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what is the optimal value for the phred score for rna seq data?
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The "optimal value" is as high as possible. I think the Illumina limit at the moment is something like 43, but most people will settle for a "good enough" value that is considerably lower (e.g. 20 or 30).Comment
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It's probably good you're not doing too much on DE/transcript reconstruction as I would have said the read numbers were a little low for anything other than gene level expression testing.Originally posted by arcolombo698 View Post
As already pointed out, you should be fine to do variant calling from the expressed genes, where there is sufficient depth - but you will want to put some kind of depth threshold for the variant calling (also there will be instances of allele specific expression and presumably RNA editing in the data). With different numbers of reads per sample, obviously some samples will have better coverage than others. For differential expression studies this is not so much of an issue when the data is normalised to the number of mapped reads anyway.Last edited by Bukowski; 12-25-2013, 06:58 AM.Comment
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thank you for the response
are there resources recommended that gives insight about understanding sufficient depth of reads for variant calling and other common problems? it seems sufficient to have read lengths of 13M.. i have a sample read with 7M , this seems also low.
these samples have all passed QC with no duplicated sequences (all the repeated sequences have been trimmed out...dont want duplicated sequences)
would appreciate resources on how to properly process VCF , and also how to analyze the VCF results, error checking etc...Comment
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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...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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