We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles.
Therefore, we proposed a simple model for mutation correction and used it to predict intron phase distributions for all species again.
We applied a simple model for mutation correction in which only mutations [see Additional file 2] that change the GC content of a codon but do not affect the translated amino acid are allowed.
To accommodate this source of error in fast-evolving species, we proposed a simple model for mutation correction and used it to re-predict the intron phase distributions for all species in the genome-wide dataset.
Most such analyses have assumed a constant-sized ancestral population, an 'infinite sites' model for mutation, a constant rate of recombination across the genome, and no sequencing error (9– 12).
While EASE-ASA combines two models for exposed and buried residues, EASE-SS is composed of three models for mutations in α-helices, β-sheets, and coils.
This artifact silencing system resulted in a mean 21-fold increased sensitivity for 29 minority variants of 17 codons in our model assay for mutations most commonly associated with HIV-1 drug resistance.
