I'm kind of confused with data normalization in CLC, I Compared RNA-seq results after doing normalization in data or on an experiment using legacy version and also using original data and normalized experiment in new version. The new version won't let me to do normalization on data and I can only do normalization on an experiment. Results are completely different, what do you recommend?
You're paying a non-trivial amount of money for CLC and that comes with support. You might just call them and ask about these sorts of things.
I agree with Jin Gu, we use the raw counts of bacterial transcriptomes (at least 3 repetitions by condition) from CLC and do normalisation and Deseq in R.
Thanks every one, I contacted CLC and as you all suggested, as they have edgeR in version 7.0.4, I can just use raw counts without normalization.
For RNAseq normalization the most popular methods are trimmed mean of M values (TMM) implemented in edgeR package and DEseq package normalization (both implemented in R).
Also have look at these two papers:
Dillies M-AM -a., Rau A, Aubert J et al. (2012) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in bioinformatics.
Li S, Łabaj PP, Zumbo P et al. (2014) Detecting and correcting systematic variation in large-scale RNA sequencing data. Nature Biotechnology, 32.
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