README.md

In twbattaglia/MicrobeDS: Microbiome Datasets

Microbiome datasets

A repository for large-scale microbiome datasets.

Install

install.packages("remotes")

remotes::install_github("twbattaglia/MicrobeDS")

Usage

# Load Library
library(MicrobeDS)

# Load selected datasets
data('HMPv35')

# Check number of samples
nsamples(HMPv35)

# Check sample metadata
sample_data(HMPv35)

Datasets

This package contains datasets provided by large-scale microbiome studies. Each dataset is formatted for use with phyloseq. (https://joey711.github.io/phyloseq/).

Hartwig

Description: Hartwig Medical Foundation (HMF) Number of samples: 3574 (4115 before clinical and lowly abundant filtering) Data source: Study: https://doi.org/10.1016/j.cell.2024.03.021 Processing Unmapped reads from metastatic tumors profiled with PathSeq/Kraken2. Type: OTU-table, Sample metadata Abstract: Microbial communities are resident to multiple niches of the human body and are important modulators of the host immune system and responses to anticancer therapies. Recent studies have shown that complex microbial communities are present within primary tumors. To investigate the presence and relevance of the microbiome in metastases, we integrated mapping and assembly-based metagenomics, genomics, transcriptomics, and clinical data of 4,160 metastatic tumor biopsies. We identified organ-specific tropisms of microbes, enrichments of anaerobic bacteria in hypoxic tumors, associations between microbial diversity and tumor-infiltrating neutrophils, and the association of Fusobacterium with resistance to immune checkpoint blockade (ICB) in lung cancer. Furthermore, longitudinal tumor sampling revealed temporal evolution of the microbial communities and identified bacteria depleted upon ICB. Together, we generated a pan-cancer resource of the metastatic tumor microbiome which may contribute to advancing treatment strategies.

Genus-level mapped counts

data(Hartwig)

# Check number of samples
nsamples(Hartwig)

# Check sample metadata
ntaxa(Hartwig)

TCGA

Description: The Cancer Genome Atlas (TCGA) Number of samples: 17625 Data source: ftp://ftp.microbio.me/pub/cancer_microbiome_analysis Study: https://pubmed.ncbi.nlm.nih.gov/32214244/ Processing Unmapped reads profiled using Kraken2 (un-normalized) Type: OTU-table, Sample metadata Abstract: Systematic characterization of the cancer microbiome provides a unique opportunity to develop cancer diagnostics that exploit non-human, microbial-derived molecules in a major human disease. Based on recent studies showing significant microbial contributions in select cancer types1–10, we re-examined treatment-naïve whole genome and whole transcriptome sequencing studies (n=18,116 samples) from 33 cancer types in The Cancer Genome Atlas11 (TCGA) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major cancer types.

Taxonomy

# Taxonomy contains only Kingdom & Genus level 
# due to missing intermediate taxonomy annotations
tax_table(TCGA) %>% 
 as.data.frame() %>% 
 head()

List of contaminants

data("TCGA.contaminants")
head(TCGA.contaminants)

TCGA.filtered = TCGA %>% 
  subset_taxa(!(Genus %in% TCGA.contaminants$Genus))

Normalize & remove batch effects

library(microbiome)
library(edgeR)
library(limma)
library(snm)

# Normalized (will take a long time)
covDesignNorm <- model.matrix(~0 + sample_type +
                              data_submitting_center_label +
                              platform +
                              experimental_strategy +
                              tissue_source_site_label +
                              portion_is_ffpe,
                              data = meta(TCGA))
colnames(covDesignNorm) <- gsub('([[:punct:]])|\\s+','',colnames(covDesignNorm))
dge <- DGEList(counts = abundances(TCGA))
keep <- filterByExpr(dge, covDesignNorm)
dge <- dge[keep, keep.lib.sizes = FALSE]
dge <- calcNormFactors(dge, method = "TMM")
vdge <- voom(dge, design = covDesignNorm, plot = TRUE, save.plot = TRUE, normalize.method="none")

# Remove batch effects (runs long)
bio.var.sample.type <- model.matrix(~sample_type, data = meta(TCGA))
adj.var <- model.matrix(~data_submitting_center_label +
                        platform +
                        experimental_strategy +
                        tissue_source_site_label +
                        portion_is_ffpe,
                        data = meta(TCGA))
colnames(bio.var.sample.type) <- gsub('([[:punct:]])|\\s+','',colnames(bio.var.sample.type))
colnames(adj.var) <- gsub('([[:punct:]])|\\s+','',colnames(adj.var))
voom.snm <- snm(raw.dat = vdge$E,
                bio.var = bio.var.sample.type,
                adj.var = adj.var,
                rm.adj = TRUE,
                verbose = TRUE,
                diagnose = TRUE)
voom.snm.data = voom.snm$norm.dat
colnames(voom.snm.data) = colnames(vdge$E)

# Create phyloseq with updated abundance
TCGA.snm = TCGA
otu_table(TCGA.snm) = otu_table(voom.snm.data, taxa_are_rows = T)

HMPv13

Description: Human Microbiome Project (HMP) V1-V3 amplicon Number of samples: 3285 Data source: https://qiita.ucsd.edu/study/description/1927 Study: https://www.ncbi.nlm.nih.gov/pubmed/22699609 Processing QIIME 1.9.1, SortMeRNA, Closed-reference OTU-picking at 97% identity Type: OTU-table, Sample metadata, Representative Tree Abstract: This HMP production phase represents pyrosequencing of 16S rRNA genes amplified from multiple body sites across hundreds of human subjects. There are two time points represented for a subset of these subjects. Using default protocol v4.2., data for the 16S window spanning V3-V5 was generated for all samples, with a second 16S window spanning V1-V3 generated for a majority of the samples. 16S rRNA sequencing is being used to characterize the complexity of microbial communities at individual body sites, and to determine whether there is a core microbiome at each site. Several body sites will be studied, including the gastrointestinal and female urogenital tracts, oral cavity, nasal and pharyngeal tract, and skin.

HMPv35

Description: Human Microbiome Project (HMP) V3-V5 amplicon Number of samples: 6000 Data source: https://qiita.ucsd.edu/study/description/1928 Study: https://www.ncbi.nlm.nih.gov/pubmed/22699609 Processing QIIME 1.9.1, SortMeRNA, Closed-reference OTU-picking at 97% identity Type: OTU-table, Sample metadata, Representative Tree Abstract: This HMP production phase represents pyrosequencing of 16S rRNA genes amplified from multiple body sites across hundreds of human subjects. There are two time points represented for a subset of these subjects. Using default protocol v4.2., data for the 16S window spanning V3-V5 was generated for all samples, with a second 16S window spanning V1-V3 generated for a majority of the samples. 16S rRNA sequencing is being used to characterize the complexity of microbial communities at individual body sites, and to determine whether there is a core microbiome at each site. Several body sites will be studied, including the gastrointestinal and female urogenital tracts, oral cavity, nasal and pharyngeal tract, and skin.

MovingPictures

Description: Time series of the human microbiome Number of samples: 1967 Data source: https://qiita.ucsd.edu/study/description/550 Study: https://www.ncbi.nlm.nih.gov/pubmed/21624126 Additional source: http://www.ebi.ac.uk/ena/data/view/PRJEB13679 Processing QIIME 1.9.1, SortMeRNA, Closed-reference OTU-picking at 97% identity Type: OTU-table, Sample metadata, Representative Tree Abstract: Understanding the normal temporal variation in the human microbiome is critical to developing treatments for putative microbiome-related afflictions such as obesity, Crohn's disease, inflammatory bowel disease, and malnutrition. Sequencing and computational technologies however have been a limiting factor in performing dense timeseries analysis of the human microbiome. Here we present the largest human microbiota timeseries analysis to date, covering two individuals at four body sites over 396 timepoints. Results: We find that despite stable differences between body sites and individuals, daily fluctuations in an individual's microbiota appear characteristic. Additionally, only a small fraction of the total within body site microbial communities appears to be present across all time points, suggesting that if a core temporal microbiome exists it is small. Many more taxa appear to be persistent but non-permanent community members. DNA sequencing and computational advances described here provide the ability to go beyond infrequent snapshots of our human-associated microbial ecology to high-resolution assessments of temporal variations over protracted periods, within and between body habitats and individuals. This capacity will allow us to define normal variation and pathologic states, and for assessing responses to therapeutic interventions.

RISK_CCFA

Description: The treatment-naive microbiome in new-onset Crohn's disease Number of samples: 1359 Data source: https://qiita.ucsd.edu/study/description/1939 Additional source: http://www.ebi.ac.uk/ena/data/view/PRJEB13679 Study: https://www.ncbi.nlm.nih.gov/pubmed/24629344 Processing QIIME 1.9.1, SortMeRNA, Closed-reference OTU-picking at 97% identity Type: OTU-table, Sample metadata, Representative Tree Abstract: Inflammatory bowel diseases (IBDs), including Crohn's disease (CD), are genetically linked to host pathways that implicate an underlying role for aberrant immune responses to intestinal microbiota. However, patterns of gut microbiome dysbiosis in IBD patients are inconsistent among published studies. Using samples from multiple gastrointestinal locations collected prior to treatment in new-onset cases, we studied the microbiome in the largest pediatric CD cohort to date. An axis defined by an increased abundance in bacteria which include Enterobacteriaceae, Pasteurellacaea, Veillonellaceae, and Fusobacteriaceae, and decreased abundance in Erysipelotrichales, Bacteroidales, and Clostridiales, correlates strongly with disease status. Microbiome comparison between CD patients with and without antibiotic exposure indicates that antibiotic use amplifies the microbial dysbiosis associated with CD. Comparing the microbial signatures between the ileum, the rectum, and fecal samples indicates that at this early stage of disease, assessing the rectal mucosal-associated microbiome offers unique potential for convenient and early diagnosis of CD.

TwinsUK

Description: Genetic Determinants of the Gut Microbiome in UK Twins Number of samples: 1024 Data source: https://qiita.ucsd.edu/study/description/2014 Study: https://www.ncbi.nlm.nih.gov/pubmed/25417156 Processing QIIME 1.9.1, SortMeRNA, Closed-reference OTU-picking at 97% identity Type: OTU-table, Sample metadata, Representative Tree Abstract: Host genetics and the gut microbiome can both influence host metabolic phenotypes. However, whether host genetic variation shapes the gut microbiome and interacts with it to affect host phenotype is unclear. Here, we addressed this question by comparing microbiotas across more than 1,000 fecal samples obtained from members of the TwinsUK population, including 416 twin-pairs. We identified a variety of microbial taxa whose abundances were influenced by host genetics. The most heritable taxon, the family Christensenellaceae, formed a co-occurrence network with other heritable Bacteria and with methanogenic Archaea, and was enriched in individuals with low BMI. Addition of Christensenella minuta to an obese donor fecal microbiome resulted in reduced weight gain and an altered fecal microbiota in recipient germfree mice. Our findings indicate that host genetics influence the composition of the human gut microbiome and can do so in ways that impact host metabolism.

qa10394

Description: Effects of preservation and storage conditions on the fecal microbiome Number of samples: 1522 Data source: https://qiita.ucsd.edu/study/description/10394 Additional source: http://www.ebi.ac.uk/ena/data/view/PRJEB13595 Study: http://msystems.asm.org/content/1/3/e00021-16 Processing QIIME 1.9.1, SortMeRNA, Closed-reference OTU-picking at 97% identity Type: OTU-table, Sample metadata, Representative Tree Abstract: Microbiome studies using fecal samples have allowed us to discover a lot about human health, animal evolution, and basic host-microbe interactions. In most studies, the workflow of conducting a microbiome study involves collecting and stabilizing a sample until it can be processed in a laboratory so that the community experiences little to no change until analysis. One widely used method is freezing at -20C or -80C. However, in scenarios were freezing is not a viable option, an alternative preservation method must used, but the effectiveness of commonly used preservatives is not well understood. To better understand the effects of different storage preservatives and storage conditions on the microbial community of fecal samples, we exposed human and canine fecal samples to a variety of commonly used preservation methods including freezing, using ethanol, RNALater, Omni-gene gut, and FTA cards, and a range of temperature conditions.

twbattaglia/MicrobeDS documentation built on April 10, 2024, 11:28 p.m.