An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis
MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have eva...
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| Published in | Proteomics (Weinheim) Vol. 5; no. 13; pp. 3475 - 3490 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Weinheim
WILEY-VCH Verlag
01.08.2005
WILEY‐VCH Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1615-9853 1615-9861 1615-9861 |
| DOI | 10.1002/pmic.200500126 |
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| Abstract | MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false‐positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed‐sequence search as demonstrated in this study based on a validated data set. The availability of open‐source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of “consensus scoring”, i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs.∁ |
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| AbstractList | MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false-positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed-sequence search as demonstrated in this study based on a validated data set. The availability of open-source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of "consensus scoring", i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs. complement.MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false-positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed-sequence search as demonstrated in this study based on a validated data set. The availability of open-source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of "consensus scoring", i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs. complement. MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false-positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed-sequence search as demonstrated in this study based on a validated data set. The availability of open-source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of "consensus scoring", i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs. complement. MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false-positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed-sequence search as demonstrated in this study based on a validated data set. The availability of open-source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of "consensus scoring", i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs.[comp] MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false‐positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed‐sequence search as demonstrated in this study based on a validated data set. The availability of open‐source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of “consensus scoring”, i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs.∁ |
| Author | Miller, Christine A. Adkins, Joshua N. Omenn, Gilbert S. Connolly, Lisa M. Kapp, Eugene A. Schütz, Frédéric Simpson, Richard J. Chakel, John A. Fenyo, David Eng, Jimmy K. Meza, Jose E. |
| Author_xml | – sequence: 1 givenname: Eugene A. surname: Kapp fullname: Kapp, Eugene A. organization: Joint ProteomicS Laboratory, Ludwig Institute for Cancer Research (Melbourne Branch)/Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia – sequence: 2 givenname: Frédéric surname: Schütz fullname: Schütz, Frédéric organization: Joint ProteomicS Laboratory, Ludwig Institute for Cancer Research (Melbourne Branch)/Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia – sequence: 3 givenname: Lisa M. surname: Connolly fullname: Connolly, Lisa M. organization: Joint ProteomicS Laboratory, Ludwig Institute for Cancer Research (Melbourne Branch)/Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia – sequence: 4 givenname: John A. surname: Chakel fullname: Chakel, John A. organization: Agilent Technologies, Santa Clara, CA, USA – sequence: 5 givenname: Jose E. surname: Meza fullname: Meza, Jose E. organization: Agilent Technologies, Santa Clara, CA, USA – sequence: 6 givenname: Christine A. surname: Miller fullname: Miller, Christine A. organization: Agilent Technologies, Santa Clara, CA, USA – sequence: 7 givenname: David surname: Fenyo fullname: Fenyo, David organization: GE Healthcare, Bio-Sciences, Piscataway, NJ, USA – sequence: 8 givenname: Jimmy K. surname: Eng fullname: Eng, Jimmy K. organization: Institute for Systems Biology, Seattle, WA, USA – sequence: 9 givenname: Joshua N. surname: Adkins fullname: Adkins, Joshua N. organization: Pacific Northwest National Laboratory, Richland, WA, USA – sequence: 10 givenname: Gilbert S. surname: Omenn fullname: Omenn, Gilbert S. organization: University of Michigan Medical School, Ann Arbor, MI, USA – sequence: 11 givenname: Richard J. surname: Simpson fullname: Simpson, Richard J. email: richard.simpson@ludwig.edu.au organization: Joint ProteomicS Laboratory, Ludwig Institute for Cancer Research (Melbourne Branch)/Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16047398$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim |
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| References | Beer, I., Barnea, E., Ziv, T., Admon, A., Proteomics 2004, 4, 950-960. Simpson, R. J., Connolly, L. M., Eddes, J. S., Pereira, J. J. et al., Electrophoresis 2000, 21, 1707-1732. Simpson, R. J., Eur. J. Pharm. Sci. Rev. 2004, 9, 25-36. Kersey, P. J., Duarte, J., Williams, A., Karavidopoulou, Y. et al., Proteomics 2004, 4, 1985-1988. Craig, R., Beavis, R. C., Rapid Commun. Mass Spectrom. 2003, 17, 2310-2316. Eng, J. K., McCormack, A. L., Yates III, J. R., J. Am. Soc. Mass Spectrom. 1994, 5, 976-989. Omenn, G. S., Proteomics 2004, 4, 1235-1240. Anderson, N. L., Polanski, M., Pieper, R., Gatlin, T. et al., Mol. Cell. Proteomics 2004, 3, 311-326. Craig, R., Beavis, R. C., Bioinformatics 2004, 20, 1466-1467. Pappin, D. J., Hojrup, P., Bleasby, A. J., Curr. Biol. 1993, 3, 327-332. Veenstra, T. D., Conrads, T. P., Issaq, H. J., Electrophoresis 2004, 25, 1278-1279. Adkins, J. N., Varnum, S. M., Auberry, K. J., Moore, R. J. et al., Mol. Cell. Proteomics 2002, 1, 947-955. Kearney, P., Thibault, P., J. Bioinform. Comput. Biol. 2003, 1, 183-200. Moritz, R. L., Ji, H., Schutz, F., Connolly, L. M. et al., Anal. Chem. 2004, 76, 4811-4824. Anderson, N. L., Anderson, N. G., Mol. Cell. Proteomics 2002, 1, 845-867. Keil, B., Specificity of Proteolysis, Springer-Verlag, Berlin 1992. Qian, W. J., Liu, T., Monroe, M. E., Strittmatter, E. F. et al., J. Proteome Res. 2005, 4, 53-62. Pedrioli, P. G., Eng, J. K., Hubley, R., Vogelzang, M. et al., Nat. Biotechnol. 2004, 22, 1459-1466. Field, H. I., Fenyo, D., Beavis, R. C., Proteomics 2002, 2, 36-47. Wysocki, V. H., Tsaprailis, G., Smith, L. L., Breci, L. A., J. Mass Spectrom. 2000, 35, 1399-1406. Resing, K. A., Meyer-Arendt, K., Mendoza, A. M., Aveline-Wolf, L. D. et al., Anal. Chem. 2004, 76, 3556-3568. Clauser, K., Negvizhskii, A., Carr, S., Aebersold, R., Baldwin, M., Burlingame, A. et al., Mol. Cell. Proteomics 2004, 3, 531-533. Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L. et al., J. Proteome Res. 2004, 3, 958-964. Keller, A., Nesvizhskii, A. I., Kolker, E., Aebersold, R., Anal. Chem. 2002, 74, 5383-5392. Sadygov, R. G., Cociorva, D., Yates III, J. R., Nat. Methods 2004, 1, 195-202. Fenyo, D., Beavis, R. C., Anal. Chem. 2003, 75, 768-774. Washburn, M. P., Wolters, D., Yates, J. R., 3rd, Nat. Biotechnol. 2001, 19, 242-247. Nesvizhskii, A. I., Aebersold, R., Drug Discov. Today 2004, 9, 173-181. Baldwin, M. A., Mol. Cell. Proteomics 2004, 3, 1-9. Olsen, J. V., Ong, S. E., Mann, M., Mol. Cell. Proteomics 2004, 3, 608-614. Tabb, D. L., MacCoss, M. J., Wu, C. C., Anderson, S. D., Yates, J. R., 3rd, Anal. Chem. 2003, 75, 2470-2477. Cargile, B. J., Bundy, J. L., Stephenson, J. L., Jr., J. Proteome Res. 2004, 3, 1082-1085. Reid, G. E., Roberts, K. D., Kapp, E. A., Simpson, R. J., J. Proteome Res. 2004, 3, 751-759. Perkins, D. N., Pappin, D. J., Creasy, D. M., Cottrell, J. S., Electrophoresis 1999, 20, 3551-3567. R Developement Core Team, A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria 2005, http://www.R-project.org. Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J., Gygi, S. P., J. Proteome Res. 2003, 2, 43-50. Keller, A., Purvine, S., Nesvizhskii, A. I., Stolyar, S. et al., Omics 2002, 6, 207-212. Marshall, J., Jankowski, A., Furesz, S., Kireeva, I. et al., J. Proteome Res. 2004, 3, 364-382. Kapp, E. A., Schutz, F., Reid, G. E., Eddes, J. S. et al., Anal. Chem. 2003, 75, 6251-6264. Link, A. J., Eng, J., Schieltz, D. M., Carmack, E. et al., Nat. Biotechnol. 1999, 17, 676-682. Nesvizhskii, A. I., Keller, A., Kolker, E., Aebersold, R., Anal. Chem. 2003, 75, 4646-4658. 2004; 22 2004; 20 2002; 74 2000; 21 2004; 25 2002; 6 2004; 9 2004; 4 2002; 1 2004; 3 2002; 2 1999; 20 2003; 17 2005 1992 2004; 1 2003; 75 1993; 3 2004; 76 2000; 35 1999; 17 2001; 19 2003; 2 2005; 4 2003; 1 1994; 5 e_1_2_1_41_2 e_1_2_1_40_2 e_1_2_1_22_2 e_1_2_1_23_2 e_1_2_1_20_2 e_1_2_1_21_2 e_1_2_1_42_2 e_1_2_1_26_2 e_1_2_1_27_2 e_1_2_1_24_2 e_1_2_1_25_2 e_1_2_1_28_2 e_1_2_1_29_2 Simpson R. J. (e_1_2_1_7_2) 2004; 9 e_1_2_1_6_2 e_1_2_1_30_2 e_1_2_1_4_2 e_1_2_1_5_2 e_1_2_1_2_2 e_1_2_1_11_2 R Developement Core Team (e_1_2_1_31_2) 2005 e_1_2_1_34_2 e_1_2_1_3_2 e_1_2_1_12_2 e_1_2_1_33_2 e_1_2_1_32_2 e_1_2_1_10_2 e_1_2_1_15_2 e_1_2_1_38_2 e_1_2_1_16_2 e_1_2_1_37_2 e_1_2_1_13_2 e_1_2_1_36_2 e_1_2_1_14_2 e_1_2_1_35_2 e_1_2_1_19_2 e_1_2_1_8_2 e_1_2_1_17_2 e_1_2_1_9_2 e_1_2_1_18_2 e_1_2_1_39_2 |
| References_xml | – reference: Field, H. I., Fenyo, D., Beavis, R. C., Proteomics 2002, 2, 36-47. – reference: Cargile, B. J., Bundy, J. L., Stephenson, J. L., Jr., J. Proteome Res. 2004, 3, 1082-1085. – reference: Kersey, P. J., Duarte, J., Williams, A., Karavidopoulou, Y. et al., Proteomics 2004, 4, 1985-1988. – reference: Tabb, D. L., MacCoss, M. J., Wu, C. C., Anderson, S. D., Yates, J. R., 3rd, Anal. Chem. 2003, 75, 2470-2477. – reference: Keller, A., Nesvizhskii, A. I., Kolker, E., Aebersold, R., Anal. Chem. 2002, 74, 5383-5392. – reference: Moritz, R. L., Ji, H., Schutz, F., Connolly, L. M. et al., Anal. Chem. 2004, 76, 4811-4824. – reference: Anderson, N. L., Polanski, M., Pieper, R., Gatlin, T. et al., Mol. Cell. Proteomics 2004, 3, 311-326. – reference: Keil, B., Specificity of Proteolysis, Springer-Verlag, Berlin 1992. – reference: Geer, L. Y., Markey, S. P., Kowalak, J. A., Wagner, L. et al., J. Proteome Res. 2004, 3, 958-964. – reference: Beer, I., Barnea, E., Ziv, T., Admon, A., Proteomics 2004, 4, 950-960. – reference: Veenstra, T. D., Conrads, T. P., Issaq, H. J., Electrophoresis 2004, 25, 1278-1279. – reference: Marshall, J., Jankowski, A., Furesz, S., Kireeva, I. et al., J. Proteome Res. 2004, 3, 364-382. – reference: Craig, R., Beavis, R. C., Rapid Commun. Mass Spectrom. 2003, 17, 2310-2316. – reference: Pappin, D. J., Hojrup, P., Bleasby, A. J., Curr. Biol. 1993, 3, 327-332. – reference: Simpson, R. J., Eur. J. Pharm. Sci. Rev. 2004, 9, 25-36. – reference: Pedrioli, P. G., Eng, J. K., Hubley, R., Vogelzang, M. et al., Nat. Biotechnol. 2004, 22, 1459-1466. – reference: Kearney, P., Thibault, P., J. Bioinform. Comput. Biol. 2003, 1, 183-200. – reference: Craig, R., Beavis, R. C., Bioinformatics 2004, 20, 1466-1467. – reference: Washburn, M. P., Wolters, D., Yates, J. R., 3rd, Nat. Biotechnol. 2001, 19, 242-247. – reference: Perkins, D. N., Pappin, D. J., Creasy, D. 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| Snippet | MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform... |
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| SubjectTerms | Algorithms Benchmarking Blood Proteins - chemistry Computational Biology Databases, Protein False Positive Reactions Humans Internet MASCOT Mass Spectrometry Mass Spectrometry - methods PeptideProphet Peptides - chemistry Proteomics - methods Reference Standards ROC Curve Sensitivity and Specificity SEQUEST Software Sonar Spectrum Mill Trypsin - pharmacology X!Tandem |
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| Title | An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis |
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