Job History   Goto: Job Submission

Demos and Case Studies

Following are some ready to execute demos and cases for understanding features of MetagenoNets
Run Case Study
Input Type Name Download
Primary Data IBD2-Abundance.txt Download
Meta Data IBD2-Metadata.txt Download
NodeMeta Data IBD2-Nodemeta.txt Download
Secondary Data IBD2-Secondary.txt Download

Title: HMP2 gut microbiota and IBD

Analysis pertains to to the public data provided by the study,"Dynamics of metatranscription in the inflammatory bowel disease gut microbiome". In order to infer microbial (and inter-omic) association patterns in inflammatory bowel disease, taxonomic and functional profiles corresponding to the metagenomic study (downloadable from MetagenoNets demo page) were obtained from the Inflammatory Bowel Disease Multi'omics Database pertaining to HMP2 (https://ibdmdb.org/). We considered the zeroth day sample corresponding to all the subjects in the study (76 IBD – 48 CD, 28 UC; 24 nonIBD). Both taxonomic as well as functional profiles along with given metadata was utilized to create integrated/ microbial association networks using MetagenoNets.

DOI: https://doi.org/10.1038/s41564-017-0089-z

.

All files may be accessed and downloaded for your reference.

Run this Case using the Run Case Study button above.

Run Demo
Input Type Name Download
Primary Data TrGenus.txt Download
Meta Data TrMetadata.txt Download
NodeMeta Data TrNodeMetaData.txt Download
Secondary Data TrSecondary.txt Download

Title: Tribal and urban gut microbiome data

Analysis pertains to the public data provided by the study, "Lifestyle-Induced Microbial Gradients: An Indian Perspective". Study considered 155 16S metagenomic samples collected from 75 tribal and 80 urban gut microbiome samples. Inferred function potential of the microbiota profiles of this study was used as secondary inter-omic data. Both taxonomic as well as functional profiles along with given metadata was utilized to create integrated/ microbial association networks using MetagenoNets.

All files may be accessed and downloaded for your reference.

DOI: https://doi.org/10.3389/fmicb.2019.02874

Run this demo using the Run Demo button above.

Run Demo
Input Type Name Download
Primary Data IBD_Prim.txt Download
Meta Data IBD_Meta.txt Download
Node MetaData IBD_Node_meta.txt Download
Secondary Data IBD_Secondary.txt Download

Title: IBD Gut and Tissue microbiome data

Analysis pertains to the public data provided by the study titled, "Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment". Study considered 228 16S metagenomic samples (stool and biopsy) collected from Ocean State Crohn's and Colitis Area Registry (OSCCAR) and the Prospective Registry in IBD Study at MGH (PRISM) database. Authors originally observed that "there were major shifts in oxidative stress pathways, as well as decreased carbohydrate metabolism and amino acid biosynthesis in favor of nutrient transport and uptake. The microbiome of ileal Crohn's disease was notable for increases in virulence and secretion pathways". Both taxonomic as well as inferred functional profiles along with given metadata was utilized to create integrated/ microbial association networks using MetagenoNets.

DOI: https://doi.org/10.1186/gb-2012-13-9-r79

Execute this demo using the Run Demo button above.

Run Demo
Input Type Name Download
Primary Data Hyper_Primary.txt Download
Meta Data Hyper_Metadata.txt Download
NodeMeta Data Hyper_Node_metadata.txt Download
Secondary Data Hyper_Secondary.txt Download

Title: Hypertension gut microbiome data

Analysis pertains to the public data provided by the study, "Gut microbiota dysbiosis contributes to the development of hypertension". Authors performed shotgun metagenomic sequencing of fecal samples from a cohort of 196 Chinese individuals. In order to establish the role of gut microbiota dysbiosis as a key factor for blood pressure (BP) changes that triggers the pathogenesis of hypertension, they classified the cohort into 41 healthy controls, 56 pre-hypertension (pHTN), and 99 hypertension (HTN) samples according to their BP levels. We utilized the abundance data as well as metadata provided in this study. Only the differentiating set of genera reported by the authors were used to construct network using MetagenoNets for better clarity and reproducibility of results. Both taxonomic as well as functional profiles along with given metadata was utilized to create integrated/ microbial association networks using MetagenoNets.

DOI: https://doi.org/10.1186/s40168-016-0222-x

All files may be accessed and downloaded for your reference.

Run this demo using the Run Demo button above.