Great, glad that helped @Monish_V
Right now, I don’t think this accession has especially large inputs. However, I do think you’ll likely want to trim these reads. Review the FastQC plots – even after the default trimming was applied, the reads still have some artifact! If you are not sure how to interpret FastQC plots, see the tool form for a link out to the original documentation where these are explained. Forums like Biostars.org also have a lot of discussion (the tools work the same in Galaxy!).
Also – the NCBI record may have more details about the library chemistry that can help with making QA choices. Then, if that isn’t enough, you can specify to trim by regions if you can’t get a match to a specific adaptor choice – the first 13 bases or so seem to be the target.
So, while mapping tools can sometimes compensate for lower quality read content (in particular, ignoring the ends of reads and mapping the rest), if you have a sample that presents with problems, running some more QA is where to start. After that, you can investigate more – try mapping with BLASTN, reviewing other annotations on those genome regions, that sort of custom review. UCSC is a good place to visualize this kind of work as discussed in topics like this one.
Hope this works out! And if you get an error, and QA isn’t enough, we can try to dig in and see if there is any information in the administrative job logs. Sharing the history with the error result is the best way to start this kind of review.
Thanks! 