LDhat Nextflow Pipeline

LDhat pipeline configured to work with gene-conversion type recombination

• About
• Getting Started
  • Set up using conda
  • Set up using docker
• Quick Start and Output
  • run_ldhat.nf
  • run_theta_est.nf
• Pipeline Options and Advanced Usage
  • run_ldhat.nf
  • run_theta_est.nf
• Issues and Contributing
• Contact

About

Nextflow pipeline for LDhat, specifically for the LDhat pairwise module, configured to work with gene-conversion type recombination. Based on the journal article https://academic.oup.com/genetics/article/160/3/1231/6052507 and the ldhat package https://github.com/auton1/LDhat. Additionaly, a theta estimate pipeline has been implemented based on equation 1 from the journal article.

Getting Started

This LDhat nextflow pipeline is designed to be run on linux and requires nextflow to be installed. Dependencies are resolved either via conda or docker images. Support for HPC, docker, singularity, AWS and many other systems are provided via nextflow.

While it is possible to resolve the dependencies using conda for running on macOS, its recommended that this option be used on linux systems for which it has been extensively test. If running on macOS it recommended that docker be used with the provided image, in which case it is similar to running in a linux environment.

It is also possible to install and run the program on Windows via wsl.

Set up using conda

Instructions for installing nextflow and dependencies via conda

1. Clone the repo

   git clone https://github.com/sid-krish/Nextflow_LDhat.git

2. Install the conda package manager: Miniconda download
3. Install nextflow

   conda install -c bioconda nextflow

4. Adjust settings in nextflow.config file, by default it is configured to work with docker with modest resources. Disable the use of docker by setting the docker option to false. Disabling the use of container engines will cause conda packages to be used by default:

   docker {
       enabled = false
   }

5. The pipeline is now ready to run, and all dependencies will be automatically resolved with conda.

Set up using docker

Instructions for installing nextflow and using the provided docker image for dependencies

1. Clone the repo

    git clone https://github.com/sid-krish/Nextflow_LDhat.git

2. Install nextflow Nextflow install
3. Install docker desktop Docker install.
4. Adjust settings in nextflow.config file, by default it is configured to work with docker with modest resources.
5. In the nextflow.config file comment the following line:

   // conda='environment.yaml'
   

6. Ensure docker is running.
7. The pipeline is now ready to run, all the required dependencies are present in the docker image, that the pipeline is preconfigured to use.

Quick Start and Output

The following quick start example makes use of the files in toy_dataset.zip.

This example is designed to help ensure that run_ldhat.nf (rho estimation pipeline) and run_theta_est.nf (theta estimation pipeline) are configured properly and to demonstrate a typical workflow. The toy datasets were simulated using the simulation pipeline Nextflow_LDhat_Sim.

run_ldhat.nf

run_ldhat.nf will by default estimate the theta per site and use it for generating the appropriate lookup table for the number of sequences in the fasta file. The lookup table is then used for the rho estimate.

The default settings of the run_ldhat.nf pipeline have been configured to work with the quick start example as is.

For this example the command to run is:

nextflow run run_ldhat.nf --input_fasta toy_dataset/example.fa

The output files will be saved to 'LDhat_Output'. The file ending with pairwise_outfile.txt and processed_results.csv have the rho estimates and file ending with theta_est.csv will have the theta (per site) estimate.

The result in the file ending with processed_results.csv should be:

max_rho,max_lk
9.000,-41836138.059

The result in the file ending with theta_est.csv should be:

0.010264296769556949

run_theta_est.nf

run_theta_est.nf is used to get the theta (per site) estimate only.

For this example the command to run is:

nextflow run run_theta_est.nf --input_fasta toy_dataset/example.fa 

The output files will be saved to 'LDhat_Theta_Output'. The file ending with theta_est.csv will have the theta (per site) estimate. It should have the following results:

0.010264296769556949

Pipeline Options and Advanced Usage

run_ldhat.nf

In the workflow section of the run_ldhat.nf script, the following parameters can be adjusted, these are the available options for the pipeline.

params.recom_tract_len = 1000
params.lookup_grid = "101,100" // The range of rho values used to generate lookup tables

params.prefix_filename = 'none' // prefix string to output filenames to help distinguish runs
params.input_fasta = 'none'

It is also possible to enable the DOWNSAMPLED_LOOKUP_TABLE process to automatically use the downsampled lookup tables generated by lookup_table_gen.nf pipeline in rhometa. To do this the LOOKUP_TABLE_LDPOP process call needs to be commented and the script needs to be adjusted accordingly.

run_theta_est.nf

In the workflow section of the run_theta_est.nf script, the following parameters can be adjusted, these are the available options for the pipeline.

params.prefix_filename = 'none' // prefix string to output filenames to help distinguish runs
params.input_fasta = 'none'

Issues and Contributing

If you have any issues please open an issue with the details and steps for reproducing the issue. If you have any questions please open a issue with the tag "question" or alternatively email one of the authors from the contact section.

If you have a suggestion that would make this better, please fork the repo and create a pull request.

Contact

Sid Krishnan - sidaswar.krishnan-1@student.uts.edu.au, sid.kr15n@gmail.com
Aaron Darling - aaron.darling@uts.edu.au
Matt DeMaere - matthew.demaere@uts.edu.au