I am new to RNA-Seq technique and NGS technology, but learning quickly. We currently are doing switchgrass transcriptome assembly and RNA-Seq based on this assembly. We have 5 runs from 454 FLX, each run using half-plate (~400,000 reads per run) and each run is a different condition (drought, bug infested, control, different strain) We are about to acquire Illumina data, 7 lanes, each lane producing about 29million reads (avg read length 55bp). My interest is in making the best quality transcriptome assembly given all this data. I know MIRA3 can do hybrid assemblies, but using all of the reads from both technologies would require over 100GB of RAM. The best computer we have is 25GB RAM. We were able to assemble all 5 454 runs together, but treat it lightly since there are different strains involved (haven't done any SNP discovery yet). Does anyone know of a pipeline to get the best qualitative assembly with all this data? Maybe assemble 454 and Illumina separately then combine them in CAP3? Any ideas??
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you might wish to have a look at this blog post
his compute cluster is basically
"two Dell PowerEdge 1900 servers joined together with ROCKS clustering software v5.0. Each server had: two Intel Quad Core E5345 (2.33 Ghz, 1333 Mhz FSB, 2x4MB L2 Cache) CPU’s and 16 GB of 667 Mhz DDR2 RAM. The cluster had a combined total of 580 GB disk space." -
I think it would be more memory efficient to assembly individual Illumina runs, then treat them as 454 reads (combining them with my original 454 reads) and assemble in MIRA3. I also know that I can map Illumina reads to a backbone (say my 454 assembly) and it has the option of creating new contigs if reads don't match up to the backbone. But can it extend/improve the 454 contigs/singletons I used as the backbone??
Or I assemble 454 separately and Illumina separately, then assemble all 454 contigs, Illumina contigs, singletons & unassembled reads through CAPS3?
Has anyone tried or prefer one of these methods?Comment
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Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.
We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing...
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