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z2zt5Bum9c#_a?y)MN&#Pq4@C#M8yg8kZ13wO=f+-$n+?#4o0F3k+I}pQfVrrG6slj zxwJDw`t;Hv83|IVquYiaFcP~FK5Y+T5AxhQkaU2O&(TvlkgE9z9K?-7w?%=GU%fXU z5H%G}G(L>l680ylrgku(1+5{xYCYHydO?FI{lFR3rG8sM{}hRy)P>tno)HszD=TV|#o&Qb2&W)LdGrh?4c0AhB_fmB2E(3h`RVTZm0scQfgfHCtA-A*njkTA<6 zJ!QjkPvGDUxb}cbbGM$}k_QhSpx42?=XwxM-hdE_c_YQ5Ha1%lga9yA3B*XzGx(>r zBidpIng7*iCjyldZPO0i-avUmVm63m2Mn5!Lj@>%fM_T{o}di*1q8siZ~_K;d5q9y zHq88bgc%5Mqp8a0JJ zNP0jrPwxqR8M$ANA8seiNm^@YSource code for stdpopsim.citations

diff --git a/main/_modules/stdpopsim/engines.html b/main/_modules/stdpopsim/engines.html index eb7f184..efea743 100644 --- a/main/_modules/stdpopsim/engines.html +++ b/main/_modules/stdpopsim/engines.html @@ -198,9 +198,24 @@

Source code for stdpopsim.engines

             warnings.warn(
                 "The demographic model has mutation rate "
                 f"{demographic_model.mutation_rate}, but this simulation used the "
-                f"contig's mutation rate {contig.mutation_rate}. Diversity levels "
-                "may be different than expected for this species. For details see "
-                "documentation at "
+                "contig's mutation rate "
+                f"{contig.mutation_rate}. "
+                "Diversity levels may be different than expected for this species. "
+                "For details see documentation at "
+                "https://popsim-consortium.github.io/stdpopsim-docs/stable/tutorial.html"
+            )
+
+    def _warn_recombination_rate_mismatch(self, contig, demographic_model):
+        if demographic_model.recombination_rate is not None and not math.isclose(
+            demographic_model.recombination_rate, contig.recombination_map.mean_rate
+        ):
+            warnings.warn(
+                "The demographic model has recombination rate "
+                f"{demographic_model.recombination_rate}, but this simulation used the "
+                "contig's recombination rate "
+                f"{contig.recombination_map.mean_rate}. "
+                "Patterns of linkage may be different than expected for this species. "
+                "For details see documentation at "
                 "https://popsim-consortium.github.io/stdpopsim-docs/stable/tutorial.html"
             )
@@ -337,6 +352,7 @@

Source code for stdpopsim.engines

         # TODO: remove this after a release or two. See #745.
         self._warn_zigzag(demographic_model)
         self._warn_mutation_rate_mismatch(contig, demographic_model)
+        self._warn_recombination_rate_mismatch(contig, demographic_model)
 
         rng = np.random.default_rng(seed)
         seeds = rng.integers(1, 2**31 - 1, size=2)
@@ -354,7 +370,7 @@ 

Source code for stdpopsim.engines

             )
         elif (gc_frac is not None) and (gc_frac > 0.0):
             # the recombination map is a map of double-stranded breaks,
-            # so we need to separate out GC from crossovers
+            # so we need to separate out gene conversion from crossing-over
             gc_rate = contig.recombination_map.mean_rate * gc_frac / (1 - gc_frac)
             recombination_map = msprime.RateMap(
                 position=recombination_map.position,
diff --git a/main/_modules/stdpopsim/genomes.html b/main/_modules/stdpopsim/genomes.html
index 96e831d..7cda533 100644
--- a/main/_modules/stdpopsim/genomes.html
+++ b/main/_modules/stdpopsim/genomes.html
@@ -80,6 +80,7 @@ 

Source code for stdpopsim.genomes

 Infrastructure for defining information about species' genomes and genomic
 regions to be simulated.
 """
+
 import logging
 import warnings
 import attr
@@ -94,7 +95,7 @@ 

Source code for stdpopsim.genomes

 
 def _uniform_rate_map(left, right, length, rate):
     """
-    Makes a RateMap that is
+    Makes a uniform RateMap
     """
     pos = [
         0.0,
@@ -118,8 +119,12 @@ 

Source code for stdpopsim.genomes

 
     :ivar chromosomes: A list of :class:`.Chromosome` objects.
     :vartype chromosomes: list
+    :ivar assembly_name: The name of the genome assembly.
+    :vartype assembly_name: str
+    :ivar assembly_accession: The ID of the genome assembly accession.
+    :vartype assembly_accession: str
     :ivar bacterial_recombination: Whether recombination is via horizontal gene
-        transfer (if this is True) or via crossover and possibly gene
+        transfer (if this is True) or via crossing-over and possibly gene
         conversion (if this is False). Default: False.
     :vartype bacterial_recombination: bool
     :ivar citations: A list of :class:`.Citation` objects
@@ -130,11 +135,9 @@ 

Source code for stdpopsim.genomes

     :vartype length: int
     """
 
-    # TODO document the assembly_name and accession
-
     chromosomes = attr.ib(factory=list)
-    assembly_name = attr.ib(default=None, kw_only=True)
-    assembly_accession = attr.ib(default=None, kw_only=True)
+    assembly_name = attr.ib(type=str, default=None, kw_only=True)
+    assembly_accession = attr.ib(type=str, default=None, kw_only=True)
     bacterial_recombination = attr.ib(type=bool, default=False, kw_only=True)
     citations = attr.ib(factory=list, kw_only=True)
 
@@ -252,7 +255,7 @@ 

Source code for stdpopsim.genomes

     def mean_recombination_rate(self):
         """
         The length-weighted mean recombination rate across all chromosomes.
-        (Note that this is the rate of crossovers, not all double-stranded breaks.)
+        (Note that this is the rate of crossing-over, not all double-stranded breaks.)
         """
         length = self.length
         mean_recombination_rate = 0
@@ -280,15 +283,15 @@ 

Source code for stdpopsim.genomes

 class Chromosome:
     """
     Class representing a single chromosome for a species. Note that although
-    the recombination rate for a Chromosome is the rate of crossovers,
-    the recombination map for a Contig gives the rate of both crossovers
+    the recombination rate for a Chromosome is the rate of crossing-over,
+    the recombination map for a Contig gives the rate of both crossing-over
     and gene conversion, so will differ if the gene conversion fraction is nonzero.
 
     :ivar str ~.id: The string identifier for the chromosome.
     :ivar int length: The length of the chromosome.
     :ivar float mutation_rate: The mutation rate used when simulating this
         chromosome.
-    :ivar float recombination_rate: The rate of crossovers used when simulating
+    :ivar float recombination_rate: The rate of crossing-over used when simulating
         this chromosome (if not using a genetic map).
     :ivar float gene_conversion_fraction: The gene conversion fraction used when
         simulating this chromosome.
@@ -326,18 +329,21 @@ 

Source code for stdpopsim.genomes

     :vartype mutation_rate: float
     :ivar ploidy: The ploidy of the contig. Defaults to 2 (diploid).
     :vartype ploidy: int
+    :ivar genetic_map: The genetic map for the region, that gives
+        the rates of crossing-over. A :class:`.GeneticMap` object.
+    :vartype genetic_map: .GeneticMap
     :ivar recombination_map: The recombination map for the region, that gives
         the rates of double-stranded breaks. Note that if gene conversion
         fraction is nonzero, then this map can have a larger rate than the
         corresponding chromosome's recombination rate, since the chromosome's
-        rate describes only crossovers. A :class:`msprime.RateMap` object.
+        rate describes only crossing-over. A :class:`msprime.RateMap` object.
     :vartype recombination_map: msprime.RateMap
     :ivar bacterial_recombination: Whether the model of recombination is
         by horizontal gene transfer or not (default is False, i.e.,
-        recombination is by crossovers and possibly gene conversion).
+        recombination is by crossing-over and possibly gene conversion).
     :ivar gene_conversion_fraction: The fraction of recombinations that resolve
         as gene conversion events. The recombination map gives the rates of
-        *both* crossovers and gene conversions, with a fraction of gene
+        *both* crossing-over and gene conversion, with a fraction of gene
         conversions given by this value. Defaults to None, i.e., no gene
         conversion. Must be None or between 0 and 1. Must be None if
         bacterial_recombination is True.
@@ -464,6 +470,7 @@ 

Source code for stdpopsim.genomes

         length_multiplier=1,
         length=None,
         mutation_rate=None,
+        recombination_rate=None,
         use_species_gene_conversion=False,
         inclusion_mask=None,
         exclusion_mask=None,
@@ -529,7 +536,11 @@ 

Source code for stdpopsim.genomes

                         )
             if mutation_rate is None:
                 mutation_rate = u_tot / L_tot
-            r = r_tot / L_tot
+            if recombination_rate is None:
+                r = r_tot / L_tot
+            else:
+                r = recombination_rate
+                r_tot = r * L_tot
             if use_species_gene_conversion is True:
                 if gcf_tot > 0:
                     gene_conversion_fraction = gcf_tot / r_tot
@@ -581,25 +592,28 @@ 

Source code for stdpopsim.genomes

 
             if genetic_map is None:
                 gm = None
+                if recombination_rate is None:
+                    r = chrom.recombination_rate
+                else:
+                    r = recombination_rate
                 if length_multiplier != 1:
                     logger.debug(
                         f"Making flat chromosome {length_multiplier} * {chrom.id}"
                     )
                     recomb_map = msprime.RateMap.uniform(
-                        round(length_multiplier * chrom.length),
-                        chrom.recombination_rate,
+                        round(length_multiplier * chrom.length), r
                     )
                 else:
                     logger.debug(
                         f"Making flat contig from {left} to {right} for {chrom.id}"
                     )
-                    recomb_map = _uniform_rate_map(
-                        left, right, chrom.length, chrom.recombination_rate
-                    )
+                    recomb_map = _uniform_rate_map(left, right, chrom.length, r)
             else:
                 if length_multiplier != 1:
-                    raise ValueError("Cannot use length multiplier with empirical maps")
-                logger.debug(f"Getting map for {chrom.id} from {genetic_map}")
+                    raise ValueError("Cannot use length multiplier with genetic map")
+                if recombination_rate is not None:
+                    raise ValueError("Cannot use recombination rate with genetic map")
+                logger.debug(f"Getting genetic map for {chrom.id} from {genetic_map}")
                 gm = species.get_genetic_map(genetic_map)
                 recomb_map = gm.get_chromosome_map(chrom.id)
                 recomb_map = recomb_map.slice(left=left, right=right)
@@ -895,15 +909,15 @@ 

Source code for stdpopsim.genomes

     def __str__(self):
         gmap = "None" if self.genetic_map is None else self.genetic_map.id
         s = (
-            "Contig(length={:.2G}, recombination_rate={:.2G}, "
-            "mutation_rate={:.2G}, bacterial_recombination={}, "
+            "Contig(length={:.2G}, mutation_rate={:.2G}, "
+            "recombination_rate={:.2G}, bacterial_recombination={}, "
             "gene_conversion_fraction={}, gene_conversion_length={}, "
             "genetic_map={}, origin={}, dfe_list={}, "
             "interval_list={})"
         ).format(
             self.length,
-            self.recombination_map.mean_rate,
             self.mutation_rate,
+            self.recombination_map.mean_rate,
             self.bacterial_recombination,
             self.gene_conversion_fraction,
             self.gene_conversion_length,
diff --git a/main/_modules/stdpopsim/models.html b/main/_modules/stdpopsim/models.html
index bccd072..6c0d633 100644
--- a/main/_modules/stdpopsim/models.html
+++ b/main/_modules/stdpopsim/models.html
@@ -79,6 +79,7 @@ 

Source code for stdpopsim.models

 """
 Common infrastructure for specifying demographic models.
 """
+
 import copy
 import textwrap
 
@@ -152,6 +153,10 @@ 

Source code for stdpopsim.models

     :ivar mutation_rate: The mutation rate associated with the demographic model.
         If no mutation rate is given, the species' default mutation rate is used.
     :vartype mutation_rate: float
+    :ivar recombination_rate: The recombination rate associated with the demographic
+        model. If no recombination rate is given, the species' default recombination
+        rate is used.
+    :vartype recombination_rate: float
     """
 
     def __init__(
@@ -170,12 +175,14 @@ 

Source code for stdpopsim.models

         populations=None,
         model=None,
         mutation_rate=None,
+        recombination_rate=None,
     ):
         self.id = id
         self.description = description
         self.long_description = long_description
         self.generation_time = 1 if generation_time is None else generation_time
         self.mutation_rate = mutation_rate
+        self.recombination_rate = recombination_rate
         self.citations = [] if citations is None else citations
         self.qc_model = qc_model
 
@@ -226,12 +233,13 @@ 

Source code for stdpopsim.models

         long_desc = "\n║                     ".join(long_desc_lines)
         s = (
             "Demographic model:\n"
-            f"║  id               = {self.id}\n"
-            f"║  description      = {self.description}\n"
-            f"║  long_description = {long_desc}\n"
-            f"║  generation_time  = {self.generation_time}\n"
-            f"║  mutation_rate    = {self.mutation_rate}\n"
-            f"║  citations        = {[cite.doi for cite in self.citations]}\n"
+            f"║  id                 = {self.id}\n"
+            f"║  description        = {self.description}\n"
+            f"║  long_description   = {long_desc}\n"
+            f"║  generation_time    = {self.generation_time}\n"
+            f"║  mutation_rate      = {self.mutation_rate}\n"
+            f"║  recombination_rate = {self.recombination_rate}\n"
+            f"║  citations          = {[cite.doi for cite in self.citations]}\n"
             f"║{self.model}"
         )
         return s
diff --git a/main/_modules/stdpopsim/slim_engine.html b/main/_modules/stdpopsim/slim_engine.html
index 5ce15ff..0b927c3 100644
--- a/main/_modules/stdpopsim/slim_engine.html
+++ b/main/_modules/stdpopsim/slim_engine.html
@@ -1706,6 +1706,7 @@ 

Source code for stdpopsim.slim_engine

         # TODO: remove this after a release or two. See #745.
         self._warn_zigzag(demographic_model)
         self._warn_mutation_rate_mismatch(contig, demographic_model)
+        self._warn_recombination_rate_mismatch(contig, demographic_model)
 
         run_slim = not slim_script
 
diff --git a/main/_modules/stdpopsim/species.html b/main/_modules/stdpopsim/species.html
index 8cfe851..a63df87 100644
--- a/main/_modules/stdpopsim/species.html
+++ b/main/_modules/stdpopsim/species.html
@@ -80,6 +80,7 @@ 

Source code for stdpopsim.species

 Infrastructure for defining basic information about species and
 organising the species catalog.
 """
+
 import logging
 
 import attr
@@ -251,6 +252,7 @@ 

Source code for stdpopsim.species

         length_multiplier=1,
         length=None,
         mutation_rate=None,
+        recombination_rate=None,
         use_species_gene_conversion=False,
         inclusion_mask=None,
         exclusion_mask=None,
@@ -290,6 +292,9 @@ 

Source code for stdpopsim.species

             ``genetic_map`` argument.
         :param float mutation_rate: The per-base mutation rate. If none is given,
             the mutation rate defaults to the rate specified by species chromosomes.
+        :param float recombination_rate: The per-base recombination rate. If none is
+            given, the recombination rate defaults to the rate specified by species
+            chromosomes. Ignored when ``genetic_map`` argument is specified.
         :param bool use_species_gene_conversion: If set to True the parameters for gene
             conversion of the species chromosome are used if available. For "generic"
             contigs the gene conversion fraction and length are given by the mean
@@ -322,6 +327,7 @@ 

Source code for stdpopsim.species

             length_multiplier=length_multiplier,
             length=length,
             mutation_rate=mutation_rate,
+            recombination_rate=recombination_rate,
             use_species_gene_conversion=use_species_gene_conversion,
             inclusion_mask=inclusion_mask,
             exclusion_mask=exclusion_mask,
@@ -363,7 +369,7 @@ 

Source code for stdpopsim.species

 
         :param str id: The string identifier for the DFE.
             A complete list of IDs for each species can be found in the
-            # TODO add that section to the species catalogo
+            # TODO add that section to the species catalog
             "DFE" subsection for the species in the
             :ref:`sec_catalog`.
         :rtype: :class:`DFE`
@@ -399,8 +405,7 @@ 

Source code for stdpopsim.species

         #     A complete list of IDs for each species can be found in the
         #     "Genetic Maps" subsection for the species in the :ref:`sec_catalog`.
         # :rtype: :class:`GeneticMap`
-        # :return: A :class:`GeneticMap` that defines the frequency of
-        #     recombinations across the genome.
+        # :return: A :class:`GeneticMap` with recombination rate across the genome.
         for gm in self.genetic_maps:
             if gm.id == id:
                 return gm
diff --git a/main/_sources/api.rst.txt b/main/_sources/api.rst.txt
index a65155c..bdb7454 100644
--- a/main/_sources/api.rst.txt
+++ b/main/_sources/api.rst.txt
@@ -100,7 +100,7 @@ Gene conversion attributes are visible through the `gene_conversion_fraction`
 and `gene_conversion_length` properties of the species' `genome`.
 The "gene conversion fraction" gives the (average) fraction of
 double-stranded breaks that resolve as gene conversion
-(a.k.a non-crossover) events,
+(a.k.a non-crossing-over) events,
 and the "gene conversion length" is the mean length of the gene conversion tracts.
 So, if the rate of crossing over (which is often referred to as
 "recombination rate") is `r` and the gene conversion fraction is `f`,
@@ -109,9 +109,9 @@ and the gene conversion rate is `r * f / (1-f)`.
 
 A consequence of this is that
 the `recombination_map` attribute of a :class:`Contig` is the rate of
-double-stranded break initiation, not the rate of crossovers. The terminology
+double-stranded break initiation, not the rate of crossing-over. The terminology
 conflicts somewhat with the `recombination_rate` property of
-a :class:`Chromosome`, which specifies the rate of crossovers. So, creating
+a :class:`Chromosome`, which specifies the rate of crossing-over. So, creating
 a contig with `use_species_gene_conversion=True` will result in a Contig
 with larger "recombination rates" than otherwise, because these rates
 include gene conversion as well as crossing over:
diff --git a/main/_sources/catalog.rst.txt b/main/_sources/catalog.rst.txt
index db294da..e5c6b91 100644
--- a/main/_sources/catalog.rst.txt
+++ b/main/_sources/catalog.rst.txt
@@ -14,11 +14,11 @@ your simulation.
 It is organised around a number of choices that you'll need to make about the
 :class:`.Species` you wish to simulate:
 
-1. Which **chromosome**? (ie. which :class:`.Genome` object?)
-2. Which **genetic map**? (ie. which :class:`.GeneticMap` object?)
-3. Which **model of demographic history**? (ie. which :class:`.DemographicModel` object)
-4. Which **distribution of fitness effects** (ie. which :class:`.DFE` object) within
-5. which **annotation track**? (ie. which :class:`.Annotation` object)
+1. Which **chromosome** (:class:`.Genome` object)?
+2. Which **recombination/genetic map** (:class:`.GeneticMap` object)?
+3. Which **model of demographic history** (:class:`.DemographicModel` object)?
+4. Which **distribution of fitness effects** (:class:`.DFE` object) within
+5. which **annotation track** (:class:`.Annotation` object)?
 
 For instance, suppose you are interested in simulating modern human samples of
 
diff --git a/main/_sources/development.rst.txt b/main/_sources/development.rst.txt
index 3ce88dc..e8234a1 100644
--- a/main/_sources/development.rst.txt
+++ b/main/_sources/development.rst.txt
@@ -9,7 +9,7 @@ Development
 We envision at least three main types of ``stdpopsim`` developers:
 
 1. Contributors of new species, demographic models, and other features
-   (such as recombination maps, annotations, DFE models)
+   (such as recombination/genetic maps, annotations, DFE models)
 2. API developers
 3. Documentation and tutorial curators
 
@@ -21,7 +21,7 @@ See the appropriate sections below:
 
 * `Adding a new species`_
 * `Adding a new demographic model`_
-* `Adding a genetic map or annotation`_
+* `Adding a recombination/genetic map or annotation`_
 * `Adding a DFE model`_
 
 `API developers` work on infrastructure development for the PopSim Consortium,
@@ -546,9 +546,6 @@ with a brief discussion of possible courses of action to take when components ha
 1. The **genome assembly** should consist of a list of chromosomes or scaffolds and their lengths.
    Having a good quality assembly with complete chromosomes, or at least very long scaffolds,
    is essential for chromosome-level simulations produced by ``stdpopsim``.
-   Species with less complete genome builds typically do not have genetic maps
-   or good estimates of recombination rates,
-   making chromosome-level simulation much less useful.
    Thus, currently, ``stdpopsim`` only supports adding species with near-complete
    chromosome-level genome assemblies (i.e., close to one contig per chromosome).
 
@@ -563,11 +560,11 @@ with a brief discussion of possible courses of action to take when components ha
 
 3. An **average recombination rate**
    should be specified for each chromosome (per generation per bp).
-   Ideally, one would want to specify a fine-scale chromosome-level **recombination map**,
-   since the recombination rate is known to vary widely across chromosomes.
-   If a recombination map exists for your species,
-   you may specify it separately (see `Adding a genetic map or annotation`_).
-   Nonetheless, you should specify a default (average) recombination rate for each chromosome.
+   Ideally, one would want to specify a fine-scale chromosome-level **genetic map**,
+   since the recombination rate is known to vary widely across and along chromosomes.
+   If a genetic map exists for your species,
+   you may specify it separately (see `Adding a recombination/genetic map or annotation`_).
+   Nonetheless, you should also specify a default (average) recombination rate for each chromosome.
    As with mutation rates, if there is no information on the variation of recombination rates
    across chromosomes, the average genome-wide recombination rate can be specified for all chromosomes.
    Furthermore, if your species of interest does not have direct estimates of recombination rates,
@@ -800,7 +797,7 @@ accompanied by the appropriate ``stdpopsim.Citation`` objects.
         common_name="A. thaliana",
         genome=_genome,
         generation_time=1.0,
-        population_size=10 ** 4,
+        population_size=10_000,
         ploidy=_species_ploidy,
         citations=[
             stdpopsim.Citation(
@@ -1026,7 +1023,7 @@ Misspecification of the model can generate unrealistic patterns of genetic
 variation that will affect downstream analyses.
 So, having at least one detailed demographic model is recommended for every species.
 A given species might have more than one demographic model,
-fit from different data or by different methods.
+fit from different data or by different methods or different assumptions/parameters.
 
 -----------------------------------
 What models are appropriate to add?
@@ -1040,7 +1037,7 @@ such as population splits and changes in the amount of gene flow between populat
 The values of different parameters should be specified in units of "number of individuals"
 (for population sizes) and generations (for times).
 Sometimes, you will need to convert values published in the literature
-to these units by making some assumptions on the mutation rate;
+to these units by making some assumptions on the mutation rate (sometimes even recombination rate);
 typically the same assumptions made by the study that published the demographic model.
 
 
@@ -1113,6 +1110,7 @@ We provide below a template block of code for these two operations:
           citations=...,
           generation_time=...,
           mutation_rate=...,
+          recombination_rate=...,
           population_configurations=...,
           migration_matrix=...,
           demographic_events=...,
@@ -1120,8 +1118,8 @@ We provide below a template block of code for these two operations:
 
       _species.add_demographic_model(_model_func_name())
 
-A demographic model is thus defined using ten different attributes.
-The first seven attributes are quite straightforward:
+A demographic model is thus defined using up to eleven different attributes.
+The first eight attributes are quite straightforward:
 
 * ``id`` (`string`): A unique, short-hand identifier for this demographic model.
   This id contains a short description written in camel case,
@@ -1167,6 +1165,16 @@ The first seven attributes are quite straightforward:
   However, note that this is quite uncommon, so you should make sure this is the case
   before you set the mutation rate to ``None``.
 
+* ``recombination_rate`` (`double`): The recombination rate assumed during the inference
+  of this demographic model, if any (per bp per generation).
+  While demographic model inference might make less assumptions about recombination rates
+  than mutation rates, we provide this option for completness.
+  Namely, a demographic model might have been inferred under the assumption of a specific
+  recombination rate, which does not match with the species' recombination rate implemented
+  in ``stdpopsim``.
+  Also, some demographic models were inferred under the assumptions of a specifc ratio of
+  mutation to recombination rates.
+
 The final three attributes
 (``population_configurations``, ``migration_matrix``, and ``demographic_events``)
 describe the inferred demographic history that you wish to code.
@@ -1185,7 +1193,7 @@ then we highly recommend that you take some time to read through its
    parameter of interest.
    In your coded model, you should use some reasonable point estimate,
    such as the value associated with the the maximum likelihood fit,
-   or the mean posterior (for Bayesian methods).
+   or the mean of posterior distribution for Bayesian methods.
 
 ------------------------------------
 Adding a parameter table to the docs
@@ -1293,11 +1301,19 @@ implemented by the reviewer.
 The original demographic model and its registered QC model are compared as part of
 the ``stdpopsim`` `Unit tests`_.
 
-**********************************
-Adding a genetic map or annotation
-**********************************
+************************************************
+Adding a recombination/genetic map or annotation
+************************************************
 
-Some species have sub-chromosomal recombination maps or genomic annotations available.
+.. note::
+    Recombination map and genetic map are terms used to describe
+    maps of recombination rates that vary across and along chromosomes.
+    In the ``stdpopsim`` code and documentation, we use the term
+    **genetic map** to refer specifically to a "crossing-over rate map" and
+    **recombination map** to refer to a "crossing-over and gene conversion rate map.""
+    See :ref:`further details ` on this distinction.
+
+Some species have sub-chromosomal genetic maps or genomic annotations available.
 These files are large enough that adding them directly to the package would quickly
 cause slow package installation and loading,
 so these files are downloaded as-needed from AWS
@@ -1307,13 +1323,13 @@ the procedure for annotations is similar (but see the important note below).
 
 Genetic maps can be added to
 `stdpopsim` by creating a new `GeneticMap` object and providing a formatted file
-detailing recombination rates to a designated `stdpopsim` maintainer who then uploads
+detailing recombination rates to a designated ``stdpopsim`` maintainer who then uploads
 it to AWS. If there is one for your species that you wish to include, create a space
 delimited file with four columns: Chromosome, Position(bp), Rate(cM/Mb), and Map(cM).
 Each chromosome should be placed in a separate file and with the chromosome id in the
 file name in such a way that it can be programatically parsed out. IMPORTANT: chromosome
 ids must match those provided in the genome definition exactly! Below is an example start
-to a recombination map file (see `here
+to a genetic map file (see `here
 `__
 for more details)::
 
@@ -1321,9 +1337,9 @@ for more details)::
     chr1 32807 5.016134 0
     chr1 488426 4.579949 0
 
-Once you have the recombination map files formatted, tar and gzip them into a single
+Once you have the genetic map files formatted, tar and gzip them into a single
 compressed archive. The gzipped tarball must be FLAT (there are no directories in the
-tarball). This file will be sent to one of the `stdpopsim` uploaders for placement in the
+tarball). This file will be sent to one of the ``stdpopsim`` uploaders for placement in the
 AWS cloud once the new genetic map(s) are approved. Finally, you must add a `GeneticMap`
 object to the file named for your species in the ``stdpopsim/catalog//`` directory
 (the one that contains all the simulation code for that species,
@@ -1335,7 +1351,7 @@ see `Getting set up to add a new species`_):
         doi="FILL_ME", author="FILL_ME", year=9999, reasons={stdpopsim.CiteReason.GEN_MAP}
     )
     """
-    The file_pattern argument is a pattern that matches the recombination map filenames,
+    The file_pattern argument is a pattern that matches the genetic map filenames,
     where '{id}' is replaced with the 'id' field of a given chromosome.
     """
     _gm = stdpopsim.GeneticMap(
@@ -1385,13 +1401,13 @@ or increment the version number if the previous file already has one.
 When the file is downloaded locally to the cache, it is given a standardized name
 that will be the same regardless of which file is pulled from AWS.
 
+****************************************
+Lifting over a recombination/genetic map
+****************************************
 
-**************************
-Lifting over a genetic map
-**************************
 Existing genetic maps will need to be lifted over to a new assembly, if and when the
-current assembly is updated in `stdpopsim`. This process can be partially automated by running
-the liftOver maintenance code.
+current assembly is updated in ``stdpopsim``. This process can be partially automated by running
+the ``liftOver`` maintenance code.
 
 First, you must download and install the ``liftOver`` executable from the
 `UCSC Genome Browser Store `__.
@@ -1445,10 +1461,10 @@ system, the following can instead be used:
 
 The newly lifted over maps will be formatted in a compressed archive and
 automatically named using the assembly name from the chain file.
-This file will be sent to one of the `stdpopsim` uploaders for placement in the
+This file will be sent to one of the ``stdpopsim`` uploaders for placement in the
 AWS cloud, once the new map is approved. Finally, you must add a `GeneticMap`
 object to the file named for your species in the `stdpopsim/catalog//`
-directory, as shown in `Adding a genetic map or annotation`_.
+directory, as shown in `Adding a recombination/genetic map or annotation`_.
 
 Again, once all this is done, submit a PR containing the code changes and wait for
 directions on whom to send the compressed archive of genetic maps to
@@ -1771,7 +1787,7 @@ to check locally how well your tests are covering your code by asking
     $ pytest --cov-report html --cov=stdpopsim tests/
 
 this will output a directory of html files for you to browse test coverage
-for every file in `stdpopsim` in a reasonably straightfoward graphical
+for every file in ``stdpopsim`` in a reasonably straightfoward graphical
 way. To see them, direct your web browser to the `htmlcov/index.html` file.
 You'll be looking for lines of code that are highlighted yellow or red
 indicated that tests do not currently cover that bit of code.
diff --git a/main/_sources/introduction.rst.txt b/main/_sources/introduction.rst.txt
index 3dfd1ca..2d90a0d 100644
--- a/main/_sources/introduction.rst.txt
+++ b/main/_sources/introduction.rst.txt
@@ -79,18 +79,18 @@ major additions to the catalog:
     *A community-maintained standard library of population genetic models*,
     eLife 9:e54967; doi: https://doi.org/10.7554/eLife.54967
 
-  - M. Elise Lauterbur, Maria Izabel A. Cavassim, Ariella L. Gladstein, Graham Gower,
-    Nathaniel S. Pope, Georgia Tsambos, Jeff Adrion, Saurabh Belsare, Arjun Biddanda,
-    Victoria Caudill, Jean Cury, Ignacio Echevarria, Benjamin C. Haller, Ahmed R. Hasan,
+  - M Elise Lauterbur, Maria Izabel A Cavassim, Ariella L Gladstein, Graham Gower,
+    Nathaniel S Pope, Georgia Tsambos, Jeff Adrion, Saurabh Belsare, Arjun Biddanda,
+    Victoria Caudill, Jean Cury, Ignacio Echevarria, Benjamin C Haller, Ahmed R Hasan,
     Xin Huang, Leonardo Nicola Martin Iasi, Ekaterina Noskova, Jana Obšteter,
-    Vitor Antonio Corrêa Pavinato, Alice Pearson, David Peede, Manolo F. Perez,
-    Murillo F. Rodrigues, Chris C. R. Smith, Jeffrey P. Spence, Anastasia Teterina,
-    Silas Tittes, Per Unneberg, Juan Manuel Vazquez, Ryan K. Waples, Anthony Wilder Wohns,
-    Yan Wong, Franz Baumdicker, Reed A. Cartwright, Gregor Gorjanc, Ryan N. Gutenkunst,
-    Jerome Kelleher, Andrew D. Kern, Aaron P. Ragsdale, Peter L. Ralph, Daniel R. Schrider,
+    Vitor Antonio Corrêa Pavinato, Alice Pearson, David Peede, Manolo F Perez,
+    Murillo F Rodrigues, Chris C R Smith, Jeffrey P Spence, Anastasia Teterina,
+    Silas Tittes, Per Unneberg, Juan Manuel Vazquez, Ryan K Waples, Anthony Wilder Wohns,
+    Yan Wong, Franz Baumdicker, Reed A Cartwright, Gregor Gorjanc, Ryan N Gutenkunst,
+    Jerome Kelleher, Andrew D Kern, Aaron P Ragsdale, Peter L Ralph, Daniel R Schrider,
     Ilan Gronau (2023)
     *Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations*,
-    eLife 12:RP84874; doi: https://doi.org/10.7554/eLife.84874.2
+    eLife 12:RP84874; doi: https://doi.org/10.7554/eLife.84874
 
 Bibtex records::
 
@@ -121,26 +121,26 @@ Bibtex records::
         publisher = {eLife Sciences Publications, Ltd},
     }
 
-    @article{10.7554/eLife.84874.2,
+    @article{10.7554/eLife.84874,
         title={Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations},
-	author = {M. Elise Lauterbur and Maria Izabel A. Cavassim and Ariella L. Gladstein and Graham Gower and
-           Nathaniel S. Pope and Georgia Tsambos and Jeff Adrion and Saurabh Belsare and Arjun Biddanda and
-           Victoria Caudill and Jean Cury and Ignacio Echevarria and Benjamin C. Haller and Ahmed R. Hasan and
-           Xin Huang and Leonardo Nicola Martin Iasi and Ekaterina Noskova and Jana Ob{\v{s}}teter and
-           Vitor Antonio Corr{\^{e}}a Pavinato and Alice Pearson and David Peede and Manolo F. Perez and
-           Murillo F. Rodrigues and Chris C. R. Smith and Jeffrey P. Spence and Anastasia Teterina and
-           Silas Tittes and Per Unneberg and Juan Manuel Vazquez and Ryan K. Waples and Anthony Wilder Wohns and
-           Yan Wong and Franz Baumdicker and Reed A. Cartwright and Gregor Gorjanc and Ryan N. Gutenkunst and
-           Jerome Kelleher and Andrew D. Kern and Aaron P. Ragsdale and Peter L. Ralph and Daniel R. Schrider and
-           Ilan Gronau},
-	doi = {10.7554/elife.84874.2},
-	url = {https://doi.org/10.7554/elife.84874.2},
-	journal = {eLife},
-	volume={12},
-	pages={RP84874},
-	year = 2023,
-	month = {may},
-	publisher = {{eLife} Sciences Publications, Ltd},
+        author = {Lauterbur, M Elise and Cavassim, Maria Izabel A and Gladstein, Ariella L and Gower, Graham and
+           Pope, Nathaniel S and Tsambos, Georgia and Adrion, Jeffrey R and Belsare, Saurabh and Biddanda, Arjun and
+           Caudill, Victoria and Cury, Jean and Echevarria, Ignacio and Haller, Benjamin C and Hasan, Ahmed R and
+           Huang, Xin and Iasi,  Leonardo Nicola Martin and Noskova, Ekaterina and Ob{\v{s}}teter, Jana and
+           Pavinato, Vitor Antonio Corr{\^{e}}a and Pearson, Alice and Peede, David and Perez, Manolo F and
+           Rodrigues, Murillo F and Smith, Chris C R and Spence, Jeffrey P and Teterina, Anastasia and
+           Tittes, Silas and Unneberg, Per and Vazquez, Juan Manuel and Waples, Ryan K and Wohns, Anthony Wilder and
+           Wong, Yan and Baumdicker, Franz and Cartwright, Reed A and Gorjanc, Gregor and Gutenkunst, Ryan N and
+           Kelleher, Jerome and Kern, Andrew D and Ragsdale, Aaron P and Ralph, Peter L and Schrider, Daniel R and
+           Gronau, Ilan},
+        doi = {10.7554/elife.84874},
+        url = {https://doi.org/10.7554/elife.84874},
+        journal = {eLife},
+        volume={12},
+        pages={RP84874},
+        year = 2023,
+        month = {may},
+        publisher = {{eLife} Sciences Publications, Ltd},
     }
 
 
diff --git a/main/_sources/tutorial.rst.txt b/main/_sources/tutorial.rst.txt
index f9d1ff4..e061187 100644
--- a/main/_sources/tutorial.rst.txt
+++ b/main/_sources/tutorial.rst.txt
@@ -18,7 +18,7 @@ we need to make several choices about what will be simulated:
 
 1. which species
 2. which contig (i.e., how much of what chromosome)
-3. which genetic map
+3. which recombination/genetic map
 4. which demographic model
 5. how many samples from each population of the model
 6. whether to simulate selection (via a distribution of fitness effects),
@@ -26,13 +26,20 @@ we need to make several choices about what will be simulated:
 7. which simulation engine to use
 
 These choices are nested:
-the species determines what contigs, genetic maps, and demographic models are available,
+the species determines what contigs, recombination/genetic maps, and demographic models are available,
 and the demographic model determines how many populations can be sampled from.
 Currently, the two choices of simulation engine are ``msprime`` and ``SLiM``,
 and incorporating selection into simulations is only possible using the ``SLiM`` engine.
 Below are examples of making these choices in various contexts,
 using both CLI and Python interfaces.
 
+.. note::
+    Recombination map and genetic map are terms used to describe
+    maps of recombination rates that vary across and along chromosomes.
+    In the ``stdpopsim`` code and documentation, we use the term
+    **genetic map** to refer specifically to a "crossing-over rate map" and
+    **recombination map** to refer to a "crossing-over and gene conversion rate map.""
+    See :ref:`further details ` on this distinction.
 
 .. _sec_cli_tute:
 
@@ -65,8 +72,8 @@ available run
     :ellipsis: 30
 
 This shows the species currently supported by ``stdpopsim``. This means that
-``stdpopsim`` knows various traits of these species including chromosome size
-and recombination rates. Once we've selected a species, in this case humans, we
+``stdpopsim`` knows various traits of these species, including chromosome size,
+recombination rate(s), etc. Once we've selected a species, in this case humans, we
 can look at the help again as follows.
 
 .. command-output:: stdpopsim HomSap --help
@@ -75,13 +82,13 @@ can look at the help again as follows.
 For conciseness we do not show all the output here but this time you should see
 a different output which shows options for performing the simulation itself and
 the species default parameters. This includes selecting the demographic model,
-chromosome, recombination map, distribution of fitness effects, and number of
+chromosome, genetic map, distribution of fitness effects, and number of
 samples.
 
 The most basic simulation we can run is to simulate a diploid genome
 - i.e., a single individual -
 using the species' defaults as seen in the species help (``stdpopsim HomSap --help``).
-These defaults include constant size population named ``pop_0``, a uniform recombination map based
+These defaults include constant size population named ``pop_0``, a uniform genetic map based
 on the average recombination rate (either genome-wide or within a chromosome, if
 specified), and the mutation rate shown above.
 To save time we will specify that the simulation use
@@ -133,16 +140,17 @@ Note that the number of samples from each population are simply specified as
 Omitted populations will have no samples in the resulting tree sequence.
 
 .. note::
-    Many demographic models were inferred or calibrated using a mutation rate that
-    differs from the cataloged species' mutation rate. Simulations using the CLI now
-    automatically use the *model's* specified mutation rate instead of the species
-    rate, so that expected levels of diversity more closely match those observed in
-    the data that were used to infer the demographic model. For generic demographic
-    models or those without associated mutation rates, the species mutation rate is
-    used.
-
-Now we want to add an empirical recombination map to make the simulation more
-realistic. We can look up the available recombination maps using the
+    Many demographic models were inferred or calibrated using a mutation rate
+    or recombination rate that differs from the cataloged species' rate.
+    Simulations using the CLI now automatically use the *model's* specified
+    mutation rate or recombination rate instead of the species rate,
+    so that expected levels of diversity more closely match those observed in
+    the data that were used to infer the demographic model.
+    For generic demographic models or those without associated mutation or
+    recombination rates, the species rates are used.
+
+Now we want to add an empirical genetic map to make the simulation more
+realistic. We can look up the available genetic maps using the
 ``--help-genetic-maps`` flag (here, truncated for space):
 
 .. command-output:: stdpopsim HomSap --help-genetic-maps
@@ -279,7 +287,7 @@ for more discussion.
 Simulating genomes with selection
 ---------------------------------
 
-The examples above simulate contigs given a species, a recombination map, and a demographic model;
+The examples above simulate contigs given a species, a genetic map, and a demographic model;
 but assume that all genetic variation is neutral (has no impact on fitness).
 It is possible to incorporate selection in the
 simulations by (1) specifying the Distribution of Fitness Effects (DFE) for all new mutations
@@ -502,16 +510,16 @@ Set up the contig
 
 We'll next define the contig, which contains information about the genome length we
 want to simulate and recombination and mutation rates. Here, we use the human
-chromosome 22. If no recombination map is specified, we assume a uniform genetic map
-based on the average recombination rate for that chromosome.
+chromosome 22. If no genetic map is specified, we assume a
+uniform recombination rate set to the average recombination rate for that chromosome.
 
 .. code-block:: python
 
    contig = species.get_contig("chr22")
 
-   # default is a flat genetic map
+   # default is a uniform genetic map
    print("mean recombination rate:", f"{contig.recombination_map.mean_rate:.3}")
-   # mean recombination rate: 1.44e-08
+   # mean recombination rate: 2.11e-08
 
    # and the default mutation rate is based on the species default
    print("mean mutation rate:", contig.mutation_rate)
@@ -522,11 +530,11 @@ based on the average recombination rate for that chromosome.
    # model mutation rate: 2.35e-08
 
 The Gutenkunst OOA model was inferred using a mutation rate much larger than the
-default mutation rate in the `stdpopsim` catalog. As such, simulating using this
-model and default rate will result in levels of diversity substantially lower than
-expected for the human population data that this model was inferred from. To match
-observed diversity in humans, we should instead use the mutation rate associated
-with the demographic model:
+default mutation rate for humans in the ``stdpopsim`` catalog. As such,
+simulating using this model and default rate will result in levels of diversity
+substantially lower than expected for the human population data that this model
+was inferred from. To match observed diversity in humans, we should instead use
+the mutation rate associated with the demographic model:
 
 .. code-block:: python
 
@@ -534,6 +542,8 @@ with the demographic model:
    print(contig.mutation_rate == model.mutation_rate)
    # True
 
+Similar functionality exists for the recombination rate, if a model specifies one.
+
 Choose a sampling scheme and simulate
 -------------------------------------
 
@@ -581,7 +591,7 @@ Running a generic model
 
 Next, we will simulate using a "generic" model, with piecewise constant
 population size. This time, we will simulate a given genome length under
-a flat recombination map, using an estimate of the
+a uniform genetic map, using an estimate of the
 human effective population size from the :ref:`sec_catalog`.
 
 Choose a species
@@ -613,8 +623,8 @@ single population of constant over all time.
 Each species has a "default" population size, ``species.population_size``,
 which for humans is 10,000.
 
-Choose a contig and recombination map
--------------------------------------
+Choose a contig and genetic map
+-------------------------------
 
 Next, we set the contig information. Again, we could use any of the chromosomes
 listed in the :ref:`sec_catalog` (or a fraction of a chromosome, using the
@@ -635,7 +645,7 @@ of length 1 Mb:
     # 1.29e-8
 
 The "sequence length" is the length in base pairs. Since we are using a generic
-contig, we cannot specify a recombination map so we get a "flat" map of
+contig, we cannot specify a genetic map so we get a uniform map with a
 constant recombination rate. The mutation rate defaults to the species average
 mutation rate, as no mutation rate was provided when defining the contig.
 
@@ -742,11 +752,11 @@ simulation with ``SLiM`` instead of ``msprime`` only requires specifying
 ``SLiM`` as the *simulation engine*.
 Here is a simple example.
 
-Choose the species, contig, and recombination map
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Choose the species, contig, and genetic map
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-First, let's set up a simulation of 10 Mb of human chromosome 22 with a flat
-recombination map, drawing 100 diploids from the Tennesen et al (2012) model of
+First, let's set up a simulation of 10 Mb of human chromosome 22 with a uniform
+genetic map, drawing 100 diploids from the Tennesen et al (2012) model of
 African history, ``Africa_1T12`` (which has a single population named `AFR`).
 Since ``SLiM`` must simulate the entire population, sample size does not affect the
 run time of the simulation, only the size of the output tree sequence (and,
@@ -763,7 +773,7 @@ this very much either).
    contig = species.get_contig(
        "chr22", left=10e6, right=20e6, mutation_rate=model.mutation_rate
    )
-   # default is a flat genetic map with average rate across chr22
+   # default is a uniform genetic map with average rate across chr22
    samples = {"AFR": 100}
 
 
@@ -1039,7 +1049,6 @@ and get one using the :meth:`.Species.get_dfe` method.
     # ║                     mutation rate ratio of 2.31 nonsynonymous to 1 synonymous mutation
     # ║  ...
 
-
 Once we have the DFE, we can add it to the Contig,
 specifying the set of ``intervals`` that it will apply to:
 
@@ -1466,7 +1475,7 @@ we can see that this model has id ``AmericanAdmixture_4B11``,
 and allows samples to be drawn from 4 contemporary populations representing African,
 European, Asian and African-American groups.
 
-Using the ``--help-genetic-maps`` option, we can also see what recombination maps
+Using the ``--help-genetic-maps`` option, we can also see what genetic maps
 are available:
 
 .. command-output:: stdpopsim HomSap --help-genetic-maps
@@ -1643,7 +1652,7 @@ species has the ID ``AraTha``:
     species = stdpopsim.get_species("AraTha")
 
 After skimming the :ref:`Catalog ` to see our options, we'll specify our
-desired chromosome ``chr4`` and recombination map ``SalomeAveraged_TAIR10``.
+desired chromosome ``chr4`` and genetic map ``SalomeAveraged_TAIR10``.
 
 .. code-block:: python
 
diff --git a/main/api.html b/main/api.html
index 0cf09f4..ab311a1 100644
--- a/main/api.html
+++ b/main/api.html
@@ -166,7 +166,7 @@ 

Quick referenceSpecies object for the specified id.

Parameters:
-

id (str) – The string identifier for the requested species. E.g. “HomSap”. +

id (str) – The string identifier for the requested species. E.g. “HomSap”. A complete list of species, and their IDs, can be found in the Catalog.

@@ -186,40 +186,40 @@

Quick reference
Variables:
@@ -230,7 +230,7 @@

Quick referenceid.

Parameters:
-

id (str) – The string identifier for the set of annotations +

id (str) – The string identifier for the set of annotations A complete list of IDs for each species can be found in the “Annotations” subsection for the species in the Catalog.

@@ -246,7 +246,7 @@

Quick reference
-get_contig(chromosome=None, genetic_map=None, length_multiplier=1, length=None, mutation_rate=None, use_species_gene_conversion=False, inclusion_mask=None, exclusion_mask=None, left=None, right=None)[source]
+get_contig(chromosome=None, genetic_map=None, length_multiplier=1, length=None, mutation_rate=None, recombination_rate=None, use_species_gene_conversion=False, inclusion_mask=None, exclusion_mask=None, left=None, right=None)[source]

Returns a Contig instance describing a section of genome that is to be simulated based on empirical information for a given species and chromosome.

@@ -263,24 +263,27 @@

Quick reference
Parameters:
    -
  • chromosome (str) – The ID of the chromosome to simulate. +

  • chromosome (str) – The ID of the chromosome to simulate. A complete list of chromosome IDs for each species can be found in the “Genome” subsection for the species in the Catalog. If the chromosome is not given, we specify a “generic” contig with given length.

  • -
  • genetic_map (str) – If specified, obtain recombination rate information +

  • genetic_map (str) – If specified, obtain recombination rate information from the genetic map with the specified ID. If None, simulate using a default uniform recombination rate on a region with the length of the specified chromosome. The default rates are species- and chromosome- specific, and can be found in the Catalog. (Default: None)

  • -
  • length_multiplier (float) – If specified, simulate a region of length +

  • length_multiplier (float) – If specified, simulate a region of length length_multiplier times the length of the specified chromosome with the same chromosome-specific mutation and recombination rates. This option cannot currently be used in conjunction with the genetic_map argument.

  • -
  • mutation_rate (float) – The per-base mutation rate. If none is given, +

  • mutation_rate (float) – The per-base mutation rate. If none is given, the mutation rate defaults to the rate specified by species chromosomes.

  • -
  • use_species_gene_conversion (bool) – If set to True the parameters for gene +

  • recombination_rate (float) – The per-base recombination rate. If none is +given, the recombination rate defaults to the rate specified by species +chromosomes. Ignored when genetic_map argument is specified.

  • +
  • use_species_gene_conversion (bool) – If set to True the parameters for gene conversion of the species chromosome are used if available. For “generic” contigs the gene conversion fraction and length are given by the mean values across all chromosomes.

  • @@ -292,14 +295,14 @@

    Quick referencefloat) – Used with a “generic” contig, specifies the +
  • length (float) – Used with a “generic” contig, specifies the length of genome sequence for this contig. For a generic contig, mutation and recombination rates are equal to the genome-wide average across all autosomal chromosomes.

  • -
  • left (float) – The left coordinate (inclusive) of the region to +

  • left (float) – The left coordinate (inclusive) of the region to keep on the chromosome. Defaults to 0. Remaining regions will have missing data when simulated.

  • -
  • right (float) – The right coordinate (exclusive) of the region to +

  • right (float) – The right coordinate (exclusive) of the region to keep on the chromosome. Defaults to the length of the chromosome. Remaining regions will have missing data when simulated.

@@ -319,7 +322,7 @@

Quick referenceid.

Parameters:
-

id (str) – The string identifier for the demographic model. +

id (str) – The string identifier for the demographic model. A complete list of IDs for each species can be found in the “Demographic Models” subsection for the species in the Catalog.

@@ -339,9 +342,9 @@

Quick referenceid.

Parameters:
-

id (str) – The string identifier for the DFE. +

id (str) – The string identifier for the DFE. A complete list of IDs for each species can be found in the -# TODO add that section to the species catalogo +# TODO add that section to the species catalog “DFE” subsection for the species in the Catalog.

@@ -363,14 +366,16 @@

Quick reference
Variables:
    -
  • chromosomes (list) – A list of Chromosome objects.

  • -
  • bacterial_recombination (bool) – Whether recombination is via horizontal gene -transfer (if this is True) or via crossover and possibly gene +

  • chromosomes (list) – A list of Chromosome objects.

  • +
  • assembly_name (str) – The name of the genome assembly.

  • +
  • assembly_accession (str) – The ID of the genome assembly accession.

  • +
  • bacterial_recombination (bool) – Whether recombination is via horizontal gene +transfer (if this is True) or via crossing-over and possibly gene conversion (if this is False). Default: False.

  • -
  • citations (list) – A list of Citation objects +

  • citations (list) – A list of Citation objects providing the source for the genome assembly, mutation rate, recombination rate, and gene conversion estimates.

  • -
  • length (int) – The total length of the genome.

  • +
  • length (int) – The total length of the genome.

@@ -389,7 +394,7 @@

Quick referenceid.

Parameters:
-

id (str) – The string ID of the chromosome. +

id (str) – The string ID of the chromosome. A complete list of chromosome IDs for each species can be found in the “Genome” subsection for the species in the Catalog.

@@ -420,7 +425,7 @@

Quick reference property mean_recombination_rate

The length-weighted mean recombination rate across all chromosomes. -(Note that this is the rate of crossovers, not all double-stranded breaks.)

+(Note that this is the rate of crossing-over, not all double-stranded breaks.)

@@ -435,25 +440,25 @@

Quick reference class stdpopsim.Chromosome[source]

Class representing a single chromosome for a species. Note that although -the recombination rate for a Chromosome is the rate of crossovers, -the recombination map for a Contig gives the rate of both crossovers +the recombination rate for a Chromosome is the rate of crossing-over, +the recombination map for a Contig gives the rate of both crossing-over and gene conversion, so will differ if the gene conversion fraction is nonzero.

Variables:
    -
  • ~.id (str) – The string identifier for the chromosome.

  • -
  • length (int) – The length of the chromosome.

  • -
  • mutation_rate (float) – The mutation rate used when simulating this +

  • ~.id (str) – The string identifier for the chromosome.

  • +
  • length (int) – The length of the chromosome.

  • +
  • mutation_rate (float) – The mutation rate used when simulating this chromosome.

  • -
  • recombination_rate (float) – The rate of crossovers used when simulating +

  • recombination_rate (float) – The rate of crossing-over used when simulating this chromosome (if not using a genetic map).

  • -
  • gene_conversion_fraction (float) – The gene conversion fraction used when +

  • gene_conversion_fraction (float) – The gene conversion fraction used when simulating this chromosome.

  • -
  • gene_conversion_length (float) – The mean tract length of gene +

  • gene_conversion_length (float) – The mean tract length of gene conversion events used when simulating this chromosome.

  • synonyms (list of str) – List of synonyms that may be used when requesting this chromosome by ID, e.g. from the command line interface.

  • -
  • ploidy (int) – The ploidy used when simulating this chromosome.

  • +
  • ploidy (int) – The ploidy used when simulating this chromosome.

@@ -472,23 +477,25 @@

Quick reference
Variables:
    -
  • mutation_rate (float) – The rate of mutation per base per generation.

  • -
  • ploidy (int) – The ploidy of the contig. Defaults to 2 (diploid).

  • +
  • mutation_rate (float) – The rate of mutation per base per generation.

  • +
  • ploidy (int) – The ploidy of the contig. Defaults to 2 (diploid).

  • +
  • genetic_map (.GeneticMap) – The genetic map for the region, that gives +the rates of crossing-over. A GeneticMap object.

  • recombination_map (msprime.RateMap) – The recombination map for the region, that gives the rates of double-stranded breaks. Note that if gene conversion fraction is nonzero, then this map can have a larger rate than the corresponding chromosome’s recombination rate, since the chromosome’s -rate describes only crossovers. A msprime.RateMap object.

  • +rate describes only crossing-over. A msprime.RateMap object.

  • bacterial_recombination – Whether the model of recombination is by horizontal gene transfer or not (default is False, i.e., -recombination is by crossovers and possibly gene conversion).

  • -
  • gene_conversion_fraction (float) – The fraction of recombinations that resolve +recombination is by crossing-over and possibly gene conversion).

  • +
  • gene_conversion_fraction (float) – The fraction of recombinations that resolve as gene conversion events. The recombination map gives the rates of -both crossovers and gene conversions, with a fraction of gene +both crossing-over and gene conversion, with a fraction of gene conversions given by this value. Defaults to None, i.e., no gene conversion. Must be None or between 0 and 1. Must be None if bacterial_recombination is True.

  • -
  • gene_conversion_length (float) – The mean tract length of gene conversions (if +

  • gene_conversion_length (float) – The mean tract length of gene conversions (if bacterial_recombination is False), or horizontally tranferred segments (if it is True). Must be present, and above 1 if either gene_conversion_fraction is given or bacterial_recombination is True.

  • @@ -496,14 +503,14 @@

    Quick referencebool) – If True, mask_intervals specify regions to exclude. If False, +
  • exclude (bool) – If True, mask_intervals specify regions to exclude. If False, mask_intervals specify regions in keep.

  • -
  • dfe_list (list) – A list of DFE objects. +

  • dfe_list (list) – A list of DFE objects. By default, the only DFE is completely neutral.

  • interval_list – A list of np.array objects containing integers. By default, the inital interval list spans the whole chromosome with the neutral DFE.

  • -
  • coordinates (tuple) – The location of the contig on a named chromosome, +

  • coordinates (tuple) – The location of the contig on a named chromosome, as a tuple of the form (chromosome, left, right). If None, the contig is assumed to be generic (i.e. it does not inherit a coordinate system from a larger chromosome).

  • @@ -585,12 +592,12 @@

    Quick reference
    Parameters:
      -
    • id (str) – A unique identifier for the single site.

    • -
    • coordinate (int) – The coordinate of the site on the contig.

    • -
    • description (str) – A short description of the single site mutation +

    • id (str) – A unique identifier for the single site.

    • +
    • coordinate (int) – The coordinate of the site on the contig.

    • +
    • description (str) – A short description of the single site mutation model as it would be used in written text, e.g., “Strong selective sweep”.

    • -
    • long_description (str) – If necessary, a more detailed summary.

    • +
    • long_description (str) – If necessary, a more detailed summary.

@@ -673,7 +680,7 @@

Quick reference
-static species_contig(*, species, chromosome=None, genetic_map=None, length_multiplier=1, length=None, mutation_rate=None, use_species_gene_conversion=False, inclusion_mask=None, exclusion_mask=None, left=None, right=None)[source]
+static species_contig(*, species, chromosome=None, genetic_map=None, length_multiplier=1, length=None, mutation_rate=None, recombination_rate=None, use_species_gene_conversion=False, inclusion_mask=None, exclusion_mask=None, left=None, right=None)[source]

Build a Contig for a species.

@@ -687,9 +694,9 @@

Quick reference
Variables:
    -
  • doi (str) – The DOI for the publication providing the definitive reference.

  • -
  • author (str) – Short author list, .e.g, “Author 1 et. al”.

  • -
  • year (int) – Year of publication as a 4 digit integer, e.g. 2008.

  • +
  • doi (str) – The DOI for the publication providing the definitive reference.

  • +
  • author (str) – Short author list, .e.g, “Author 1 et. al”.

  • +
  • year (int) – Year of publication as a 4 digit integer, e.g. 2008.

@@ -727,12 +734,12 @@

Quick reference
Variables:
    -
  • ~.id (str) – String that uniquely identifies the annotation.

  • +
  • ~.id (str) – String that uniquely identifies the annotation.

  • species (Species) – The species to which this annotation applies.

  • -
  • url (str) – The URL where the packed and compressed GFF3 can be found.

  • -
  • intervals_url (str) – The URL of the intervals cache of the annotations.

  • -
  • intervals_sha256 (str) – The SHA256 checksum of the annotations cache.

  • -
  • ~.description (str) – One line description of the annotation.

  • +
  • url (str) – The URL where the packed and compressed GFF3 can be found.

  • +
  • intervals_url (str) – The URL of the intervals cache of the annotations.

  • +
  • intervals_sha256 (str) – The SHA256 checksum of the annotations cache.

  • +
  • ~.description (str) – One line description of the annotation.

  • citations (list of Citation) – List of citations for the annotation.

  • file_pattern – The pattern used to map individual chromosome id strings to files

  • @@ -773,19 +780,22 @@

    Quick reference
    Variables:
      -
    • ~.id (str) – The unique identifier for this model. DemographicModel IDs should be +

    • ~.id (str) – The unique identifier for this model. DemographicModel IDs should be short and memorable, and conform to the stdpopsim naming conventions for demographic models.

    • -
    • ~.description (str) – A short description of this model as it would be used in +

    • ~.description (str) – A short description of this model as it would be used in written text, e.g., “Three population Out-of-Africa”. This should describe the model itself and not contain author or year information.

    • -
    • long_description (str) – A concise, but detailed, summary of the model.

    • -
    • generation_time (int) – Mean inter-generation interval, in years.

    • +
    • long_description (str) – A concise, but detailed, summary of the model.

    • +
    • generation_time (int) – Mean inter-generation interval, in years.

    • citations (list of Citation) – A list of Citation, that describe the primary reference(s) for the model.

    • -
    • mutation_rate (float) – The mutation rate associated with the demographic model. +

    • mutation_rate (float) – The mutation rate associated with the demographic model. If no mutation rate is given, the species’ default mutation rate is used.

    • +
    • recombination_rate (float) – The recombination rate associated with the demographic +model. If no recombination rate is given, the species’ default recombination +rate is used.

@@ -802,10 +812,10 @@

Quick reference
Parameters:
    -
  • individuals_per_population (dict) – A dict of the form +

  • individuals_per_population (dict) – A dict of the form {population_name:number_of_samples} giving sample counts per population.

  • -
  • ploidy (int) – The ploidy of the samples.

  • +
  • ploidy (int) – The ploidy of the samples.

@@ -842,9 +852,9 @@

Quick reference
Variables:
    -
  • ~.id (str) – The id of the population.

  • -
  • ~.description (str) – a short description of the population

  • -
  • sampling_time (int) – an integer value which indicates how many +

  • ~.id (str) – The id of the population.

  • +
  • ~.description (str) – a short description of the population

  • +
  • sampling_time (int) – an integer value which indicates how many generations prior to the present individuals should samples should be drawn from this population. If None, sampling not allowed from this population (default = 0).

  • @@ -876,7 +886,7 @@

    Quick referenceContig is the rate of -double-stranded break initiation, not the rate of crossovers. The terminology +double-stranded break initiation, not the rate of crossing-over. The terminology conflicts somewhat with the recombination_rate property of -a Chromosome, which specifies the rate of crossovers. So, creating +a Chromosome, which specifies the rate of crossing-over. So, creating a contig with use_species_gene_conversion=True will result in a Contig with larger “recombination rates” than otherwise, because these rates include gene conversion as well as crossing over:

    @@ -941,18 +951,18 @@

    Quick reference
    Variables:
      -
    • ~.mutation_types (list) – A list of MutationType objects associated +

    • ~.mutation_types (list) – A list of MutationType objects associated with the DFE. Defaults to an empty list.

    • -
    • ~.proportions (list) – A list of the proportions of new mutations that +

    • ~.proportions (list) – A list of the proportions of new mutations that fall in to each of the mutation types (must sum to 1).

    • -
    • ~.id (str) – The unique identifier for this model. DFE IDs should be +

    • ~.id (str) – The unique identifier for this model. DFE IDs should be short and memorable, and conform to the stdpopsim naming conventions for DFE models.

    • -
    • ~.description (str) – A short description of this model as it would be used in +

    • ~.description (str) – A short description of this model as it would be used in written text, e.g., “Lognormal DFE”. This should describe the DFE itself and not contain author or year information.

    • -
    • long_description (str) – A concise, but detailed, summary of the DFE model.

    • +
    • long_description (str) – A concise, but detailed, summary of the DFE model.

    • citations (list of Citation) – A list of Citations, that describe the primary reference(s) for the DFE model.

    @@ -1003,13 +1013,13 @@

    Quick reference
    Variables:
      -
    • distribution_type (list) – A one-letter abbreviation for the distribution of +

    • distribution_type (list) – A one-letter abbreviation for the distribution of fitness effects that each new mutation of this type draws from (see below).

    • distribution_args – Arguments for the distribution type.

    • -
    • dominance_coeff (float) – The dominance coefficient (negative = underdominance, +

    • dominance_coeff (float) – The dominance coefficient (negative = underdominance, 0 = recessive, 0.5 = additive, 1.0 = completely dominant, > 1.0 = overdominant) Default: 0.5.

    • -
    • convert_to_substitution (bool) – Whether to retain any fixed mutations in the +

    • convert_to_substitution (bool) – Whether to retain any fixed mutations in the simulation: if not, we cannot ask about their frequency once fixed. (Either way, they will remain in the tree sequence).

    • dominance_coeff_list (list of floats) – Either None (the default) or a list of floats describing @@ -1051,9 +1061,9 @@

      Quick reference

    @@ -1073,17 +1083,17 @@

    Quick reference
    Variables:
      -
    • start_time (float) – The number of generations in the past at which the change +

    • start_time (float) – The number of generations in the past at which the change in mutation fitness begins.

    • -
    • end_time (float) – The number of generations in the past at which the change +

    • end_time (float) – The number of generations in the past at which the change in mutation fitness ends.

    • -
    • single_site_id (str) – The string ID of the single site at which to change +

    • single_site_id (str) – The string ID of the single site at which to change the mutation fitness, see Contig.add_single_site().

    • -
    • population (str) – The name of the population in which to change +

    • population (str) – The name of the population in which to change the mutation fitness. If None (the default), then all populations are affected.

    • -
    • selection_coeff (float) – The new selection coefficient of the mutation.

    • -
    • dominance_coeff (float) – The new dominance coefficient of the mutation.

    • +
    • selection_coeff (float) – The new selection coefficient of the mutation.

    • +
    • dominance_coeff (float) – The new dominance coefficient of the mutation.

    @@ -1100,15 +1110,15 @@

    Quick reference
    Variables:
      -
    • start_time (float) – The number of generations in the past at which to start +

    • start_time (float) – The number of generations in the past at which to start checking the allele frequency condition.

    • -
    • end_time (float) – The number of generations in the past at which to stop +

    • end_time (float) – The number of generations in the past at which to stop checking the allele frequency condition.

    • -
    • single_site_id (str) – The string ID of the single site at which to check +

    • single_site_id (str) – The string ID of the single site at which to check the allele frequency condition, see Contig.add_single_site().

    • -
    • population (str) – The name of the population in which to check +

    • population (str) – The name of the population in which to check the allele frequency condition.

    • -
    • op (float) – The comparison operator used for the allele frequency condition, +

    • op (float) – The comparison operator used for the allele frequency condition, one of (“>”, “>=”, “<”, “<=”).

    • allele_frequency – The allele frequency to compare against.

    @@ -1160,27 +1170,27 @@

    Quick reference
    Parameters:
      -
    • single_site_id (str) – The string ID of the single site at which to introduce +

    • single_site_id (str) – The string ID of the single site at which to introduce the mutation, see Contig.add_single_site().

    • -
    • population (int) – The name of the population in which the mutation is +

    • population (int) – The name of the population in which the mutation is introduced and the sweep occurs.

    • -
    • mutation_generation_ago (float) – The number of generations in the past at which +

    • mutation_generation_ago (float) – The number of generations in the past at which to introduce the mutation.

    • -
    • selection_coeff (float) – The selection coefficient of the beneficial +

    • selection_coeff (float) – The selection coefficient of the beneficial mutation during the sweep. Must be greater than or equal to zero.

    • -
    • dominance_coeff (float) – The dominance coefficient of the beneficial +

    • dominance_coeff (float) – The dominance coefficient of the beneficial mutation during the sweep. Defaults to 0.5.

    • -
    • start_generation_ago (float) – The number of generations in the past at which the +

    • start_generation_ago (float) – The number of generations in the past at which the sweep starts and the mutation switches from neutral to beneficial. Defaults to mutation_generation_ago (i.e. a “hard” sweep).

    • -
    • end_generation_ago (float) – The number of generations in the past at which the +

    • end_generation_ago (float) – The number of generations in the past at which the sweep ends and the mutation switches from beneficial to neutral. Defaults to 0 (i.e. the end of the simulation).

    • -
    • min_freq_at_start (float) – The minimum allowed frequency for the +

    • min_freq_at_start (float) – The minimum allowed frequency for the mutation in population at the start of the sweep (optional).

    • -
    • min_freq_at_end (float) – The minimum allowed frequency for the +

    • min_freq_at_end (float) – The minimum allowed frequency for the mutation in population at the end of the sweep (optional).

    • -
    • globally_adaptive (bool) – If True (default), the mutation is beneficial in all +

    • globally_adaptive (bool) – If True (default), the mutation is beneficial in all populations. If False, the mutation is beneficial only in the population of origin.

    @@ -1205,7 +1215,7 @@

    Generic models
    Parameters:
      -
    • N0 (float) – The initial effective population size

    • +
    • N0 (float) – The initial effective population size

    • args – Each subsequent argument is a tuple (t, N) which gives the time at which the size change takes place and the population size.

    @@ -1230,12 +1240,12 @@

    Generic models
    Parameters:
      -
    • NA (float) – The initial ancestral effective population size

    • -
    • N1 (float) – The effective population size of population 1

    • -
    • N2 (float) – The effective population size of population 2

    • -
    • T (float) – Time of split between populations 1 and 2 (in generations)

    • -
    • M12 (float) – Migration rate from population 1 to 2

    • -
    • M21 (float) – Migration rate from population 2 to 1

    • +
    • NA (float) – The initial ancestral effective population size

    • +
    • N1 (float) – The effective population size of population 1

    • +
    • N2 (float) – The effective population size of population 2

    • +
    • T (float) – Time of split between populations 1 and 2 (in generations)

    • +
    • M12 (float) – Migration rate from population 1 to 2

    • +
    • M21 (float) – Migration rate from population 2 to 1

    @@ -1259,7 +1269,7 @@

    Generic modelsid.

    Parameters:
    -

    id (str) – The string identifier for the requested engine. +

    id (str) – The string identifier for the requested engine. The currently supported engines are “msprime” and “slim”.

    Returns:
    @@ -1305,8 +1315,8 @@

    Generic models
    Variables:
      -
    • ~.id (str) – The unique identifier for the simulation engine.

    • -
    • ~.description (str) – A short description of this engine.

    • +
    • ~.id (str) – The unique identifier for the simulation engine.

    • +
    • ~.description (str) – A short description of this engine.

    • citations (list of Citation) – A list of citations for the simulation engine.

    @@ -1317,7 +1327,7 @@

    Generic models
    Return type:
    -

    str

    +

    str

    @@ -1334,8 +1344,8 @@

    Generic modelsContig) – The contig, defining the length, mutation rate, and recombination rate(s).

  • samples (dict containing number of individuals per population) – The samples to be obtained from the simulation.

  • -
  • seed (int) – The seed for the random number generator.

  • -
  • dry_run (bool) – If True, the simulation engine will return None without +

  • seed (int) – The seed for the random number generator.

  • +
  • dry_run (bool) – If True, the simulation engine will return None without running the simulation.

@@ -1373,12 +1383,12 @@

Generic models
Parameters:
    -
  • msprime_model (str) – The msprime simulation model to be used. +

  • msprime_model (str) – The msprime simulation model to be used. One of hudson, dtwf, smc, or smc_prime. See msprime API documentation for details.

  • -
  • msprime_change_model (list of (float, str) tuples) – A list of (time, model) tuples, which +

  • msprime_change_model (list of (float, str) tuples) – A list of (time, model) tuples, which changes the simulation model to the new model at the time specified.

  • -
  • dry_run (bool) – If True, end_time=0 is passed to msprime.simulate() +

  • dry_run (bool) – If True, end_time=0 is passed to msprime.simulate() to initialise the simulation and then immediately return.

  • **kwargs – Further arguments passed to msprime.sim_ancestry()

@@ -1439,27 +1449,27 @@

Generic models
Parameters:
    -
  • seed (int) – The seed for the random number generator.

  • -
  • extended_events (list) – A list of ExtendedEvents to be +

  • seed (int) – The seed for the random number generator.

  • +
  • extended_events (list) – A list of ExtendedEvents to be passed to SLiM, e.g. produced by selective_sweep().

  • -
  • slim_path (str) – The full path to the slim executable, or the name of +

  • slim_path (str) – The full path to the slim executable, or the name of a command in the current PATH.

  • -
  • slim_script (bool) – If true, the simulation will not be executed. +

  • slim_script (bool) – If true, the simulation will not be executed. Instead the generated SLiM script will be printed to stdout.

  • -
  • slim_scaling_factor (float) – Rescale model parameters by the given value, +

  • slim_scaling_factor (float) – Rescale model parameters by the given value, to speed up simulation. Population sizes and generation times are divided by this factor, whereas the mutation rate, recombination rate, and growth rates are multiplied by the factor. See SLiM manual: 5.5 Rescaling population sizes to improve simulation performance.

  • -
  • slim_burn_in (float) – Length of the burn-in phase, in units of N +

  • slim_burn_in (float) – Length of the burn-in phase, in units of N generations.

  • -
  • dry_run (bool) – If True, run the setup and then end the simulation.

  • +
  • dry_run (bool) – If True, run the setup and then end the simulation.

  • logfile – Name of file to write a log of summary statistics (currently, only mean and SD of fitness values per population). Defaults to None, meaning “do not log”.

  • logfile_interval – How often to write to the log file, in generations.

  • -
  • keep_mutation_ids_as_alleles (bool) – If true, alleles will be coded by integer +

  • keep_mutation_ids_as_alleles (bool) – If true, alleles will be coded by integer mutation ids (assigned by SLiM) rather than by randomly-generated nucleotides.

diff --git a/main/catalog.html b/main/catalog.html index 92632ff..f6b76ac 100644 --- a/main/catalog.html +++ b/main/catalog.html @@ -101,7 +101,7 @@
  • Bos taurus @@ -352,11 +352,11 @@ It is organised around a number of choices that you’ll need to make about the Species you wish to simulate:

      -
    1. Which chromosome? (ie. which Genome object?)

    2. -
    3. Which genetic map? (ie. which GeneticMap object?)

    4. -
    5. Which model of demographic history? (ie. which DemographicModel object)

    6. -
    7. Which distribution of fitness effects (ie. which DFE object) within

    8. -
    9. which annotation track? (ie. which Annotation object)

    10. +
    11. Which chromosome (Genome object)?

    12. +
    13. Which recombination/genetic map (GeneticMap object)?

    14. +
    15. Which model of demographic history (DemographicModel object)?

    16. +
    17. Which distribution of fitness effects (DFE object) within

    18. +
    19. which annotation track (Annotation object)?

    For instance, suppose you are interested in simulating modern human samples of

      @@ -2111,16 +2111,24 @@

      Genome

      HolsteinFriesian_1M13

      -

      Piecewise size changes in Holstein-Friesian cattle.

      +

      Piecewise size model for Holstein-Friesian cattle (MacLeod et al., 2013).

      -

      Piecewise size changes in Holstein-Friesian cattle.

      +

      Piecewise size model for Holstein-Friesian cattle (MacLeod et al., 2013).

      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 and Table S1. + from MacLeod et al. (2013). Effective population sizes were estimated based + on runs of homozygosity observed in a single individual, + using the following assumptions: + a generation interval of 5 years (page 2213), + a mutation rate of 0.94e-8 (pages 2221 and 2215), and + a recombination rate of 1e-8 (pages 2215 and 2221). + Effective population sizes are given in Figure 4A and Table S1. + The single individual is the bull Walkway Chief Mark. + Mark and his father are two of the most influentiual sires in this breed - see + Larkin et al. (2012) http://www.pnas.org/cgi/doi/10.1073/pnas.1114546109.

      Details

      @@ -2128,7 +2136,7 @@

      Genome

      HolsteinFriesian_1M13

  • Description:
    -

    Piecewise size changes in Holstein-Friesian cattle.

    +

    Piecewise size model for Holstein-Friesian cattle (MacLeod et al., 2013).

    Num populations:

    1

    @@ -2272,6 +2280,10 @@

    Genome

    Recombination rate

    +

    1.0e-0

    +

    Per-base per-generation recombination rate

    + diff --git a/main/changelogs.html b/main/changelogs.html index f8d0e7c..ab0abcb 100644 --- a/main/changelogs.html +++ b/main/changelogs.html @@ -122,7 +122,7 @@

    [0.2.1a] - 2024-07-06stdpopsim proper

    New features:

      diff --git a/main/cli_arguments.html b/main/cli_arguments.html index 0de70da..db40b74 100644 --- a/main/cli_arguments.html +++ b/main/cli_arguments.html @@ -314,7 +314,7 @@

      AedAegPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -403,7 +403,7 @@

      AnaPlaPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -493,7 +493,7 @@

      AnoCarPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -582,7 +582,7 @@

      AnoGamPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -672,7 +672,7 @@

      ApiMelPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -765,7 +765,7 @@

      AraThaPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -873,7 +873,7 @@

      BosTauPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -964,7 +964,7 @@

      CaeElePositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1061,7 +1061,7 @@

      CanFamPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1157,7 +1157,7 @@

      ChlReiPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1250,7 +1250,7 @@

      DroMelPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1358,7 +1358,7 @@

      DroSecPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1448,7 +1448,7 @@

      EscColPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1537,7 +1537,7 @@

      GasAcuPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1626,7 +1626,7 @@

      HelAnnPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1715,7 +1715,7 @@

      HelMelPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1808,7 +1808,7 @@

      HomSapPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -1916,7 +1916,7 @@

      MusMusPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -2006,7 +2006,7 @@

      OrySatPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -2096,7 +2096,7 @@

      PanTroPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -2187,7 +2187,7 @@

      PapAnuPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -2287,7 +2287,7 @@

      PhoSinPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -2389,7 +2389,7 @@

      PonAbePositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      @@ -2487,7 +2487,7 @@

      StrAgaPositional Arguments

      samples
      -

      The number of samples to draw from each population. At least two samples must be specified. The number of arguments that will be accepted depends on the simulation model that is specified: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      +

      The number of samples to draw from each population. At least two genomes must be sampled, say, for one diploid individual. The number of arguments accepted depends on the specified simulation model: for a model that has n populations, we can specify the number of samples to draw from each of these populations.We do not need to provide sample numbers of each of the populations; those that are omitted are set to zero.

      diff --git a/main/development.html b/main/development.html index 0321922..74ad88b 100644 --- a/main/development.html +++ b/main/development.html @@ -79,8 +79,8 @@
    • Implementing tests for the review of a demographic model
    -
  • Adding a genetic map or annotation
  • -
  • Lifting over a genetic map
  • +
  • Adding a recombination/genetic map or annotation
  • +
  • Lifting over a recombination/genetic map
  • Adding a DFE model
  • -

    A demographic model is thus defined using ten different attributes. -The first seven attributes are quite straightforward:

    +

    A demographic model is thus defined using up to eleven different attributes. +The first eight attributes are quite straightforward:

    The final three attributes (population_configurations, migration_matrix, and demographic_events) @@ -1180,7 +1187,7 @@

    Coding the demographic model @@ -1272,9 +1279,18 @@

    Implementing tests for the review of a demographic modelstdpopsim Unit tests.

    -
    -

    Adding a genetic map or annotation

    -

    Some species have sub-chromosomal recombination maps or genomic annotations available. +

    +

    Adding a recombination/genetic map or annotation

    +
    +

    Note

    +

    Recombination map and genetic map are terms used to describe +maps of recombination rates that vary across and along chromosomes. +In the stdpopsim code and documentation, we use the term +genetic map to refer specifically to a “crossing-over rate map” and +recombination map to refer to a “crossing-over and gene conversion rate map.”” +See further details on this distinction.

    +
    +

    Some species have sub-chromosomal genetic maps or genomic annotations available. These files are large enough that adding them directly to the package would quickly cause slow package installation and loading, so these files are downloaded as-needed from AWS @@ -1283,22 +1299,22 @@

    Adding a genetic map or annotationstdpopsim maintainer who then uploads it to AWS. If there is one for your species that you wish to include, create a space delimited file with four columns: Chromosome, Position(bp), Rate(cM/Mb), and Map(cM). Each chromosome should be placed in a separate file and with the chromosome id in the file name in such a way that it can be programatically parsed out. IMPORTANT: chromosome ids must match those provided in the genome definition exactly! Below is an example start -to a recombination map file (see here +to a genetic map file (see here for more details):

    Chromosome Position(bp) Rate(cM/Mb) Map(cM)
     chr1 32807 5.016134 0
     chr1 488426 4.579949 0
     
    -

    Once you have the recombination map files formatted, tar and gzip them into a single +

    Once you have the genetic map files formatted, tar and gzip them into a single compressed archive. The gzipped tarball must be FLAT (there are no directories in the -tarball). This file will be sent to one of the stdpopsim uploaders for placement in the +tarball). This file will be sent to one of the stdpopsim uploaders for placement in the AWS cloud once the new genetic map(s) are approved. Finally, you must add a GeneticMap object to the file named for your species in the stdpopsim/catalog/<SPECIES_ID>/ directory (the one that contains all the simulation code for that species, @@ -1307,7 +1323,7 @@

    Adding a genetic map or annotationdoi="FILL_ME", author="FILL_ME", year=9999, reasons={stdpopsim.CiteReason.GEN_MAP} ) """ -The file_pattern argument is a pattern that matches the recombination map filenames, +The file_pattern argument is a pattern that matches the genetic map filenames, where '{id}' is replaced with the 'id' field of a given chromosome. """ _gm = stdpopsim.GeneticMap( @@ -1355,11 +1371,11 @@

    Adding a genetic map or annotation -

    Lifting over a genetic map

    +
    +

    Lifting over a recombination/genetic map

    Existing genetic maps will need to be lifted over to a new assembly, if and when the -current assembly is updated in stdpopsim. This process can be partially automated by running -the liftOver maintenance code.

    +current assembly is updated in stdpopsim. This process can be partially automated by running +the liftOver maintenance code.

    First, you must download and install the liftOver executable from the UCSC Genome Browser Store. Next, you must download the appropriate chain files, again from UCSC @@ -1404,10 +1420,10 @@

    Lifting over a genetic mapstdpopsim uploaders for placement in the AWS cloud, once the new map is approved. Finally, you must add a GeneticMap object to the file named for your species in the stdpopsim/catalog/<SPECIES_ID>/ -directory, as shown in Adding a genetic map or annotation.

    +directory, as shown in Adding a recombination/genetic map or annotation.

    Again, once all this is done, submit a PR containing the code changes and wait for directions on whom to send the compressed archive of genetic maps to (currently Andrew Kern is the primary uploader but please wait to send files @@ -1678,7 +1694,7 @@

    Code Coveragestdpopsim in a reasonably straightfoward graphical way. To see them, direct your web browser to the htmlcov/index.html file. You’ll be looking for lines of code that are highlighted yellow or red indicated that tests do not currently cover that bit of code.

    diff --git a/main/index.html b/main/index.html index 3da251b..a766583 100644 --- a/main/index.html +++ b/main/index.html @@ -154,8 +154,8 @@

    Welcome to stdpopsim’s documentation!GitHub workflow
  • Adding a new species
  • Adding a new demographic model
  • -
  • Adding a genetic map or annotation
  • -
  • Lifting over a genetic map
  • +
  • Adding a recombination/genetic map or annotation
  • +
  • Lifting over a recombination/genetic map
  • Adding a DFE model
  • Coding standards
  • Naming conventions
  • diff --git a/main/introduction.html b/main/introduction.html index 8f63383..b0a07a2 100644 --- a/main/introduction.html +++ b/main/introduction.html @@ -156,18 +156,18 @@

    Citationshttps://doi.org/10.7554/eLife.54967

    -
  • M. Elise Lauterbur, Maria Izabel A. Cavassim, Ariella L. Gladstein, Graham Gower, -Nathaniel S. Pope, Georgia Tsambos, Jeff Adrion, Saurabh Belsare, Arjun Biddanda, -Victoria Caudill, Jean Cury, Ignacio Echevarria, Benjamin C. Haller, Ahmed R. Hasan, +

  • M Elise Lauterbur, Maria Izabel A Cavassim, Ariella L Gladstein, Graham Gower, +Nathaniel S Pope, Georgia Tsambos, Jeff Adrion, Saurabh Belsare, Arjun Biddanda, +Victoria Caudill, Jean Cury, Ignacio Echevarria, Benjamin C Haller, Ahmed R Hasan, Xin Huang, Leonardo Nicola Martin Iasi, Ekaterina Noskova, Jana Obšteter, -Vitor Antonio Corrêa Pavinato, Alice Pearson, David Peede, Manolo F. Perez, -Murillo F. Rodrigues, Chris C. R. Smith, Jeffrey P. Spence, Anastasia Teterina, -Silas Tittes, Per Unneberg, Juan Manuel Vazquez, Ryan K. Waples, Anthony Wilder Wohns, -Yan Wong, Franz Baumdicker, Reed A. Cartwright, Gregor Gorjanc, Ryan N. Gutenkunst, -Jerome Kelleher, Andrew D. Kern, Aaron P. Ragsdale, Peter L. Ralph, Daniel R. Schrider, +Vitor Antonio Corrêa Pavinato, Alice Pearson, David Peede, Manolo F Perez, +Murillo F Rodrigues, Chris C R Smith, Jeffrey P Spence, Anastasia Teterina, +Silas Tittes, Per Unneberg, Juan Manuel Vazquez, Ryan K Waples, Anthony Wilder Wohns, +Yan Wong, Franz Baumdicker, Reed A Cartwright, Gregor Gorjanc, Ryan N Gutenkunst, +Jerome Kelleher, Andrew D Kern, Aaron P Ragsdale, Peter L Ralph, Daniel R Schrider, Ilan Gronau (2023) Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations, -eLife 12:RP84874; doi: https://doi.org/10.7554/eLife.84874.2

  • +eLife 12:RP84874; doi: https://doi.org/10.7554/eLife.84874

    Bibtex records:

    @@ -198,20 +198,20 @@

    Citationspublisher = {eLife Sciences Publications, Ltd}, } -@article{10.7554/eLife.84874.2, +@article{10.7554/eLife.84874, title={Expanding the stdpopsim species catalog, and lessons learned for realistic genome simulations}, - author = {M. Elise Lauterbur and Maria Izabel A. Cavassim and Ariella L. Gladstein and Graham Gower and - Nathaniel S. Pope and Georgia Tsambos and Jeff Adrion and Saurabh Belsare and Arjun Biddanda and - Victoria Caudill and Jean Cury and Ignacio Echevarria and Benjamin C. Haller and Ahmed R. Hasan and - Xin Huang and Leonardo Nicola Martin Iasi and Ekaterina Noskova and Jana Ob{\v{s}}teter and - Vitor Antonio Corr{\^{e}}a Pavinato and Alice Pearson and David Peede and Manolo F. Perez and - Murillo F. Rodrigues and Chris C. R. Smith and Jeffrey P. Spence and Anastasia Teterina and - Silas Tittes and Per Unneberg and Juan Manuel Vazquez and Ryan K. Waples and Anthony Wilder Wohns and - Yan Wong and Franz Baumdicker and Reed A. Cartwright and Gregor Gorjanc and Ryan N. Gutenkunst and - Jerome Kelleher and Andrew D. Kern and Aaron P. Ragsdale and Peter L. Ralph and Daniel R. Schrider and - Ilan Gronau}, - doi = {10.7554/elife.84874.2}, - url = {https://doi.org/10.7554/elife.84874.2}, + author = {Lauterbur, M Elise and Cavassim, Maria Izabel A and Gladstein, Ariella L and Gower, Graham and + Pope, Nathaniel S and Tsambos, Georgia and Adrion, Jeffrey R and Belsare, Saurabh and Biddanda, Arjun and + Caudill, Victoria and Cury, Jean and Echevarria, Ignacio and Haller, Benjamin C and Hasan, Ahmed R and + Huang, Xin and Iasi, Leonardo Nicola Martin and Noskova, Ekaterina and Ob{\v{s}}teter, Jana and + Pavinato, Vitor Antonio Corr{\^{e}}a and Pearson, Alice and Peede, David and Perez, Manolo F and + Rodrigues, Murillo F and Smith, Chris C R and Spence, Jeffrey P and Teterina, Anastasia and + Tittes, Silas and Unneberg, Per and Vazquez, Juan Manuel and Waples, Ryan K and Wohns, Anthony Wilder and + Wong, Yan and Baumdicker, Franz and Cartwright, Reed A and Gorjanc, Gregor and Gutenkunst, Ryan N and + Kelleher, Jerome and Kern, Andrew D and Ragsdale, Aaron P and Ralph, Peter L and Schrider, Daniel R and + Gronau, Ilan}, + doi = {10.7554/elife.84874}, + url = {https://doi.org/10.7554/elife.84874}, journal = {eLife}, volume={12}, pages={RP84874}, diff --git a/main/objects.inv b/main/objects.inv index 7602f9d03077b3a3ce490e877c0136052fdc64dd..9dc32b3dc31f9cc4392af99f692c2eb375cfd598 100644 GIT binary patch delta 3916 zcmV-S53}%_AF3aagMarSsaa`F*^^}EO_ne5#LlQJPcr+2f=Gx&OcES`w5&YsZ|v{w zmu!RJT@(q}PF1R6L48i+(&%o0Y{}G1cp_OeoyRdxNy>PPlHIRvNTfs#A_DGkQEsCsH5%#7!;6rF+;6)AosAu}6D30zZ`WRhB8HZ*kuyk_f^K3M@HDtt3l zJocD^{2tH4kcRY9(7!SoyE~If;c?1*W5Fr$JnU^u`&A+&i~W$of2j@94Ji>yo3sFd zI9>}%9um&t)PDvE#vRiv6(m%kMDPTxWH$Y+)vAr@ zI={RySX_{r3hFbNQi1<+jfiQ)eL<3dxwqV-p^26bB=<{_M2RvDYPMDb9M8ks4qK3I z>Cy~_d=bMEQ)9t=G>=rsf_FEWDTpyb;7Lr+a*-G?}WcDT>LlgA3~NXwBkhatIav_C^!Z*iQp##&j)tm}~#a z(%_bX;TqaqX`=VwlPtxp{os;Omu{J);G|?q;&jEnnc=XPF09|i`|&N=CK1g&sN8eZ{WOJC9|69ViC_^m_W1q4sYj(NSW-?XNmIer z8Mq!>BAwelA8!*>{M_To-qihoG1QX1M4hzs0Dp;=>LciexqgB|s!gCa(K zMFHbc1QR+5m67m#mF1B~AAu=RVoi9XS$~7i>xLtU7%cYaQw|8pTDeFEm91L-TK#!c zixs>LiLW$tRVYb0s;2XQ|M{;(AewhO-zI+p4)A4R;e=XLb$h9^ZUI;-xR<&6u)4j3 z)h$QhA$!J_>?Y+G8UukL1Dn*NY;|df`6bng_v*kbg;N6EUSItPHP5B{xE&ntu|oE9^Mf^@dp+qKosD1u=aZ>u^`Nk6c~S3>pudLGt`k zIU!ir@=S2C*??z~=#7dqZx1jlbAo&b2hN80tnAo19=7!yF)tDK^Xoh3xKGb3Y{@1OCY#(f&3|aZ-2gjdleyv< zIJ8^ld653hQ^>2)Tr;3_%TdmPa_VuNC$FG05R@8fm5L_HS%@ORjyL9j_7x}P30>4Kkl`@ERl&ne0Kn!Wy@TaKJ-2>$4apjY@D-*omBzLHg<L7Zi#zC;ao;jM8fGm(d^hcc{^?O$|V#c&Y-qIfF5TgeHzQGaL1GvsNHGW? 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