8
Figure 8
Differential distribution of bacterial taxa that discriminate maximum pain levels in patients with recurrent abdominal pain. The distribution of bacterial taxa in patients with recurrent abdominal pain was used to classify the subjects based on the maximum levels of abdominal pain. Bacterial taxa (specified in leftmost column) were analyzed using randomForest and confirmed by feature selection using Boruta. The list is sorted first by Mann-Whitney U score followed by the largest disparity in medians for each group. Taxa represent the lowest taxonomic depth (Genus) that is labeled by RDP Classifier (at ≥ 80% bootstrap cut off). The degree of abdominal pain was differentiated by the maximum level of pain recorded during a 14-day period. Red rectangles display the HM (high- medium level) maximum abdominal pain phenotype. Light blue rectangles display the L0 (low-zero level) maximum abdominal pain phenotype. Boxes represent the first quartile, median, and third quartile of the distribution of OTUs for each pain group. Empty circles represent outliers that are 1.5x greater than the respective interquartile ranges. Shown are OTUs with increased levels of maximum pain in children with HM versus L0 maximum abdominal pain phenotypes.
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