edgeR and DESeq are the popular R packages for differential expression analyses. For time course data, you may also need packages to do clustering or building co-expression modules. You'll have a lot of options to do these in R. I'd suggest you to read some papers about time-course experiment with both microarray and mRNA-Seq, you'll get a better idea on how you analyze this type of data.
This is tricky. Specifically, borrowing lines from Simon Anders, it depends on what question you are asking?
1) Are transcript concentrations different in two samples?
2) Can the difference between transcripts be attributed to experimental conditions, i.e., how sure can we be that the difference is due to experimental treatment and not due to fluctuations that went out of experimenter's control (i.e., not due to biological variation).
If you data comes simply from two different time points, I guess Cuffdiff (which answers question 1) is suitable for your work. If you are looking at samples before and after treatment, then 2) (Deseq and edgeR) answers this question.
Have a look at this post for more details.
Isn't the Timecourse package designed for microarray data? Then, if this is the case, it may not be suitable to use with RNA-seq outputs
In general, I think most microarray analysis tools are also OK for RNA-Seq if you use things like log2(RPKM + 0.1) as your expression measurement. For example, see some of the benchmarks in the following papers:
So, I think the timecourse package is a good recommendation
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Charles Warden
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Has anyone used DyNB for time-course analysis of RNA-seq data? What is your opinion of the tool?
Thanks