Add Bos taurus

stdpopsim/__init__.py

@@ -21,6 +21,7 @@
 # We import these here to build the catalog, but the internal functions
 # defined are not part of the external API.
 from .catalog import AraTha  # NOQA
+from .catalog import BosTau  # NOQA
 from .catalog import CanFam  # NOQA
 from .catalog import DroMel  # NOQA
 from .catalog import EscCol  # NOQA

stdpopsim/catalog/BosTau/__init__.py

@@ -0,0 +1,190 @@
+"""
+Catalog definitions for Bos Taurus
+"""
+import collections
+
+import msprime
+
+import stdpopsim
+from . import genome_data
+
+###########################################################
+#
+# Genome definition
+#
+###########################################################
+
+# De novo assembly of the cattle reference genome with single-molecule sequencing.
+_RosenEtAl = stdpopsim.Citation(
+    doi="https://doi.org/10.1093/gigascience/giaa021",
+    year="2020",
+    author="Rosen et al.",
+)
+
+# Frequency of mosaicism points towards mutation-prone early cleavage
+# cell divisions in cattle.
+_HarlandEtAl = stdpopsim.Citation(
+    author="Harland et al.",
+    year="2017",
+    # BioRxiv preprint
+    doi="https://doi.org/10.1101/079863",
+)
+
+# Cattle Sex-Specific Recombination and Genetic Control from a
+# Large Pedigree Analysis.
+_MaEtAl = stdpopsim.Citation(
+    author="Ma et al.",
+    year="2015",
+    doi="https://doi.org/10.1371/journal.pgen.1005387",
+)
+
+# Inferring Demography from Runs of Homozygosity in Whole-Genome Sequence,
+# with Correction for Sequence Errors.
+_MacLeodEtAl = stdpopsim.Citation(
+    doi="https://doi.org/10.1093/molbev/mst125",
+    year="2013",
+    author="MacLeod et al.",
+)
+
+# 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).
+# From Ma et al. (2015), 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 lengths is 2628394923 bp.
+# 24.35 / 2628394923 = 9.26e-9 per bp per generation.
+_genome_wide_recombination_rate = 9.26e-9
+
+_recombination_rate_data = collections.defaultdict(
+    lambda: _genome_wide_recombination_rate
+)
+# Set some exceptions for non-recombining chrs.
+_recombination_rate_data["MT"] = 0
+
+_chromosomes = []
+for name, data in genome_data.data["chromosomes"].items():
+    _chromosomes.append(
+        stdpopsim.Chromosome(
+            id=name,
+            length=data["length"],
+            synonyms=data["synonyms"],
+            # Harland et al. (2017), sex-averaged estimate per bp per generation.
+            mutation_rate=1.2e-8,
+            recombination_rate=_recombination_rate_data[name],
+        )
+    )
+
+_genome = stdpopsim.Genome(
+    chromosomes=_chromosomes,
+    mutation_rate_citations=[
+        _HarlandEtAl.because(stdpopsim.CiteReason.MUT_RATE),
+    ],
+    recombination_rate_citations=[_MaEtAl.because(stdpopsim.CiteReason.REC_RATE)],
+    assembly_citations=[_RosenEtAl.because(stdpopsim.CiteReason.ASSEMBLY)],
+)
+
+_species = stdpopsim.Species(
+    id="BosTau",
+    name="Bos Taurus",
+    common_name="Cattle",
+    genome=_genome,
+    generation_time=5,
+    generation_time_citations=[_MacLeodEtAl.because(stdpopsim.CiteReason.GEN_TIME)],
+    population_size=62000,
+    population_size_citations=[_MacLeodEtAl.because(stdpopsim.CiteReason.POP_SIZE)],
+)
+
+stdpopsim.register_species(_species)
+
+
+###########################################################
+#
+# Demographic models
+#
+###########################################################
+
+
+def _HolsteinFriesan_1M13():
+    id = "HolsteinFriesian_1M13"
+    description = "Piecewise size changes in Holstein-Friesian cattle."
+    long_description = """
+    The piecewise-constant population size model of Holstein-Friesian cattle
+    from MacLeod et al. 2013. Population sizes were estimated from inferred
+    runs of homozygosity, with parameter values taken from Figure 4A by visual
+    inspection of the plots.
+    """
+    populations = [
+        stdpopsim.Population(id="Holstein-Friesian", description="Holstein-Friesian"),
+    ]
+    citations = [_MacLeodEtAl.because(stdpopsim.CiteReason.DEM_MODEL)]
+
+    return stdpopsim.DemographicModel(
+        id=id,
+        description=description,
+        long_description=long_description,
+        populations=populations,
+        citations=citations,
+        generation_time=_species.generation_time,
+        population_configurations=[
+            msprime.PopulationConfiguration(
+                initial_size=90, growth_rate=0.0166, metadata=populations[0].asdict()
+            )
+        ],
+        # 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
+        demographic_events=[
+            msprime.PopulationParametersChange(
+                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(_HolsteinFriesan_1M13())

stdpopsim/catalog/BosTau/genome_data.py

@@ -0,0 +1,38 @@
+# File autogenerated from Ensembl REST API. Do not edit.
+data = {
+    "assembly_accession": "GCA_002263795.2",
+    "assembly_name": "ARS-UCD1.2",
+    "chromosomes": {
+        "1": {"length": 158534110, "synonyms": []},
+        "2": {"length": 136231102, "synonyms": []},
+        "3": {"length": 121005158, "synonyms": []},
+        "4": {"length": 120000601, "synonyms": []},
+        "5": {"length": 120089316, "synonyms": []},
+        "6": {"length": 117806340, "synonyms": []},
+        "7": {"length": 110682743, "synonyms": []},
+        "8": {"length": 113319770, "synonyms": []},
+        "9": {"length": 105454467, "synonyms": []},
+        "10": {"length": 103308737, "synonyms": []},
+        "11": {"length": 106982474, "synonyms": []},
+        "12": {"length": 87216183, "synonyms": []},
+        "13": {"length": 83472345, "synonyms": []},
+        "14": {"length": 82403003, "synonyms": []},
+        "15": {"length": 85007780, "synonyms": []},
+        "16": {"length": 81013979, "synonyms": []},
+        "17": {"length": 73167244, "synonyms": []},
+        "18": {"length": 65820629, "synonyms": []},
+        "19": {"length": 63449741, "synonyms": []},
+        "20": {"length": 71974595, "synonyms": []},
+        "21": {"length": 69862954, "synonyms": []},
+        "22": {"length": 60773035, "synonyms": []},
+        "23": {"length": 52498615, "synonyms": []},
+        "24": {"length": 62317253, "synonyms": []},
+        "25": {"length": 42350435, "synonyms": []},
+        "26": {"length": 51992305, "synonyms": []},
+        "27": {"length": 45612108, "synonyms": []},
+        "28": {"length": 45940150, "synonyms": []},
+        "29": {"length": 51098607, "synonyms": []},
+        "X": {"length": 139009144, "synonyms": []},
+        "MT": {"length": 16338, "synonyms": []},
+    },
+}

stdpopsim/catalog/HomSap/__init__.py

@@ -2,15 +2,12 @@
 Catalog definitions for Homo Sapiens
 """
 import math
-import logging
 
 import msprime
 
 import stdpopsim
 from . import genome_data
 
-logger = logging.getLogger(__name__)
-
 ###########################################################
 #
 # Genome definition

stdpopsim/catalog/PonAbe/__init__.py

@@ -8,10 +8,6 @@
 import stdpopsim
 from . import genome_data
 
-import logging
-
-logger = logging.getLogger(__name__)
-
 ###########################################################
 #
 # Genome definition