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We demonstrated that PTMClust improved on both true positives (correct modification position predictions) and false positives (misplaces modification positions) when applied to the outputs of SIMS, InsPecT, MODmap, and InsPecT post-processed with PTMFinder, a PTM refinement algorithm.
Then, we determined the average modification position for each group (rounded to the nearest position) and computed a histogram of the modification position error.
Finally, the observed modification position is assumed to be a noisy version of the true position.
All other positions in the peptide sequence have zero probability of being the true modification position.
We modeled the modification position error (x n − z n ) between the observed modification position x n and the true modification position z n with a discrete probability distribution, given as (4) where the likelihood function ϕ accounts for the modification position error.
Our analysis shows that many of the mismatches are due to incorrect modification position assignments: 229 of the 790 spectra that both SIMS and InsPecT, mapped to the same peptide sequence have mismatched modification position.
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Further, this shows that thiophosphate siRNAs with the same nucleotide sequence but with different sulfur modification positions have different silencing effects.
Similarly, InsPecT result matched 860 reference sequences but misplaced 239 modification positions.
We next examined the abilities of our algorithm to identify PTM groups corresponding to bona fide PTMs, fine-tune observed modification masses and correct for misplaced modification positions.
More specifically, for SIMS, PTMClust decreased the number of misplaced modifications by ∼40% (106 fewer misplaced modification positions) to produce 791 correct matches, an increase of ∼15%.
However, these search methods suffer from two problems: mass measurement inaccuracy and uncertainty in predicting modification positions, which limit their accuracy and precision.
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