Tweet
Hi! I have a values of the mean per-base read coverage for each gene. My question what is it? How to calculate RPKM using these "the mean per-base read coverage". Thank you.
-
-
Can I use these values for the comparison of gene transcription between several distant samples? -
Going from a single number for each gene (which is what I presume you have in 'mean per-base read coverage') to something that encompasses all reads (the 'M' in RPKM) using the length of each gene (the 'K') seems hard if not impossible.
As for using the values for a comparison, normalizing them, making some broad assumptions and looking for gross changes, then I think that you could do so. I'd back up everything you find with other evidence but for a rough estimate of differential then your values will give you guidance.Comment
-
Thank you Rick. The "mean per-base read coverage" was computed on exons. So, if knowing gene length is it still hard to perform such a calculations?Comment
-
I suppose if you assume that all of your reads are the same length and if you know the lengths of your exons then you can determine the number of reads for each exon. From that information you can sum up the number of reads for all exons and sum up the lengths of all exons. From that information you can calculate RPKM for each exon. The assumption of all reads being the same length is the most critical part.Comment
-
Thank you very much. Say we have a gene of length 1000 bp and average read per-base equal to say 15. So what is the calculation procedure in numbers to deduct the RPKM?Comment
-
First, get the number of reads for each gene, call this number Rz . This would be Rz = L * A / X where:
L = length of the gene
A = average reads per base
X = length of a read
And lower-case 'z' represents the gene number (e.g., R1 is gene #1, R2 is gene #2, etc.)
Second, sum up the number of reads for all of the genes. Divide by 1,000,000. Call this number 'M'. In other words M = sum(Rz) / 1000000
Then for each gene you can determine the RPKM via Rz / L / MComment
-
Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...Today, 05:17 AM -
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
-
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...
Comment