rnaSPAdes is giving error

[Gauravshree786]

I have a paired end transcriptome dataset for plant and i want assembly of it but i have tried many time rnaSPAses is not working fine . Can any one help me with that

[jennaj]

Welcome @Gauravshree786

Hopefully we can help!

I’m going to link some resources below but I’m also curious about your goal? Do you want to assemble a transcriptome? For just one sample or many? Do you want to compare your samples to a known transcriptome? For differential expression or something else? If you are following a protocol, you can share that back here too for context!

I’m asking since there are alternative ways to “assemble” this kind of data that do not involve a full assembly and we can help to point you to methods for these.

QA

For assembly, the first item to review is how the reads are preprocessed. Assembly is very sensitive to read quality but also the content. Untrimmed adaptor can lead to processing issues and contaminated results.

@jennaj

Multiple paired end dataset QA example

• Hands-on: Reference-based RNA-Seq data analysis / Reference-based RNA-Seq data analysis / Transcriptomics (see the initial steps)
• More examples → Galaxy Training!

A demonstration is in the Quality Control Q20-L20 public workflow.
How to find it →

@jennaj

• Search here GalaxyProject.org → GTN Pan-Galactic Workflow Search
• With the term Quality Control Q20-L20

Protocol

The second item to review is the sequencing depth. Higher coverage (deeper depth) doesn’t always add in more coverage information once resolved into an assembly. This means (most?) samples can be reduced to reduce the chance of overloading the algorithm.

If your species is known, you could try mapping your reads then reviewing the depth that way. You might notice where the pile ups are happening, and get an idea about how you could reduce depth while retaining overall coverage.

The help here applies to most assembly projects. Reduce the coverage then try tuning the tool parameters.

@jennaj

Troubleshooting options include:

1. Use Sub-sample sequences files e.g. to reduce coverage. This is a pretty common solution. More coverage is not necessarily “better” during initial assembly steps, and for scientific reasons, too.

2. Tune parameters. Review publications that involve your target species. Defaults are unlikely to be the best fit for real data. To match things up: know that options are labeled on the tool form with the command line flags (try a browser search) and the Galaxy command line is captured on the same place you found the other logs (“i” icon).

Transcriptomics

Finally, if you are curious about how psudo-assembly works, we have some polished tutorials and example workflows in our Galaxy Training Network (GTN) Tutorials!

Transcriptomics / Tutorial List

I’ll also add in this in case you are new to Galaxy! The QA module may be interesting!

Learning Pathway: Introduction to Galaxy and Sequence analysis

Then we have production quality workflows in the IWC Workflow Library. These can be imported and used intact or you can modify them to suit your goals.

Intergalactic Workflow Commission

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Let’s start there! And I was just guessing about your error being a memory failure. If it was something else, or if you are not sure, you are welcome to share back a link to your history and we can help to troubleshoot what is specifically going wrong.

• How to get faster help with your question → FAQ: Sharing your History

[Gauravshree786]

Actually, I have multiple plant samples and need to perform de novo assembly and further DGE and annotation. Can you please help me with that?

[jennaj]

Hi @Gauravshree786

Thanks for explaining! Then our tutorials and workflows will likely work for you! The first two hands-on items have full materials to walk you through the steps.

• Transcriptomics / Tutorial List
  • Hands-on: Reference-based RNA-Seq data analysis / Reference-based RNA-Seq data analysis / Transcriptomics
  • Hands-on: De novo transcriptome reconstruction with RNA-Seq / De novo transcriptome reconstruction with RNA-Seq / Transcriptomics

Once you are comfortable with the basic path, you can consider these IWC workflows. These can be adjusted to accept a custom reference genome as needed. All annotation will need to be sourced from public repositories.

• https://iwc.galaxyproject.org/
  • RNA-Seq Analysis: Paired-End Read Processing and Quantification | IWC
  • RNA-Seq Differential Expression Analysis with Visualization | IWC
  • Gene Ontology and KEGG Pathway Enrichment Analysis | IWC

If you don’t have your reference data yet, this is how to get genome data from NCBI into Galaxy But you can use any source that you want and process will be the same! Please ask if you have questions.

• FAQ: NCBI reference data

If the analysis is new to you, starting with the tutorial version will be easier since these cover what is happening at each step and why, and each has a video with more.

If you species doesn’t have a reference genome, you can also explore tools like Salmon for transcriptomic analysis. We have some help in topics tagged with salmon but there isn’t a tutorial. The assembly and annotation would be predictive and non-trivial. Locating a publication would be the first step, then you could adapt that to a Galaxy protocol.

Hope this helps!