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TSKITetude

This project is an attempt to analyze SMARTER data using tskit. Most of the analyses are done using cnr-ibba/nf-treeseq nextflow pipeline able which is able to transform SMARTER data in tskit TreeSequence files. Within this project, there's the tskitetude python library which does data conversion and is then used in the nextflow pipeline. There's also a notebook folder with some example code useful for analysis. Files and folders are structured as follow:

TSKITetude/
├── config
├── data
├── Dockerfile
├── docs
├── experiments
├── Makefile
├── poetry.lock
├── pyproject.toml
├── README.md
├── results-*
├── scripts
├── tests
├── TODO.md
├── tskitetude
└── tskit-pipeline
  • config: configuration directory for analyses with nextflow
  • data: data folder (not tracked)
  • Dockerfile: create a docker image with all TSKITetude dependencies. Required
  • docs: jupyter-book documentation folder
    • notebooks: folder with IPython notebooks files
  • experiments: testing stuff folder. Not tracked
  • Makefile: automate some boring stuff
  • poetry.lock: managed by Poetry during installation. Don't touch
  • pyproject.toml: required to manage this project with poetry. Dependencies can be managed using poetry (see Managing dependencies)
  • README.md: this file
  • results-*: results folders not tracked with Git. Usually is the nextflow results folder
  • scripts: scripts used to perform analyses
  • test: test code for tskitetude python module
  • TODO.md: TODO file
  • tskitetude: source code of this project

This folder is managed using git, and to avoid committing every IPython notebook output you require the nbstripout software installed, which will be called every time you do a git add or git status command: this will strip out notebook results on the fly without erasing the outputs of your local notebooks (and without requiring you to strip out results before committing with Git).

Install poetry

You require poetry to work with this project. Install poetry with https://python-poetry.org/docs/#installing-with-the-official-installer. Then install poetry-bumpversion plugin (globally) with:

poetry self add poetry-bumpversion

Install from git repository

Simply clone this project with

git clone https://github.com/bunop/TSKITetude.git

Set-Up

Recover (or install) this environment by entering in the cloned folder and type:

poetry install

This will install all poetry dependencies, including nbstripout.

Activate poetry environment

In your TSKITetude folder, simply type:

poetry shell

to activate the poetry environment. Next, set up the git filter and attributes provided by nbstripout with:

nbstripout --install

This will install nbstripout in your Poetry bin path.

Get SMARTER-data

Collect SMARTER genotypes

Connect to SMARTER FTPs site and download Sheep and Goat datasets:

cd SHEEP/OAR3
mget SMARTER-OA-OAR3-top-0.4.10.*

Convert into forward coding

SMARTER data is stored in illumina top. It is possible to convert data into forward coordinates with plink: This could be a more fast alternative to SNPconvert.py coming from SMARTER-database project. First, crate a TSV file with the SNP ids, the old allele codes and the new allele codes. This could be done using this script:

python scripts/top2forward.py > data/OAR3_top2forward.csv

Next, you can convert the data with plink:

plink --chr-set 26 no-xy no-mt --allow-no-sex --bfile data/SMARTER-OA-OAR3-top-0.4.10 \
    --update-alleles data/OAR3_top2forward.csv --make-bed --out data/SMARTER-OA-OAR3-forward-0.4.10

The tskit-pipeline

The proposed pipeline is supposed to work starting from the entire SMARTER database: all the required samples are selected by the pipeline relying on a list of samples (like the one required by the --keep option of plink). Then there are three different approaches to genereate TreeSequences object using the nextflow pipeline: the est-sfs approach, the reference approach and the compara approach.

The est-sfs approach

This approach requires to select a list of samples from the SMARTER including the samples required to estimate ancestor alleles: the former samples will be referred as focal samples, the latter ancient. The two groups will be processed separately: data will be converted into VCF and normalized against the reference sequence. Focal samples will be phased and imputed using Beagle before being joined with ancient samples to determine ancestor alleles using est-sfs software (see Keightley and Jackson 2018). Then the output will be used to define samples and TreeSequences using tsinfer. Ages will be finally estimated with tsdate

This approach was superseded by the compara and reference allele extraction, since we are not sure that those ancient samples can be considered as ancestors instead of just outgroups, since they are living in the same time frame of the focal samples.

The reference approach

This approach requires to select a list of samples from the SMARTER database, like the est-sfs approach: however no ancient samples are required and the ancestral alleles are extracted from a reference sequence.

This approach should be the reference approach against other methods, since no assumptions are made on ancestors.

The compara approach

Even this approach requires to select a list of samples from the SMARTER database. In addition, you require a CSV file in which the ancestral alleles are stored. This file can be generated using the collect_compara_ancestors script:

collect_compara_ancestors --assembly oar3 --chip_name IlluminaOvineSNP50 --output data/ancestors-OAR3-50K.csv

With this approach, the ancestral alleles are extracted from the ensembl compara database, when an alignment between sheep and goat assemblies is available. This datafile is specific to the assembly and the chip used to genotype the samples, in this case we can call the analysis on OAR3 for the IlluminaOvineSNP50 (50K) chip.

Compare background samples with three different approaches

Select background samples

Background samples can be selected by using the notebooks/03-smarter_database.ipynb notebook: this notebook was used to select only Ovis aries background samples from the SMARTER database. The output is a list of samples to be used in the pipeline mainly by plink with the --keep option. In addition, three different lists (european_mouflon, sardinian_mouflon, spanish_mouflon) are then created to extract ancestor alleles using est-sfs.

Call pipeline on background samples

Call the pipeline with est-sfs on background samples:

nextflow run cnr-ibba/nf-treeseq -r v0.2.1 -profile singularity -params-file config/smarter-sheeps.json -resume \
    --outdir "results-estsfs/background_samples" --with_estsfs

Call the pipeline using reference alleles (the outgroup samples are not used):

nextflow run cnr-ibba/nf-treeseq -r v0.2.1 -profile singularity -params-file config/smarter-sheeps.json -resume \
    --outdir "results-reference/background_samples" --reference_ancestor

Call the pipeline using the compara reference alleles (the outgroup samples are not used):

nextflow run cnr-ibba/nf-treeseq -r v0.2.1 -profile singularity -params-file config/smarter-sheeps.json -resume \
    --outdir "results-compara/background_samples" --compara_ancestor data/ancestors-OAR3-50K.csv

Compare 50K samples with repetitions

In order to do a fair simulation, we need to extract all 50K SNPs from the SMARTER dataset: this will avoid the particular case when I extract all HD samples, which will results in a HD subset. By extracting only the 50K dataset, all simulations should be comparable. Let's collect SNP names for 50K SNPs:

python scripts/getSNPnames.py --chip_name IlluminaOvineSNP50 > data/50k_snp_ids.txt

Next, we can extract the 50K dataset from the whole forward file using plink:

plink --chr-set 26 no-xy no-mt --allow-no-sex --bfile data/SMARTER-OA-OAR3-forward-0.4.10 \
    --extract data/50k_snp_ids.txt --make-bed --out data/SMARTER-OA-OAR3-forward-0.4.10-50K

To test the effect of the number of samples / breed when creating TreeSequence objects, we select randomly [1, 2, 5, 10, 15, 20] breeds having each one a number of samples between 20 and 30 (see notebooks/09-smarter_50k.ipynb for details) and we repeat this random extraction (without repetition) 5 times. For each combination, a input file is created inside data folder, and the pipeline can be called called with:

bash scripts/50K_simulations_reference.sh

To call the pipeline using the reference approach. In order to call the pipeline with the compara approach, you can use the following script:

bash scripts/50K_simulations_compara.sh

The est-sfs approach is not used in this case, since we cannot define the output groups that can be used to estimate the ancestral alleles.