DJANGO tpm: Bacterial tpm meta-barcoding diversity profilings of urban sediments
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB40142 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR4557620_1.fastq.gz | Fastqsanger.gz | |||
| ERR4557620_2.fastq.gz | Fastqsanger.gz | |||
| ERR4557630_1.fastq.gz | Fastqsanger.gz | |||
| ERR4557630_2.fastq.gz | Fastqsanger.gz | |||
| ERR4557640_1.fastq.gz | Fastqsanger.gz |
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Project description:BackgroundThe global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swept through every part of the world. Because of its impact, international efforts have been underway to identify the variants of SARS-CoV-2 by genome sequencing and to understand the gene expression changes in COVID-19 patients compared to healthy donors using RNA sequencing (RNA-seq) assay. Within the last two and half years since the emergence of SARS-CoV-2, a large number of OMICS data of COVID-19 patients have accumulated. Yet, we are still far from understanding the disease mechanism. Further, many people suffer from long-term effects of COVID-19; calling for a more systematic way to data mine the generated OMICS data, especially RNA-seq data.MethodsBy searching gene expression omnibus (GEO) using the key terms, COVID-19 and RNA-seq, 108 GEO entries were identified. Each of these studies was manually examined to categorize the studies into bulk or single-cell RNA-seq (scRNA-seq) followed by an inspection of their original articles.ResultsThe currently available RNA-seq data were generated from various types of patients' samples, and COVID-19 related sample materials have been sequenced at the level of RNA, including whole blood, different components of blood [e.g., plasma, peripheral blood mononuclear cells (PBMCs), leukocytes, lymphocytes, monocytes, T cells], nasal swabs, and autopsy samples (e.g., lung, heart, liver, kidney). Of these, RNA-seq studies using whole blood, PBMCs, nasal swabs and autopsy/biopsy samples were reviewed to highlight the major findings from RNA-seq data analysis.ConclusionsBased on the bulk and scRNA-seq data analysis, severe COVID-19 patients display shifts in cell populations, especially those of leukocytes and monocytes, possibly leading to cytokine storms and immune silence. These RNA-seq data form the foundation for further gene expression analysis using samples from individuals suffering from long COVID.
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