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|
#########################################################################
# MacSyFinder - Detection of macromolecular systems in protein dataset #
# using systems modelling and similarity search. #
# Authors: Sophie Abby, Bertrand Neron #
# Copyright (c) 2014-2024 Institut Pasteur (Paris) and CNRS. #
# See the COPYRIGHT file for details #
# #
# This file is part of MacSyFinder package. #
# #
# MacSyFinder is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# MacSyFinder is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details . #
# #
# You should have received a copy of the GNU General Public License #
# along with MacSyFinder (COPYING). #
# If not, see <https://www.gnu.org/licenses/>. #
#########################################################################
import os
import argparse
import random
from macsypy.hit import CoreHit, ModelHit, Loner, MultiSystem, HitWeight
from macsypy.config import Config, MacsyDefaults
from macsypy.gene import CoreGene, ModelGene, Exchangeable, GeneStatus
from macsypy.profile import ProfileFactory
from macsypy.model import Model
from macsypy.registries import ModelLocation
from macsypy.cluster import Cluster
from macsypy.system import System, RejectedCandidate
from macsypy.solution import find_best_solutions, combine_clusters, combine_multisystems, Solution
from tests import MacsyTest
def _build_clusters(cfg, profile_factory):
model_name = 'foo'
model_location = ModelLocation(path=os.path.join(cfg.models_dir()[0], model_name))
models = {}
cg_sctn_flg = CoreGene(model_location, "sctN_FLG", profile_factory)
cg_sctj_flg = CoreGene(model_location, "sctJ_FLG", profile_factory)
cg_flgB = CoreGene(model_location, "flgB", profile_factory)
cg_tadZ = CoreGene(model_location, "tadZ", profile_factory)
cg_sctn = CoreGene(model_location, "sctN", profile_factory)
cg_sctj = CoreGene(model_location, "sctJ", profile_factory)
cg_gspd = CoreGene(model_location, "gspD", profile_factory)
cg_abc = CoreGene(model_location, "abc", profile_factory)
cg_sctc = CoreGene(model_location, "sctC", profile_factory)
###########
# Model A #
###########
models['A'] = Model("foo/A", 10)
mgA_sctn = ModelGene(cg_sctn, models['A'])
mgA_sctn_hom = Exchangeable(cg_sctn_flg, mgA_sctn)
mgA_sctn.add_exchangeable(mgA_sctn_hom)
mgA_sctj = ModelGene(cg_sctj, models['A'])
mgA_sctj_an = Exchangeable(cg_sctj_flg, mgA_sctj)
mgA_sctj.add_exchangeable(mgA_sctj_an)
mgA_gspd = ModelGene(cg_gspd, models['A'])
mgA_gspd_an = Exchangeable(cg_flgB, mgA_gspd)
mgA_gspd.add_exchangeable(mgA_gspd_an)
mgA_abc = ModelGene(cg_abc, models['A'])
mgA_abc_ho = Exchangeable(cg_tadZ, mgA_abc)
mgA_abc.add_exchangeable(mgA_abc_ho)
models['A'].add_mandatory_gene(mgA_sctn)
models['A'].add_mandatory_gene(mgA_sctj)
models['A'].add_accessory_gene(mgA_gspd)
models['A'].add_forbidden_gene(mgA_abc)
models['A']._min_mandatory_genes_required = 2
models['A']._min_genes_required = 2
###########
# Model B #
###########
models['B'] = Model("foo/B", 10)
mgB_sctn_flg = ModelGene(cg_sctn_flg, models['B'])
mgB_sctj_flg = ModelGene(cg_sctj_flg, models['B'])
mgB_flgB = ModelGene(cg_flgB, models['B'])
mgB_tadZ = ModelGene(cg_tadZ, models['B'])
models['B'].add_mandatory_gene(mgB_sctn_flg)
models['B'].add_mandatory_gene(mgB_sctj_flg)
models['B'].add_accessory_gene(mgB_flgB)
models['B'].add_accessory_gene(mgB_tadZ)
models['B']._min_mandatory_genes_required = 1
models['B']._min_genes_required = 2
###########
# Model C #
###########
models['C'] = Model("foo/C", 10)
mgC_sctn_flg = ModelGene(cg_sctn_flg, models['C'])
mgC_sctj_flg = ModelGene(cg_sctj_flg, models['C'])
mgC_flgB = ModelGene(cg_flgB, models['C'])
mgC_tadZ = ModelGene(cg_tadZ, models['C'])
mgC_gspd = ModelGene(cg_gspd, models['C'])
models['C'].add_mandatory_gene(mgC_sctn_flg)
models['C'].add_mandatory_gene(mgC_sctj_flg)
models['C'].add_mandatory_gene(mgC_flgB)
models['C'].add_accessory_gene(mgC_tadZ)
models['C'].add_accessory_gene(mgC_gspd)
models['C']._min_mandatory_genes_required = 1
models['C']._min_genes_required = 2
###########
# Model D #
###########
models['D'] = Model("foo/D", 10)
mgD_abc = ModelGene(cg_abc, models['D'])
mgD_sctn = ModelGene(cg_sctn, models['D'])
models['D'].add_mandatory_gene(mgD_abc)
models['D'].add_accessory_gene(mgD_sctn)
models['D']._min_mandatory_genes_required = 1
models['D']._min_genes_required = 1
###########
# Model E #
###########
models['E'] = Model("foo/E", 10)
mgE_gspd = ModelGene(cg_gspd, models['E'])
models['E'].add_accessory_gene(mgE_gspd)
models['E']._min_mandatory_genes_required = 0
models['E']._min_genes_required = 1
###########
# Model F #
###########
models['F'] = Model("foo/F", 10)
mgF_abc = ModelGene(cg_abc, models['F'])
models['F'].add_mandatory_gene(mgF_abc)
models['F']._min_mandatory_genes_required = 1
models['F']._min_genes_required = 1
#####################
# Model G idem as C #
#####################
models['G'] = Model("foo/G", 10)
mgG_sctn_flg = ModelGene(cg_sctn_flg, models['G'])
mgG_sctj_flg = ModelGene(cg_sctj_flg, models['G'])
mgG_flgB = ModelGene(cg_flgB, models['G'])
mgG_tadZ = ModelGene(cg_tadZ, models['G'])
mgG_gspd = ModelGene(cg_gspd, models['G'])
models['G'].add_mandatory_gene(mgG_sctn_flg)
models['G'].add_mandatory_gene(mgG_sctj_flg)
models['G'].add_mandatory_gene(mgG_flgB)
models['G'].add_accessory_gene(mgG_tadZ)
models['G'].add_accessory_gene(mgG_gspd)
#####################
# Model H idem as D #
#####################
models['H'] = Model("foo/H", 10)
mgH_abc = ModelGene(cg_abc, models['H'])
mgH_sctn = ModelGene(cg_sctn, models['H'])
models['H'].add_mandatory_gene(mgH_abc)
models['H'].add_accessory_gene(mgH_sctn)
models['H']._min_mandatory_genes_required = 1
models['H']._min_genes_required = 1
###########
# Model I #
###########
models['I'] = Model("foo/I", 10)
mgI_abc = ModelGene(cg_abc, models['I'])
mgI_flgB = ModelGene(cg_flgB, models['I'])
mgI_tadZ = ModelGene(cg_tadZ, models['I'])
models['I'].add_mandatory_gene(mgI_abc)
models['I'].add_mandatory_gene(mgI_flgB)
models['I'].add_accessory_gene(mgI_tadZ)
models['I']._min_mandatory_genes_required = 1
models['I']._min_genes_required = 1
###########
# model J #
###########
models['J'] = Model("foo/J", 10)
mgJ_abc = ModelGene(cg_abc, models['J'])
mgJ_gspd = ModelGene(cg_gspd, models['J'])
mgJ_tadZ = ModelGene(cg_tadZ, models['J'])
mgJ_sctc = ModelGene(cg_sctc, models['J'])
models['J'].add_mandatory_gene(mgJ_abc)
models['J'].add_mandatory_gene(mgJ_gspd)
models['J'].add_accessory_gene(mgJ_tadZ)
models['J'].add_accessory_gene(mgJ_sctc)
models['J']._min_mandatory_genes_required = 1
models['J']._min_genes_required = 1
###########
# model K #
###########
models['K'] = Model("foo/K", 10)
mgK_flgB = ModelGene(cg_flgB, models['K'])
mgK_sctn_flg = ModelGene(cg_sctn_flg, models['K'])
mgK_sctj_flg = ModelGene(cg_sctj_flg, models['K'])
mgK_sctn = ModelGene(cg_sctn, models['K'])
models['K'].add_mandatory_gene(mgK_flgB)
models['K'].add_mandatory_gene(mgK_sctn_flg)
models['K'].add_accessory_gene(mgK_sctj_flg)
models['K'].add_accessory_gene(mgK_sctn)
models['K']._min_mandatory_genes_required = 1
models['K']._min_genes_required = 1
###########
# model L #
###########
models['L'] = Model("foo/L", 10)
mgL_flgB = ModelGene(cg_flgB, models['L'])
mgL_sctn_flg = ModelGene(cg_sctn_flg, models['L'])
mgL_sctj_flg = ModelGene(cg_sctj_flg, models['L'])
mgL_sctn = ModelGene(cg_sctn, models['L'], loner=True)
models['L'].add_mandatory_gene(mgL_flgB)
models['L'].add_mandatory_gene(mgL_sctn_flg)
models['L'].add_accessory_gene(mgL_sctj_flg)
models['L'].add_accessory_gene(mgL_sctn)
###########
# model M #
###########
models['M'] = Model("foo/L", 10)
mgM_sctj = ModelGene(cg_sctj, models['M'])
mgM_gspd = ModelGene(cg_gspd, models['M'])
mgM_sctn = ModelGene(cg_sctn, models['M'], multi_system=True)
mgM_tadZ = ModelGene(cg_tadZ, models['M'])
mgM_abc = ModelGene(cg_abc, models['M'])
models['M'].add_mandatory_gene(mgM_sctj)
models['M'].add_mandatory_gene(mgM_gspd)
models['M'].add_accessory_gene(mgM_sctn)
models['M'].add_accessory_gene(mgM_tadZ)
models['M'].add_accessory_gene(mgM_abc)
###########
# model N #
###########
models['N'] = Model("foo/N", 10)
mgN_flgB = ModelGene(cg_flgB, models['N'])
mgN_sctn_flg = ModelGene(cg_sctn_flg, models['N'])
mgN_sctj = ModelGene(cg_sctj, models['N'])
mgN_sctj_flg = ModelGene(cg_sctj_flg, models['N'])
mgN_sctn = ModelGene(cg_sctn, models['N'], loner=True)
mgN_tadZ = ModelGene(cg_tadZ, models['N'], loner=True)
models['N'].add_mandatory_gene(mgN_flgB)
models['N'].add_mandatory_gene(mgN_sctn_flg)
models['N'].add_accessory_gene(mgN_sctj)
models['N'].add_accessory_gene(mgN_sctj_flg)
models['N'].add_accessory_gene(mgN_sctn)
models['N'].add_accessory_gene(mgN_tadZ)
###########
# model O #
###########
models['O'] = Model("foo/O", 10)
mgO_sctj = ModelGene(cg_sctj, models['O'], multi_system=True)
mgO_sctj_flg = Exchangeable(cg_sctj_flg, mgO_sctj)
mgO_sctj.add_exchangeable(mgO_sctj_flg)
mgO_gspd = ModelGene(cg_gspd, models['O'], loner=True, multi_system=True)
mgO_sctn = ModelGene(cg_sctn, models['O'], multi_system=True)
mgO_sctn_flg = Exchangeable(cg_sctn_flg, mgO_sctn)
mgO_sctn.add_exchangeable(mgO_sctn_flg)
mgO_tadZ = ModelGene(cg_tadZ, models['O'], loner=True)
mgO_abc = ModelGene(cg_abc, models['O'])
models['O'].add_mandatory_gene(mgO_sctj)
models['O'].add_mandatory_gene(mgO_gspd)
models['O'].add_accessory_gene(mgO_sctn)
models['O'].add_accessory_gene(mgO_tadZ)
models['O'].add_neutral_gene(mgO_abc)
ch_sctj = CoreHit(cg_sctj, "hit_sctj", 803, "replicon_id", 1, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_sctn = CoreHit(cg_sctn, "hit_sctn", 803, "replicon_id", 2, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_gspd = CoreHit(cg_gspd, "hit_gspd", 803, "replicon_id", 3, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_sctn_flg = CoreHit(cg_sctn_flg, "hit_sctn_flg", 803, "replicon_id", 4, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_sctj = CoreHit(cg_sctj, "hit_sctj", 803, "replicon_id", 5, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_sctj_flg = CoreHit(cg_sctj_flg, "hit_sctj_flg", 803, "replicon_id", 6, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_flgB = CoreHit(cg_flgB, "hit_flgB", 803, "replicon_id", 7, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_tadZ = CoreHit(cg_tadZ, "hit_tadZ", 803, "replicon_id", 8, 1.0, 1.0, 1.0, 1.0, 10, 20)
ch_abc = CoreHit(cg_abc, "hit_abc", 803, "replicon_id", 9, 1.0, 1.0, 1.0, 1.0, 10, 20)
hit_weights = HitWeight(**cfg.hit_weights())
clusters = {}
clusters['c1'] = Cluster([ModelHit(ch_sctj, gene_ref=mgA_sctj, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_sctn, gene_ref=mgA_sctn, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_gspd, gene_ref=mgA_gspd, gene_status=GeneStatus.ACCESSORY)
],
models['A'], hit_weights)
clusters['c2'] = Cluster([ModelHit(ch_sctj, gene_ref=mgA_sctj, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_sctn, gene_ref=mgA_sctn, gene_status=GeneStatus.MANDATORY)],
models['A'], hit_weights)
clusters['c3'] = Cluster([ModelHit(ch_sctj_flg, gene_ref=mgB_sctj_flg, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_tadZ, gene_ref=mgB_tadZ, gene_status=GeneStatus.ACCESSORY),
ModelHit(ch_flgB, gene_ref=mgB_flgB, gene_status=GeneStatus.ACCESSORY)],
models['B'], hit_weights)
clusters['c4'] = Cluster([ModelHit(ch_sctj_flg, gene_ref=mgC_sctj_flg, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_tadZ, gene_ref=mgC_tadZ, gene_status=GeneStatus.ACCESSORY),
ModelHit(ch_flgB, gene_ref=mgC_flgB, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_gspd, gene_ref=mgC_gspd, gene_status=GeneStatus.ACCESSORY)],
models['C'], hit_weights)
clusters['c5'] = Cluster([ModelHit(ch_abc, gene_ref=mgD_abc, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_sctn, gene_ref=mgD_sctn, gene_status=GeneStatus.ACCESSORY)],
models['D'], hit_weights)
clusters['c6'] = Cluster([ModelHit(ch_gspd, gene_ref=mgE_gspd, gene_status=GeneStatus.ACCESSORY)],
models['E'], hit_weights)
clusters['c7'] = Cluster([ModelHit(ch_abc, gene_ref=mgF_abc, gene_status=GeneStatus.MANDATORY)],
models['F'], hit_weights)
clusters['c8'] = Cluster([ModelHit(ch_flgB, gene_ref=mgI_flgB, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_tadZ, gene_ref=mgI_tadZ, gene_status=GeneStatus.ACCESSORY)],
models['I'], hit_weights)
clusters['c9'] = Cluster([ModelHit(ch_abc, gene_ref=mgJ_abc, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_tadZ, gene_ref=mgJ_tadZ, gene_status=GeneStatus.ACCESSORY)],
models['J'], hit_weights)
clusters['c10'] = Cluster([ModelHit(ch_flgB, gene_ref=mgK_flgB, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_sctn, gene_ref=mgK_sctn, gene_status=GeneStatus.ACCESSORY)],
models['K'], hit_weights)
clusters['c11'] = Cluster([ModelHit(ch_flgB, gene_ref=mgL_flgB, gene_status=GeneStatus.MANDATORY),
ModelHit(ch_sctn_flg, gene_ref=mgL_sctn_flg, gene_status=GeneStatus.MANDATORY)],
models['L'], hit_weights)
clusters['c12'] = Cluster([ModelHit(ch_sctj_flg, gene_ref=mgL_sctj_flg, gene_status=GeneStatus.ACCESSORY),
ModelHit(ch_sctn, gene_ref=mgL_sctn, gene_status=GeneStatus.ACCESSORY)],
models['L'], hit_weights)
clusters['c13'] = Cluster([Loner(ch_sctn, gene_ref=mgL_sctn, gene_status=GeneStatus.ACCESSORY)],
models['L'], hit_weights)
clusters['c14'] = Cluster([ModelHit(ch_sctj, mgM_sctj, gene_status=GeneStatus.MANDATORY),
MultiSystem(ch_sctn, gene_ref=mgM_sctn, gene_status=GeneStatus.ACCESSORY),
ModelHit(ch_gspd, gene_ref=mgM_gspd, gene_status=GeneStatus.ACCESSORY)
],
models['M'], hit_weights)
clusters['c15'] = Cluster([ModelHit(ch_tadZ, gene_ref=mgM_tadZ, gene_status=GeneStatus.ACCESSORY),
ModelHit(ch_abc, gene_ref=mgM_abc, gene_status=GeneStatus.ACCESSORY)
],
models['M'], hit_weights)
clusters['c16'] = Cluster([MultiSystem(ch_sctn, gene_ref=mgM_sctn, gene_status=GeneStatus.ACCESSORY)],
models['M'], hit_weights)
clusters['c17'] = Cluster([ModelHit(ch_flgB, mgL_flgB, GeneStatus.MANDATORY),
ModelHit(ch_sctn_flg, mgL_sctn_flg, GeneStatus.MANDATORY)],
models['N'], hit_weights)
clusters['c18'] = Cluster([ModelHit(ch_sctj, mgN_sctj, GeneStatus.MANDATORY),
ModelHit(ch_sctj_flg, mgL_sctj_flg, GeneStatus.MANDATORY)],
models['N'], hit_weights)
clusters['c19'] = Cluster([Loner(ch_sctn, mgL_sctn, GeneStatus.ACCESSORY)],
models['N'], hit_weights)
clusters['c20'] = Cluster([Loner(ch_tadZ, mgN_tadZ, GeneStatus.ACCESSORY)],
models['N'], hit_weights)
clusters['c21'] = Cluster([ModelHit(ch_sctj, mgO_sctj, GeneStatus.MANDATORY),
ModelHit(ch_abc, mgO_abc, GeneStatus.NEUTRAL),
ModelHit(ch_tadZ, mgO_tadZ, GeneStatus.ACCESSORY)],
models['O'], hit_weights)
clusters['c22'] = Cluster([ModelHit(ch_sctn_flg, mgO_sctn_flg, GeneStatus.ACCESSORY),
ModelHit(ch_gspd, mgO_gspd, GeneStatus.MANDATORY),
ModelHit(ch_tadZ, mgO_tadZ, GeneStatus.ACCESSORY)],
models['O'], hit_weights)
clusters['c23'] = Cluster([Loner(ch_gspd, mgO_gspd, gene_status=GeneStatus.MANDATORY)],
models['O'], hit_weights)
clusters['c24'] = Cluster([MultiSystem(ch_gspd, mgO_gspd, gene_status=GeneStatus.MANDATORY)],
models['O'], hit_weights)
clusters['c25'] = Cluster([MultiSystem(ch_sctn, mgO_sctn, gene_status=GeneStatus.ACCESSORY)],
models['O'], hit_weights)
clusters['c26'] = Cluster([MultiSystem(ch_sctj_flg, mgO_sctj_flg, gene_status=GeneStatus.MANDATORY)],
models['O'], hit_weights)
return models, clusters
def _build_systems(models, clusters, cfg):
systems = {}
# we need to tweek the replicon_id to have stable ressults
# whatever the number of tests ran
# or the tests order
systems['A'] = System(models['A'], [clusters['c1'], clusters['c2']], cfg.redundancy_penalty()) # 5 hits
systems['A'].id = "replicon_id_A"
systems['B'] = System(models['B'], [clusters['c3']], cfg.redundancy_penalty()) # 3 hits
systems['B'].id = "replicon_id_B"
systems['C'] = System(models['C'], [clusters['c4']], cfg.redundancy_penalty()) # 4 hits
systems['C'].id = "replicon_id_C"
systems['D'] = System(models['D'], [clusters['c5']], cfg.redundancy_penalty()) # 2 hits
systems['D'].id = "replicon_id_D"
systems['E'] = System(models['E'], [clusters['c6']], cfg.redundancy_penalty()) # 1 hit
systems['E'].id = "replicon_id_E"
systems['F'] = System(models['F'], [clusters['c7']], cfg.redundancy_penalty()) # 1 hit
systems['F'].id = "replicon_id_F"
systems['G'] = System(models['G'], [clusters['c4']], cfg.redundancy_penalty()) # 4 hits
systems['G'].id = "replicon_id_G"
systems['H'] = System(models['H'], [clusters['c5']], cfg.redundancy_penalty()) # 2 hits
systems['H'].id = "replicon_id_H"
systems['I'] = System(models['I'], [clusters['c8']], cfg.redundancy_penalty()) # 2 hits
systems['I'].id = "replicon_id_I"
systems['J'] = System(models['J'], [clusters['c9']], cfg.redundancy_penalty()) # 2 hits
systems['J'].id = "replicon_id_J"
systems['K'] = System(models['K'], [clusters['c10']], cfg.redundancy_penalty()) # 2 hits
systems['K'].id = "replicon_id_K"
return systems
class SolutionTest(MacsyTest):
def setUp(self) -> None:
args = argparse.Namespace()
args.sequence_db = self.find_data("base", "test_1.fasta")
args.db_type = 'gembase'
args.models_dir = self.find_data('models')
self.cfg = Config(MacsyDefaults(), args)
# we need to reset the ProfileFactory
# because it's a like a singleton
# so other tests are influenced by ProfileFactory and it's configuration
# for instance search_genes get profile without hmmer_exe
self.profile_factory = ProfileFactory(self.cfg)
self.models, self.clusters = _build_clusters(self.cfg, self.profile_factory)
self.systems = _build_systems(self.models, self.clusters, self.cfg)
def test_gt(self):
s1 = Solution([self.systems[k] for k in 'AB']) # 5 + 3 = 8 hits, 2 syst, 0.875, [2, 2, 3, 5, 5, 6, 7, 8]
s2 = Solution([self.systems[k] for k in 'HIJ']) # 2 + 2 + 2 = 6 hits, 3 syst, 0.722, [2, 9, 7, 8, 8, 9]
s3 = Solution([self.systems[k] for k in 'DC']) # 4 + 2 = 6 hits, 2 syst 0.900, [2, 9, 3, 6, 7, 8]
s4 = Solution([self.systems[k] for k in 'CF']) # 4 + 1 = 5 hits, 2 syst, 0.900, [3, 6, 7, 8, 9]
s5 = Solution([self.systems[k] for k in 'EC']) # 1 + 4 = 5 hits, 2 syst, 0.900, [3, 3, 6, 7, 8]
s6 = Solution([self.systems[k] for k in 'DB']) # 2 + 3 = 5 hits, 2 syst, 0.875, [2, 9, 6, 7, 8]
self.assertGreater(s1, s2) # s1 more hits than s2
self.assertGreater(s2, s3) # s2 more systems than s3
self.assertGreater(s5, s6) # s5 greater awholeness than s6
self.assertGreater(s4, s5) # s4 "bigger positions than s5
def test_lt(self):
s1 = Solution([self.systems[k] for k in 'AB']) # 5 + 3 = 8 hits, 2 syst, 0.875, [2, 2, 3, 5, 5, 6, 7, 8]
s2 = Solution([self.systems[k] for k in 'HIJ']) # 2 + 2 + 2 = 6 hits, 3 syst, 0.722, [2, 9, 7, 8, 8, 9]
s3 = Solution([self.systems[k] for k in 'DC']) # 4 + 2 = 6 hits, 2 syst 0.900, [2, 9, 3, 6, 7, 8]
s4 = Solution([self.systems[k] for k in 'CF']) # 4 + 1 = 5 hits, 2 syst, 0.900, [3, 6, 7, 8, 9]
s5 = Solution([self.systems[k] for k in 'EC']) # 1 + 4 = 5 hits, 2 syst, 0.900, [3, 3, 6, 7, 8]
s6 = Solution([self.systems[k] for k in 'DB']) # 2 + 3 = 5 hits, 2 syst, 0.875, [2, 9, 6, 7, 8]
self.assertLess(s2, s1) # s1 more hits than s2
self.assertLess(s3, s2) # s2 more systems than s3
self.assertLess(s6, s5) # s5 greater awholeness than s6
self.assertLess(s5, s4) # s4 "bigger positions than s5
def test_sorting(self):
s1 = Solution([self.systems[k] for k in 'AB']) # 5 + 3 = 8 hits, 2 syst, 0.875, [2, 2, 3, 5, 5, 6, 7, 8]
s2 = Solution([self.systems[k] for k in 'HIJ']) # 2 + 2 + 2 = 6 hits, 3 syst, 0.722, [2, 9, 7, 8, 8, 9]
s3 = Solution([self.systems[k] for k in 'DC']) # 4 + 2 = 6 hits, 2 syst 0.900, [2, 9, 3, 6, 7, 8]
s4 = Solution([self.systems[k] for k in 'CF']) # 4 + 1 = 5 hits, 2 syst, 0.900, [3, 6, 7, 8, 9]
s5 = Solution([self.systems[k] for k in 'EC']) # 1 + 4 = 5 hits, 2 syst, 0.900, [3, 3, 6, 7, 8]
s6 = Solution([self.systems[k] for k in 'DB']) # 2 + 3 = 5 hits, 2 syst, 0.875, [2, 9, 6, 7, 8]
expected_order = [s6, s5, s4, s3, s2, s1]
shuffled_sol = expected_order[:]
random.shuffle(shuffled_sol)
sorted_sol = sorted(shuffled_sol)
self.assertEqual(expected_order, sorted_sol)
def test_systems(self):
s1 = Solution([self.systems[k] for k in 'CD'])
self.assertEqual([self.systems['D'], self.systems['C']],
s1.systems)
def test_score(self):
s = Solution([self.systems[k] for k in 'CD'])
self.assertEqual(s.score, 4.5)
s = Solution([self.systems[k] for k in 'HIJ'])
self.assertEqual(s.score, 4.5)
def test_average_wholeness(self):
s = Solution([self.systems[k] for k in 'CD'])
self.assertEqual(s.average_wholeness, 0.9)
s = Solution([self.systems[k] for k in 'HIJ'])
self.assertEqual(s.average_wholeness, 0.7222222222222222)
def test_hits_number(self):
s = Solution([self.systems[k] for k in 'CD'])
self.assertEqual(s.hits_number, 6)
s = Solution([self.systems[k] for k in 'AB'])
self.assertEqual(s.hits_number, 8)
def test_hits_positions(self):
s = Solution([self.systems[k] for k in 'CD'])
self.assertEqual(s.hits_positions,
[2, 9, 3, 6, 7, 8])
s = Solution([self.systems[k] for k in 'AB'])
self.assertEqual(s.hits_positions,
[2, 2, 3, 5, 5, 6, 7, 8])
def test_iteration(self):
# be careful the systems are ordered in Solution
systems = [self.systems[k] for k in 'HIJ']
s = Solution(systems)
got = [syst for syst in s]
self.assertEqual(systems,
got)
class SolutionExplorerTest(MacsyTest):
@classmethod
def setUpClass(cls) -> None:
# to turn on debugging
# uncomment the 3 following lines
# import macsypy
# macsypy.init_logger()
# macsypy.logger_set_level('DEBUG')
pass
def setUp(self) -> None:
args = argparse.Namespace()
args.sequence_db = self.find_data("base", "test_1.fasta")
args.db_type = 'gembase'
args.models_dir = self.find_data('models')
self.cfg = Config(MacsyDefaults(), args)
# we need to reset the ProfileFactory
# because it's a like a singleton
# so other tests are influenced by ProfileFactory and it's configuration
# for instance search_genes get profile without hmmer_exe
self.profile_factory = ProfileFactory(self.cfg)
self.models, self.clusters = _build_clusters(self.cfg, self.profile_factory)
self.systems = _build_systems(self.models, self.clusters, self.cfg)
def test_find_best_solution(self):
systems = [self.systems[k] for k in 'ABCD']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
# sorted_syst = [('replicon_id_C', 3.0), ('replicon_id_B', 2.0), ('replicon_id_A', 1.5), ('replicon_id_D', 1.5)]
# replicon_id_C ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_B ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB']
# replicon_id_A ['hit_sctj', 'hit_sctn', 'hit_gspd', 'hit_sctj', 'hit_sctn']
# replicon_id_D ['hit_abc', 'hit_sctn']
# C and D are compatible 4.5
# B and A are compatible 3.5
# B and D are compatible 3.5
# So the best Solution expected is C D 4.5
best_sol, score = find_best_solutions(sorted_syst)
expected_sol = [Solution([self.systems[k] for k in 'CD'])]
# The order of solutions are not relevant
# The order of systems in each solutions are not relevant
# transform list in set to compare them
self.assertEqual(score, 4.5)
self.assertEqual(best_sol, expected_sol)
systems = [self.systems[k] for k in 'ABC']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
# sorted_syst = [('replicon_id_C', 3.0), ('replicon_id_B', 2.0), ('replicon_id_A', 1.5)]
# replicon_id_C ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_B ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB']
# replicon_id_A ['hit_sctj', 'hit_sctn', 'hit_gspd', 'hit_sctj', 'hit_sctn']
# C is alone 3.0
# B and A are compatible 3.5
# So the best Solution expected is B and A
best_sol, score = find_best_solutions(sorted_syst)
expected_sol = [Solution([self.systems[k] for k in 'BA'])]
self.assertEqual(score, 3.5)
self.assertEqual(best_sol, expected_sol)
systems = [self.systems[k] for k in 'BCE']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
# sorted_syst = [('replicon_id_C', 3.0), ('replicon_id_B', 2.0), ('replicon_id_E', 0.5)]
# replicon_id_C ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_B ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB']
# replicon_id_E ['hit_gspd']
# C is alone 3.0
# B and E are compatible 2.5
# So the best Solution expected is C
best_sol, score = find_best_solutions(sorted_syst)
expected_sol = [Solution([self.systems[k] for k in 'C'])]
self.assertEqual(score, 3.0)
self.assertEqual(best_sol, expected_sol)
# systems = [('replicon_id_C', 3.0), ('replicon_id_B', 2.0), ('replicon_id_A', 1.5),
# ('replicon_id_D', 1.5), ('replicon_id_E', 0.5)]
# replicon_id_C ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_B ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB']
# replicon_id_A ['hit_sctj', 'hit_sctn', 'hit_gspd', 'hit_sctj', 'hit_sctn']
# replicon_id_D ['hit_abc', 'hit_sctn']
# replicon_id_E ['hit_gspd']
# C and D are compatible 4.5
# B and A are compatible 3.5
# B and E are compatible 2.5
# D and E are compatible 2.0
systems = [self.systems[k] for k in 'ABCDE']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
best_sol, score = find_best_solutions(sorted_syst)
expected_sol = [Solution([self.systems[k] for k in 'CD'])]
self.assertEqual(score, 4.5)
self.assertEqual(best_sol, expected_sol)
# systems = [('replicon_id_C', 3.0), ('replicon_id_B', 2.0), ('replicon_id_A', 1.5),
# ('replicon_id_D', 1.5), ('replicon_id_E', 0.5), ('replicon_id_F', 1.0)]
# replicon_id_C ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_B ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB']
# replicon_id_A ['hit_sctj', 'hit_sctn', 'hit_gspd', 'hit_sctj', 'hit_sctn']
# replicon_id_D ['hit_abc', 'hit_sctn']
# replicon_id_E ['hit_gspd']
# replicon_id_F ['hit_abc']
# C and D are compatible 4.5
# C and F are compatible 4.0
# B and A and F are compatible 4.5
# B and D and E are compatible 4.0
# B and E and F are compatible 3.5
# So the best Solution expected are C D / B A F
systems = [self.systems[k] for k in 'ABCDEF']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
best_sol, score = find_best_solutions(sorted_syst)
expected_sol = [Solution([self.systems[k] for k in 'BAF']), # 3 + 5 + 1 = 9 hits
Solution([self.systems[k] for k in 'CD'])] # 4 + 2 = 7 hits
self.assertEqual(best_sol[0].score, 4.5)
self.assertEqual(best_sol[1].score, 4.5)
# test if the composition is right
self.assertEqual(best_sol, expected_sol)
# test if solution order is right
systems = [self.systems[k] for k in 'ABCDGH']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
# sorted_syst = [('replicon_id_C', 3.0), ('replicon_id_B', 2.0), ('replicon_id_A', 1.5), ('replicon_id_D', 1.5)
# ('replicon_id_G', 3.0), ('replicon_id_H', 1.5)]
# replicon_id_A ['hit_sctj', 'hit_sctn', 'hit_gspd', 'hit_sctj', 'hit_sctn']
# replicon_id_B ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB']
# replicon_id_C ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_D ['hit_abc', 'hit_sctn']
# replicon_id_G ['hit_sctj_flg', 'hit_tadZ', 'hit_flgB', 'hit_gspd']
# replicon_id_H ['hit_abc', 'hit_sctn']
# C and D are compatible 4.5 wholeness = 0.8 + 1.0 = 1.8
# C and H are compatible 4.5 0.8 + 1.0 = 1.8
# G and D are compatible 4.5 0.8 + 1.0 = 1.8
# G and H are compatible 4.5 0.8 + 1.0 = 1.8
# B and A are compatible 3.5 0.75 + 1.0 = 1.75
# So the best Solution expected are C D / C H / G D / G H with score 4.5
best_sol, score = find_best_solutions(sorted_syst)
expected_sol = [Solution([self.systems[k] for k in 'CD']), # 4 + 2 hits
Solution([self.systems[k] for k in 'CH']), # 4 + 2
Solution([self.systems[k] for k in 'GD']), # 4 + 2
Solution([self.systems[k] for k in 'GH'])] # 4 + 2
self.assertEqual(score, 4.5)
self.assertEqual(best_sol, expected_sol)
systems = [self.systems[k] for k in 'HJKI']
sorted_syst = sorted(systems, key=lambda s: (- s.score, s.id))
best_sol, score = find_best_solutions(sorted_syst)
# check if solution is ordered by wholeness average (3rd criterion)
# first criterion nb of hits
# second citerion nb of systems
# replicon_id_H ['hit_abc', 'hit_sctn']
# replicon_id_I ['hit_flgB', 'hit_tadZ']
# replicon_id_J ['hit_abc', 'hit_tadZ']
# replicon_id_K ['hit_flgB', 'hit_sctn']
# score Nb hits nb sys wholeness
expected_sol = [Solution([self.systems[k] for k in 'HI']), # 1.5 + 1.5 = 3.0 4 2 1.0
Solution([self.systems[k] for k in 'JK'])] # 1.5 + 1.5 = 3.0 4 2 0.5
self.assertEqual(score, 3.0)
self.assertEqual(best_sol, expected_sol)
def test_combine_clusters(self):
##################################################
# with 3 regular clusters 0 loner 0 multisystyem
##################################################
combinations = combine_clusters([self.clusters['c1'], self.clusters['c2'], self.clusters['c3']],
{},
multi_loci=False)
self.assertEqual(combinations,
[
(self.clusters['c1'],),
(self.clusters['c2'],),
(self.clusters['c3'],)
])
# the same in multi loci
combinations = combine_clusters([self.clusters['c1'], self.clusters['c2'], self.clusters['c3']],
{},
multi_loci=True)
exp_combs = [
(self.clusters['c1'],),
(self.clusters['c2'],),
(self.clusters['c3'],),
(self.clusters['c1'], self.clusters['c2']),
(self.clusters['c1'], self.clusters['c3']),
(self.clusters['c2'], self.clusters['c3']),
(self.clusters['c1'], self.clusters['c2'], self.clusters['c3'])
]
self.assertEqual(combinations, exp_combs)
###########################################
# with 2 RC + 1 L not included in cluster
###########################################
combinations = combine_clusters([self.clusters['c11'], self.clusters['c12']],
{},
multi_loci=False)
exp_combs = [
(self.clusters['c11'],),
(self.clusters['c12'],)
]
self.assertEqual(combinations, exp_combs)
# the same in multi loci
combinations = combine_clusters([self.clusters['c11'], self.clusters['c12']],
{},
multi_loci=True)
exp_combs = [
(self.clusters['c11'],),
(self.clusters['c12'],),
(self.clusters['c11'], self.clusters['c12'])
]
self.assertEqual(combinations, exp_combs)
##################################
# with 2 RC + 1 L already in RC 2
##################################
# c11 = flgB, sctn_flg
# c12 = sctj_flg, sctn
# c13 = Loner sctn
combinations = combine_clusters([self.clusters['c11'], self.clusters['c12']],
{'sctN': self.clusters['c13']},
multi_loci=False)
exp_combs = [
(self.clusters['c11'],),
(self.clusters['c12'],),
(self.clusters['c11'], self.clusters['c13']),
(self.clusters['c13'],),
]
self.assertEqual(combinations, exp_combs)
# the same in multi loci
combinations = combine_clusters([self.clusters['c11'], self.clusters['c12']],
{'sctN': self.clusters['c13']},
multi_loci=True)
exp_combs = [
(self.clusters['c11'],),
(self.clusters['c12'],),
(self.clusters['c11'], self.clusters['c12']),
(self.clusters['c11'], self.clusters['c13']),
(self.clusters['c13'],),
]
self.assertEqual(combinations, exp_combs)
###########################################
# with 2 RC with one containing a MS #
###########################################
combinations = combine_clusters([self.clusters['c14'], self.clusters['c15']],
{'sctN': self.clusters['c16']},
multi_loci=True)
# c14 contains a MS
# c15 do not contains MS
# c16 is the artificial cluster with only sctn
exp_combs = [
(self.clusters['c14'],),
(self.clusters['c15'],),
(self.clusters['c14'], self.clusters['c15']),
(self.clusters['c15'], self.clusters['c16']),
(self.clusters['c16'],)]
self.assertEqual(combinations, exp_combs)
###########################################
# with 2 RC + 2 L not included in cluster
###########################################
combinations = combine_clusters([self.clusters['c17'], self.clusters['c18']],
{'sctn': self.clusters['c19'],
'tadZ': self.clusters['c20']
},
multi_loci=False)
exp_combs = [
(self.clusters['c17'],),
(self.clusters['c18'],),
(self.clusters['c17'], self.clusters['c19']),
(self.clusters['c18'], self.clusters['c19']),
(self.clusters['c19'],),
(self.clusters['c17'], self.clusters['c20']),
(self.clusters['c18'], self.clusters['c20']),
(self.clusters['c20'],),
(self.clusters['c17'], self.clusters['c19'], self.clusters['c20']),
(self.clusters['c18'], self.clusters['c19'], self.clusters['c20']),
(self.clusters['c19'], self.clusters['c20'])
]
self.assertEqual(combinations, exp_combs)
# the same in multi loci
combinations = combine_clusters([self.clusters['c17'], self.clusters['c18']],
{'sctn': self.clusters['c19'],
'tadZ': self.clusters['c20']
},
multi_loci=True)
exp_combs = [
(self.clusters['c17'],),
(self.clusters['c18'],),
(self.clusters['c17'], self.clusters['c18']),
(self.clusters['c17'], self.clusters['c19']),
(self.clusters['c18'], self.clusters['c19']),
(self.clusters['c17'],self.clusters['c18'], self.clusters['c19']),
(self.clusters['c19'],),
(self.clusters['c17'], self.clusters['c20']),
(self.clusters['c18'], self.clusters['c20']),
(self.clusters['c17'],self.clusters['c18'], self.clusters['c20']),
(self.clusters['c20'],),
(self.clusters['c17'], self.clusters['c19'], self.clusters['c20']),
(self.clusters['c18'], self.clusters['c19'], self.clusters['c20']),
(self.clusters['c17'], self.clusters['c18'], self.clusters['c19'], self.clusters['c20']),
(self.clusters['c19'], self.clusters['c20'])
]
# print("\n####################################################################")
# for comb in exp_combs:
# print([c.id for c in comb])
# print("==============================")
# for comb in combinations:
# print([c.id for c in comb])
self.assertEqual(combinations, exp_combs)
def test_combine_multisystems(self):
# key id hits
# c21 c47 sctj abc tadz
# c22 c48 sctn_flg gspd tadz
# c23 c49 Loner gspd
# c24 c50 MS gspd
# c25 c51 MS sctn
# c26 c52 MS sctj_flg
rejected_clst = RejectedCandidate(self.models['O'],
[self.clusters['c21']],
'fake_reason')
combinations = combine_multisystems(rejected_clst,
[self.clusters['c24'], self.clusters['c25'], self.clusters['c26']]
)
exp_combs = [
(self.clusters['c21'], self.clusters['c24']),
(self.clusters['c21'], self.clusters['c25']),
(self.clusters['c21'], self.clusters['c24'], self.clusters['c25'])
]
self.assertEqual(combinations, exp_combs)
rejected_clst = RejectedCandidate(self.models['O'],
[self.clusters['c22']],
'fake_reason')
combinations = combine_multisystems([rejected_clst],
[self.clusters['c24'], self.clusters['c25'], self.clusters['c26']]
)
exp_combs = [
(self.clusters['c22'], self.clusters['c26'])
]
self.assertEqual(combinations, exp_combs)
rejected_clst = RejectedCandidate(self.models['O'],
[self.clusters['c21'], self.clusters['c23']],
'fake_reason')
combinations = combine_multisystems([rejected_clst],
[self.clusters['c24'], self.clusters['c25'], self.clusters['c26']]
)
exp_combs = [
(self.clusters['c21'], self.clusters['c23'], self.clusters['c25'])
]
self.assertEqual(combinations, exp_combs)
# print("\n####################################################################")
# for comb in exp_combs:
# print([c.id for c in comb])
# print("==============================")
# for comb in combinations:
# print([c.id for c in comb])
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