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
-
The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist...04-22-2024, 07:01 AM -
Proteins are often described as the workhorses of the cell, and identifying their sequences is key to understanding their role in biological processes and disease. Currently, the most common technique used to determine protein sequences is mass spectrometry. While still a valuable tool, mass spectrometry faces several limitations and requires a highly experienced scientist familiar with the equipment to operate it. Additionally, other proteomic methods, like affinity assays, are constrained...04-04-2024, 04:25 PM
Comment