Hi again,
I know most people, expert bioinformaticians, use DESeq and EdgeR packages in RBioconductor to analyze differential expression from RNA-Seq data…
Unfortunatelly, I'm experimentalist and my knowledge in bioinformatics are rather low, that way we are using CLC Genome Workbech to analyze our data. I'm using default parameters, but not really confident with the final result. So it would be really great if someone having more experience in this software could give me some tips to perform an accurate analysis.
What is the protocol/pipeline you use? what are the specified parameters? specific settings?
I'm working with bacteria, in a time-course experiments. I have 4 time-points in 3 different conditions to compare, and RNA-Seq was performed to 3 biological replicates. It means that the total number of 4*3*3 samples 36 samples for one experiment.
Thank you in advance!
Hi Cristina, I previously used CLC for RNA-Seq analysis and fiddled with lots of parameters, my final output delivered a low number of differentially expressed genes (DEG) that were uninformative. I ended up getting clearer results that I trusted and understood better after abandoning CLC and using DESeq and EdgeR (with DESeq being my preferred method). Therefore, I would advise to use CLC as more of a preliminary analysis tool. Although, command line analysis is quite difficult at times to learn and incredibly frustrating, it's not impossible. Many others have stood where you're standing, resulting in a lot of blog posts etc. available online. I don't know where you are at in your career, but learning these tools has made me feel ahead of the game and more trusting of my results. I feel publishing with these types of R packages are more credible in the literature as well. I would encourage you to seek further support in your research community and start fiddling around with RStudio and these various packages. Message me if you'd like to see some codes I've used or have any other questions. Hope this helps! - Camerron
Hi Cristina, I previously used CLC for RNA-Seq analysis and fiddled with lots of parameters, my final output delivered a low number of differentially expressed genes (DEG) that were uninformative. I ended up getting clearer results that I trusted and understood better after abandoning CLC and using DESeq and EdgeR (with DESeq being my preferred method). Therefore, I would advise to use CLC as more of a preliminary analysis tool. Although, command line analysis is quite difficult at times to learn and incredibly frustrating, it's not impossible. Many others have stood where you're standing, resulting in a lot of blog posts etc. available online. I don't know where you are at in your career, but learning these tools has made me feel ahead of the game and more trusting of my results. I feel publishing with these types of R packages are more credible in the literature as well. I would encourage you to seek further support in your research community and start fiddling around with RStudio and these various packages. Message me if you'd like to see some codes I've used or have any other questions. Hope this helps! - Camerron
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