Nucleic Acids Research, 2006, Vol. 34, Web Server issue W263 innovatively and therefore MODi can interpret a tandem mass spectrum when hundreds of modiﬁcation types are considered and the number of potential PTMs in a peptide is large. MODi is different from existing methods based on sequence tags in that it aligns multiple sequence tags to a candidate peptide and isolates regions that might include post-translationally modiﬁed amino acids, while most of the previous methods align a single tag to a candidate peptide and try to identify modiﬁcations over the entire peptide. By ﬁrst ﬁxing unmodi- ﬁed regions in a peptide and then interpreting potentially modiﬁed amino acids in relatively small regions in between these unmodiﬁed tags, MODi can greatly reduce the search space for PTMs. In our experiments, MODi demonstrates its power by identifying uncommon modiﬁcations and artefacts such as di-methylation, acrylamide adduct (propionamide), cysteine oxidation to cysteic acid and tryptophan oxidation to formylky- nurenin. Such results are in accordance with the recent publication by Pevzner group where a variety of PTMs are reported (18). Owing to the efﬁciency gain obtained by our method, MODi runs in a reasonable amount of time. We tested MODi on a web server of Intel Xeon 3.06 GHz dual processor with 2 GB memory, running Windows Server 2003. For a dataset of 5684 spectra, with 211 different PTM types downloaded from http://www.unimod.org and nine protein sequences, obtained from Mascot’s protein ID results, in the Protein DB, it took 360 s. When the same dataset was run with a database of 20 proteins (additional 11 random proteins from IPI human database), it took 1070 s. The current version of MODi assumes that the number of candidate proteins is limited to 20 or less, and focuses on ﬁnding PTMs in tandem mass spectra. Candidate proteins can be easily identiﬁed using tandem mass spectra that do not contain PTMs using any of existing methods such as SEQUEST, Mascot or X!Tandem (7,8,19). In order to conduct protein identiﬁcation on the same web server, instead of using separate software tools, we are planning to generalize the current version of MODi so that it also includes the protein identiﬁcation step. ACKNOWLEDGEMENTS The authors thank Dr J. B. Kwon (Ewha Womans University) for providing purified histone from Hela cells. This work was supported by 21C Frontier Functional Proteomics Project from Korean Ministry of Science and Technology (FPR05A2-340 and FPR05A2-480) and through the Center for Cell Signaling Research (CCSR) at Ewha Womans University. Funding to pay the Open Access publication charges for this article was provided by 21C Frontier Functional Proteomics Project from Korean Ministry of Science and Technology (FPR05A2-340). Conflict of interest statement. None declared. REFERENCES 1. Baenziger,J.U. (2003) A major step on the road to understanding a unique posttranslational modification and its role in a genetic disease. Cell, 113, 421–422. 2. Wilkins,M.R. et al. (1999) High-throughput mass spectrometric discovery of protein post-translational modifications. J. Mol. Biol., 289, 645–657. 3. Mann,M. and Jensen,O.N. (2003) Proteomic analysis of post-translational modifications. Nat. Biotechnol., 21, 255–261. 4. Jensen,O.N. (2004) Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr. Opin. Chem. Biol., 8, 33–41. 5. Seo,J. and Lee,K.J. (2004) Post-translational modifications and their biological functions: proteomic analysis and systematic approaches. J. Biochem. Mol. Biol., 37, 35–44. 6. Aebersold,R. and Mann,M. (2003) Mass spectrometry-based proteomics. Nature, 422, 198–207. 7. Eng,J.K., McCormack,A.L. and Yates,J.R. (1994) An approach to correlate tandem mass-spectral data of peptides with amino-acid-sequences in a protein database. J. Am. Soc. Mass Spectrom., 5, 976–989. 8. Perkins,D.N., Pappin,D.J.C., Creasy,D.M. and Cottrell,J.S. (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis, 20, 3551–3567. 9. Bafna,V. and Edwards,N. (2001) SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database. Bioinformatics, 17 (Suppl. 1), 13–21. 10. Chen,T., Kao,M., Tepel,M., Rush,J. and Church,G.M. (2001) Dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry. J. Comput. Biol., 8, 325–337. 11. Frank,A. and Pevzner,P. (2005) PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal. Chem., 77, 964–973. 12. Ma,B., Zhang,K., Hendrie,C., Liang,C., Li,M., Doherty-Kirby,A. and Lajoie,G. (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Comm. Mass Spec., 17, 2337–2342. 13. Mann,M. and Wilm,M. (1994) Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem., 66, 4390–4399. 14. Tabb,D.L., Saraf,A. and Yates,J.R. (2003) GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. Anal. Chem., 75, 6415–6421. 15. Tanner,S., Hongjun,S., Frank,A., Wang,L.C., Zandi,E., Mumby,M., Pevzner,P. and Bafna,V. (2005) InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. Anal. Chem., 77, 4626–4639. 16. Matthiesen,R., Bunkenborg,J., Stensballe,A., Jensen,O., Welinder,K. and Bauw,G. (2004) Database-independent, database-dependent, and extended interpretation of peptide mass spectra in VEMS V2.0. Proteomics, 4, 2583–2593. 17. Matthiesen,R., Trelle,M., Hojrup,P., Bunkenborg,J. and Jensen,O. (2005) VEMS 3.0: algorithms and computational tools for tandem mass spectrometry based identification of post-translational modifications in proteins. J. Proteome Res., 4, 2338–2347. 18. Tsur,D., Tanner,S., Zandi,E., Bafna,V. and Pevzner,P. (2005) Identification of post-translational modifications by blind search of mass spectra. Nat. Biotechnol., 23, 1562–1566. 19. Craig,R. and Beavis,R. (2003) A method for reducing the time required to match protein sequences with tandem mass spectra. Rapid Comm. Mass Spec., 17, 2310–2316.