Back

Variant pathogenic prediction by locus variability, the importance of the last picture of evolution.

Cabrera, J. L.; Enriquez, J. A.; Garcia, J.; Sanchez-Cabo, F.

2020-11-08 bioinformatics
10.1101/2020.11.06.371195 bioRxiv
Show abstract

Accurate pathogenic detection for single nucleotide variants (SNVs) is a key problem to perform variant ranking in whole exome sequencing studies. Several in silico tools have been developed to identify deleterious variants. Locus variability, computed as Shannon entropy from gnomAD/helixMTdb variant allele frequencies can be used as pathogenic variants predictor. In this study we evaluate the use of Shannon entropy in non-coding mitochondrial DNA and also in coding regions with an additional selective pressure other than that imposed by the genetic code, as are splice-sites. To benchmark this functionality in non-coding mitochondrial variants, Shannon entropy was compared with HmtVar disease score, outperforming it in non-coding SNVs (AUCH=0.99 in ROC curve and PR-AUCH=1.00 in Precision-recall curve). In the same way, for splice-sites variants, Shannon entropy was compared against two state-of-the-art ensemble predictors ada score and rf score, matching their overall performance both in ROC curves (AUCH=0.95) and Precision-recall curves (PR-AUC=0.97). Therefore, locus variability could aid in variant ranking process for these specific types of SNVs. Contactjlcabreraa@cnic.es; fscabo@cnic.es

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.