remove_redundant_psms (bool): If True, redundant PSMs (e.g.num_compared_psms (int): maximum number of PSMs (sorted by score, starting with the best scoring PSM) that are compared.If the score difference between the best and secondbest PSM If False, all PSMs for one spectrum are removed accept_conflicting_psms (bool): If True, multiple PSMs for one spectrum can be reported if their score difference is below.threshold_is_log10 (bool): True, if log10 scale has been used for score_diff_threshold.score_diff_threshold (float): minimum score difference between the best PSM and the first rejected PSM of one spectrum.Of the following quality criteria (Proteome Bioinformatics,Įds: S.J. This way of generating a target decoy database lead to the fulfillment ‘RR’ for trypsin) are not shuffled and are reported by the engineĪs unmutable peptides in a text file, so that they can be excluded in It is further ensured that unmutable peptides This ensures that no unequal distribution of target and decoy peptidesįurther, every peptide is shuffled, while the amindo acids where theĮnzyme cleaves aremaintained at their original position.Įvery peptide is only shuffled once and the shuffling result is stored.Īs a result it is ensured that if a peptide occurs multiple times it is Protein gets a tag which highlight its double occurence in the database. The shuffling peptide method is described below.Īs one of the first steps redundant sequences are filtered and the The engine currently available generates a very stringent target decoyĭatabase by peptide shuffling but also offers the possibility to Generate Target Decoy 1_0_0 UNode _execute ( ) ¶Ĭreates a target decoy database based on shuffling of peptides or generate_target_decoy_1_0_0 ( *args, **kwargs ) ¶ Generate Target Decoy 1_0_0 ¶ class _target_decoy_1_0_0. Ties can be resolved by sorting them by Bayes PEP. column “combined PEP”: The PEP as computed within the engine combination PSMsįor optimal ranking, PSMs should be sorted by combined PEP.column “Bayes PEP”: The multi-engine PEP, see explanation above.Returns a merged CSV file with all PSMs that were found and two added The sliding window size can be defined by adjusting the Ursgal parameter Including all the search result scores from the individual search enginesĪs well as the FDR based on the “combined PEP”. PSM receives a PEP based on the target/decoy ratio of the surrounding PSMs.įinally, all groups are merged and the results reported in one output, Using a sliding window over all PSMs within each group (sorted by MEP). Then, the combined PEP (the final score) is computed similar to PeptideShaker This is done for each PSM group separately. Percolator for different search engines, that is PEP (MEP) for each PSM based on the PEPs reported by, for example, The combined PEP approach uses Bayes’ theorem to calculate a multiengine ThisĪpproach is based on the assumption that the search engines agree on theĭecoys and false-positives as they agree on the targets. Higher quality subset and thus its PSMs receive a higher score. Shared by multiple engines contains fewer decoy hits and thus represents a Intersections of a three-set Venn diagram. Would result in seven PSM groups, which can be visualized by the seven For each search engine, the reported PSMs are treatedĪs a set and the logical combinations of all sets are treated separatelyĪs done in the “combined FDR” approach. Similar to “combined FDR”, “combined PEP” “combined FDR” approach (Jones et al., 2009), elements of PeptideShaker,Īnd elements of Bayes’ theorem. “Combined PEP” is a hybrid approach combining elements of the combine_pep_1_0_0 ( *args, **kwargs ) ¶Ĭombining Multiengine with “Combined PEP”
These parameters are stored in mand_dict Returns:Ĭombine PEP 1_0_0 ¶ class _pep_1_0_0. The main function is imported and then executed using theīuilding the list of parameters that will be passed to the That were defined in preflight (stored in mand_dict) _execute ( ) ¶Įxecuting the combine_FDR_0_1 main function with parameters Returns a merged CSV file with all PSMs that were found and an addedĬolumn “Combined FDR Score”. EachĬSV requires a PEP column, for instance by post-processing with Percolator. Input should be multiple CSV files from different search engines. “Improving sensitivity in proteome studies by analysis of false discovery Jones AR, Siepen JA, Hubbard SJ, Paton NW (2009):
combine_FDR_0_1 ( *args, **kwargs ) ¶Īn implementation of the “combined FDR Score” algorithm, as described in:
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