Question: Normalization on Single End and Paired End reads
gravatar for Ismaelrymy
7 months ago by
Ismaelrymy0 wrote:

Hi there.

I want to assemble a transcriptome with Trinity using reads that are both paired- and single-end. Due to memory issues I have to normalize the reads, but the normalization process in Trinity is not able to handle this kind of data. Is there any application in Galaxy that I can use to normalize the reads prior to the assembly process? Data is in fastqsanger format

Thank you very much for your time.

error from the randomization step: Error, pairs.K25.stats is empty. Be sure to check your fastq reads and ensure that the read names are identical except for the /1 or /2 designation. at /opt/packages/trinity/2.2.0/util/ line 882.

assembly galaxy • 414 views
ADD COMMENTlink modified 7 months ago by Jennifer Hillman Jackson25k • written 7 months ago by Ismaelrymy0
gravatar for Jennifer Hillman Jackson
7 months ago by
United States
Jennifer Hillman Jackson25k wrote:


This utility has not been wrapped for Galaxy in the ToolShed ( However, it might be available at the Trinity Galaxy server They may also be able to provide you with more resources versus using Galaxy Main, as that is a domain specific server. Apply for an account to see if they can help.

That said, I wouldn't expect the tool to accept both paired and single end sequences as input. Trinity hosted at Galaxy Main does not, even though that is a command-line usage option. Inputs are expected to all be the same length and contain only matched paired-end datasets.

If you just want a random sample, convert with Fastq-to-Tabular (for the forward or reverse reads), use Select random lines from a file, then convert the format back with Tabular-to-Fastq. Then sync up the other side of the pair with the tools FASTQ interlacer and FASTQ de-interlacer.

Your final option is to set up your own Galaxy server and allocate sufficient memory resources to process the entire dataset.

Thanks! Jen, Galaxy team

ADD COMMENTlink written 7 months ago by Jennifer Hillman Jackson25k
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