Skip to content

GeneExpression

Jump to bottom Edit New page
Birgit Meldal edited this page May 13, 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 and sub typing of gene expression:

*Are there any known drugs or other factors that might modulate the expression of selected genes? How do drugs affect expression in different backgrounds?

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? We can also collaborate with #annotations on structures.

Initial work: https://amp.pharm.mssm.edu/covid19/ https://www.nature.com/articles/s41467-019-08831-9 https://science.sciencemag.org/content/367/6473/45.full https://www.biorxiv.org/content/10.1101/2020.03.22.002386v1

Comparing drug response RNAseq profiles, as well as making it possible to detect chronic diseases that may arise from CoVID-19. https://github.com/NCBI-Codeathons/ViraVate

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!)

  1. Virus Seer and ViraVate: https://github.com/NCBI-Codeathons/ViraVate ViraVate is a command-line tool to identify whether differences between a user-provided gene expression matrix of any case vs. control (e.g. diabetics vs. non-diabetics) are associated with differentially expressed genes in infected vs. uninfected groups across a database of human-viral infections.

  2. Normative mouse lung expression and eQTL data for a genetically diverse family (BXDs) generated using the Affymetrix M430 array at www.genenetwork.org: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=160&InfoPageName=HZI_0408_R . The data can be analyzed within GeneNetwork (link to www.genenetwork.org; select Species = Mouse; Group = BXD Family; Type = Lung mRNA; Dataset = HZI Lung M430v2 (Apr08) RMA or other.

  3. Influenza infected lung RNAseq expression data for mouse family (BXD) generate 3 days after infection by Klaus Schughart and colleague (very close to publication; Rob Williams will check about opening data for our team). Data will be on www.genenetwork.org at: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=807&InfoPageName=HZI_LungBXD_RNA-Seq_1116

  4. Normative human lung expression data (RNAseq) for 578 humans from GTEx v8 is available in www.genenetwork.org for analysis: http://gn2.genenetwork.org/show_trait?trait_id=ENSG00000130234&dataset=GTEXv8_Lung_tpm_0220 For example, ACE2 expression data here: https://genenetwork.org/show_trait?trait_id=ENSG00000130234&dataset=GTEXv8_Lung_tpm_0220 . The entire GTEx Lung RNAseq also available from GEO or from GTExPortal.org/home/datasets along with eQTL results.

  5. Normative human lung expression data (Affy HuRSTA) for 1230 humans (GN445, GEO GSE23546) available in GeneNetwork for analysis (www.genenetwork.org) here: http://gn1.genenetwork.org/webqtl/main.py?FormID=sharinginfo&GN_AccessionId=455. The data can be analyzed within GeneNetwork (link to www.genenetwork.org; select Species = Human; Group = Lung: Normal Gene Expression; Type = Lung mRNA; Dataset = Super Series GSE23546. For example, ACE2 expression data here: https://genenetwork.org/show_trait?trait_id=100134205_TGI_at&dataset=GSE23546HLT0613

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

Participants

(Feel free to add your name)

Add a custom footer
Add a custom sidebar
Clone this wiki locally