void DigestSimulation::digest(SimTypes::FeatureMapSim& feature_map)
  {
    LOG_INFO << "Digest Simulation ... started" << std::endl;

    if ((String)param_.getValue("enzyme") == String("none"))
    {
      //peptides = proteins;
      // convert all proteins into peptides

      // for each protein_hit in the FeatureMap
      for (std::vector<ProteinHit>::iterator protein_hit = feature_map.getProteinIdentifications()[0].getHits().begin();
           protein_hit != feature_map.getProteinIdentifications()[0].getHits().end();
           ++protein_hit)
      {
        // generate a PeptideHit hit with the correct link to the protein
        PeptideHit pep_hit(1.0, 1, 0, AASequence::fromString(protein_hit->getSequence()));
        PeptideEvidence pe;
        pe.setProteinAccession(protein_hit->getAccession());
        pep_hit.addPeptideEvidence(pe);

        // add the PeptideHit to the PeptideIdentification
        PeptideIdentification pep_id;
        pep_id.insertHit(pep_hit);

        // generate Feature with correct Intensity and corresponding PeptideIdentification
        Feature f;
        f.getPeptideIdentifications().push_back(pep_id);
        f.setIntensity(protein_hit->getMetaValue("intensity"));

        // copy intensity meta-values and additional annotations from Protein to Feature
        StringList keys;
        protein_hit->getKeys(keys);
        for (StringList::const_iterator it_key = keys.begin(); it_key != keys.end(); ++it_key)
        {
          f.setMetaValue(*it_key, protein_hit->getMetaValue(*it_key));
        }

        // add Feature to SimTypes::FeatureMapSim
        feature_map.push_back(f);
      }

      return;
    }


    UInt min_peptide_length = param_.getValue("min_peptide_length");
    bool use_log_model = param_.getValue("model") == "trained" ? true : false;
    UInt missed_cleavages = param_.getValue("model_naive:missed_cleavages");
    double cleave_threshold = param_.getValue("model_trained:threshold");

    EnzymaticDigestion digestion;
    digestion.setEnzyme(digestion.getEnzymeByName((String)param_.getValue("enzyme")));
    digestion.setLogModelEnabled(use_log_model);
    digestion.setLogThreshold(cleave_threshold);

    std::vector<AASequence> digestion_products;

    // keep track of generated features
    std::map<AASequence, Feature> generated_features;

    // Iterate through ProteinHits in the FeatureMap and digest them
    for (std::vector<ProteinHit>::iterator protein_hit = feature_map.getProteinIdentifications()[0].getHits().begin();
         protein_hit != feature_map.getProteinIdentifications()[0].getHits().end();
         ++protein_hit)
    {
      // determine abundance of each digestion product (this is quite long now...)
      // we assume that each digestion product will have the same abundance
      // note: missed cleavages reduce overall abundance as they combine two (or more) single peptides

      // how many "atomic"(i.e. non-cleavable) peptides are created?
      digestion.setMissedCleavages(0);
      Size complete_digest_count = digestion.peptideCount(AASequence::fromString(protein_hit->getSequence()));
      // compute average number of "atomic" peptides summed from all digestion products
      Size number_atomic_whole = 0;
      Size number_of_digestion_products = 0;
      for (Size i = 0; (i <= missed_cleavages) && (i < complete_digest_count); ++i)
      {
        number_atomic_whole += (complete_digest_count - i) * (i + 1);
        number_of_digestion_products += (complete_digest_count - i);
      }

      // mean number of "atomic" peptides per digestion product is now: number_atomic_whole / number_of_digestion_products
      // -> thus abundance of a digestion product is: #proteins / avg#of"atomic"peptides
      // i.e.: protein->second / (number_atomic_whole / number_of_digestion_products)

      Map<String, SimTypes::SimIntensityType> intensities;
      StringList keys;
      protein_hit->getKeys(keys);
      for (StringList::const_iterator it_key = keys.begin(); it_key != keys.end(); ++it_key)
      {
        if (!it_key->hasPrefix("intensity"))
          continue;
        intensities[*it_key] = std::max(SimTypes::SimIntensityType(1), SimTypes::SimIntensityType(protein_hit->getMetaValue(*it_key))
                                        * SimTypes::SimIntensityType(number_of_digestion_products)
                                        / SimTypes::SimIntensityType(number_atomic_whole)); // order changed for numeric stability
      }

      // do real digest
      digestion.setMissedCleavages(missed_cleavages);
      digestion.digest(AASequence::fromString(protein_hit->getSequence()), digestion_products);

      for (std::vector<AASequence>::const_iterator dp_it = digestion_products.begin();
           dp_it != digestion_products.end();
           ++dp_it)
      {
        if (dp_it->size() < min_peptide_length)
          continue;

        // sum equal peptide's intensities
        // *dp_it -> peptide
        // If we see this Peptide the first time -> generate corresponding feature
        if (generated_features.count(*dp_it) == 0)
        {
          PeptideHit pep_hit(1.0, 1, 0, *dp_it);

          PeptideIdentification pep_id;
          pep_id.insertHit(pep_hit);

          // create feature
          Feature f;
          f.getPeptideIdentifications().push_back(pep_id);
          // set intensity to 0 to avoid problems when summing up
          f.setIntensity(0.0);

          // copy all non-intensity meta values
          StringList lkeys;
          protein_hit->getKeys(lkeys);
          for (StringList::iterator key = lkeys.begin(); key != lkeys.end(); ++key)
          {
            if (!key->hasPrefix("intensity"))
            {
              f.setMetaValue(*key, protein_hit->getMetaValue(*key));
            }
          }

          // insert into map
          generated_features.insert(std::make_pair(*dp_it, f));
        }

        // sum up intensity values
        generated_features[*dp_it].setIntensity(generated_features[*dp_it].getIntensity() + intensities["intensity"]);
        // ... same for other intensities (iTRAQ...)
        for (Map<String, SimTypes::SimIntensityType>::const_iterator it_other = intensities.begin(); it_other != intensities.end(); ++it_other)
        {
          if (!generated_features[*dp_it].metaValueExists(it_other->first))
          {
            generated_features[*dp_it].setMetaValue(it_other->first, it_other->second);
          }
          else
          {
            generated_features[*dp_it].setMetaValue(it_other->first, SimTypes::SimIntensityType(generated_features[*dp_it].getMetaValue(it_other->first)) + it_other->second);
          }
        }

        // add current protein accession
        // existing proteins accessions ...
        std::set<String> protein_accessions = generated_features[*dp_it].getPeptideIdentifications()[0].getHits()[0].extractProteinAccessions();

        // ... add accession of current protein
        protein_accessions.insert(protein_hit->getAccession());

        std::vector<PeptideIdentification> pep_idents = generated_features[*dp_it].getPeptideIdentifications();
        std::vector<PeptideHit> pep_hits = pep_idents[0].getHits();

        for (std::set<String>::const_iterator s_it = protein_accessions.begin(); s_it != protein_accessions.end(); ++s_it)
        {
          PeptideEvidence pe;
          pe.setProteinAccession(*s_it);
          pep_hits[0].addPeptideEvidence(pe);
        }
        pep_idents[0].setHits(pep_hits);
        generated_features[*dp_it].setPeptideIdentifications(pep_idents);
      }
    }

    // add generated_features to FeatureMap
    for (std::map<AASequence, Feature>::iterator it_gf = generated_features.begin();
         it_gf != generated_features.end();
         ++it_gf)
    {
      // round up intensity
      (it_gf->second).setIntensity(ceil((it_gf->second).getIntensity()));
      feature_map.push_back(it_gf->second);
    }

  }
예제 #2
0
  ExitCodes main_(int, const char**)
  {
    String inputfile_name = getStringOption_("in");
    String outputfile_name = getStringOption_("out");

    vector<ProteinIdentification> proteins;
    vector<PeptideIdentification> peptides;
    IdXMLFile().load(inputfile_name, proteins, peptides);

    Size n_prot_ids = proteins.size();
    Size n_prot_hits = IDFilter::countHits(proteins);
    Size n_pep_ids = peptides.size();
    Size n_pep_hits = IDFilter::countHits(peptides);


    // Filtering peptide identification according to set criteria

    double rt_high = numeric_limits<double>::infinity(), rt_low = -rt_high;
    if (parseRange_(getStringOption_("precursor:rt"), rt_low, rt_high))
    {
      LOG_INFO << "Filtering peptide IDs by precursor RT..." << endl;
      IDFilter::filterPeptidesByRT(peptides, rt_low, rt_high);
    }

    double mz_high = numeric_limits<double>::infinity(), mz_low = -mz_high;
    if (parseRange_(getStringOption_("precursor:mz"), mz_low, mz_high))
    {
      LOG_INFO << "Filtering peptide IDs by precursor m/z...";
      IDFilter::filterPeptidesByMZ(peptides, mz_low, mz_high);
    }


    // Filtering peptide hits according to set criteria

    if (getFlag_("unique"))
    {
      LOG_INFO << "Removing duplicate peptide hits..." << endl;
      IDFilter::removeDuplicatePeptideHits(peptides);
    }

    if (getFlag_("unique_per_protein"))
    {
      LOG_INFO << "Filtering peptides by unique match to a protein..." << endl;
      IDFilter::keepUniquePeptidesPerProtein(peptides);
    }

    double peptide_significance = getDoubleOption_("thresh:pep");
    if (peptide_significance > 0)
    {
      LOG_INFO << "Filtering by peptide significance threshold..." << endl;
      IDFilter::filterHitsBySignificance(peptides, peptide_significance);
    }

    double pred_rt_pv = getDoubleOption_("rt:p_value");
    if (pred_rt_pv > 0)
    {
      LOG_INFO << "Filtering by RT prediction p-value..." << endl;
      IDFilter::filterPeptidesByRTPredictPValue(
        peptides, "predicted_RT_p_value", pred_rt_pv);
    }

    double pred_rt_pv_1d = getDoubleOption_("rt:p_value_1st_dim");
    if (pred_rt_pv_1d > 0)
    {
      LOG_INFO << "Filtering by RT prediction p-value (first dim.)..." << endl;
      IDFilter::filterPeptidesByRTPredictPValue(
        peptides, "predicted_RT_p_value_first_dim", pred_rt_pv_1d);
    }

    String whitelist_fasta = getStringOption_("whitelist:proteins").trim();
    if (!whitelist_fasta.empty())
    {
      LOG_INFO << "Filtering by protein whitelisting (FASTA input)..." << endl;
      // load protein accessions from FASTA file:
      vector<FASTAFile::FASTAEntry> fasta;
      FASTAFile().load(whitelist_fasta, fasta);
      set<String> accessions;
      for (vector<FASTAFile::FASTAEntry>::iterator it = fasta.begin();
           it != fasta.end(); ++it)
      {
        accessions.insert(it->identifier);
      }
      IDFilter::keepHitsMatchingProteins(peptides, accessions);
      IDFilter::keepHitsMatchingProteins(proteins, accessions);
    }

    vector<String> whitelist_accessions =
      getStringList_("whitelist:protein_accessions");
    if (!whitelist_accessions.empty())
    {
      LOG_INFO << "Filtering by protein whitelisting (accessions input)..."
               << endl;
      set<String> accessions(whitelist_accessions.begin(),
                             whitelist_accessions.end());
      IDFilter::keepHitsMatchingProteins(peptides, accessions);
      IDFilter::keepHitsMatchingProteins(proteins, accessions);
    }

    String whitelist_peptides = getStringOption_("whitelist:peptides").trim();
    if (!whitelist_peptides.empty())
    {
      LOG_INFO << "Filtering by inclusion peptide whitelisting..." << endl;
      vector<PeptideIdentification> inclusion_peptides;
      vector<ProteinIdentification> inclusion_proteins; // ignored
      IdXMLFile().load(whitelist_peptides, inclusion_proteins,
                       inclusion_peptides);
      bool ignore_mods = getFlag_("whitelist:ignore_modifications");
      IDFilter::keepPeptidesWithMatchingSequences(peptides, inclusion_peptides,
                                                  ignore_mods);
    }

    vector<String> whitelist_mods = getStringList_("whitelist:modifications");
    if (!whitelist_mods.empty())
    {
      LOG_INFO << "Filtering peptide IDs by modification whitelisting..."
               << endl;
      set<String> good_mods(whitelist_mods.begin(), whitelist_mods.end());
      IDFilter::keepPeptidesWithMatchingModifications(peptides, good_mods);
    }

    String blacklist_fasta = getStringOption_("blacklist:proteins").trim();
    if (!blacklist_fasta.empty())
    {
      LOG_INFO << "Filtering by protein blacklisting (FASTA input)..." << endl;
      // load protein accessions from FASTA file:
      vector<FASTAFile::FASTAEntry> fasta;
      FASTAFile().load(blacklist_fasta, fasta);
      set<String> accessions;
      for (vector<FASTAFile::FASTAEntry>::iterator it = fasta.begin();
           it != fasta.end(); ++it)
      {
        accessions.insert(it->identifier);
      }
      IDFilter::removeHitsMatchingProteins(peptides, accessions);
      IDFilter::removeHitsMatchingProteins(proteins, accessions);
    }

    vector<String> blacklist_accessions =
      getStringList_("blacklist:protein_accessions");
    if (!blacklist_accessions.empty())
    {
      LOG_INFO << "Filtering by protein blacklisting (accessions input)..."
               << endl;
      set<String> accessions(blacklist_accessions.begin(),
                             blacklist_accessions.end());
      IDFilter::removeHitsMatchingProteins(peptides, accessions);
      IDFilter::removeHitsMatchingProteins(proteins, accessions);
    }

    String blacklist_peptides = getStringOption_("blacklist:peptides").trim();
    if (!blacklist_peptides.empty())
    {
      LOG_INFO << "Filtering by exclusion peptide blacklisting..." << endl;
      vector<PeptideIdentification> exclusion_peptides;
      vector<ProteinIdentification> exclusion_proteins; // ignored
      IdXMLFile().load(blacklist_peptides, exclusion_proteins,
                       exclusion_peptides);
      bool ignore_mods = getFlag_("blacklist:ignore_modifications");
      IDFilter::removePeptidesWithMatchingSequences(
        peptides, exclusion_peptides, ignore_mods);
    }

    vector<String> blacklist_mods = getStringList_("blacklist:modifications");
    if (!blacklist_mods.empty())
    {
      LOG_INFO << "Filtering peptide IDs by modification blacklisting..."
               << endl;
      set<String> bad_mods(blacklist_mods.begin(), blacklist_mods.end());
      IDFilter::removePeptidesWithMatchingModifications(peptides, bad_mods);
    }


    if (getFlag_("best:strict"))
    {
      LOG_INFO << "Filtering by best peptide hits..." << endl;
      IDFilter::keepBestPeptideHits(peptides, true);
    }


    Int min_length = 0, max_length = 0;
    if (parseRange_(getStringOption_("length"), min_length, max_length))
    {
      LOG_INFO << "Filtering by peptide length..." << endl;
      if ((min_length < 0) || (max_length < 0))
      {
        LOG_ERROR << "Fatal error: negative values are not allowed for parameter 'length'" << endl;
        return ILLEGAL_PARAMETERS;
      }
      IDFilter::filterPeptidesByLength(peptides, Size(min_length),
                                       Size(max_length));
    }

    // Filter by digestion enzyme product

    String protein_fasta = getStringOption_("digest:fasta").trim();
    if (!protein_fasta.empty())
    {
      LOG_INFO << "Filtering peptides by digested protein (FASTA input)..." << endl;
      // load protein accessions from FASTA file:
      vector<FASTAFile::FASTAEntry> fasta;
      FASTAFile().load(protein_fasta, fasta);

      // Configure Enzymatic digestion
      EnzymaticDigestion digestion;
      String enzyme = getStringOption_("digest:enzyme").trim();
      if (!enzyme.empty())
      {
        digestion.setEnzyme(enzyme);
      }

      String specificity = getStringOption_("digest:specificity").trim();
      if (!specificity.empty())
      {
        digestion.setSpecificity(digestion.getSpecificityByName(specificity));
      }

      Int missed_cleavages = getIntOption_("digest:missed_cleavages");
      bool ignore_missed_cleavages = true;
      if (missed_cleavages > -1)
      {
        ignore_missed_cleavages = false;
        if (digestion.getSpecificity() == EnzymaticDigestion::SPEC_FULL)
        {
          LOG_WARN << "Specificity not full, missed_cleavages option is redundant" << endl;
        }
        digestion.setMissedCleavages(missed_cleavages);
      }
      
      bool methionine_cleavage = false;
      if (getFlag_("digest:methionine_cleavage"))
      {
        methionine_cleavage = true;
      }

      // Build the digest filter function
      IDFilter::DigestionFilter filter(fasta, 
                                       digestion, 
                                       ignore_missed_cleavages, 
                                       methionine_cleavage);
      // Filter peptides
      filter.filterPeptideEvidences(peptides);
    }


    if (getFlag_("var_mods"))
    {
      LOG_INFO << "Filtering for variable modifications..." << endl;
      // gather possible variable modifications from search parameters:
      set<String> var_mods;
      for (vector<ProteinIdentification>::iterator prot_it = proteins.begin();
           prot_it != proteins.end(); ++prot_it)
      {
        const ProteinIdentification::SearchParameters& params =
          prot_it->getSearchParameters();
        for (vector<String>::const_iterator mod_it =
               params.variable_modifications.begin(); mod_it !=
               params.variable_modifications.end(); ++mod_it)
        {
          var_mods.insert(*mod_it);
        }
      }
      IDFilter::keepPeptidesWithMatchingModifications(peptides, var_mods);
    }

    double pep_score = getDoubleOption_("score:pep");
    // @TODO: what if 0 is a reasonable cut-off for some score?
    if (pep_score != 0)
    {
      LOG_INFO << "Filtering by peptide score..." << endl;
      IDFilter::filterHitsByScore(peptides, pep_score);
    }

    Int min_charge = numeric_limits<Int>::min(), max_charge =
      numeric_limits<Int>::max();
    if (parseRange_(getStringOption_("charge"), min_charge, max_charge))
    {
      LOG_INFO << "Filtering by peptide charge..." << endl;
      IDFilter::filterPeptidesByCharge(peptides, min_charge, max_charge);
    }

    Size best_n_pep = getIntOption_("best:n_peptide_hits");
    if (best_n_pep > 0)
    {
      LOG_INFO << "Filtering by best n peptide hits..." << endl;
      IDFilter::keepNBestHits(peptides, best_n_pep);
    }

    Int min_rank = 0, max_rank = 0;
    if (parseRange_(getStringOption_("best:n_to_m_peptide_hits"), min_rank,
                    max_rank))
    {
      LOG_INFO << "Filtering by peptide hit ranks..." << endl;
      if ((min_rank < 0) || (max_rank < 0))
      {
        LOG_ERROR << "Fatal error: negative values are not allowed for parameter 'best:n_to_m_peptide_hits'" << endl;
        return ILLEGAL_PARAMETERS;
      }
      IDFilter::filterHitsByRank(peptides, Size(min_rank), Size(max_rank));
    }

    double mz_error = getDoubleOption_("mz:error");
    if (mz_error > 0)
    {
      LOG_INFO << "Filtering by mass error..." << endl;
      bool unit_ppm = (getStringOption_("mz:unit") == "ppm");
      IDFilter::filterPeptidesByMZError(peptides, mz_error, unit_ppm);
    }


    // Filtering protein identifications according to set criteria

    double protein_significance = getDoubleOption_("thresh:prot");
    if (protein_significance > 0)
    {
      LOG_INFO << "Filtering by protein significance threshold..." << endl;
      IDFilter::filterHitsBySignificance(proteins, protein_significance);
    }

    double prot_score = getDoubleOption_("score:prot");
    // @TODO: what if 0 is a reasonable cut-off for some score?
    if (prot_score != 0)
    {
      LOG_INFO << "Filtering by protein score..." << endl;
      IDFilter::filterHitsByScore(proteins, prot_score);
    }

    Size best_n_prot = getIntOption_("best:n_protein_hits");
    if (best_n_prot > 0)
    {
      LOG_INFO << "Filtering by best n protein hits..." << endl;
      IDFilter::keepNBestHits(proteins, best_n_prot);
    }

    if (getFlag_("remove_decoys"))
    {
      LOG_INFO << "Removing decoy hits..." << endl;
      IDFilter::removeDecoyHits(peptides);
      IDFilter::removeDecoyHits(proteins);
    }


    // Clean-up:

    if (!getFlag_("keep_unreferenced_protein_hits"))
    {
      LOG_INFO << "Removing unreferenced protein hits..." << endl;
      IDFilter::removeUnreferencedProteins(proteins, peptides);
    }

    IDFilter::updateHitRanks(proteins);
    IDFilter::updateHitRanks(peptides);

    // remove non-existant protein references from peptides (and optionally:
    // remove peptides with no proteins):
    bool rm_pep = getFlag_("delete_unreferenced_peptide_hits");
    if (rm_pep) LOG_INFO << "Removing peptide hits without protein references..." << endl;
    IDFilter::updateProteinReferences(peptides, proteins, rm_pep);

    IDFilter::removeEmptyIdentifications(peptides);
    // we want to keep "empty" protein IDs because they contain search meta data

    // update protein groupings if necessary:
    for (vector<ProteinIdentification>::iterator prot_it = proteins.begin();
         prot_it != proteins.end(); ++prot_it)
    {
      bool valid = IDFilter::updateProteinGroups(prot_it->getProteinGroups(),
                                                 prot_it->getHits());
      if (!valid)
      {
        LOG_WARN << "Warning: While updating protein groups, some proteins were removed from groups that are still present. The new grouping (especially the group probabilities) may not be completely valid any more." << endl;
      }

      valid = IDFilter::updateProteinGroups(
        prot_it->getIndistinguishableProteins(), prot_it->getHits());
      if (!valid)
      {
        LOG_WARN << "Warning: While updating indistinguishable proteins, some proteins were removed from groups that are still present. The new grouping (especially the group probabilities) may not be completely valid any more." << endl;
      }
    }

    // some stats
    LOG_INFO << "Before filtering:\n"
             << n_prot_ids << " protein identification(s) with "
             << n_prot_hits << " protein hit(s),\n"
             << n_pep_ids << " peptide identification(s) with "
             << n_pep_hits << " peptides hit(s).\n"
             << "After filtering:\n"
             << proteins.size() << " protein identification(s) with "
             << IDFilter::countHits(proteins) << " protein hit(s),\n"
             << peptides.size() << " peptide identification(s) with "
             << IDFilter::countHits(peptides) << " peptides hit(s)." << endl;

    IdXMLFile().store(outputfile_name, proteins, peptides);

    return EXECUTION_OK;
  }
예제 #3
0
  void ProteinResolver::buildingISDGroups_(vector<ProteinEntry> & protein_nodes, vector<PeptideEntry> & peptide_nodes,
                                           vector<ISDGroup> & isd_groups)
  {
    EnzymaticDigestion digestor;
    String enzyme_name = param_.getValue("resolver:enzyme");
    digestor.setEnzyme(digestor.getEnzymeByName(enzyme_name));
    UInt min_size = param_.getValue("resolver:min_length");
    UInt missed_cleavages = param_.getValue("resolver:missed_cleavages");
    digestor.setMissedCleavages(missed_cleavages);


    //-------------------------------------------------------------
    // building ISD Groups
    //-------------------------------------------------------------

    vector<AASequence> temp_peptides;
    map<String, set<Size> > peptides;

    for (Size i = 0; i < protein_data_.size(); ++i)
    {
      protein_nodes[i].fasta_entry = &protein_data_[i];
      protein_nodes[i].traversed = false;
      protein_nodes[i].index = i;
      protein_nodes[i].protein_type = ProteinEntry::secondary;
      protein_nodes[i].weight = AASequence(protein_data_[i].sequence).getMonoWeight();
      protein_nodes[i].coverage = 0.;
      protein_nodes[i].number_of_experimental_peptides = 0;
      digestor.digest(AASequence(protein_data_[i].sequence), temp_peptides);
      for (Size j = 0; j < temp_peptides.size(); ++j)
      {
        if (temp_peptides[j].size() >= min_size)
        {
          peptides[temp_peptides[j].toUnmodifiedString()].insert(i);
        }
      }
    }
    // important to resize
    peptide_nodes.resize(peptides.size());
    vector<PeptideEntry>::iterator pep_node = peptide_nodes.begin();
    Size peptide_counter = 0;

    for (map<String, set<Size> >::iterator i  = peptides.begin(); i != peptides.end(); ++i, ++pep_node, ++peptide_counter)
    {
      pep_node->index = peptide_counter;
      pep_node->traversed = false;
      pep_node->sequence = (*i).first;
      pep_node->experimental = false;
      for (set<Size>::iterator j = (*i).second.begin(); j != (*i).second.end(); ++j)
      {
        pep_node->proteins.push_back(&protein_nodes[*j]);
        protein_nodes[*j].peptides.push_back(&*pep_node);
      }
    }
    //ISDGraph constructed
    Size isd_group_counter = 0;
    Size i = 0;
    for (vector<ProteinEntry>::iterator prot_node = protein_nodes.begin(); prot_node != protein_nodes.end(); ++prot_node)
    {
      ++i;
      if (!prot_node->traversed)
      {
        prot_node->traversed = true;
        ISDGroup group;
        group.index = isd_group_counter;
        ++isd_group_counter;
        traversProtein_(&*prot_node, group);
        isd_groups.push_back(group);
      }
    }
  }
예제 #4
0
  ExitCodes main_(int, const char **)
  {
    vector<ProteinIdentification> protein_identifications;

    vector<PeptideIdentification> identifications;
    PeptideIdentification peptide_identification;
    DateTime date_time = DateTime::now();
    String date_time_string = date_time.get();
    peptide_identification.setIdentifier("In-silico_digestion" + date_time_string);

    ProteinIdentification protein_identification;

    protein_identifications.push_back(ProteinIdentification());
    //-------------------------------------------------------------
    // parsing parameters
    //-------------------------------------------------------------
    String inputfile_name = getStringOption_("in");
    String outputfile_name = getStringOption_("out");

    //input file type
    FileHandler fh;
    FileTypes::Type out_type = FileTypes::nameToType(getStringOption_("out_type"));

    if (out_type == FileTypes::UNKNOWN)
    {
      out_type = fh.getTypeByFileName(outputfile_name);
      writeDebug_(String("Output file type: ") + FileTypes::typeToName(out_type), 2);
    }

    if (out_type == FileTypes::UNKNOWN)
    {
      LOG_ERROR << ("Error: Could not determine output file type!") << std::endl;
      return PARSE_ERROR;
    }

    Size min_size = getIntOption_("min_length");
    Size max_size = getIntOption_("max_length");
    Size missed_cleavages = getIntOption_("missed_cleavages");


    bool has_FASTA_output = (out_type == FileTypes::FASTA);

    //-------------------------------------------------------------
    // reading input
    //-------------------------------------------------------------
    std::vector<FASTAFile::FASTAEntry> protein_data;
    FASTAFile().load(inputfile_name, protein_data);
    //-------------------------------------------------------------
    // calculations
    //-------------------------------------------------------------

    // This should be updated if more cleavage enzymes are available
    ProteinIdentification::SearchParameters search_parameters;
    String enzyme = getStringOption_("enzyme");
    EnzymaticDigestion digestor;
    if (enzyme == "Trypsin")
    {
      digestor.setEnzyme(EnzymaticDigestion::ENZYME_TRYPSIN);
      digestor.setMissedCleavages(missed_cleavages);
      search_parameters.enzyme = ProteinIdentification::TRYPSIN;
    }
    else if (enzyme == "none")
    {
      search_parameters.enzyme = ProteinIdentification::NO_ENZYME;
    }
    else
    {
      LOG_ERROR << "Internal error in Digestor, when evaluating enzyme name! Please report this!" << std::endl;
      return ILLEGAL_PARAMETERS;
    }

    vector<String> protein_accessions(1);
    PeptideHit temp_peptide_hit;

    protein_identifications[0].setSearchParameters(search_parameters);
    protein_identifications[0].setDateTime(date_time);
    protein_identifications[0].setSearchEngine("In-silico digestion");
    protein_identifications[0].setIdentifier("In-silico_digestion" + date_time_string);

    std::vector<FASTAFile::FASTAEntry> all_peptides;

    Size dropped_bylength(0);   // stats for removing candidates

    for (Size i = 0; i < protein_data.size(); ++i)
    {
      if (!has_FASTA_output)
      {
        protein_accessions[0] = protein_data[i].identifier;
        ProteinHit temp_protein_hit;
        temp_protein_hit.setSequence(protein_data[i].sequence);
        temp_protein_hit.setAccession(protein_accessions[0]);
        protein_identifications[0].insertHit(temp_protein_hit);
        temp_peptide_hit.setProteinAccessions(protein_accessions);
      }

      vector<AASequence> temp_peptides;
      if (enzyme == "none")
      {
        temp_peptides.push_back(AASequence(protein_data[i].sequence));
      }
      else
      {
        digestor.digest(AASequence(protein_data[i].sequence), temp_peptides);
      }

      for (Size j = 0; j < temp_peptides.size(); ++j)
      {
        if ((temp_peptides[j].size() >= min_size) &&
            (temp_peptides[j].size() <= max_size))
        {
          if (!has_FASTA_output)
          {
            temp_peptide_hit.setSequence(temp_peptides[j]);
            peptide_identification.insertHit(temp_peptide_hit);
            identifications.push_back(peptide_identification);
            peptide_identification.setHits(std::vector<PeptideHit>());   // clear
          }
          else   // for FASTA file output
          {
            FASTAFile::FASTAEntry pep(protein_data[i].identifier, protein_data[i].description, temp_peptides[j].toString());
            all_peptides.push_back(pep);
          }
        }
        else
        {
          ++dropped_bylength;
        }
      }
    }

    //-------------------------------------------------------------
    // writing output
    //-------------------------------------------------------------

    if (has_FASTA_output)
    {
      FASTAFile().store(outputfile_name, all_peptides);
    }
    else
    {
      IdXMLFile().store(outputfile_name,
                        protein_identifications,
                        identifications);
    }

    Size pep_remaining_count = (has_FASTA_output ? all_peptides.size() : identifications.size());
    LOG_INFO << "Statistics:\n"
             << "  total #peptides after digestion:         " << pep_remaining_count + dropped_bylength << "\n"
             << "  removed #peptides (length restrictions): " << dropped_bylength << "\n"
             << "  remaining #peptides:                     " << pep_remaining_count << std::endl;

    return EXECUTION_OK;
  }
START_TEST(EnzymaticDigestion, "$Id$")

/////////////////////////////////////////////////////////////
    EnzymaticDigestion * e_ptr = 0;
    EnzymaticDigestion* e_nullPointer = 0;
    START_SECTION((EnzymaticDigestion()))
    e_ptr = new EnzymaticDigestion;
    TEST_NOT_EQUAL(e_ptr, e_nullPointer)
END_SECTION

START_SECTION([EXTRA] ~EnzymaticDigestion())
    delete e_ptr;
END_SECTION

START_SECTION((EnzymaticDigestion(const EnzymaticDigestion &rhs)))
    EnzymaticDigestion ed;
    ed.setMissedCleavages(1234);
    ed.setEnzyme("no cleavage");
    ed.setSpecificity(EnzymaticDigestion::SPEC_SEMI);
    
    EnzymaticDigestion ed2(ed);
    
    TEST_EQUAL(ed.getMissedCleavages(), ed2.getMissedCleavages());
    TEST_EQUAL(ed.getEnzymeName(), ed2.getEnzymeName());
    TEST_EQUAL(ed.getSpecificity(), ed2.getSpecificity());

END_SECTION

START_SECTION((EnzymaticDigestion & operator=(const EnzymaticDigestion &rhs)))
    EnzymaticDigestion ed;
    ed.setMissedCleavages(1234);
/////////////////////////////////////////////////////////////

EnzymaticDigestion* e_ptr = 0;
EnzymaticDigestion* e_nullPointer = 0;
START_SECTION((EnzymaticDigestion()))
	e_ptr = new EnzymaticDigestion;
	TEST_NOT_EQUAL(e_ptr, e_nullPointer)
END_SECTION

START_SECTION([EXTRA] ~EnzymaticDigestion())
	delete e_ptr;
END_SECTION

START_SECTION((EnzymaticDigestion(const EnzymaticDigestion& rhs) ))
  EnzymaticDigestion ed;
  ed.setMissedCleavages(1234);
  ed.setEnzyme(EnzymaticDigestion::SIZE_OF_ENZYMES);
  ed.setSpecificity(EnzymaticDigestion::SPEC_SEMI);
  ed.setLogModelEnabled(true);
  ed.setLogThreshold(81231);
  
  EnzymaticDigestion ed2(ed);

  TEST_EQUAL(ed.getMissedCleavages(), ed2.getMissedCleavages());
  TEST_EQUAL(ed.getEnzyme(), ed2.getEnzyme());
  TEST_EQUAL(ed.getSpecificity(), ed2.getSpecificity());
  TEST_EQUAL(ed.isLogModelEnabled(), ed2.isLogModelEnabled());
  TEST_EQUAL(ed.getLogThreshold(), ed2.getLogThreshold());

END_SECTION