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.
Unconfigured Ad
Collapse
X
-
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
-
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
-
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 / M
Comment
Latest Articles
Collapse
-
by SEQadmin2
CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).
Despite this, “CRISPR helped turn genome editing from a specialized technique into...-
Channel: Articles
07-31-2026, 11:01 AM -
-
by SEQadmin2
Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.
The systematic characterization of the human proteome has...-
Channel: Articles
07-20-2026, 11:48 AM -
-
by SEQadmin2
Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...-
Channel: Articles
07-09-2026, 11:10 AM -
ad_right_rmr
Collapse
News
Collapse
| Topics | Statistics | Last Post | ||
|---|---|---|---|---|
|
Started by SEQadmin2, 08-06-2026, 07:41 AM
|
0 responses
13 views
0 reactions
|
Last Post
by SEQadmin2
08-06-2026, 07:41 AM
|
||
|
Started by SEQadmin2, 08-03-2026, 10:13 AM
|
0 responses
31 views
0 reactions
|
Last Post
by SEQadmin2
08-03-2026, 10:13 AM
|
||
|
Started by SEQadmin2, 07-31-2026, 02:55 AM
|
0 responses
40 views
0 reactions
|
Last Post
by SEQadmin2
07-31-2026, 02:55 AM
|
||
|
Started by SEQadmin2, 07-24-2026, 12:17 PM
|
0 responses
26 views
0 reactions
|
Last Post
by SEQadmin2
07-24-2026, 12:17 PM
|
Comment