Trying to create a workflow to analyze my nanopore sequencing data

[jennaj]

Hi @nanohacker1

For this part

@nanohacker1

Also another question: porechop is taking reeeaaaally long - >24 h for a single file - is this normal? how can I make it chop faster?

it could just mean the job was queued for a while, and is now executing. Or maybe it is still queued? This is how to check:

@jennaj

When working at UseGalaxy.org, UseGalaxy.eu, or UseGalaxy.org.au, your analysis jobs execute at large scale academic clusters. Hundreds of thousands of jobs process each month!

• Use a Workflow for batch work.
• Allow queued (grey color) and executing (yellow-orange) datasets to finish processing.
• Deleting and rerunning puts your job back at the end of the queue again!
• Job priority is the exactly the same for everyone.
• Be patient. Galaxy is a free service, when a lot of people are using it, you may have to wait longer than usual (especially for ‘big’ jobs, e.g. alignments).

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Then for your questions: you could try comparing how the genomic versus transcriptomic DE results turn out. But in general, if you are working with a known reference genome and known annotated genes, using that is usually a good idea.

• Featurecounts → Map against a reference genome first, then generating the counts with this tool.
• Salmon → Maps against a transcriptome at runtime.
• DESeq2 can be used with either (counts, TMP values), but EdgeR and Limma will expect just counts.

We have a couple of guides that can help to get the reference data in order. I’m not sure why you are mixing up data sources. You can also use the built-in hg38 reference genome instead of loading up that fasta separately.

@jennaj

• Start here Correct reference transcriptome for Salmon quant on existing RNASTAR alignments - #2 by jennaj
• Then this post has an example where I loaded all the file choices from UCSC, reformatted, and tagged the “matched files”. Correct reference transcriptome for Salmon quant on existing RNASTAR alignments - #4 by jennaj
• Suggested data formatting for these tools. FAQ: Extended Help for Differential Expression Analysis Tools
• More is under reference-transcriptome and reference-genome and reference-annotation
• Human data has extra tips in this guide Reference genomes at public Galaxy servers: GRCh38/hg38 example

If you want to review some existing workflows for ideas, please start here.

• Single Cell / Tutorial List
• Transcriptomics / Tutorial List

Hope this helps! You should be able to get a good solid core of reference data organized for human. From there you can then explore and compare all of these tools in your workflow and make choices. The same reference data can be used with all of them.