Sentence examples for prediction of variant from inspiring English sources

The examples discussed are related to prediction of variant effects, but description of methods and evaluation measures is general and thereby not application domain specific.

Possibly for millions of proteins SNAP2noali provides a reliable prediction of variant effects and allows for a quick assessment of functionally relevant positions in novel proteins.

For this reason, comprehensive testing as performed for the E. Coli LacI repressor or the HIV-1 protease is an invaluable source of information for computational prediction of variant effects.

We chose this strategy because variant pathogenicity is inversely correlated with the frequency of the non-reference allele in the general population (Coventry et al., 2010), with prediction of variant deleteriousness being more reliable for exonic and splice-site variants than for non-coding variants (Shihab et al., 2015).

RUNES incorporates data from ENSEMBL's Variant Effect Predictor software [ 24], and produces comparisons to NCBI dbSNP, known disease mutations from the Human Gene Mutation Database [ 25], and performs additional in silico prediction of variant consequences using RefSeq and ENSEMBL gene annotations.

RUNES incorporates data from ENSEMBL's Variant Effect Predictor (VEP) software [ 24], produces comparisons to NCBI dbSNP, known disease variants from the Human Gene Mutation Database [ 25], and performs additional in silico prediction of variant consequences using RefSeq and ENSEMBL gene annotations [ 26].

No domain term is included for the prediction of variants in genes KCNJ2, CAV3, SCN4B, AKAP9, SNTA1, KCNJ5, CACNA2D1, CACNB2, GPD1L, SCN1B, MYBPC3 and TNNT2, because domains of these genes are not sufficiently represented in our LQTS, BrS and HCM training data.

We anticipate that techniques that can consider the combined effects of multiple variants will refine and improve predictions of variant function.

Because of the variability provided by different computational methods, in silico predictions of variant pathogenicity were not used to evaluate missense PPVs.

Computational predictions of variants at the splice sites were precise by the three algorithms used in this work, but only HSF was able to identify all the cryptic alternative sites of exons 20, 23 and 24.

This includes the prediction of the variant effect on the different transcripts it affects, a feature closely modeled after the Variant Effect Predictor (VEP) from Ensembl [ 17].