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__init__.py

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+"""
+Catalog definitions for Bos Taurus
+"""
+import math
+import logging
+
+import msprime
+
+import stdpopsim
+from . import genome_data
+
+logger = logging.getLogger(__name__)
+
+###########################################################
+#
+# Genome definition
+#
+###########################################################
+
+# Parse list of chromosomes into a list of Chromosome objects which contain the
+# chromosome name, length, mutation rate, and recombination rate
+
+_chromosomes = []
+for name, data in genome_data.data["chromosomes"].items():
+    _chromosomes.append(stdpopsim.Chromosome(
+        id=name, length=data["length"],
+        synonyms=data["synonyms"],
+        mutation_rate=1.2e-8,
+        recombination_rate=9.26e-9)) # 25.5 crossovers per meiosis in males and 23.2 crossovers per meiosis in females gives an average of 24.35 crossovers per meiosis
+                                     # The sum of chromosome lenghts is 2628394923 bp. The global recombination rate is 24.35/ 2628394923 = 9.26e-9 per base pair per generation
+
+
+# A citation for the chromosome parameters. Additional citations may be needed if
+# the mutation or recombination rates come from other sources. In that case create
+# additional citations with the appropriate reasons specified (see API documentation
+# for stdpopsim.citations)
+
+
+
+_RosenEtAl = stdpopsim.Citation(
+    doi="https://doi.org/10.1093/gigascience/giaa021",
+    year="2020",
+    author="Rosen et al.",
+    reasons={stdpopsim.CiteReason.ASSEMBLY})
+
+_HarlandEtAl = stdpopsim.Citation(
+        # Frequency of mosaicism points towards mutation-prone early cleavage cell  divisions in cattle.
+        author="Harland et al.",
+        year=2020,
+        url="https://www.biorxiv.org/content/10.1101/079863v2.full.pdf") # BioRxiv preprint
+
+_MaEtAl = stdpopsim.Citation(
+        # Cattle Sex-Specific Recombination and Genetic Control from a Large Pedigree Analysis
+        author="Ma et al.",
+        year=2015,
+        doi="https://doi.org/10.1371/journal.pgen.1005387")
+
+
+# Create a genome object
+
+_genome = stdpopsim.Genome(
+    chromosomes=_chromosomes,
+    # Mutation rate is a sex-averaged estimate per base pair per generation
+     mutation_rate_citations=[
+        _CoppietersEtAl.because(stdpopsim.CiteReason.MUT_RATE),
+        ],
+    # Recombination rate has been derived from dairy cattle crossovers per meiosis, by taking the average between females and males and then dividing by the 
+    # whole genome length (equal to the sum of chromosome lengths used above)
+    recombination_rate_citations=[
+        _MaEtAl.because(stdpopsim.CiteReason.REC_RATE)
+        ],
+    assembly_citations=[
+        _RosenEtAl.because(stdpopsim.CiteReason.ASSEMBLY)
+        ],
+  )
+
+# Create a Species Object
+_gen_time_citation = stdpopsim.Citation(
+    doi="https://doi.org/10.1093/molbev/mst125",
+    year="2013",
+    author="MacLeod et al.",
+    reasons={stdpopsim.CiteReason.GEN_TIME})
+
+_pop_size_citation = stdpopsim.Citation(
+    doi="https://doi.org/10.1093/molbev/mst125",
+    year="2013",
+    author="MacLeod et al.",
+    reasons={stdpopsim.CiteReason.POP_SIZE})
+
+_species = stdpopsim.Species(
+    id="BosTau",
+    name="Bos Taurus",
+    common_name="Cattle",
+    genome=_genome,
+    generation_time=5,
+    generation_time_citations=[_gen_time_citation],
+    population_size=90,
+    population_size_citations=[_pop_size_citation]
+)
+
+stdpopsim.register_species(_species)
+
+
+###########################################################
+#
+# Demographic models
+#
+###########################################################
+
+def _inferred_1_M_13():
+    id = "IonaInferredDemography"
+    description = "Iona MacLeod's Inferred Demographic Model for Bos Taurus"
+    long_description = """
+    The Runs of Homozygosity-based infer of Demography from MacLeod et al. 2013. Only the Holstein breed has been taken into account for this inference.
+    """
+    populations = [
+        stdpopsim.Population(id="FILL ME", description="FILL ME"),
+    ]
+    citations = [
+        stdpopsim.Citation(
+            author="MacLeod et al.",
+            year="2013",
+            doi="https://doi.org/10.1093/molbev/mst125",
+            reasons={stdpopsim.CiteReason.DEM_MODEL})
+    ]
+
+    generation_time = 5
+
+    # parameter value definitions based on published values
+
+    return stdpopsim.DemographicModel(
+        id=id,
+        description=description,
+        long_description=long_description,
+        populations=populations,
+        citations=citations,
+        generation_time=generation_time,
+        population_configurations=[
+            msprime.PopulationConfiguration(
+                initial_size=90, growth_rate=0.0166,
+                metadata=populations[0].asdict())
+        ],
+
+
+        demographic_events=[
+            msprime.PopulationParametersChange(
+                # Here 'time' should be in generation notation ie. how many generations ago were that Ne (effective population size) and growth rate.
+                # Growth rate is "per generation exponential growth rate": -alpha= [ln(initial_pop_size/next_stage_pop_size)/generation_span_in_years]
+                # For example: ln(90/120)/3= -0.095894024
+                time=1, initial_size=90, growth_rate=-0.095894024, population_id=0), #Ne 90 to 120
+            msprime.PopulationParametersChange(
+                time=4, growth_rate=-0.24465639, population_id=0),  #Ne 120 to 250
+            msprime.PopulationParametersChange(
+                time=7, growth_rate=-0.0560787, population_id=0),   #Ne 250 to 350 
+            msprime.PopulationParametersChange(
+                time=13, growth_rate=-0.1749704, population_id=0),  #Ne 350 to 1000
+            msprime.PopulationParametersChange(
+                time=19, growth_rate=-0.0675775, population_id=0),  #Ne 1000 to 1500
+            msprime.PopulationParametersChange(
+                time=25, growth_rate=-0.0022129, population_id=0),  #Ne 1500 to 2000
+            msprime.PopulationParametersChange(
+                time=155, growth_rate=-0.0007438, population_id=0), #Ne 2000 to 2500
+            msprime.PopulationParametersChange(
+                time=455, growth_rate=-0.0016824, population_id=0), #Ne 2500 to 3500
+            msprime.PopulationParametersChange(
+                time=655, growth_rate=-0.0006301, population_id=0), #Ne 3500 to 7000
+            msprime.PopulationParametersChange(
+                time=1755, growth_rate=-0.0005945, population_id=0),    #Ne 7000 to 10000
+            msprime.PopulationParametersChange(
+                time=2355, growth_rate=-0.0005306, population_id=0),    #Ne 10000 to 17000
+            msprime.PopulationParametersChange(
+                time=3355, growth_rate=-0.0000434, population_id=0),    #Ne 17000 to 62000
+            msprime.PopulationParametersChange(
+                time=33155, growth_rate=-0.0000, population_id=0),      #Ne 62000 (model has "coalesced")
+            msprime.PopulationParametersChange(
+                time=933155, growth_rate=-0.0, population_id=0),
+
+        ],
+    )
+
+
+_species.add_demographic_model(_inferred_1_M_13())