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Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed
Author(s) -
Hicks Stephanie,
Wheeler David A.,
Plon Sharon E.,
Kimmel Marek
Publication year - 2011
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.21490
Subject(s) - missense mutation , multiple sequence alignment , biology , algorithm , sequence alignment , uniprot , alignment free sequence analysis , protein function , genetics , mutation , hum , sequence (biology) , protein sequencing , computational biology , computer science , gene , peptide sequence , art , performance art , art history
Multiple algorithms are used to predict the impact of missense mutations on protein structure and function using algorithm‐generated sequence alignments or manually curated alignments. We compared the accuracy with native alignment of SIFT, Align‐GVGD, PolyPhen‐2, and Xvar when generating functionality predictions of well‐characterized missense mutations ( n = 267) within the BRCA1, MSH2, MLH1 , and TP53 genes. We also evaluated the impact of the alignment employed on predictions from these algorithms (except Xvar) when supplied the same four alignments including alignments automatically generated by (1) SIFT, (2) Polyphen‐2, (3) Uniprot, and (4) a manually curated alignment tuned for Align‐GVGD. Alignments differ in sequence composition and evolutionary depth. Data‐based receiver operating characteristic curves employing the native alignment for each algorithm result in area under the curve of 78–79% for all four algorithms. Predictions from the PolyPhen‐2 algorithm were least dependent on the alignment employed. In contrast, Align‐GVGD predicts all variants neutral when provided alignments with a large number of sequences. Of note, algorithms make different predictions of variants even when provided the same alignment and do not necessarily perform best using their own alignment. Thus, researchers should consider optimizing both the algorithm and sequence alignment employed in missense prediction. Hum Mutat 32:1–8, 2011. © 2011 Wiley‐Liss, Inc.