This is the documentation for pyslim, a Python API for reading and modifying tskit tree sequence files produced by SLiM, or modifying files produced by other programs (e.g., msprime, fwdpy11, and tsinfer) for use in SLiM.
SLiM can read and write tree sequences, which store genetic genealogies for entire populations. These can be used to efficiently encode both the state of the population at various points during a simulation as well as the complete genomic ancestry. Furthermore, SLiM can “load” a saved tree sequence file to recreate the exact state of the population at the time it was saved. To do this, SLiM stores some additional information in the basic tree sequence file.
A tree sequence is a way of storing both the full genetic history and the genotypes of a bunch of genomes. See the tskit documentation for more description of the tree sequence and underlying data structure, and definitions of the important terms. Each (haploid) genome is associated with a node, and the “focal” nodes are called sample nodes or simply samples. Many operations on tree sequences act on the sample nodes by default (see the tskit data model for more on this topic), and the tree sequence always describes the genealogy of the entire genome of all the samples, at at least over the simulated time period. (Other nodes in the tree sequence represent ancestral genomes about which we might have only partial information). SLiM simulates diploid organisms, so each individual usually has two nodes; many operations you might want to do involve first finding the individuals you want, and then looking at their nodes.
What does SLiM record in the tree sequence?¶
Suppose we’ve run a very small simulation with SLiM. The genetic relationships between the various diploid individuals who were alive over the course of the simulation might look something like the picture on the left below. Note that individuals (circles) are diploid, so that each contains two chromosomes or nodes (shaded rectangles), and that relationships are between the nodes, not the individuals.
At the end of the simulation we are typically only interested in the genetic relationships between the nodes in those individuals which are still alive; other parts of the genealogy are irrelevant. To save having to store this unnecessary genealogical information, SLiM simplifies the tree sequence as it goes along, retaining only certain parts of the genetic genealogy. When the tree sequence is output, the result then looks something like the situation in the figure below, in which many of the nodes and individuals have been removed.
Who and what is in the tree sequence?¶
OK, who and what exactly is left in the tree sequence after the unnecessary information has been removed? Figure 2 depicts the terminology.
In the recorded tree sequence the individuals who are alive at the end of the simulation have their nodes marked as samples, and so we have their full genetic ancestry. The sample nodes, and the individuals containing them, are always present in the tree sequence.
In contrast to the individuals containing sample nodes, you can see that all the other circles, representing historical (i.e., dead) individuals, have vanished, although sometimes their nodes remain. By default, only individuals with sample nodes are recorded in the tree sequence; that means the other, remaining, nodes lose any information about which individuals they were in (the tutorial explains ways to retain this information.
As well as the historical individuals, many historical nodes have been removed too, along with with their genealogical relationships (i.e. the lines, which in tree-sequence-speak are known as “edges”). The deleted nodes are simply those that are not needed to reconstruct the relationships between the sample nodes. For example, we remove nodes leading to a dead end (e.g. in individuals who had no offspring). Similarly, as time goes on, recombination events in conjunction with genetic drift can gradually reduce the genetic contribution of parts of older genomes to the current generation. The generated tree sequence therefore need not contain historical nodes whose genetic contribution to the samples has been whittled down to zero. Finally, to reconstruct relationships between samples, strictly we only need to keep a node if it represents the genetic most recent common ancestor (MRCA) of at least two samples. So by default, we also remove historical nodes that are only “on the line to” a sample, but do not represent a branching point (i.e. coalescent event) on the tree.
What else can I find out from the tree sequence?¶
Enough information is stored in the tree sequence
to completely reconstruct the state of the SLiM simulation
(except for user-defined data, like a
Most of this is stored as metadata, which pyslim makes accessible: