Transcript mapping without a reference genome

[aalyaphillips]

Moreover, the Third Generation Company from Japan started the production of game consoles recently named Famicom and made NES (Nintendo Entertainment System) game consoles. Beginning then on, support was begun to get the re-perceived and welcomed energetically until at last an early game was grown Super Mario Brothers. 7xRight now, amazing game comfort seemed to contend Sega and Nintendo. Jupiter Ed App After afierce rivalry among Sega and Nintendo, another contender, Sony, accompanied the Playstation. Sony supplanted cartridge with a CD and delivered most extreme outcomes and achievement. After Sony Play station got mainstream, the prevalence of Sega and Nintendo were slowly down. Beginning from age 6, sony playstation was formed into Sony Playstation 2, which utilizes DVD rather than CD.

[gringer]

Trinity would be nice, but it's not working so well on my little computer with 24GB memory. This seems a little strange to me because there is at least one genome assembler (Ray) that has a significantly lower memory footprint. I'll keep trying though (probably involving some code modification).

I have easy access to small clusters with distributed memory (I think something like 120GB over 5 nodes), but the Inchworm process is not distributed, so that's probably not going to help.

[HESmith]

I would recommend Trinity, which is designed specifically for your application. The reference is http://www.nature.com/nbt/journal/v2.../nbt.1883.html.

[gringer]

How should I map transcript data in the absence of a good reference genome sequence?

I'm playing around with NGS data for an organism that has a fairly bad reference genome sequence (Schmidtea mediterranea, FWIW). In order to work out how bad, I split the reference sequence up to 100bp sequences, filtered out low-complexity sequences, then remapped back to the original sequence using bowtie2, and found quite a few places that mapped to >20 different contigs (~2000 by my rough guess using uniq/sort). This means that I'm not particularly comfortable using this genome for mapping our RNAseq data.

We now have heaps of RNAseq data and would like to look at differential expression and splice variants. We have some reference transcriptome sequences to map to, but due to some variation in reported sequences, can't be sure if two contigs from the transcriptome are different gene copies, or different isoforms of the same gene. So I have a few questions about the analysis:

Would it be better to create a new reference transcriptome from our data (about 450M reads, 35/50 paired-end on a SOLiD4, expected transcriptome size is about 20-30Mb), or use the previously published transcriptomes?

I like what cufflinks can do in identifying isoforms, but aren't sure how it responds to transcriptome mappings. Can it combine multiple contigs and notice that they are actually the same gene?

Is there any point in using tophat on a transcriptome, given that there shouldn't be any large breaks in the reference transcript sequences?

Other comments / questions would be appreciated, because this will be the first "real" analysis that I have done.