Galaxy tutorial "Differential abundance testing of small RNAs" corrupted

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#1

Hi

I am working with the MicroRNA differential expression and tested the “Differential abundance testing of small RNAs” tutorial but it was corrupted in many ways.

I even tried with the tutorials own data. but still no result.

you can see the problem in steps such as : Converting bam to fastq, manipulate fastqc and the whole Hands-on: Hierarchical alignment to rRNA and miRNA reference sequences (3) step.
So anyone figure out what is the problem /.?

#2

Hi @amir,

thanks for checking. Which Galaxy instance are you using (usegalaxy.eu?) and can you please send an error report for those broken tools? I will have a look then.

Thanks,
Bjoern

#3

Hi,

I remember last time I did it, there was a missing step on a resulting fastq. The fastq should be groom-ed since they did not show up in the tool next step? (I think it was just after Converting bam to fastq)

#4

i saw this too

#5

There is no error to send , problem is, when you convert fastq to bam :
1: Hisat doesnt recognize the data, and you should change the data type to fastqsanger.gz manually again. because Convert bam to Fastq tool, make Fastq data and not fastqsanger.
2: After Converting, and using hisat2, the result does not has any volume(byte) to work with
I suggest to do it from A to Z. then you will see the problems.
Thank you for your attention

#6

Hello,
I am running into the same issue, after using hisat2 for the second time the workflow says to convert the file from a Bam( the output format for hisat2) and convert to a fastq. After following the same steps to convert that I did earlier in the workflow the files are empty. Is there any issue with the data provided or a step missing?

#7

Also, during the Read quality checking steps, when verifying the data after running fastQC, The workflow says the Illumina Universal Adapter should present, but it is not. This makes me think something is off about the data. Has anyone else experienced the same thing?

#8

Yes. I saw that too. Just use rRNA reference and map it to your data with Bowtie(use that option to leave you the not mapped ones) and after that map the to your reference genome. it clears your data.