akcorut/kGWASflow
kGWASflow is a Snakemake workflow for performing k-mers-based GWAS.
Overview
Topics: snakemake genomics gwas gwas-pipeline kmers bioinformatics-pipeline ngs sciworkflows bioinformatics conda pipeline association-mapping bioinformatics-tool gwas-tools structural-variation workflow
Latest release: v1.3.0, Last update: 2024-06-11
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Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.
When using Mamba, run
mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy
to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via
conda activate snakemake
Step 2: Deploy workflow
With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:
mkdir -p path/to/project-workdir
cd path/to/project-workdir
In all following steps, we will assume that you are inside of that directory. Then run
snakedeploy deploy-workflow https://github.com/akcorut/kGWASflow . --tag v1.3.0
Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.
Step 3: Configure workflow
To configure the workflow, adapt config/config.yml to your needs following the instructions below.
Step 4: Run workflow
The deployment method is controlled using the --software-deployment-method (short --sdm) argument.
To run the workflow with automatic deployment of all required software via conda/mamba, use
snakemake --cores all --sdm conda
Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.
For further options such as cluster and cloud execution, see the docs.
Step 5: Generate report
After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using
snakemake --report report.zip
Configuration
The following section is imported from the workflow’s config/README.md.
To configure this workflow, you need to edit config/config.yaml file based on your needs. The explanation for each parameter is provided in the config.yaml file. Below parameters has to be set in order to run this workflow:
-
samples: the path of the sample sheet -
phenotype: the path of the phenotype sheet -
ref:fasta:the path of the refrence genome fasta file (Required for Post-GWAS mapping steps)
Add samples information to config/samples.tsv. For each sample, you need to specify the sample name under the sample_name column and the corresponding library name under the library_name column.
- Each sample has a
library_name, which can be e.g. a library name, sequencing run or lane. - Each sample has a
sample_name, which associates it with the biological sample it comes from. - For each sample, define the paths for your (columns
fq1,fq2) FASTQ files (these can point to any path in your system). - As an alternative, you can provide an SRA (sequence read archive) accession (starting with e.g. ERR or SRR) by using a column
sra. If ypu provde SRA accession instead of FASTQ file paths, the workflow will automatically download the corresponding paired end reads from SRA. If both local file paths and SRA accession are provided, the local files will be preferred.
| sample_name | library_name | fq1 | fq2 | sra |
|---|---|---|---|---|
| 33-16 | HB1JEADXX_GATCAG | /path/to/your/local/fastq_1/33-16_HB1JEADXX_GATCAG_1.fastq.gz | /path/to/your/local/fastq_2/33-16_HB1JEADXX_GATCAG_2.fastq.gz | |
| 33-16 | HB1LCADXX_GATCAG | /path/to/your/local/fastq_1/33-16_HB1LCADXX_GATCAG_1.fastq.gz | /path/to/your/local/fastq_2/33-16_HB1LCADXX_GATCAG_2.fastq.gz | |
| 33-16 | HLC27CCXX_L7 | /path/to/your/local/fastq_1/33-16/33-16_HLC27CCXX_L7_1.fastq.gz | /path/to/your/local/fastq_2/33-16_HLC27CCXX_L7_2.fastq.gz | |
| 38-11 | HLCY2CCXX_L3 | /path/to/your/local/fastq_1/38-11/38-11_HLCY2CCXX_L3_1.fastq.gz | /path/to/your/local/fastq_2/38-11_HLCY2CCXX_L3_2.fastq.gz | |
| 4226 | H174VBGXX | /path/to/your/local/fastq_1/4226/4226_H174VBGXX_1.fastq.gz | /path/to/your/local/fastq_2/4226_H174VBGXX_2.fastq.gz | |
| 4226 | HLC27CCXX_L3 | /path/to/your/local/fastq_1/4226/4226_HLC27CCXX_L3_1.fastq.gz | /path/to/your/local/fastq_2/4226/4226_HLC27CCXX_L3_2.fastq.gz | |
| 4722 | H3CWKBGXX_GCCAAT | /path/to/your/local/fastq_1/4722_H3CWKBGXX_GCCAAT_1.fastq.gz | /path/to/your/local/fastq_2/4722_H3CWKBGXX_GCCAAT_2.fastq.gz | |
| 4722 | HLCY2CCXX_L3 | /path/to/your/local/fastq_1/4722_HLCY2CCXX_L3_1.fastq.gz | /path/to/your/local/fastq_2/4722_HLCY2CCXX_L3_2.fastq.gz | |
| A188 | HLC27CCXX_L3 | /path/to/your/local/fastq_1/A188_HLC27CCXX_L3_1.fastq.gz | /path/to/your/local/fastq_2/A188_HLC27CCXX_L3_2.fastq.gz | |
| A188 | H174VBGXX | /path/to/your/local/fastq_1/A188_H174VBGXX_1.fastq.gz | /path/to/your/local/fastq_2/A188_H174VBGXX_2.fastq.gz |
| sample_name | library_name | fq1 | fq2 | sra |
|---|---|---|---|---|
| C00022105 | C00022105 | SRR3050845 | ||
| C00022106 | C00022106 | SRR3050846 | ||
| C00022107 | C00022107 | SRR3050847 | ||
| C00022108 | C00022108 | SRR3050848 | ||
| C00022109 | C00022109 | SRR3050849 | ||
| C00022110 | C00022110 | SRR3050850 | ||
| C00022119 | C00022119 | SRR3050851 | ||
| C00022112 | C00022112 | SRR3050852 | ||
| C00022113 | C00022113 | SRR3050853 | ||
| C00022114 | C00022114 | SRR3050854 |
Add phenotype information to config/phenos.tsv. For each phenotype you need to specify the name of the phenotype (column pheno_name) and the path of the correspondig phenotype file (column pheno_path).
Below is an example phenos.tsv file. First columns corresponds to phenotype name and the second column corresponds to its file path.
| pheno_name | pheno_path |
|---|---|
| Flowering_Time | path/to/pheno/Flowering_Time.pheno |
| Seed_Oil | path/to/pheno/Seed_Oil.pheno |
| UpperLeaf_Angle | path/to/pheno/UpperLeaf_Angle.pheno |
Note: The name in in the pheno_name column must match to name of the .pheno file. For example, if pheno_name name is "Flowering_Time", .phone file must be named as Flowering_Time.pheno. See above table for examples.
The format of the phenotye file has to match the description in the kmersGWAS manual:
The phenotype file should be with two columns separated by tabs (\t), with “accession_id” and “phenotype_value” in the header. The first column lists the name of the individuals, as in the kmers_table, and the second, phenotype_value, the phenotypic values in numerical form.
Below is an example of a phenotype file (pheno_name.pheno):
| accession_id | phenotype_value |
|---|---|
| 33-16 | 81.704 |
| 38-11 | 83.33 |
| 4226 | 73.066 |
| 4722 | 83.016 |
| A188 | 67.996 |
| A214N | 87.855 |
| A239 | 73.357 |
Linting and formatting
Linting results
/tmp/tmpu3hokbpi/akcorut-kGWASflow-27654c8/workflow/rules/common.smk:98: SyntaxWarning: invalid escape sequence '\ '
logger.info(" | |/ / | |_ | \ \/ \/ / /\ \ \___ \| _| |/ _ \ \ /\ / / ")
/tmp/tmpu3hokbpi/akcorut-kGWASflow-27654c8/workflow/rules/common.smk:99: SyntaxWarning: invalid escape sequence '\ '
logger.info(" | <| |__| | \ /\ / ____ \ ____) | | | | (_) \ V V / ")
/tmp/tmpu3hokbpi/akcorut-kGWASflow-27654c8/workflow/rules/common.smk:100: SyntaxWarning: invalid escape sequence '\ '
logger.info(" |_|\_\\_____| \/ \/_/ \_\_____/|_| |_|\___/ \_/\_/ ")
/tmp/tmpu3hokbpi/akcorut-kGWASflow-27654c8/workflow/rules/common.smk:101: SyntaxWarning: invalid escape sequence '\ '
logger.info("")
/tmp/tmpu3hokbpi/akcorut-kGWASflow-27654c8/workflow/rules/common.smk:102: SyntaxWarning: invalid escape sequence '\_'
logger.info(" kGWASflow: A Snakemake Workflow for k-mers Based GWAS ")
# ================================================================================== #
_ _______ __ _____ __ _
| | / ____\ \ / /\ / ____|/ _| |
| | _| | __ \ \ /\ / / \ | (___ | |_| | _____ __
| |/ / | |_ | \ \/ \/ / /\ \ \___ \| _| |/ _ \ \ /\ / /
| <| |__| | \ /\ / ____ \ ____) | | | | (_) \ V V /
|_|\_\_____| \/ \/_/ \_\_____/|_| |_|\___/ \_/\_/
kGWASflow: A Snakemake Workflow for k-mers Based GWAS
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Formatting results
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