Latest
Open
RNA-Seq
ChIP-Seq
SNP
Assembly
Forum
Planet
All »
Home
Log In
Question: Normalization on Single End and Paired End reads
0
gravatar for Ismaelrymy
7 months ago by
Ismaelrymy • 0
Ismaelrymy • 0 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/insilico_read_normalization.pl line 882.
assembly galaxy • 414 views
ADD COMMENT • link •
modified 7 months ago by Jennifer Hillman Jackson ♦ 25k • written 7 months ago by Ismaelrymy • 0
0
gravatar for Jennifer Hillman Jackson
7 months ago by
Jennifer Hillman Jackson ♦ 25k
United States
Jennifer Hillman Jackson ♦ 25k wrote:

Hello,

This utility has not been wrapped for Galaxy in the ToolShed (https://usegalaxy.org). However, it might be available at the Trinity Galaxy server http://ncgas.org/galaxy_trinity_faq.php. They may also be able to provide you with more resources versus using Galaxy Main https://usegalaxy.org, 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. https://galaxyproject.github.io/

Thanks! Jen, Galaxy team
ADD COMMENT • link written 7 months ago by Jennifer Hillman Jackson ♦ 25k
Please log in to add an answer.

Similar posts • Search »

Content
Help
Access
Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 183 users visited in the last hour