def __init__(self):
genome = stdpopsim.Genome(chromosomes=[])
_species = stdpopsim.Species(
id="tesspe", name="Test species", genome=genome)
super().__init__(
species=_species,
name="test_map",
url="http://example.com/genetic_map.tar.gz",
file_pattern="prefix_{name}.txt")
def __init__(self):
genome = stdpopsim.Genome(chromosomes=[])
_species = stdpopsim.Species(id="TesSpe",
name="Test species",
common_name="Testy McTestface",
genome=genome)
super().__init__(species=_species,
id="test_annotation",
url="http://example.com/annotation.gff.gz",
zarr_url="http://example.com/annotation.zip",
file_name="annotation.gff.gz")
def __init__(self):
genome = stdpopsim.Genome(chromosomes=[])
_species = stdpopsim.Species(id="TesSpe",
name="Test species",
common_name="Testy McTestface",
genome=genome)
super().__init__(
species=_species,
id="test_map",
url="http://example.com/genetic_map.tar.gz",
sha256="1234", # url doesn't exist, so this will never be checked
file_pattern="prefix_{name}.txt")
def __init__(self):
genome = stdpopsim.Genome(chromosomes=[])
_species = stdpopsim.Species(id="TesSpe",
name="Test species",
common_name="Testy McTestface",
genome=genome)
super().__init__(
species=_species,
id="test_annotation",
url="http://example.com/annotation.gff.gz",
zarr_url="http://example.com/annotation.zip",
zarr_sha256="1234", # this shouldn't be checked anywhere
description="test annotation",
)
def __init__(self):
genome = stdpopsim.Genome(chromosomes=[])
_species = stdpopsim.Species(
id="TesSpe",
ensembl_id="test_species",
name="Test species",
common_name="Testy McTestface",
genome=genome,
)
super().__init__(
species=_species,
id="test_annotation",
url="http://example.com/annotation.gff.gz",
intervals_url="http://example.com/annotation.zip",
intervals_sha256="1234", # this shouldn't be checked anywhere
gff_sha256="6789",
description="test annotation",
file_pattern="yolo_{id}.txt",
annotation_source="your mom",
annotation_type="test",
)
"LGg": _overall_rate,
"LGh": _overall_rate,
"MT": _overall_rate,
}
_genome = stdpopsim.Genome.from_data(
genome_data.data,
recombination_rate=_recombination_rate,
mutation_rate=_mutation_rate,
citations=[_BourgeoisEtAl],
)
_species = stdpopsim.Species(
id="AnoCar",
ensembl_id="anolis_carolinensis",
name="Anolis carolinensis",
common_name="Anole lizard",
genome=_genome,
generation_time=1.5,
# they live between 1-2 years after they are able to mate
# they mature 8 to 9 months after they are born
# can live up to 8 years in captivity
population_size=3.05e6,
# poulation size caculated from theta caculations
# theta = 4Neu, theta from table 1
# Ne averaged across the 5 populations from BourgeoisEtAl
citations=[_LovernEtAl, _BourgeoisEtAl],
)
stdpopsim.register_species(_species)
),
],
)
stdpopsim.utils.append_common_synonyms(_genome)
_species = stdpopsim.Species(
id="ChlRei",
ensembl_id="chlamydomonas_reinhardtii",
name="Chlamydomonas reinhardtii",
common_name="Chlamydomonas reinhardtii",
genome=_genome,
generation_time=1 / 876,
population_size=1.4 * 1e-7,
citations=[
stdpopsim.Citation(
author="Ness et al.",
year=2016,
doi="https://doi.org/10.1093/molbev/msv272",
reasons={stdpopsim.CiteReason.POP_SIZE}, # Quebec population
),
stdpopsim.Citation(
author="Vítová et al",
year=2011,
doi="https://doi.org/10.1007/s00425-011-1427-7",
reasons={stdpopsim.CiteReason.GEN_TIME},
),
],
)
stdpopsim.register_species(_species)
reasons={stdpopsim.CiteReason.ASSEMBLY},
)
_genome = stdpopsim.Genome.from_data(
genome_data.data,
recombination_rate=_recombination_rate,
mutation_rate=_mutation_rate,
citations=[
_NeneEtAl,
_JunejaEtAl,
_CrawfordEtAl,
_KeightleyEtAl,
],
)
_species = stdpopsim.Species(
id="AedAeg",
ensembl_id="aedes_aegypti_lvpagwg",
name="Aedes aegypti",
common_name="Yellow fever mosquito",
genome=_genome,
generation_time=1 / 15,
# the estimated population size today the modern Senegal forest population
population_size=1e6,
citations=[_CrawfordEtAl],
)
stdpopsim.register_species(_species)
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,
citations=[
_HarlandEtAl.because(stdpopsim.CiteReason.MUT_RATE),
_MaEtAl.because(stdpopsim.CiteReason.REC_RATE),
_RosenEtAl.because(stdpopsim.CiteReason.ASSEMBLY),
],
)
_species = stdpopsim.Species(
id="BosTau",
ensembl_id="bos_taurus",
name="Bos Taurus",
common_name="Cattle",
genome=_genome,
generation_time=5,
population_size=62000,
citations=[_MacLeodEtAl],
)
stdpopsim.register_species(_species)
_species = stdpopsim.Species(
id="AnaPla",
ensembl_id="anas_platyrhynchos",
name="Anas platyrhynchos",
common_name="Mallard",
# description="The 'mallard' species complex consists of 14 hybridizing and "
# "recently diverged species living around the world, ranging from the holarctic "
# "mallard with >15M individuals today in North America alone to "
# "endangered endemics in Hawaii and New Zealand. The assembly, "
# "recombination rates, and default Ne were estimtaed with wild Chinese "
# "mallards.",
genome=_genome,
# generation time estimate from Lavertsky et al. (2020):
# Generation time (G) was calculated as G = \alpha + (s/(1 − s)),
# where \alpha is the age of maturity and s is the expected adult
# survival rate (Sather et al., 2005). The age of maturity for mallard-
# like ducks generally is one year (i.e., \alpha = 1), and the average
# adult survival rate is 0.54 (range: 0.34–0.74) and 0.54 (range: 0.4–0.70)
# for mallards and black ducks, respectively (Nichols, Obrecht, & Hines, 1987).
# Using an overall survival rate average of 0.54 for the two species, we
# estimated the generation time to be 4.0 years.
generation_time=4,
# choosing Ne based on theta = 4 Ne u from Guo et al 2021
# theta = 0.003 (Figure 1), u as above (the paper uses a rate from chicken)
population_size=156000,
citations=[
_LavretskyEtAl2020,
_GuoEtAl2020,
],
)
assembly_citations=[
stdpopsim.Citation(
doi="https://doi.org/10.1093/nar/gkm965",
year="2007",
author="Swarbreck et al.",
reasons={stdpopsim.CiteReason.ASSEMBLY})])
_species = stdpopsim.Species(
id="AraTha",
name="Arabidopsis thaliana",
common_name="A. thaliana",
genome=_genome,
generation_time=1.0,
generation_time_citations=[stdpopsim.Citation(
doi="https://doi.org/10.1890/0012-9658(2002)083[1006:GTINSO]2.0.CO;2",
year="2002",
author="Donohue",
reasons={stdpopsim.CiteReason.GEN_TIME})],
population_size=10**4,
population_size_citations=[stdpopsim.Citation(
doi="https://doi.org/10.1016/j.cell.2016.05.063",
year="2016",
author="1001GenomesConsortium",
reasons={stdpopsim.CiteReason.POP_SIZE})]
)
stdpopsim.register_species(_species)
###########################################################
#
# Genetic maps
#
))
_genome = stdpopsim.Genome(
chromosomes=_chromosomes,
assembly_name=genome_data.data["assembly_name"],
assembly_accession=genome_data.data["assembly_accession"],
citations=[
_SchriderEtAl.because(stdpopsim.CiteReason.MUT_RATE),
_DosSantosEtAl,
_HoskinsEtAl,
_ComeronEtAl.because(stdpopsim.CiteReason.REC_RATE),
],
)
stdpopsim.utils.append_common_synonyms(_genome)
_species = stdpopsim.Species(
id="DroMel",
ensembl_id="drosophila_melanogaster",
name="Drosophila melanogaster",
common_name="D. melanogaster",
genome=_genome,
generation_time=0.1,
# Population size is the older of two population sizes estimated by
# Li and Stephan in a two-epoch model of African populations.
# N_A0 is given as 8.603e6, and N_A1 (used here) is 5 times smaller.
population_size=1720600,
citations=[_LiAndStephan],
)
stdpopsim.register_species(_species)
mutation_rate=1.5e-8,
recombination_rate=float(mean_rr)))
_genome = stdpopsim.Genome(chromosomes=_chromosomes,
mutation_rate_citations=[
_nater2017.because(
stdpopsim.CiteReason.MUT_RATE)
])
_species = stdpopsim.Species(
id="PonAbe",
name="Pongo abelii",
common_name="Sumatran orangutan",
genome=_genome,
generation_time=20,
generation_time_citations=[
_locke2011.because(stdpopsim.CiteReason.GEN_TIME)
],
population_size=1.79e4,
population_size_citations=[
_locke2011.because(stdpopsim.CiteReason.POP_SIZE)
])
stdpopsim.register_species(_species)
###########################################################
#
# Genetic maps
#
###########################################################
_NelsonEtAl = stdpopsim.Citation(
doi="https://doi.org/10.1111/mec.14122",
year=2017,
author="Nelson et al.",
reasons={stdpopsim.CiteReason.GEN_TIME},
)
_WallbergEtAl = stdpopsim.Citation(
doi="https://doi.org/10.1038/ng.3077",
year=2014,
author="Wallberg et al.",
reasons={stdpopsim.CiteReason.POP_SIZE},
)
_species = stdpopsim.Species(
id="ApiMel",
ensembl_id="apis_mellifera",
name="Apis mellifera",
common_name="Apis mellifera (DH4)",
genome=_genome,
generation_time=2,
population_size=2e05,
citations=[
_WallbergEtAl,
_NelsonEtAl,
],
)
stdpopsim.register_species(_species)
_chromosomes = []
for line in _chromosome_data.splitlines():
name, length, mean_rr = line.split()[:3]
_chromosomes.append(
stdpopsim.Chromosome(
id=name,
length=int(length),
mutation_rate=1e-8, # WRONG!,
recombination_rate=float(mean_rr)))
_genome = stdpopsim.Genome(chromosomes=_chromosomes)
_species = stdpopsim.Species(
id="homsap",
name="H**o sapiens",
genome=_genome,
# TODO reference for these
generation_time=25,
population_size=10**4)
stdpopsim.register_species(_species)
###########################################################
#
# Genetic maps
#
###########################################################
_gm = stdpopsim.GeneticMap(
species=_species,
name="HapmapII_GRCh37",
author="Keightley et al",
year=2015,
doi="https://doi.org/10.1093/molbev/msu302",
reasons={stdpopsim.CiteReason.MUT_RATE},
),
],
)
stdpopsim.utils.append_common_synonyms(_genome)
_species = stdpopsim.Species(
id="HelMel",
ensembl_id="heliconius_melpomene",
name="Heliconius melpomene",
common_name="Heliconius melpomene",
genome=_genome,
generation_time=35 / 365, # 35 days
population_size=2111109,
citations=[
stdpopsim.Citation(
author="Pardo-Diaz et al",
year=2012,
doi="https://doi.org/10.1371/journal.pgen.1002752",
reasons={
stdpopsim.CiteReason.POP_SIZE, stdpopsim.CiteReason.GEN_TIME
},
),
],
)
stdpopsim.register_species(_species)
mutation_rate=2.0e-8,
recombination_rate=float(mean_rr)))
_genome = stdpopsim.Genome(chromosomes=_chromosomes,
mutation_rate_citations=[
_locke2011.because(
stdpopsim.CiteReason.MUT_RATE)
])
_species = stdpopsim.Species(
id="PonPyg",
name="Pongo pygmaeus",
common_name="Bornean orangutan",
genome=_genome,
generation_time=20,
generation_time_citations=[
_locke2011.because(stdpopsim.CiteReason.GEN_TIME)
],
population_size=1.79e4,
population_size_citations=[
_locke2011.because(stdpopsim.CiteReason.POP_SIZE)
])
stdpopsim.register_species(_species)
###########################################################
#
# Genetic maps
#
###########################################################
# 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)
)
_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
#
###########################################################
chromosomes=_chromosomes,
assembly_name=genome_data.data["assembly_name"],
assembly_accession=genome_data.data["assembly_accession"],
mutation_rate_citations=[
_wielgoss_et_al.because(stdpopsim.CiteReason.MUT_RATE),
],
assembly_citations=[_blattner_et_al.because(stdpopsim.CiteReason.ASSEMBLY)],
)
_species = stdpopsim.Species(
id="EscCol",
name="Escherichia coli",
common_name="E. coli",
# We use the K-12 strain, because the parameters we're using more
# closely match this strain than the ensembl default (HUSEC2011).
ensembl_id="escherichia_coli_str_k_12_substr_mg1655_gca_000005845",
genome=_genome,
# E. coli K-12 strain MG1655 "doubling time during steady-state growth in
# Luria-Bertani broth was 20 min".
generation_time=0.00003805175, # 1.0 / (525600 min/year / 20 min/gen)
generation_time_citations=[_sezonov_et_al.because(stdpopsim.CiteReason.GEN_TIME)],
# Hartl et al. calculated Ne for "natural isolates of E. coli",
# assuming mu=5e-10 (from Drake 1991).
population_size=1.8e8,
population_size_citations=[_hartl_et_al.because(stdpopsim.CiteReason.POP_SIZE)],
)
stdpopsim.register_species(_species)
mutation_rate=1e-5+2e-4,
recombination_rate=0.0))
# mean_conversion_rate=8.9e-11 # not implemented yet!
# mean_conversion_length=542 # not implemented yet!
#: :class:`stdpopsim.Genome` definition for E. Coli.
# Chromosome length data is based on strain K-12.
_genome = stdpopsim.Genome(
chromosomes=_chromosomes,
mutation_rate_citations=[
_perfeito_et_al.because(stdpopsim.CiteReason.MUT_RATE),
_kibota_and_lynch.because(stdpopsim.CiteReason.MUT_RATE),
],
assembly_citations=[
_blattner_et_al.because(stdpopsim.CiteReason.ASSEMBLY)])
_species = stdpopsim.Species(
id="EscCol",
name="Escherichia coli",
common_name="E. coli",
genome=_genome,
generation_time=0.00003805175, # 1.0 / (525600 min/year / 20 min/gen)
generation_time_citations=[
_sezonov_et_al.because(stdpopsim.CiteReason.GEN_TIME)],
population_size=1.8e8,
population_size_citations=[
_lapierre_et_al.because(stdpopsim.CiteReason.POP_SIZE)])
stdpopsim.register_species(_species)
# We could not auto-pull the genome data from ensemble
# so instead we used the most up-to-date assembly
# currently available from NCBI.
_genome = stdpopsim.Genome.from_data(
genome_data.data,
recombination_rate=_recombination_rate,
mutation_rate=_mutation_rate,
citations=[
_ChakrabortyEtAl,
_ComeronEtAl,
_LegrandEtAl,
],
)
# Generation time was set to that used by
# by Legrand et al. in an ABC selection of demographic
# scenarios (page 1200).
# Population size was estimated in the same paper (page 1202).
_species = stdpopsim.Species(
id="DroSec",
ensembl_id="drosophila_sechellia",
name="Drosophila sechellia",
common_name="Drosophila sechellia",
genome=_genome,
generation_time=0.05,
population_size=100000,
citations=[_LegrandEtAl],
)
stdpopsim.register_species(_species)
mutation_rate=5.49e-9, # _SchriderEtAl de novo mutation rate
recombination_rate=_recombination_rate_data[name],
)
)
_genome = stdpopsim.Genome(
chromosomes=_chromosomes,
assembly_name=genome_data.data["assembly_name"],
assembly_accession=genome_data.data["assembly_accession"],
citations=[
_SchriderEtAl.because(stdpopsim.CiteReason.MUT_RATE),
_DosSantosEtAl,
_HoskinsEtAl,
_ComeronEtAl.because(stdpopsim.CiteReason.REC_RATE),
],
)
_species = stdpopsim.Species(
id="DroMel",
ensembl_id="drosophila_melanogaster",
name="Drosophila melanogaster",
common_name="D. melanogaster",
genome=_genome,
generation_time=0.1,
population_size=1720600,
citations=[_LiAndStephan],
)
stdpopsim.register_species(_species)
reasons={stdpopsim.CiteReason.REC_RATE},
),
stdpopsim.Citation(
author="Liu et al.",
year=2016,
doi="https://10.1111/mec.13827",
reasons={stdpopsim.CiteReason.MUT_RATE},
),
],
)
_species = stdpopsim.Species(
id="GasAcu",
ensembl_id="9307941",
name="Gasterosteus aculeatus",
common_name="Three-spined stickleback",
genome=_genome,
generation_time=1,
population_size=1e4,
citations=[
stdpopsim.Citation(
author="Liu et al.",
year=2016,
doi="https://10.1111/mec.13827",
reasons={stdpopsim.CiteReason.POP_SIZE, stdpopsim.CiteReason.GEN_TIME},
),
],
)
stdpopsim.register_species(_species)
chromosomes=_chromosomes,
assembly_name=genome_data.data["assembly_name"],
assembly_accession=genome_data.data["assembly_accession"],
mutation_rate_citations=[
_nater2017.because(stdpopsim.CiteReason.MUT_RATE)
],
)
_species = stdpopsim.Species(
id="PonAbe",
name="Pongo abelii",
common_name="Sumatran orangutan",
genome=_genome,
# generation time used by Locke et al. without further citation
generation_time=20,
generation_time_citations=[
_locke2011.because(stdpopsim.CiteReason.GEN_TIME)
],
# Locke et al. inferred ancestral Ne
population_size=1.79e4,
population_size_citations=[
_locke2011.because(stdpopsim.CiteReason.POP_SIZE)
],
)
stdpopsim.register_species(_species)
###########################################################
#
# Genetic maps
#
###########################################################
recombination_rate_citations=[
_CampbellEtAl.because(stdpopsim.CiteReason.REC_RATE)
],
assembly_citations=[
_LindbladTohEtAl.because(stdpopsim.CiteReason.ASSEMBLY)
],
)
_species = stdpopsim.Species(
id="CanFam",
name="Canis familiaris",
common_name="Dog",
genome=_genome,
generation_time=3,
generation_time_citations=[
# Everyone uses 3 years because everyone else uses it.
# It's likely higher, at least in wolves:
# https://pubs.er.usgs.gov/publication/70187564
],
population_size=13000, # ancestral dog size
population_size_citations=[
_LindbladTohEtAl.because(stdpopsim.CiteReason.POP_SIZE)
],
)
stdpopsim.register_species(_species)
_gm = stdpopsim.GeneticMap(
species=_species,
id="Campbell2016_CanFam3_1",
description="Pedigree-based crossover map from 237 individuals",
long_description="""
# based on `dm6 <https://www.ncbi.nlm.nih.gov/assembly/GCF_000001215.4/>`_.
_genome = stdpopsim.Genome(chromosomes=_chromosomes,
mutation_rate_citations=[
_SchriderEtAl.because(
stdpopsim.CiteReason.MUT_RATE)
],
assembly_citations=[_DosSantosEtAl])
_species = stdpopsim.Species(
id="DroMel",
name="Drosophila melanogaster",
common_name="D. melanogaster",
genome=_genome,
generation_time=0.1,
generation_time_citations=[
_LiAndStephan.because(stdpopsim.CiteReason.GEN_TIME)
],
population_size=1720600,
population_size_citations=[
_LiAndStephan.because(stdpopsim.CiteReason.POP_SIZE)
])
stdpopsim.register_species(_species)
###########################################################
#
# Genetic maps
#
###########################################################
recombination_rate=_recombination_rate_data[name],
))
_genome = stdpopsim.Genome(
chromosomes=_chromosomes,
assembly_name=genome_data.data["assembly_name"],
assembly_accession=genome_data.data["assembly_accession"],
citations=[
_genome2001,
_tian2019.because(stdpopsim.CiteReason.MUT_RATE),
_hapmap2007.because(stdpopsim.CiteReason.REC_RATE),
],
)
stdpopsim.utils.append_common_synonyms(_genome)
_species = stdpopsim.Species(
id="HomSap",
ensembl_id="homo_sapiens",
name="H**o sapiens",
common_name="Human",
genome=_genome,
generation_time=30,
population_size=10**4,
citations=[
_tremblay2000.because(stdpopsim.CiteReason.GEN_TIME),
_takahata1993.because(stdpopsim.CiteReason.POP_SIZE),
],
)
stdpopsim.register_species(_species)
_CampbellEtAl.because(stdpopsim.CiteReason.REC_RATE),
_LindbladTohEtAl.because(stdpopsim.CiteReason.ASSEMBLY),
],
)
_species = stdpopsim.Species(
id="CanFam",
ensembl_id="canis_familiaris",
name="Canis familiaris",
common_name="Dog",
genome=_genome,
population_size=13000, # ancestral dog size
generation_time=3,
citations=[
# Everyone uses 3 years for generation time because everyone else uses it.
# It's likely higher, at least in wolves:
# https://academic.oup.com/mbe/article/35/6/1366/4990884
# Reasoning behind a generation time of 3 years:
# Consider two use cases for CanFam simulations:
# (1) for domestic dog simulations, and (2) for wolf+dog simulations
# (or ancestral dogs).
# In case (1), maybe 3 year generations are more appropriate because of human
# intervention in breeding. In case (2), you might want to match what other
# studies have done (thus using 3 year generations), or you might want to
# consider what is known about modern wolves.
_LindbladTohEtAl.because(stdpopsim.CiteReason.POP_SIZE)
],
)
stdpopsim.register_species(_species)
length=data["length"],
synonyms=data["synonyms"],
# Nater et al. 2017 used mu=1.5e-8 per generation, based on the
# assumption that it's similar to humans and chimps.
mutation_rate=1.5e-8,
recombination_rate=_recombination_rate_data[name],
)
)
_genome = stdpopsim.Genome(
chromosomes=_chromosomes,
assembly_name=genome_data.data["assembly_name"],
assembly_accession=genome_data.data["assembly_accession"],
citations=[_nater2017],
)
_species = stdpopsim.Species(
id="PonAbe",
ensembl_id="pongo_abelii",
name="Pongo abelii",
common_name="Sumatran orangutan",
genome=_genome,
# generation time used by Locke et al. without further citation
generation_time=20,
# Locke et al. inferred ancestral Ne
population_size=1.79e4,
citations=[_locke2011],
)
stdpopsim.register_species(_species)
