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TagRecon: high-throughput mutation identification through sequence tagging.

Surendra Dasari¹, Matthew C Chambers, Robbert J Slebos

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8340, USA.

Journal of Proteome Research

|February 6, 2010

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TagRecon identifies unanticipated mutations in clinical proteomic data using sequence tags. This novel algorithm enhances mutation detection sensitivity and accuracy in shotgun proteomics, outperforming existing software.

Area of Science:

Proteomics
Bioinformatics
Genomics

Background:

Shotgun proteomics generates tandem mass spectra for identifying mutated peptides in clinical samples.
Conventional database search strategies struggle with identifying sequence variations due to exact match requirements, leading to performance issues and false positives.

Purpose of the Study:

To introduce TagRecon, a novel algorithm for identifying unanticipated mutations in clinical proteomic datasets.
To evaluate TagRecon's performance against state-of-the-art software in detecting sequence mismatches.

Main Methods:

Developed TagRecon, an algorithm leveraging inferred sequence tags to identify mutations.
Compared TagRecon's performance with MyriMatch and other software on LTQ and Orbitrap datasets.
Established guidelines for filtering putative mutations from clinical samples.

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Main Results:

TagRecon identifies unmodified peptides with sensitivity comparable to MyriMatch.
TagRecon demonstrated superior performance in recognizing sequence mismatches in datasets with known variants.
Mutations were identified in up to 6% of peptides, with a small fraction matching dbSNP entries.
DNA mismatch repair-deficient RKO cell lines showed more mutant peptides than proficient SW480 lines.
Hydroxyproline modifications linked to extracellular matrix degradation were found in colon cancer tissues.

Conclusions:

TagRecon effectively identifies unanticipated mutations in clinical proteomic data.
Sequence tagging algorithms offer a valuable approach for comprehensive analysis of clinical proteomic datasets.
The findings highlight the potential of TagRecon in cancer research and diagnostics.