W262 Nucleic Acids Research, 2006, Vol. 34, Web Server issue Figure 5. Interpretation for a spectrum with precursor ion 719.663 (2+) corresponding to a peptide ‘GKGGKGLGKGGAKR’ of histone. MODi first finds a tag chain __GG _glg_gga__, computes mass offsets of the gaps, which are 41.95, 41.92, 41.94 and 41.93 Da, respectively (shown in Figure 1). Every Lysine is interpreted with an acetylation and the estimation is evaluated by a scoring scheme. Figure 6. Shows that MODi can find multiple PTMs in a single gap. It identifies a deamidation on ‘N’ and a di-methylation on ‘K’ in a gap ‘NGK’. 41.93 Da, respectively, estimates four acetylation sites from the mass offsets and evaluates the estimation by a simple scoring scheme. MODi can ﬁnd multiple PTMs in a single gap. In Figure 6, MODi identiﬁes a deamidation on ‘N’ and a di-methylation on ‘K’ in a gap ‘NGK’. In general, considering various combina- tions of possible modiﬁcations requires prohibitively huge computational time as the numbers of possible modiﬁcation sites and types increase. This example demonstrates how MODi successfully manages the time complexity of modiﬁca- tion analysis problem so that it can spend time on identifying multiple closely located PTMs in a peptide. This is possible because MODi has already identiﬁed regions in a peptide that do not contain modiﬁed residues and are anchored on those sites before starting modiﬁcation analysis for each gap. Thus, it narrowed down the search space for multiple PTMs to each gap region of a peptide, which is generally a lot shorter than the entire peptide length. DISCUSSION Identifying multiple PTMs in a single gap mandates trying all possible combinations of feasible PTMs. This implies that as the number and the type of PTMs being considered grow, the number of combinations grows exponentially. This is the reason why most of the previous methods limit the number of PTM sites and PTM types being considered. However, we manage the computational complexity of PTM identiﬁcation