Dear UCSC Team,
introduction of REVEL pathogenicity score is a really nice addition and valuable feature.
I have a question regarding the usage of the score. Scores range from 0 to 1 with higher scores predicting a greater likelihood for the variant being disease-causing.
Is there a reasonable threshold in that range for evaluation of benign or pathogenic likelihood? Benign less 0.5 and pathogenic greater or equal 0.5? How should it be applied for variant interpretation?
I did not find any hint regarding a threshold on dbNSFP.
Thanks and best regards,
Marc
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Dear Brian,
many thanks for your swift and comprehensive reply.
And thanks for pointing me to the figure. I was going to fast through the paper screening for cut-off as a keyword and I missed the explanation in the graph. Also, in the dbNSFP release notes they did not mention any cut-off for REVEL either though they give one for most of the other prediction scores.
We just finished the ESHG 2021 meeting and so many colleagues talked about the REVEL score for variant prioritization and that it was now also available in the UCSC browser. It is really a nice addition.
Best,
Marc
Von: Brian Lee <bria...@soe.ucsc.edu>
Gesendet: Wednesday, September 1, 2021 01:54
An: Marc-Alexander Rauschendorf <marc.rau...@molecularhealth.com>
Cc: gen...@soe.ucsc.edu
Betreff: Re: [genome] Request usage of REVEL score
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Dear Brian,
many thanks for your swift and comprehensive reply.
And thanks for pointing me to the figure. I was going too fast through the paper screening for cut-off as a keyword and I missed the explanation in the graph. Also, in the dbNSFP release notes they did not mention any cut-off for REVEL either though they give one for most of the other prediction scores.
To view this discussion on the web visit https://groups.google.com/a/soe.ucsc.edu/d/msgid/genome/PAXPR04MB81914E347CFD8F7EFA4D23EFF2CE9%40PAXPR04MB8191.eurprd04.prod.outlook.com.
Dear Robert,
many thanks for your reply.
Yes, the sliding scale with likelihood of higher scores for variants being more disease-causing vs. a defined cut-off is kind of a tricky thing.
We want to use it for variant prioritization and filtering. In this case a cut-off is easier to use. We will start with a cut-off at 0.5 as mentioned before. But we will re-evaluate after some time to see if this is the best approach.
Thanks for bringing the scores into UCSC browser.
Best regards,
Marc