Nextflow- Evolutionary divergence time / Divergence Time estimation using SNPs. Pipeline for standardised Quality analisis, mapping and variant calls on yeast genomes. The pipeline take input: fastqs, followed by hard filtering and outputs: html, bams, and gvcfs for joint genotyping.
The recommended way is to clone it from github:
git clone https://github.com/jimmlucas/DVT-WGS.git
cd DVT-WGS
The pipeline requires NextFlow and Docker on the target system. These are often pre-installed on HPC systems.
It is recommended that you pre-pull the Docker imagen required by the workflow, there is a script "Dockerfile" in the workflow directory to help you with this, just run:
docker pull jimmlucas/dvt
Download
If you already have the reads downloaded or perhaps you will use your data, ignore this step and proceed to the next one. However, if you plan to use reads form the DB, you can use a script "download_reads.sh" to download all the data, just nead an input list in the format .txt "Acc_List.txt". Then, run the script in the root using:
bash ./bin/download_reads.sh
Reference-genome
The pipeline requires a refence-genome, which will be index using BWA and create a dictionary. These can be done in the first step. Just be sure to move your reference-genome to the corrected directory. Remember to give all the necessary access to your reference file. Use the following command:
chmod 644 $projectDir/data/reference-genome/reference-genome/*.fasta
Befor start all the process in NextFlow, you must to know what happens in the entire workflow.
To estimate Divergence Times (DVT) in this case, I decided to use SNPs from a WGS data used in the publication Nespolo et al, 2020, to estimate DVT in sub-populations of S.eubayanus.
The finality of this workflow, step by step using NextFlow, was to connect all the process of curating data and reduce the time of processing.
The workflow was classified in order to:
- Quality-(Fastqc): Performing quality control on raw data
- Trimming-(Trimmomatic): Reducing noise and adapters
- Quality control-(Fastqc): Second round of quality control
- Mapping-(BWA): Mapping with the reference-genome
- Identification of Duplicate-(GATK): Identification of duplicate and variant calling
- Filter of SNPs-(GATK): Filtering only for SNPs
After all these processes, you get a VCF with all the filtered SNPs.
Depending on the objective of your project, you can continue with this process or skip it and proceed in other way.
Now, you can run the following command:
Using Docker:
nextflow run tutorial.nf -with-docker jimmlucas/dvt:wgs
if you want to use a local envieroment in conda:
nextflow run tutorial.nf -with-conda envWGS.yaml
Estimate Time of Divergence is not an easy task, and depending on the objetive of your project, you might have to use others methods. In this case, I decide use SNPs to determine DVT.
First of all, we have to determinate the model of substitution that we need to use. In this case, I want to introduce how to select a substitution model for phylogenetic analysisi with the sofware PAUP.
PAUP is a program develop in 1980 used for inferring models of substitution. The author is Dave Swofford, who has never relased a final version. You could use a tested version for free on his new WEBSITE
You can find the interface version or a commond-line version for Mac OS X or Windows.