PaPI: pseudo amino acid composition to score human protein-coding variants.

ABSTRACT:

Background

High throughput sequencing technologies are able to identify the whole genomic variation of an individual. Gene-targeted and whole-exome experiments are mainly focused on coding sequence variants related to a single or multiple nucleotides. The analysis of the biological significance of this multitude of genomic variant is challenging and computational demanding.

Results

We present PaPI, a new machine-learning approach to classify and score human coding variants by estimating the probability to damage their protein-related function. The novelty of this approach consists in using pseudo amino acid composition through which wild and mutated protein sequences are represented in a discrete model. A machine learning classifier has been trained on a set of known deleter

SUBMITTER: Limongelli I 

PROVIDER: S-EPMC4411653 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature