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GeneExpression

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robwwilliams edited this page Apr 5, 2020 · 68 revisions

Our own github repository is here: https://github.com/avantikalal/covid-gene-expression

Main Objective

We want to perform transcriptome analyses (RNAseq-based, array, rtPCR) on published datasets in order to better understand the interaction between human host and virus.

Skills needed

Bioinformatics in general, NGS analysis (bulk, viral, sRNA, single cell RNA), biostatistics, biology, virology, everyone is welcome!

Deliverables

Biological: Perform global transcriptome analyses with SARS-CoV-2 infected datasets (or other relevant pathogens) to search for new candidate genes for testing experimentally

Methodological: Create a packaged reproducible pipeline in Dockers or GUIX to help scientists to easily treat their transcriptome data and for us if any new dataset comes out

Workflow

1. Viral infection global analyses:

  • Transcriptome analyses to select differentially expressed genes
  • Isoform differential expression
  • Map RNAseq to virus
  • Functional and enrichment analyses
  • Search for global modulators of disease virulence and host susceptibility

2. Human-virus interaction analyses:

  • Which human mRNAs and proteins interact with or are regulating the virus and vice-versa?
  • Are there human RNA-binding proteins potentially regulating the viral genome?
  • Include these in subsequent analyses

3. Increased risk factors analyses:

  • Select datasets from co-morbidities related to severeness of Covid-19 For instance, diseases such as diabetes myelitus and hypertension; other factors like smoking, which might make the person more vulnerable to the virus;
  • Select other human tissues to check the expression of proteins interacting with the virus (selected in previous step)
  • Search for SNPs, splicing variants, regulatory regions for all genes selected in previous steps
  • Analysis of HLA types that predispose individuals and populations to COVID-19 infection and mortality (starting at http://hlacovid19.org/
  • Analysis of host expression differences in ACE2, TMPRSS2, and other key genes involved in SARS-CoV-2 infection. See ACE2 expression in normal lung from GTExv8 here: https://genenetwork.org/show_trait?trait_id=ENSG00000130234&dataset=GTEXv8_Lung_tpm_0220
  • Analysis of BXD mouse models in viral pneumonia susceptibility after viral infection. See Ace2 expression for 43 genomes here: https://genenetwork.org/show_trait?trait_id=ENSMUSG00000015405&dataset=HZI_LungBXD_RNA-Seq_1116

4. Towards the control of gene expression:

  • Are there any known drugs or other factors that might modulate the expression of these selected genes? In this section we can also communicate with #htvs (Virtual Screening) as they “will use target binding of the crystal structure of the viral protease with potential inhibitors”, so maybe there is room for collaborating their pipeline on human proteins as well?

Initial work: https://amp.pharm.mssm.edu/covid19/

5. Reporting findings to electronic medical records:

  • How do we get RNAseq findings into the hands of clinicians in both the immediate and longer (chronic disease) term?

Initial work: https://github.com/NCBI-Codeathons/omopomics -- working with OHDSI

Communication channel

Resources that might be helpful:

(Please add more here!)

https://github.com/NCBI-Codeathons/ViraVate

Perhaps future collab with these folks: https://www.hackseq.com/rna

Participants

(Feel free to add your name)

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