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Prediction of peptide observance in mass-spectrometry experiments

Title Prediction of peptide observance in mass-spectrometry experiments
Student George Rosenberger
Type Undergraduate research project
Completion Date 2010-05-25
Abstract

Prediction of peptide observance in mass-spectrometry experiments using a Random forest classifier and physicochemical features was evaluated and implemented in an automatic and unsupervised module to extend the 2DDB proteomics work-flow. The predictor was applied to multiple problems, including modeling with a selected training dataset, evaluation of prediction with varying conditions and first approaches to quantitative prediction were explored.

An algorithm was developed to select subsets of peptidomes, according to se- lected constraints, such as occurrence in different genomes or singularity in the transcriptome. The 2DDB proteomics work ow was extended to integrate genomic precursors. Rapid and automated evaluation of genomes was enabled and different predictors were applied to identify a most-likely selection of observable peptides of these genomes.

These approaches were used to provide support in purchasing synthetic peptides for further investigation and experiments.