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)?
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$\begingroup$ What type of sequencing data do you have? Are we talking about RNA-seq or DNA? $\endgroup$– bobthejoeCommented Feb 22, 2012 at 18:34
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$\begingroup$ I would reask this question on BioStar. They may have better ideas than we mere experimentalists. $\endgroup$– bobthejoeCommented Feb 24, 2012 at 10:33
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$\begingroup$ see tRNAomics: tRNA gene copy number variation and codon use provide bioinformatic evidence of a new anticodon:codon wobble pair in a eukaryote. Iben JR, Maraia RJ. RNA. 2012 Jul;18(7):1358-72. doi: 10.1261/rna.032151.111. Epub 2012 May 14. $\endgroup$– user4902Commented Nov 12, 2013 at 1:56
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2 Answers
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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.
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$\begingroup$ 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. $\endgroup$ Commented Feb 24, 2012 at 9:17
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$\begingroup$ 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. $\endgroup$ Commented Feb 24, 2012 at 10:32
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2$\begingroup$ 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. $\endgroup$ Commented Feb 24, 2012 at 14:26
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$\begingroup$ 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. $\endgroup$ Commented Feb 27, 2012 at 8:03
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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.
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$\begingroup$ 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. $\endgroup$ Commented Feb 17, 2012 at 11:33