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Is it possible to compare RPKM values across studies? This question is something I have been thinking about during my own analyses. I tend to think that under the conditions of standard poly-A enrichment and approximately equal coverage during sequencing runs, then RPKM values for genes between separate studies can be compared. However, if study X used a transcript enrichment setup in which a select number of mRNAs from a subset of genes were being selected for sequencing, can the RPKM values obtained from this study be reliably compared to study Y in which poly-A enrichment was used?
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Part II for those who are feeling ambitious: If I were to selectively enrich for a set of transcripts (A,B,C), sequence these, and obtain high RPKM measurements and attempt to compare these to a poly-A enriched sample set in which I have a fair expression measurement of most of the transcriptome (although in this case transcripts A, B, and C were unaccounted for due to low abundance) is it possible to extrapolate the normal physiological mRNA abundance of these transcripts from the enriched data series?
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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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