A compilation of fecal microbiome shotgun metagenomics from hematopoietic cell transplantation patients.
Jinyuan YanChen LiaoBradford P TaylorEmily FontanaLuigi A AmorettiRoberta J WrightEric R LittmannAnqi DaiNicholas R WatersJonathan U PeledYing TaurMiguel-Ángel PeralesBenjamin A SiranosianAmi S BhattMarcel R M van den BrinkEric G PamerJonas SchluterJoao B XavierPublished in: Scientific data (2022)
Hospitalized patients receiving hematopoietic cell transplants provide a unique opportunity to study the human gut microbiome. We previously compiled a large-scale longitudinal dataset of fecal microbiota and associated metadata, but we had limited that analysis to taxonomic composition of bacteria from 16S rRNA gene sequencing. Here we augment those data with shotgun metagenomics. The compilation amounts to a nested subset of 395 samples compiled from different studies at Memorial Sloan Kettering. Shotgun metagenomics describes the microbiome at the functional level, particularly in antimicrobial resistances and virulence factors. We provide accession numbers that link each sample to the paired-end sequencing files deposited in a public repository, which can be directly accessed by the online services of PATRIC to be analyzed without the users having to download or transfer the files. Then, we show how shotgun sequencing enables the assembly of genomes from metagenomic data. The new data, combined with the metadata published previously, enables new functional studies of the microbiomes of patients with cancer receiving bone marrow transplantation.
Keyphrases
- single cell
- bone marrow
- electronic health record
- healthcare
- end stage renal disease
- staphylococcus aureus
- big data
- case control
- newly diagnosed
- endothelial cells
- escherichia coli
- mental health
- chronic kidney disease
- cell therapy
- mesenchymal stem cells
- primary care
- randomized controlled trial
- pseudomonas aeruginosa
- prognostic factors
- stem cells
- peritoneal dialysis
- machine learning
- systematic review
- dna methylation
- deep learning
- transcription factor
- antibiotic resistance genes
- meta analyses
- adverse drug