4.5 years ago by
You are working on the Main (usegalaxy.org) public server?
Joining and splitting is not always necessary or even recommended. Often it is better to do the data prep (grooming if needed, trimming, etc), map, and then filter after for properly paired reads if you are proceeding with a variant workflow. If using an expression workflow, no filtering is necessary (the Tuxedo pipeline will filter for you) - the less done to the data outside of basic QA/QC before mapping with Tophat/2, or running Cufflinks and downstream tools, the better.
The blue "pause" state indicates that something is incorrect, likely with the metadata, with the input files to the FastQC tool. If you open the datasets, does a message in the dataset appear indicating this? Clicking to auto-detect, as prompted, can often repair the problem. This assumes that the datasets are not empty - which can sometimes occur after certain jobs running a workflow and the data is not checked to ensure that it passed that step (either the tool setting are incomplete/incorrect or the data simply did not pass the criteria set).
Also, it seems unlikely that the data wouldn't be set to a datatype that is of a "fastq" or "fasta" variety after the actions you describe, but that could also present with this problem. FastQC does not require ".fastqsanger" format, but you will want the quality scores scaled to that format (which means that you might as well assign that datatype) before running the tool on the entire dataset, unless the intention is to determine the quality score type (to determine if grooming is needed).
This wiki section describes the QC process for quality score determination/assignment:
Give the inputs a look and if you are unable to solve the problem, please share the history. Create a share link and paste it into an email sent to firstname.lastname@example.org. Make sure to include either your galaxy account's email address or undelete/unhide all datasets in the analysis path. How to share is described here: https://wiki.galaxyproject.org/Learn/Share
Best, Jen, Galaxy team