Tell me more ×
Biology Stack Exchange is a question and answer site for biology researchers, academics, and students. It's 100% free, no registration required.
up vote 11 down vote favorite
2

I just started working on predicting steady-state protein levels from different codon usage bias measures. I have whole-genome sequences (it's NGS data) from different strains of S. cerevisiae and S. paradoxus and I began to wonder if it's possible that different strains of the same species could have different copy numbers of tRNA genes? If so, would this be detectable in the sequencing data (presumably the reads were mapped onto a reference genome so maybe this copy number variation would be lost)?

share|improve this question
What type of sequencing data do you have? Are we talking about RNA-seq or DNA? – bobthejoe Feb 22 '12 at 18:34
I would reask this question on BioStar. They may have better ideas than we mere experimentalists. – bobthejoe Feb 24 '12 at 10:33

2 Answers

up vote 3 down vote
+50

I'd take that computational angle and run the sequence data through tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964). Ideally, you'd install this locally. This tool is what the UCSC folks use and it has been the best known, most widely used tRNA predictor for years. It's what we all used on Arabidopsis thaliana genome annotation back in the late 1990's.

There is also a genomic tRNA database that may have many of the predictions you seek, at least for some of your species/strains.

share|improve this answer
I don't think this will work since it's NGS data assembled against a reference genome. In fact I ran tRNAscan-SE and all strains have exactly the same tRNA GCN as the reference strains but this is a consequence of the assembly process. – Greg Slodkowicz Feb 24 '12 at 9:17
It sounds like you should try a De Novo assembly. That might be tricky since you don't have any paired-end data but that might be able to discover any novel contigs. – bobthejoe Feb 24 '12 at 10:32
2  
I like @bobthejoe 's suggestion. I never intended that tRNAscan-SE would be run against an assembled genome. Depending on the size of your reads vs. the size of a typical tRNA gene, you may be able to input the reads themselves into tRNAscan-SE. If coverage of each genome is deep enough, try that de novo ass0embly. Also run the unassembled reads from each strain through tRANscan-SE - perhaps these are indeed those extra copies of the tRNA genes you're searching. Lastly, are there any gaps in an assembly where known tRNA genes are? I still think my suggestion would work. – Larry_Parnell Feb 24 '12 at 14:26
I don't think the coverage is deep enough (1-4x) for de novo assembly but I'll try running tRNAscan-SE on the unassembled reads and maybe try to see if there's a difference in coverage of some tRNA genes which would suggest that duplicated genes have been mapped on the same locus. – Greg Slodkowicz Feb 27 '12 at 8:03
up vote 2 down vote

I would use an RNA microarray to look at those difference instead of sequencing. To delicately amplify your tRNAs in an unbiased manner would be a tricky molecular biology endeavor. I wouldn't be surprised if there were detectable and significant differences. Those experiments will be able to confirm your hypotheses regarding codon usage bias.

share|improve this answer
The data I have is for over forty Sc strains so it wouldn't be possible to do those experiments within my timeframe/budget. I'm limited to computational approaches and my best guess was that some copy number variation detection algorithm could be applied to the raw read data. – Greg Slodkowicz Feb 17 '12 at 11:33

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.