Hi All,
I know the formula for theoretical read depth is not quite applicable for RNASeq data, as previously explained very well in this Biostars post: human transcriptome size
So my question is when "coverage per sample" is reported, such as in below, is it usually estimated based on the total size of the "genome" as the "target size"? Or is it based on other experiment-driven parameters for each facility?
(if I use genome size, I get numbers that are close to the reported sample coverage numbers).
https://genohub.com/shop-by-next-gen-sequencing-project/#query=1389c0b35376676553e7c7bbad214f39
I appreciate your feedback
nfarnoud
•
written
by
noushin.farnoud
1 answer
If you read the manufacturer specs link on the right side of that form, you'll see that they recommend to use the "minimum number of reads per sample" metric rather than "minimum coverage per sample". This is simply to avoid the ambiguity in meaning.
•
written
by
Devon Ryan
Log in to answer this question.
-
Proteomics normalization: equal protein loading but unequal cell counts in clinical samples
written by Kittinun •
-
Discrepency in assembly sizes
written by hpapoli
-
How to compare the quality of assemblies
written by kirillkirilenko •
-
Adding Principal components as covariates in GWAS studies
written by anikcropscience •
-
prepDE.py read length choice for paired-end sequencing reads
written by 1234gingko •
-
Estimating genome size
written by Ketil
-
How to find Standard Error (SE) values when not provided in GWAS summary stats?
written by ydadmehr •
-
Assess and correct any GC bias in Illumina HiSeq 4000 data
written by Anand Rao
-
kmergenie to estimate genome size and then sequencing coverage
written by cook284 •
-
HMP Abundance Table (HMSCP) Interpretation for Calculate Taxa Abundance
written by felipepratalima •
