Question: De Novo Metatranscriptomic Assembly Failing - Trinity, Velvet/Oases
gravatar for Newvin
7.4 years ago by
Newvin340 wrote:

I'm attempting de-novo assembly of metatranscriptomic data, which is admittedly a very resource-intensive problem. I have ~206 million paired-end Illumina reads each 100bp long generated via RNA-seq on environmental samples. I am able to create assemblies using Trinity and Velvet/Oases using a small portion of the reads; however, when I attempt to assemble the metatranscriptome using the full set of reads, both programs will run for a day or so then fail while attempting to allocate memory. The server I am running on has 32 procs and 256GB of RAM. I should also mention that for Velvet/Oases, I am using K=61. I believe Trinity's K value is locked at 25.

I am rather new at this. Does anyone have of sense of how unreasonable my parameters are? Is the idea of assembling 200 million reads ludicrous? I may be able to perform a dereplication step that would reduce the number of reads to ~50 million. Does anyone have an assembly experience indicating that I might have more success with only 50 million reads?


ADD COMMENTlink modified 5.4 years ago by Dgg3260 • written 7.4 years ago by Newvin340
gravatar for Jeremy Leipzig
7.4 years ago by
Philadelphia, PA
Jeremy Leipzig18k wrote:

You might need to speak to Titus Brown, who has used Bloom filters to put metagenomic (perhaps not metatranscriptomic) reads into manageable piles.

ADD COMMENTlink modified 7.4 years ago • written 7.4 years ago by Jeremy Leipzig18k

I'd be interested in your results with using digital normalization, I think it might work better for metatranscriptomic data than partitioning will.

ADD REPLYlink written 7.0 years ago by Titus Brown80
gravatar for pmenzel
7.4 years ago by
pmenzel310 wrote:

Assembly of that many reads is not unreasonable. Try SOAPdenovo for the assembly. If you filter out low abundance k-mers (e.g. with -d option of SOAPdenovo), the memory consumption would decrease.

ADD COMMENTlink written 7.4 years ago by pmenzel310
gravatar for Dgg32
5.4 years ago by
Dgg3260 wrote:

I would cluster the reads with cdhit with a high identity cutoff and put the amount of reads into the fasta headers so I can keep track of them. This step alone cuts my sequences into a half without losing a single reads (but it surely mask some heterogenity of your sequences). Then Velvet with default settings will finish the job.

ADD COMMENTlink written 5.4 years ago by Dgg3260
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