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| # UnCoVar | ||
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| <picture> | ||
| <source media="(prefers-color-scheme: dark)" srcset="https://github.com/IKIM-Essen/uncovar/assets/77535027/8e17c6fc-ff7a-4c25-afc9-7888036d693e"> | ||
| <source media="(prefers-color-scheme: light)" srcset="https://github.com/IKIM-Essen/uncovar/assets/77535027/c99f5a94-749b-422e-b319-1e3700d40a8e"> | ||
| <source media="(prefers-color-scheme: dark)" srcset="https://github.com/IKIM-Essen/uncovar/assets/77535027/8e17c6fc-ff7a-4c25-afc9-7888036d693e" width="40%"> | ||
| <source media="(prefers-color-scheme: light)" srcset="https://github.com/IKIM-Essen/uncovar/assets/77535027/c99f5a94-749b-422e-b319-1e3700d40a8e" width="40%"> | ||
| <img alt="UnCoVar Logo dark/light"> | ||
| </picture> | ||
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| <h1> | ||
| Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction | ||
| and Lineage Assignment | ||
| </h1> | ||
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| ## SARS-CoV-2 Variant Calling and Lineage Assignment | ||
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| [](https://snakemake.bitbucket.io) | ||
| [](https://snakemake.bitbucket.io) | ||
| [](https://github.com/koesterlab/snakemake-workflow-sars-cov2/actions?query=branch%3Amaster+workflow%3ATests) | ||
| [](https://quay.io/repository/uncovar/uncovar) | ||
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| A reproducible and scalable workflow for transparent and robust SARS-CoV-2 | ||
| variant calling and lineage assignment with comprehensive reporting. | ||
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| ## Usage | ||
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| This workflow is written with snakemake and its usage is described in the | ||
| <details> | ||
| <Summary><b>Step 1: Install Snakemake and Snakedeploy</b></Summary> | ||
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| Snakemake and Snakedeploy are best installed via the [Mamba package manager](https://github.com/mamba-org/mamba) | ||
| (a drop-in replacement for conda). If you have neither Conda nor Mamba, it can | ||
| be installed via [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge). | ||
| For other options see [here](https://github.com/mamba-org/mamba). | ||
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| Given that Mamba is installed, run | ||
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| mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy | ||
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| to install both Snakemake and Snakedeploy in an isolated environment. For all | ||
| following commands ensure that this environment is activated via | ||
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| conda activate snakemake | ||
| </details> | ||
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| <details> | ||
| <Summary><b>Step 2: Clone or Deploy workflow</b></Summary> | ||
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| First, create an appropriate project working directory on your system and enter it: | ||
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| mkdir -p path/to/project-workdir | ||
| cd path/to/project-workdir | ||
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| In all following steps, we will assume that you are inside of that directory. | ||
| Second, run | ||
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| Given that Snakemake is installed and you want to clone the full workflow you can | ||
| do it as follows: | ||
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| git clone https://github.com/IKIM-Essen/uncovar | ||
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| Given that Snakemake and Snakedeploy are installed and available (see Step 1), | ||
| the workflow can be deployed as follows: | ||
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| snakedeploy deploy-workflow https://github.com/IKIM-Essen/uncovar . --tag v0.16.0 | ||
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| Snakedeploy will create two folders `workflow` and `config`. The former contains | ||
| the deployment of the UnCoVar workflow as a | ||
| [Snakemake module](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#using-and-combining-pre-exising-workflows), | ||
| the latter contains configuration files which will be modified in the next step | ||
| in order to configure the workflow to your needs. Later, when executing the workflow, | ||
| Snakemake will automatically find the main Snakefile in the workflow subfolder. | ||
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| </details> | ||
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| <details> | ||
| <Summary><b>Step 3: Configure workflow</b></Summary> | ||
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| ### General settings | ||
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| #### Config file | ||
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| To configure this workflow, modify `config/config.yaml` according to your | ||
| needs, following the explanations provided in the file. It is especially recommended | ||
| to provide a `BED` file with primer coordinates, when the analyzed reads derive | ||
| from amplicon-tiled sequencing, so the primers are trimmed appropriately. | ||
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| #### Sample sheet | ||
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| The sample sheet contains all samples to be analyzed by UnCoVar. | ||
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| #### Auto filling | ||
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| UnCoVar offers the possibility to automatically append paired-end sequenced | ||
| samples to the sample sheet. To load your data into the workflow execute | ||
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| snakemake --cores all --use-conda update_sample | ||
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| with the root of the UnCoVar as working directory. It is recommended to use | ||
| the following structure to when adding data automatically: | ||
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| ├── archive | ||
| ├── incoming | ||
| └── snakemake-workflow-sars-cov2 | ||
| ├── data | ||
| └── 2023-12-24 | ||
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| However, this structure is not set in stone and can be adjusted via the | ||
| `config/config.yaml` file under `data-handling`. Only the following path to the | ||
| corresponding folders, relative to the directory of UnCoVar are needed: | ||
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| - **incoming**: path of incoming data, which is moved to the data directory by | ||
| the preprocessing script. Defaults to `../incoming/`. | ||
| - **data**: path to store data within the workflow. defaults to `data/`. It is | ||
| recommend using subfolders named properly (e.g. with date) | ||
| - **archive**: path to archive data from the results from the analysis to. | ||
| Defaults to `../archive/`. | ||
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| The incoming directory should contain paired end reads in (compressed) FASTQ | ||
| format. UnCoVar automatically copies your data into the data directory and moves | ||
| all files from incoming directory to the archive. After the analysis, all results | ||
| are compressed and saved alongside the reads. | ||
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| Moreover, the sample sheet is automatically updated with the new files. Please | ||
| note, that only the part of the filename before the first '\_' character is used | ||
| as the sample name within the workflow. Technology and amplicon flag (**is_amplicon_data**) | ||
| have to be revisited manually | ||
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| #### Manual filling | ||
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| Of course, samples to be analyzed can also be added manually to the sample sheet. | ||
| For each sample, the a new line in `config/pep/samples.csv` with the following | ||
| content has to be defined: | ||
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| - **sample_name**: name or identifier of sample | ||
| - **fq1**: path to read 1 in FASTQ format | ||
| - **fq2**: path to read 2 in FASTQ format (if paired end sequencing) | ||
| - **date**: sampling date of the sample | ||
| - **is_amplicon_data**: indicates whether the data was generated with a | ||
| shotgun (0) or amplicon (1) sequencing | ||
| - **technology**: indicates the sequencing technology used to generate | ||
| the samples (illumina, ont, ion) | ||
| - **include_in_high_genome_summary**: indicates if sample should be included in the submission files (1) or not (0) | ||
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| </details> | ||
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| <details> | ||
| <Summary><b>Step 4: Run workflow</b></Summary> | ||
| Given that the workflow has been properly deployed and configured, it can be executed as follows. | ||
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| Fow running the workflow while deploying any necessary software via conda (using | ||
| the Mamba package manager by default), run Snakemake with | ||
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| snakemake --cores all --use-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. | ||
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| For further options, e.g. for cluster and cloud execution, see the docs. | ||
| </details> | ||
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| This workflow is written with Snakemake and details and tools are described in the | ||
| [Snakemake Workflow Catalog](https://snakemake.github.io/snakemake-workflow-catalog?usage=IKIM-Essen/uncovar). | ||
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| If you use this workflow in a paper, don't forget to give credits to the | ||
| authors by citing the URL of this repository and its DOI (see above). | ||
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| ## Tools, Frameworks and Packages used | ||
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| This project wouldn't be possible without several open source libraries: | ||
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| | Tool | Link | | ||
| | -------------- | ------------------------------------------------- | | ||
| | ABySS | www.doi.org/10.1101/gr.214346.116 | | ||
| | Altair | www.doi.org/10.21105/joss.01057 | | ||
| | BAMClipper | www.doi.org/10.1038/s41598-017-01703-6 | | ||
| | BCFtools | www.doi.org/10.1093/gigascience/giab008 | | ||
| | BEDTools | www.doi.org/10.1093/bioinformatics/btq033 | | ||
| | Biopython | www.doi.org/10.1093/bioinformatics/btp163 | | ||
| | bwa | www.doi.org/10.1093/bioinformatics/btp324 | | ||
| | Covariants | www.github.com/hodcroftlab/covariants | | ||
| | delly | www.doi.org/10.1093/bioinformatics/bts378 | | ||
| | ensembl-vep | www.doi.org/10.1186/s13059-016-0974-4 | | ||
| | entrez-direct | www.ncbi.nlm.nih.gov/books/NBK179288 | | ||
| | fastp | www.doi.org/10.1093/bioinformatics/bty560 | | ||
| | FastQC | www.bioinformatics.babraham.ac.uk/projects/fastqc | | ||
| | fgbio | www.github.com/fulcrum-genomics/fgbio | | ||
| | FreeBayes | www.arxiv.org/abs/1207.3907 | | ||
| | intervaltree | www.github.com/chaimleib/intervaltree | | ||
| | Jupyter | www.jupyter.org | | ||
| | kallisto | www.doi.org/10.1038/nbt.3519 | | ||
| | Kraken2 | www.doi.org/10.1186/s13059-019-1891-0 | | ||
| | Krona | www.doi.org/10.1186/1471-2105-12-385 | | ||
| | mason | www.<http://publications.imp.fu-berlin.de/962> | | ||
| | MEGAHIT | www.doi.org/10.1093/bioinformatics/btv033 | | ||
| | Minimap2 | www.doi.org/10.1093/bioinformatics/bty191 | | ||
| | MultiQC | www.doi.org/10.1093/bioinformatics/btw354 | | ||
| | pandas | pandas.pydata.org | | ||
| | Picard | broadinstitute.github.io/picard | | ||
| | PySAM | www.doi.org/10.11578/dc.20190903.1 | | ||
| | QUAST | www.doi.org/10.1093/bioinformatics/btt086 | | ||
| | RaGOO | www.doi.org/10.1186/s13059-019-1829-6 | | ||
| | ruamel.yaml | www.sourceforge.net/projects/ruamel-yaml | | ||
| | Rust-Bio-Tools | www.github.com/rust-bio/rust-bio-tools | | ||
| | SAMtools | www.doi.org/10.1093/bioinformatics/btp352 | | ||
| | Snakemake | www.doi.org/10.12688/f1000research.29032.1 | | ||
| | sourmash | www.doi.org/10.21105/joss.00027 | | ||
| | SPAdes | www.doi.org/10.1089/cmb.2012.0021 | | ||
| | SVN | www.doi.org/10.1142/s0219720005001028 | | ||
| | Tabix | www.doi.org/10.1093/bioinformatics/btq671 | | ||
| | Trinity | www.doi.org/10.1038/nprot.2013.084 | | ||
| | Varlociraptor | www.doi.org/10.1186/s13059-020-01993-6 | | ||
| | Vega-Lite | www.doi.org/10.1109/TVCG.2016.2599030 | | ||
| | Velvet | www.doi.org/10.1101/gr.074492.107 | | ||
| | vembrane | www.github.com/vembrane/vembrane | |