Informatics For Protein Identification by Tandem Mass Spectrometry; Focused on Two Most-widely Applied Algorithms, Mascot and SEQUEST
نویسندگان
چکیده
Mass spectrometry(MS) is widely applied for high throughput proteomics analysis. When large-scale proteome analysis experiments are performed, it generates massive amount of data. To search these proteomics data against protein databases, fully automated database search algorithms, such as Mascot and SEQUEST are routinely employed. At present, it is critical to reduce false positives and false negatives during such analysis. In this review we have focused on aspects of automated protein identification using tandem mass spectrometry(MS/MS) spectra and validation of the protein identifications of two most common automated protein identification algorithms Mascot and SEQUEST.
منابع مشابه
Whole-Cell Protein Identification Using the Concept of Unique Peptides
A concept of unique peptides (CUP) was proposed and implemented to identify whole-cell proteins from tandem mass spectrometry (MS/MS) ion spectra. A unique peptide is defined as a peptide, irrespective of its length, that exists only in one protein of a proteome of interest, despite the fact that this peptide may appear more than once in the same protein. Integrating CUP, a two-step whole-cell ...
متن کاملMS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
Today's highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry d...
متن کاملPepHMM: A Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search
An accurate scoring function for database search is crucial for peptide identification using tandem mass spectrometry. Although many mathematical models have been proposed to score peptides against tandem mass spectra, our method (called PepHMM, http://msms.cmb.usc.edu) is unique in that it combines information on machine accuracy, mass peak intensity, and correlation among ions into a hidden M...
متن کاملA Hidden Markov Model Based Scoring Function for Mass Spectrometry Database Search
An accurate scoring function for database search is crucial for peptide identification using tandem mass spectrometry. Although many mathematical models have been proposed to score peptides against tandem mass spectra, our method (called PepHMM, http://msms.cmb.usc.edu) is unique in that it combines information on machine accuracy, mass peak intensity, and correlation among ions into a hidden M...
متن کاملEvaluation of the Consensus of Four Peptide Identification Algorithms for Tandem Mass Spectrometry Based Proteomics.
The availability of different scoring schemes and filter settings of protein database search algorithms has greatly expanded the number of search methods for identifying candidate peptides from MS/MS spectra. We have previously shown that consensus-based methods that combine three search algorithms yield higher sensitivity and specificity compared to the use of a single search engine (individua...
متن کامل