TagRecon: High-Throughput Mutation Identification through Sequence Tagging

Shotgun proteomics produces collections of tandem mass spectra that contain all the data needed to identify mutated peptides from clinical samples. Identifying these sequence variations, however, has not been feasible with conventional database search strategies, which require exact matches between...

Full description

Saved in:
Bibliographic Details
Published in:Journal of proteome research 2010-04, Vol.9 (4), p.1716-1726
Main Authors: Dasari, Surendra, Chambers, Matthew C, Slebos, Robbert J, Zimmerman, Lisa J, Ham, Amy-Joan L, Tabb, David L
Format: Article
Language:English
Subjects:
Algorithms
Cell Line, Tumor
Chromatography, Liquid
Colonic Neoplasms - metabolism
Computational Biology - methods
Data Mining
Databases, Protein
DNA Mismatch Repair
Extracellular Matrix - metabolism
Humans
Hydroxyproline - metabolism
Models, Genetic
Mutation
Peptide Fragments - chemistry
Peptide Mapping - methods
Sequence Tagged Sites
Tandem Mass Spectrometry - methods
Yeasts - chemistry
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Shotgun proteomics produces collections of tandem mass spectra that contain all the data needed to identify mutated peptides from clinical samples. Identifying these sequence variations, however, has not been feasible with conventional database search strategies, which require exact matches between observed and expected sequences. Searching for mutations as mass shifts on specified residues through database search can incur significant performance penalties and generate substantial false positive rates. Here we describe TagRecon, an algorithm that leverages inferred sequence tags to identify unanticipated mutations in clinical proteomic data sets. TagRecon identifies unmodified peptides as sensitively as the related MyriMatch database search engine. In both LTQ and Orbitrap data sets, TagRecon outperformed state of the art software in recognizing sequence mismatches from data sets with known variants. We developed guidelines for filtering putative mutations from clinical samples, and we applied them in an analysis of cancer cell lines and an examination of colon tissue. Mutations were found in up to 6% of identified peptides, and only a small fraction corresponded to dbSNP entries. The RKO cell line, which is DNA mismatch repair deficient, yielded more mutant peptides than the mismatch repair proficient SW480 line. Analysis of colon cancer tumor and adjacent tissue revealed hydroxyproline modifications associated with extracellular matrix degradation. These results demonstrate the value of using sequence tagging algorithms to fully interrogate clinical proteomic data sets.
ISSN:1535-3893
1535-3907
DOI:10.1021/pr900850m