def main():
first_pass_results_filename = sys.argv[1]
second_pass_results_filename = sys.argv[2]
fasta_db_filename = sys.argv[3]
second_pass_proteins_filename = sys.argv[4]
output_first_pass_peptides = sys.argv[5]
output_second_pass_peptides = sys.argv[6]
output_psms_first_pass = sys.argv[7]
output_psms_updated_evalues = sys.argv[8]
output_original_high_FDR_psms = sys.argv[9]
output_updated_high_FDR_psms = sys.argv[10]
#Low FDR Original and updated evals
psm_list_first_pass = ming_psm_library.PSMset(first_pass_results_filename)
psm_list_first_pass.load_MSGF_Plus_tsvfile(first_pass_results_filename)
psm_list_first_pass.remove_duplicated_rows()
psm_list_first_pass.filter_to_fdr_by_length(0.01)
psm_list_first_pass.write_output(open(output_psms_first_pass, "w"))
update_psm_set_with_second_pass_psms(first_pass_results_filename,
second_pass_results_filename,
output_psms_updated_evalues)
#High FDR for other purposes to show things
psm_list_first_pass = ming_psm_library.PSMset(first_pass_results_filename)
psm_list_first_pass.load_MSGF_Plus_tsvfile(first_pass_results_filename)
psm_list_first_pass.remove_duplicated_rows()
psm_list_first_pass.filter_to_fdr_by_length(0.01)
psm_list_first_pass.write_output(open(output_original_high_FDR_psms, "w"))
update_psm_set_with_second_pass_psms(first_pass_results_filename,
second_pass_results_filename,
output_updated_high_FDR_psms, 0.05)
#Precursor Level
psm_list_first_pass = ming_psm_library.PSMset(first_pass_results_filename)
psm_list_first_pass.load_MSGF_Plus_tsvfile(first_pass_results_filename)
psm_list_first_pass.remove_duplicated_rows()
full_peptides_list_first_pass = library_creation.create_library_unique_peptides_filtered(
[psm_list_first_pass], filter_by_length=True)
full_peptides_list_first_pass.write_output(
open(output_first_pass_peptides, "w"))
psm_list_second_pass = ming_psm_library.PSMset(
second_pass_results_filename)
psm_list_second_pass.load_MSGF_Plus_tsvfile(second_pass_results_filename)
psm_list_second_pass.remove_duplicated_rows()
full_peptides_list_second_pass = library_creation.create_library_unique_peptides_filtered(
[psm_list_second_pass], filter_by_length=True)
full_peptides_list_second_pass.write_output(
open(output_second_pass_peptides, "w"))
def get_first_pass_variant_set(first_pass_results_filename):
psm_list_first_pass = ming_psm_library.PSMset(first_pass_results_filename)
psm_list_first_pass.load_MSGF_Plus_tsvfile(first_pass_results_filename)
psm_list_first_pass.filter_to_fdr_by_length(0.05)
print "First Pass PSMs: " + str(len(psm_list_first_pass))
full_peptides_list_first_pass = library_creation.create_library_unique_peptides_filtered(
[psm_list_first_pass], filter_by_length=True)
print "First Pass Variants: " + str(len(full_peptides_list_first_pass))
return full_peptides_list_first_pass
def save_psms_as_peptides(psm_set, output_peptide_path, fdr):
peptide_variant_set = library_creation.create_library_unique_peptides_filtered(
[psm_set], fdr, filter_by_length=True)
psm_set = ming_psm_library.PSMset("task results")
for peptide in peptide_variant_set.peptide_list:
psm_set.psms.append(peptide.get_best_psm())
output_pickle = open(output_peptide_path, 'wb')
pickle.dump(psm_set, output_pickle, pickle.HIGHEST_PROTOCOL)
output_pickle.close()
return peptide_variant_set
def main():
input_searchresults_filename = sys.argv[1]
output_peptide_list = sys.argv[2]
output_peptide_list_with_decoy_filename = sys.argv[3]
psm_list = ming_psm_library.PSMset(input_searchresults_filename)
psm_list.load_MSGF_Plus_tsvfile(input_searchresults_filename)
psm_list.filter_to_fdr_by_length(0.01)
print len(psm_list)
full_peptides_list = library_creation.create_library_unique_peptides_filtered(
[psm_list], fdr=0.01, filter_by_length=True)
output_file = open(output_peptide_list, "w")
all_peptides = [
peptide.get_stripped_sequence()
for peptide in full_peptides_list.peptide_list
]
all_peptides = list(set(all_peptides))
for peptide in all_peptides:
output_file.write(peptide + "\n")
#Now lets load the PSMs and keep all variants, and then output them with the decoys present
print "GIVING US FULL RESULT SET"
psm_list = ming_psm_library.PSMset(input_searchresults_filename)
psm_list.load_MSGF_Plus_tsvfile(input_searchresults_filename)
full_peptides_list = library_creation.create_library_unique_peptides_filtered(
[psm_list], 1.0)
output_peptide_list_with_decoy_file = open(
output_peptide_list_with_decoy_filename, "w")
output_peptide_list_with_decoy_file.write(
ming_psm_library.PeptideVariant.output_header() + "\n")
for peptide in full_peptides_list.peptide_list:
output_peptide_list_with_decoy_file.write(str(peptide) + "\n")
def main():
input_fasta_filename = sys.argv[1]
input_searchresults_filename = sys.argv[2]
output_proteins_as_list = sys.argv[3]
proteome = ming_protein_library.parse_fasta_proteome_file(
input_fasta_filename)
#for protein in proteome.protein_list:
# print protein.protein
psm_list = ming_psm_library.PSMset(input_searchresults_filename)
psm_list.load_MSGF_Plus_tsvfile(input_searchresults_filename)
full_peptides_list = library_creation.create_library_unique_peptides_filtered(
[psm_list], 0.01, filter_by_length=True)
target_peptide_strings = []
decoy_peptide_strings = []
for peptide_obj in full_peptides_list.peptide_list:
peptide_to_search = peptide_obj.get_stripped_sequence()
if peptide_obj.is_decoy():
decoy_peptide_strings.append(peptide_to_search[::-1])
else:
target_peptide_strings.append(peptide_to_search)
protein_coverage_of_targets = proteome.get_proteins_with_number_of_peptides_covered_map(
target_peptide_strings)
protein_coverage_of_decoys = proteome.get_proteins_with_number_of_peptides_covered_map(
decoy_peptide_strings)
output_file = open(output_proteins_as_list, "w")
output_file.write(
"protein\tdecoy_count\ttarget_count\ttotal_count\tlength\n")
for protein in protein_coverage_of_targets:
output_string = protein + "\t"
output_string += str(protein_coverage_of_decoys[protein]) + "\t"
output_string += str(protein_coverage_of_targets[protein]) + "\t"
output_string += str(protein_coverage_of_targets[protein] +
protein_coverage_of_decoys[protein]) + "\t"
output_string += str(len(
proteome.protein_map[protein].sequence)) + "\n"
output_file.write(output_string)
output_file.close()
def grab_results_from_multipass(task_id, user, output_peptide_directory,
output_psm_directory):
return_dict = {}
return_dict["number_psms"] = 0
return_dict["number_peptides"] = 0
return_dict["task_id"] = task_id
#Copying the psm files
path_to_psm_files_list = ming_proteosafe_library.get_proteosafe_result_file_path(
task_id, user, "updated_eval_psms_with_kl_with_ambiguity")
if len(path_to_psm_files_list) == 1:
output_psm_path = os.path.join(output_psm_directory, task_id + ".psms")
path_to_param_file = ming_proteosafe_library.get_proteosafe_result_file_path(
task_id, user, "params")[0]
#path_to_merged_results = ming_proteosafe_library.get_proteosafe_backend_result_file_path(task_id, "mergedResult", "proteomics2")[0]
print(
ming_proteosafe_library.get_proteosafe_result_file_path(
task_id, user, "mergedResult"))
path_to_merged_results = ming_proteosafe_library.get_proteosafe_result_file_path(
task_id, user, "mergedResult")[0]
print(path_to_psm_files_list[0] + " to " + output_psm_path)
#name_demangle_filenames(path_to_psm_files_list[0], output_psm_path, path_to_param_file, "filename", "filename")
name_demangle_filenames_and_instrument_collision(
path_to_psm_files_list[0], output_psm_path, path_to_param_file,
path_to_merged_results, "filename", "filename")
#Now lets generate the peptide list from the psm list
psm_set = ming_psm_library.PSMset("task results")
psm_set.load_PSM_tsvfile(output_psm_path)
output_peptide_path = output_psm_path = os.path.join(
output_peptide_directory, task_id + ".peptides")
peptide_variant_set = library_creation.create_library_unique_peptides_filtered(
[psm_set], 0.01)
peptide_variant_set.write_output(open(output_peptide_path, "w"))
return_dict["number_psms"] = len(psm_set.psms)
return_dict["number_peptides"] = len(peptide_variant_set.peptide_list)
return return_dict
def main():
input_fasta_filename = sys.argv[1]
input_searchresults_filename = sys.argv[2]
output_fasta_filename = sys.argv[3]
output_proteins_as_list = sys.argv[4]
proteome = ming_protein_library.parse_fasta_proteome_file(input_fasta_filename)
#for protein in proteome.protein_list:
# print protein.protein
psm_list = ming_psm_library.PSMset(input_searchresults_filename)
psm_list.load_MSGF_Plus_tsvfile(input_searchresults_filename)
psm_list.filter_to_fdr_by_length(0.01)
print len(psm_list)
full_peptides_list = library_creation.create_library_unique_peptides_filtered([psm_list], fdr=0.01, filter_by_length=True)
#Testing efficient version fo this
all_peptide_strings = []
for peptide_obj in full_peptides_list.peptide_list:
peptide_to_search = peptide_obj.get_stripped_sequence()
all_peptide_strings.append(peptide_obj.get_stripped_sequence())
all_proteins = proteome.get_proteins_covered_by_k_peptides(all_peptide_strings, 2, True)
all_protein_names = []
for protein in all_proteins:
all_protein_names.append(protein.protein)
output_protein_filename = output_fasta_filename
open(output_protein_filename, "w").write(json.dumps(all_protein_names))
#Outputting the list of proteins
output_protein_list_file = open(output_proteins_as_list, "w")
output_protein_list_file.write("Protein\n")
for protein in all_protein_names:
output_protein_list_file.write(protein + "\n")
exit(0)
