ambarishK / covid-gene-expression

Hackathon team: gene expression analysis for Covid-19

Covid-19 Gene Expression Work Group (Private project)

Hackathon team: gene expression analysis for Covid-19 Virtual Biohackathon (vBH)

Link to vBH Github

https://github.com/virtual-biohackathons/covid-19-bh20

Official Gene Expression Work Group Page (Public page)

https://github.com/virtual-biohackathons/covid-19-bh20/wiki/GeneExpression

Main Objective

We want to perform RNAseq-based analyses on published datasets in order to better understand the interaction between human host and virus.

Fig.1 - We want to focus on already known genes (such as ACE2 and TMPRSS2) but also on new candidate ones that may play a punctual or a general role in the interaction between host and virus. To this end, we will perform extensive RNAseq analyses as described in the workflow section below

Deliverables

Biological: Perform a global RNA-Seq analysis with SARS-CoV-2 infected datasets to search for new candidate genes for testing experimentally

Methodological: Create a packaged reproducible pipeline in Docker to help scientists to easily treat their RNA-Seq data and for us if any new dataset comes out

We have four main projects going on. To summarize them :

1. SARS-CoV-2 infection global analyses: will include global gene expression, functional and regulation of gene expression analyses.
2. Human-virus interaction analyses : will try to search RNA-binding proteins that might be key in the interaction between human and SARS-CoV-2.
3. Increased risk factors analyses: will look for gene expression data in other datasets to compare especially with selected genes from previous analyses.
4. Subtyping of expression response to drugs after COVID infection: will focus on trying to search for potential drugs that could impact in important genes for the interaction of human and virus.
5. Reporting findings to electronic medical records : will try to make these findings arrive at the hands of clinicians.

Progress tracking

We are tracking progress on project-specific boards here: https://github.com/avantikalal/covid-gene-expression/projects

Software Used during this project

Command line tools

• FastQC (https://github.com/s-andrews/FastQC)
• Fastq-screen (https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/)
• trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic)
• STAR (https://github.com/alexdobin/STAR)
• Bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)
• HISAT2 (https://daehwankimlab.github.io/hisat2/)
• TEtools (https://github.com/l-modolo/TEtools)
• TEtranscripts (http://hammelllab.labsites.cshl.edu/software)
• LIONS (www.github.com/ababaian/LIONS)
• samtools / picard (http://samtools.sourceforge.net/; https://broadinstitute.github.io/picard/)
• featureCounts (http://subread.sourceforge.net)
• MultiQC (https://github.com/ewels/MultiQC)
• KissSplice (http://kissplice.prabi.fr/)

R packages

Direct install

ggplot2

install.packages("ggplot2")

BiocManager install

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

DESEq2

BiocManager::install("DESeq2")

EdgeR

BiocManager::install("EdgeR")

Limma-Voom

BiocManager::install("limma")

Devtools install

install.packages("devtools")

SARTools

library(devtools)
install_github("PF2-pasteur-fr/SARTools", build_opts="--no-resave-data")

Code repositories

Data-sources

Virus infection studies

• SARS-MERS: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56192
• COVID: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507
• Murine coronavirus (M-CoV): https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4111/

Increased risk factors studies

References