Add Streptococcus agalactiae CC17 strains.

stdpopsim/catalog/StrAga/__init__.py

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+"""
+Catalog definitions for Streptococcus agalactiae CC17
+"""
+from . import species  # noqa: F401

stdpopsim/catalog/StrAga/genome_data.py

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+# File autogenerated from Ensembl REST API. Do not edit.
+data = {
+    "assembly_accession": "GCA_000689235.1",
+    "assembly_name": "GBCO_p1",
+    "chromosomes": {"1": {"length": 2065074, "synonyms": ["I"]}},
+}

stdpopsim/catalog/StrAga/species.py

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+import stdpopsim
+
+from . import genome_data
+
+# ref for mutation, assembly, pop size
+_DaCunha_et_al = stdpopsim.Citation(
+    author="Da Cunha et al",
+    year=2014,
+    doi="https://doi.org/10.1038/ncomms5544",
+)
+
+# per site per generation
+# Estimated in Da Cunha et al. as 0.56 SNP/Mb/Year
+# Given that the generation time is estimated at 1 generation per day we have :
+#  0.56/(1e6*365)
+_mutation_rate = {"1": 1.53e-9}
+
+# no cross-over recombination in bacteria
+_recombination_rate = {"1": 0}
+
+# gene conversion rate (HGT rate):
+# hard to estimate precisely. What is often estimated is the r/m or rho/theta ratio.
+# in Oliveira et. al, PNAS, 2016, they estimate those rates on coregenome
+# (conservative estimates) it was found for the species (not CC17 in particular) that
+# r/m = 7.42% and rho/theta = 0.964%
+# In Almeida et al, 2017, mSystems, they identified for a population of CC17,
+# 502 homoplasic SNPs over 16572 SNPs, which gives a r/m ratio of 3%
+# In Lefebure et al, Genome Biology 2007, they found that between 3% and 18% of the genes
+#  were recombinant (depending on the methods used). This illustrates also that this
+#  species do not recombine much
+# In Da Cunha et al, 2016, Nat Comms, CC17 is shown to have a cumulated size of
+# recombination fragment that is about 10% that of other recombining region of other
+# clonal complexes of the same species, so CC17 does not recombine much in a species
+# that is already not recombining a lot.
+# Overall, as most of the estimation might be conservatives one can use a relative
+# rate rho/theta = 10%.
+# _gene_conversion_rate = {"1": _mutation_rate/10}
+
+# Mean gene conversion  tract length
+# In Brochet et al., 2008, PNAS, they estimate the size of transferred DNA experimentally
+#  ([33, 334, 252, 28, 21, 105, 99, 14, 286, 49] kb), which leads to an estimation of a
+# mean tract length of 122kb, which is plausible given the size that was identified in
+# Almeida et al. 2017, mSystems. (25, 634, 778 and 880kb)
+# This estimate (as well as others) was used in Cury et al, 2021, PCI Evol Biol, and
+# discussed in more detail in response to a reviewer, that can be seen there :
+# https://evolbiol.peercommunityin.org/articles/rec?id=356
+#      > Revision round #3 > Author's reply (pdf).
+# _mean_gene_conversion_tract_length = 120000
+
+
+_genome = stdpopsim.Genome.from_data(
+    genome_data.data,
+    recombination_rate=_recombination_rate,
+    mutation_rate=_mutation_rate,
+    citations=[
+        _DaCunha_et_al.because(stdpopsim.CiteReason.ASSEMBLY),
+        _DaCunha_et_al.because(stdpopsim.CiteReason.MUT_RATE),
+    ],
+)
+
+# Generation time :
+#  No estimation was done for S agalactiae, but we can use estimates from E coli (which
+# grows a bit faster in the lab than S agalactiae), which ranges from around
+# 0.5 to 20 generations per day.
+# ref for lower range estimates : Michael A. Savageau, « Escherichia coli habitats,
+# cell types, and molecular mechanisms of gene control », The american naturalist 122,
+#  nᵒ 6 (1983): 732–744.
+# ref for higher range estimates : Mohamed Ghalayini et al., « Evolution of a Dominant
+#  Natural Isolate of Escherichia coli in the Human Gut over the Course of a Year
+# Suggests a Neutral Evolution with Reduced Effective Population Size »,
+# Applied and Environmental Microbiology 84, nᵒ 6 (5 janvier 2018): e02377-17,
+#  https://doi.org/10.1128/AEM.02377-17.
+# This is likely over estimated as it is only in the gut,
+# where there is plenty of nutrient for bacteria to grow,
+# unlike outside of it where growth might be slower.
+# So we use the value of 1 generation per day.
+
+# population size
+# We estimate it from the Watterson estimator :
+# theta = 2.Ne.mu = S / sum_{i=1}^{k=n-1}(1/k)
+# With n the number of samples and S the number of segregating sites.
+# From Da Cunha et al, we have
+# S = 3922 / 1.86Mb = 2.1×10−3 SNP/bp; k = 79; mu=1.53×10−9 SNP/bp/generation
+# So Ne ~ 140000
+
+
+_species = stdpopsim.Species(
+    id="StrAga",
+    ensembl_id="NA",
+    name="Streptococcus agalactiae",
+    common_name="Group B Streptococcus",
+    genome=_genome,
+    generation_time=1 / 365,  # year / generations
+    population_size=140000,
+    citations=[
+        _DaCunha_et_al.because(stdpopsim.CiteReason.POP_SIZE),
+        stdpopsim.Citation(
+            author="Savageau M.A.",
+            year=1983,
+            doi="https://doi.org/10.1086/284168",
+            reasons={stdpopsim.CiteReason.GEN_TIME},
+        ),
+    ],
+)
+
+stdpopsim.register_species(_species)

tests/test_StrAga.py

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+import pytest
+
+import stdpopsim
+from tests import test_species
+
+
+class TestSpeciesData(test_species.SpeciesTestBase):
+
+    species = stdpopsim.get_species("StrAga")
+
+    def test_ensembl_id(self):
+        assert self.species.ensembl_id == "NA"
+
+    def test_name(self):
+        assert self.species.name == "Streptococcus agalactiae"
+
+    def test_common_name(self):
+        assert self.species.common_name == "Group B Streptococcus"
+
+    # QC Tests. These tests are performed by another contributor
+    # independently referring to the citations provided in the
+    # species definition, filling in the appropriate values
+    # and deleting the pytest "skip" annotations.
+    @pytest.mark.skip("Population size QC not done yet")
+    def test_qc_population_size(self):
+        assert self.species.population_size == -1
+
+    @pytest.mark.skip("Generation time QC not done yet")
+    def test_qc_generation_time(self):
+        assert self.species.generation_time == -1
+
+
+class TestGenomeData(test_species.GenomeTestBase):
+
+    genome = stdpopsim.get_species("StrAga").genome
+
+    @pytest.mark.skip("Recombination rate QC not done yet")
+    @pytest.mark.parametrize(
+        ["name", "rate"],
+        {
+            "1": -1,
+        }.items(),
+    )
+    def test_recombination_rate(self, name, rate):
+        assert pytest.approx(rate, self.genome.get_chromosome(name).recombination_rate)
+
+    @pytest.mark.skip("Mutation rate QC not done yet")
+    @pytest.mark.parametrize(
+        ["name", "rate"],
+        {"1": -1}.items(),
+    )
+    def test_mutation_rate(self, name, rate):
+        assert pytest.approx(rate, self.genome.get_chromosome(name).mutation_rate)