OLAV-PMF:  A Novel Scoring Scheme for High-Throughput Peptide Mass Fingerprinting

Magnin, Jérôme; Masselot, Alexandre; Menzel, Christoph; Colinge, Jacques

doi:10.1021/pr034055m

Cited by 31 publications

(21 citation statements)

References 21 publications

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“…In this study, we extend our previous studies by including a fourth search algorithm (Phenyx) in consensus based peptide searches in order to determine whether four consensus methods that employ more than three search engines further enhances sensitivity and specificity for the identification of peptides. Phenyx is a search algorithm that relies on the OLAV scoring method for identifying peptides; it is a modification of the heuristic and descriptive approaches employed by Sequest, Mascot or X!Tandem (Magnin et al, 2004; Colinge et al, 2003). We report the performance of these four search algorithms for reliability, sensitivity, and specificity of peptide identification.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Consensus of Four Peptide Identification Algorithms for Tandem Mass Spectrometry Based Proteomics

Dagda

Sultana²,

Lyons‐Weiler

2010

J Proteomics Bioinform

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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 (individual method). We hypothesized that union of four search engines (Sequest, Mascot, X!Tandem and Phenyx) can further enhance sensitivity and specificity. ROC plots were generated to measure the sensitivity and specificity of 5460 consensus methods derived from the same dataset. We found that Mascot outperformed individual methods for sensitivity and specificity, while Phenyx performed the worst. The union consensus methods generally produced much higher sensitivity, while the intersection consensus methods gave much higher specificity. The union methods from four search algorithms modestly improved sensitivity, but not specificity, compared to union methods that used three search engines. This suggests that a strategy based on specific combination of search algorithms, instead of merely 'as many search engines as possible', may be key strategy for success with peptide identification. Lastly, we provide strategies for optimizing sensitivity or specificity of peptide identification in MS/MS spectra for different userspecific conditions.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Consensus of Four Peptide Identification Algorithms for Tandem Mass Spectrometry Based Proteomics

Dagda

Sultana²,

Lyons‐Weiler

2010

J Proteomics Bioinform

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“…Determination of a threshold value should be carefully done because if the threshold is set low then the false positives may be getting identified and the result will be corrupted, whereas a high threshold masks the correct protein from getting identified. Statistical methods have been developed so as to find a correct threshold value which can identify correct proteins [8,9]. Limitation of PMF is that the database size affects the sensitivity of the method.…”

Section: Mass Based Approachesmentioning

confidence: 99%

Computational Diagnostics Based on Proteomic Data- Review on Approaches and Algorithms

AN¹,

Badrunnisa²,

M³

et al. 2010

Int J of Bioi Res

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-Protein identification using mass spectrometry is an indispensable tool for proteomics which in recent days has evolved to give better understanding of the biology of cell and its functioning. Proteomics has wide application in diagnosing diseases such as cancer, Alzheimer's disease etc. The data obtained from the diagnostic tools like LC-MS is to be interpreted accurately so as to obtain the correct qualitative and quantitative information about the peptides present in the biological sample. Such interpretation requires and exhaustive knowledge and review about different tools that can be employed and their comparison. This article focuses on comparison of different proteomic tools available for the MS data processing and interpretation. The accuracy demanded during protein identification can be fulfilled by tag based approaches, than PMF or PFF systems. Although, there is a need of standardized matrices for the comparison of the protein identification tools, identifying the single best package for each application from the available literature is at present extremely difficult as each package has its own advantage over other. The datasets and thresholds used in these kinds of comparisons have a critical importance on the outcome of such experiments, and that the high variability in machine and experimental setups complicates analysis. The state of data standards and lack of benchmarks therefore makes it difficult to make an effective comparison. While the increasing availability of data in public repositories and tightening standards will no doubt ameliorate the problem, until this basic benchmarking problem is overcome, no single package or approach can conclusively be declared to outperform all others, expect, perhaps, in the specific circumstances used in particular studies.

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“…Please refer to [15] for a survey on the history of PMF before 2003 and refer to [16] for a summary on available PMF tools. To further improve the identification accuracy, many new algorithms are proposed recently (e.g., [17], [18], [19], [20], [21], [22]). Note that all these methods focus on single protein identification rather than protein mixture identification.…”

Section: Introductionmentioning

confidence: 99%

A Partial Set Covering Model for Protein Mixture Identification Using Mass Spectrometry Data

Yang

2011

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.

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OLAV-PMF: A Novel Scoring Scheme for High-Throughput Peptide Mass Fingerprinting

Cited by 31 publications

References 21 publications

Evaluation of the Consensus of Four Peptide Identification Algorithms for Tandem Mass Spectrometry Based Proteomics

Evaluation of the Consensus of Four Peptide Identification Algorithms for Tandem Mass Spectrometry Based Proteomics

Computational Diagnostics Based on Proteomic Data- Review on Approaches and Algorithms

A Partial Set Covering Model for Protein Mixture Identification Using Mass Spectrometry Data

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