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A genome about 6M has about 340 solexa contigs. I'd like to predict genes of each contig with the Glimmer. I don't know to do it from reference genome as training data or if i can use the long-orf to get training data from each contig. I wander if anyone do the same job and how i should do.
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Yes, it is possible to do it using the long-orfs program, but I often juct go to NCBI and retrieve genes from their taxonomy browser of a closely related strain or species and use that to do my training. Be sure to use the -r flag when using build-icm. In my experience it helps a lot. -
Yes, glimmer with the training data is perfect. However, the contigs are parts of the genome. The training data maybe not suitable for the contig. Or I should do the training for every contig?
Originally posted by Mark View PostComment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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