I would recommend working directly with R or SAS to learn the details of any "Pipeline" or tool. These are primarily a sequence of applications of standard statistical methods (generalized linear models) applied to very many (weighted) variables. I'm not saying the pipeline is not useful, but my experience has been that anyone with modest understanding of standard statistical methods can improve their understanding of the analysis by working through the analysis themselves.
Hello Toni,
My group has recently published a pipeline to computationally identify long non-coding RNAs in assembled transcriptomes.
http://www.ncbi.nlm.nih.gov/pubmed/25701574
Available at: https://github.com/frankMusacchia/Annocript
Apart from identification of lncRNAs the pipeline as performs an in depth annotation of coding transcripts (proteins, domains, GO terms, Enzyme IDs).
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