# FastqWiper
[](https://github.com/mazzalab/fastqwiper/actions/workflows/buildall_and_publish.yml) [](https://codecov.io/gh/mazzalab/fastqwiper) [](https://github.com/mazzalab/fastqwiper/issues)
[](https://anaconda.org/bfxcss/fastqwiper) [](https://anaconda.org/bfxcss/fastqwiper) [](https://anaconda.org/bfxcss/fastqwiper) [](https://anaconda.org/bfxcss/fastqwiper)
[](https://badge.fury.io/py/fastqwiper) [](https://pypi.python.org/pypi/fastqwiper/) 
[](https://hub.docker.com/r/mazzalab/fastqwiper) 
`FastqWiper` is a Snakemake-enabled application that wipes out bad reads from broken FASTQ files. Additionally, the available and pre-designed Snakemake [workflows](https://github.com/mazzalab/fastqwiper/tree/main/pipeline) allows **recovering** corrupted `fastq.gz`, **dropping** or **fixing** pesky lines, **removing** unpaired reads, and **fixing** reads interleaving.
* Compatibility: Python ≥3.7, <3.11
* OS: Windows, Linux, Mac OS (Snakemake workflows through Docker for Windows)
* Contributions: [bioinformatics@css-mendel.it](bioinformatics@css-mendel.it)
* Docker: https://hub.docker.com/r/mazzalab/fastqwiper
* Singularity: https://cloud.sylabs.io/library/mazzalab/fastqwiper/fastqwiper.sif
* Bug report: [https://github.com/mazzalab/fastqwiper/issues](https://github.com/mazzalab/fastqwiper/issues)
## USAGE
- **Case 1**. You have one or a couple (R1&R2) of **computer readable** FASTQ files which contain pesky, unformatted, uncompliant lines: Use *FastWiper* to clean them;
- **Case 2**. You have one or a couple (R1&R2) of **computer readable** FASTQ files that you want to drop unpaired reads from or fix reads interleaving: Use the FastqWiper's *Snakemake workflows*;
- **Case 3**. You have one `fastq.gz` file or a couple (R1&R2) of `fastq.gz` files which are corrupted (**unreadable**) and you want to recover healthy reads and reformat them: Use the FastqWiper's *Snakemake workflows*;
## Installation
### Case 1
This requires you to install FastqWiper and therefore does not require you to configure *workflows* also. You can do it for all OSs:
#### Use Conda
```
conda create -n fastqwiper python=3.10
conda activate fastqwiper
conda install -c bfxcss -c conda-forge fastqwiper
fastqwiper --help
```
*Hint: for an healthier experience, use* **mamba**
#### Use Pypi
```
pip install fastqwiper
fastqwiper --help
```
<br/>
`fastqwiper <options>`
```
options:
--fastq_in TEXT The input FASTQ file to be cleaned [required]
--fastq_out TEXT The wiped FASTQ file [required]
--log_frequency INTEGER The number of reads you want to print a status message
--log_out TEXT The file name of the final quality report summary
--help Show this message and exit.
```
It accepts in input and outputs **readable** `*.fastq` or `*.fastq.gz` files.
### Cases 2 & 3
There are <b>QUICK</b> and a <b>SLOW</b> methods to configure `FastqWiper`'s workflows.
#### One quick way (Docker)
1. Pull the Docker image from DockerHub:
`docker pull mazzalab/fastqwiper`
2. Once downloaded the image, type:
CMD: `docker run --rm -ti --name fastqwiper -v "YOUR_LOCAL_PATH_TO_DATA_FOLDER:/fastqwiper/data" mazzalab/fastqwiper paired 8 sample 50000000`
#### Another quick way (Singularity)
1. Pull the Singularity image from the Cloud Library:
`singularity pull library://mazzalab/fastqwiper/fastqwiper.sif`
2. Once downloaded the image (e.g., fastqwiper.sif_2023.2.70.sif), type:
CMD `singularity run --bind /scratch/tom/fastqwiper_singularity/data:/fastqwiper/data --writable-tmpfs fastqwiper.sif_2023.2.70.sif paired 8 sample 50000000`
If you want to bind the `.singularity` cache folder and the `logs` folder, you can omit `--writable-tmpfs`, create the folders `.singularity` and `logs` (`mkdir .singularity logs`) on the host system, and use this command instead:
CMD: `singularity run --bind YOUR_LOCAL_PATH_TO_DATA_FOLDER/:/fastqwiper/data --bind YOUR_LOCAL_PATH_TO_.singularity_FOLDER/:/fastqwiper/.snakemake --bind YOUR_LOCAL_PATH_TO_LOGS_FOLDER/:/fastqwiper/logs fastqwiper.sif_2023.2.70.sif paired 8 sample 50000000`
For both **Docker** and **Singularity**:
- `YOUR_LOCAL_PATH_TO_DATA_FOLDER` is the path of the folder where the fastq.gz files to be wiped are located;
- `paired` triggers the cleaning of R1 and R2. Alternatively, `single` will trigger the wipe of individual FASTQ files;
- `8` is the number of your choice of computing cores to be spawned;
- `sample` is part of the names of the FASTQ files to be wiped. <b>Be aware</b> that: for <b>paired-end</b> files (e.g., "sample_R1.fastq.gz" and "sample_R2.fastq.gz"), your files must finish with `_R1.fastq.gz` and `_R2.fastq.gz`. Therefore, the argument to pass is everything before these texts: `sample` in this case. For <b>single end</b>/individual files (e.g., "excerpt_R1_001.fastq.gz"), your file must end with the string `.fastq.gz`; the preceding text, i.e., "excerpt_R1_001" in this case, will be the text to be passed to the command as an argument.
- `50000000` is the number of rows-per-chunk (used when cores>1. It must be a number multiple of 4)
#### The slow way (Linux & Mac OS)
To enable the use of preconfigured [pipelines](https://github.com/mazzalab/fastqwiper/tree/main/pipeline), you need to install **Snakemake**. The recommended way to install Snakemake is via Conda, because it enables **Snakemake** to [handle software dependencies of your workflow](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#integrated-package-management).
However, the default conda solver is slow and often hangs. Therefore, we recommend installing [Mamba](https://github.com/mamba-org/mamba) as a drop-in replacement via
`$ conda install -c conda-forge mamba`
if you have anaconda/miniconda already installed, or directly installing `Mambaforge` as described [here](https://github.com/conda-forge/miniforge#mambaforge).
Then, create and activate a clean environment as above:
```
mamba create -n fastqwiper python=3.10
mamba activate fastqwiper
```
Finally, install a few dependencies:
```
$ mamba install -c bioconda snakemake
$ mamba install colorama click
```
#### Usage
Clone the FastqWiper repository in a folder of your choice and enter it:
```
git clone https://github.com/mazzalab/fastqwiper.git
cd fastqwiper
```
It contains, in particular, a folder `data` containing the fastq files to be processed, a folder `pipeline` containing the released pipelines and a folder `fastq_wiper` with the source files of `FastqWiper`. <br/>
Input files to be processed should be copied into the **data** folder.
Currently, to run the `FastqWiper` pipelines, the following packages need to be installed manually:
### required packages:
[gzrt](https://github.com/arenn/gzrt) (Linux build fron source [instructions](https://github.com/arenn/gzrt/blob/master/README.build), Ubuntu install [instructions](https://howtoinstall.co/en/gzrt), Mac OS install [instructions](https://formulae.brew.sh/formula/gzrt))
[BBTools](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/) (install [instructions](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/installation-guide/))
If installed from source, `gzrt` scripts need to be put on PATH. `bbmap` must be installed in the root folder of FastqWiper, as the image below
### Commands:
Copy the fastq files you want to fix in the `data` folder.
**N.b.**: In all commands above, you will pass to the workflow the name of the sample to be analyzed through the config argument: `sample_name`. Remember that your fastq files' names must finish with `_R1.fastq.gz` and `_R2.fastq.gz`, for paired fastq files, and with `.fastq.gz`, for individual fastq files, and, therefore, the text to be assigned to the variable `sample_name` must be everything before them. E.g., if your files are `my_sample_R1.fastq.gz` and `my_sample_R2.fastq.gz`, then `--config sample_name=my_sample`.
#### Paired-end files
- **Get a dry run** of a pipeline (e.g., `fix_wipe_pairs_reads_sequential.smk`):<br />
`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_pairs_reads_sequential.smk --use-conda --cores 4`
- **Generate the planned DAG**:<br />
`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_pairs_reads_sequential.smk --dag | dot -Tpdf > dag.pdf`<br /> <br />
<img src="https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_pairs_reads.png?raw=true" width="400">
- **Run the pipeline** (n.b., during the first execution, Snakemake will download and install some required remote packages and may take longer). The number of computing cores can be tuned accordingly:<br />
`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2`
Fixed files will be copied in the `data` folder and will be suffixed with the string `_fixed_wiped_paired_interleaving`.
We remind that the `fix_wipe_pairs_reads_sequential.smk` and `fix_wipe_pairs_reads_parallel.smk` pipelines perform the following actions:
- execute `gzrt` on corrupted fastq.gz files (i.e., that cannot be unzipped because of errors) and recover readable reads;
- execute `FastqWiper` on recovered reads to make them compliant with the FASTQ format (source: [Wipipedia](https://en.wikipedia.org/wiki/FASTQ_format))
- execute `Trimmomatic` on wiped reads to remove residual unpaired reads
- execute `BBmap (repair.sh)` on paired reads to fix the correct interleaving and sort fastq files.
#### Single-end files
`fix_wipe_single_reads_parallel.smk` and `fix_wipe_single_reads_sequential.smk` will not execute `trimmomatic` and BBmap's `repair.sh`.
- **Get a dry run** of a pipeline (e.g., `fix_wipe_single_reads_sequential.smk`):<br />
`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2 -np`
- **Generate the planned DAG**:<br />
`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --dag | dot -Tpdf > dag.pdf`<br /><br />
<img src="https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_single_reads.png?raw=true" width="200">
- **Run the pipeline** (n.b., The number of computing cores can be tuned accordingly):<br />
`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2`
# Author
**Tommaso Mazza**
[](https://twitter.com/irongraft)
Laboratory of Bioinformatics</br>
Fondazione IRCCS Casa Sollievo della Sofferenza</br>
Viale Regina Margherita 261 - 00198 Roma IT</br>
Tel: +39 06 44160526 - Fax: +39 06 44160548</br>
E-mail: t.mazza@css-mendel.it</br>
Web page: http://www.css-mendel.it</br>
Web page: http://bioinformatics.css-mendel.it</br>
Raw data
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"description": "# FastqWiper \n[](https://github.com/mazzalab/fastqwiper/actions/workflows/buildall_and_publish.yml) [](https://codecov.io/gh/mazzalab/fastqwiper) [](https://github.com/mazzalab/fastqwiper/issues) \n\n[](https://anaconda.org/bfxcss/fastqwiper) [](https://anaconda.org/bfxcss/fastqwiper) [](https://anaconda.org/bfxcss/fastqwiper) [](https://anaconda.org/bfxcss/fastqwiper)\n\n[](https://badge.fury.io/py/fastqwiper) [](https://pypi.python.org/pypi/fastqwiper/) \n\n[](https://hub.docker.com/r/mazzalab/fastqwiper) \n\n`FastqWiper` is a Snakemake-enabled application that wipes out bad reads from broken FASTQ files. Additionally, the available and pre-designed Snakemake [workflows](https://github.com/mazzalab/fastqwiper/tree/main/pipeline) allows **recovering** corrupted `fastq.gz`, **dropping** or **fixing** pesky lines, **removing** unpaired reads, and **fixing** reads interleaving.\n\n* Compatibility: Python \u22653.7, <3.11\n* OS: Windows, Linux, Mac OS (Snakemake workflows through Docker for Windows)\n* Contributions: [bioinformatics@css-mendel.it](bioinformatics@css-mendel.it)\n* Docker: https://hub.docker.com/r/mazzalab/fastqwiper\n* Singularity: https://cloud.sylabs.io/library/mazzalab/fastqwiper/fastqwiper.sif\n* Bug report: [https://github.com/mazzalab/fastqwiper/issues](https://github.com/mazzalab/fastqwiper/issues)\n\n\n## USAGE\n- **Case 1**. You have one or a couple (R1&R2) of **computer readable** FASTQ files which contain pesky, unformatted, uncompliant lines: Use *FastWiper* to clean them;\n- **Case 2**. You have one or a couple (R1&R2) of **computer readable** FASTQ files that you want to drop unpaired reads from or fix reads interleaving: Use the FastqWiper's *Snakemake workflows*;\n- **Case 3**. You have one `fastq.gz` file or a couple (R1&R2) of `fastq.gz` files which are corrupted (**unreadable**) and you want to recover healthy reads and reformat them: Use the FastqWiper's *Snakemake workflows*;\n\n\n## Installation\n### Case 1\nThis requires you to install FastqWiper and therefore does not require you to configure *workflows* also. You can do it for all OSs:\n\n#### Use Conda\n\n```\nconda create -n fastqwiper python=3.10\nconda activate fastqwiper\nconda install -c bfxcss -c conda-forge fastqwiper\n\nfastqwiper --help\n```\n*Hint: for an healthier experience, use* **mamba**\n\n\n#### Use Pypi\n```\npip install fastqwiper\n\nfastqwiper --help\n```\n<br/>\n\n`fastqwiper <options>`\n```\noptions:\n --fastq_in TEXT The input FASTQ file to be cleaned [required]\n --fastq_out TEXT The wiped FASTQ file [required]\n --log_frequency INTEGER The number of reads you want to print a status message\n --log_out TEXT The file name of the final quality report summary\n --help Show this message and exit.\n```\nIt accepts in input and outputs **readable** `*.fastq` or `*.fastq.gz` files.\n\n\n### Cases 2 & 3\nThere are <b>QUICK</b> and a <b>SLOW</b> methods to configure `FastqWiper`'s workflows.\n\n\n#### One quick way (Docker)\n1. Pull the Docker image from DockerHub:\n\n`docker pull mazzalab/fastqwiper`\n\n2. Once downloaded the image, type:\n\nCMD: `docker run --rm -ti --name fastqwiper -v \"YOUR_LOCAL_PATH_TO_DATA_FOLDER:/fastqwiper/data\" mazzalab/fastqwiper paired 8 sample 50000000`\n\n#### Another quick way (Singularity)\n1. Pull the Singularity image from the Cloud Library:\n\n`singularity pull library://mazzalab/fastqwiper/fastqwiper.sif`\n\n2. Once downloaded the image (e.g., fastqwiper.sif_2023.2.70.sif), type:\n\nCMD `singularity run --bind /scratch/tom/fastqwiper_singularity/data:/fastqwiper/data --writable-tmpfs fastqwiper.sif_2023.2.70.sif paired 8 sample 50000000`\n\nIf you want to bind the `.singularity` cache folder and the `logs` folder, you can omit `--writable-tmpfs`, create the folders `.singularity` and `logs` (`mkdir .singularity logs`) on the host system, and use this command instead:\n\nCMD: `singularity run --bind YOUR_LOCAL_PATH_TO_DATA_FOLDER/:/fastqwiper/data --bind YOUR_LOCAL_PATH_TO_.singularity_FOLDER/:/fastqwiper/.snakemake --bind YOUR_LOCAL_PATH_TO_LOGS_FOLDER/:/fastqwiper/logs fastqwiper.sif_2023.2.70.sif paired 8 sample 50000000`\n\nFor both **Docker** and **Singularity**:\n\n- `YOUR_LOCAL_PATH_TO_DATA_FOLDER` is the path of the folder where the fastq.gz files to be wiped are located;\n- `paired` triggers the cleaning of R1 and R2. Alternatively, `single` will trigger the wipe of individual FASTQ files;\n- `8` is the number of your choice of computing cores to be spawned;\n- `sample` is part of the names of the FASTQ files to be wiped. <b>Be aware</b> that: for <b>paired-end</b> files (e.g., \"sample_R1.fastq.gz\" and \"sample_R2.fastq.gz\"), your files must finish with `_R1.fastq.gz` and `_R2.fastq.gz`. Therefore, the argument to pass is everything before these texts: `sample` in this case. For <b>single end</b>/individual files (e.g., \"excerpt_R1_001.fastq.gz\"), your file must end with the string `.fastq.gz`; the preceding text, i.e., \"excerpt_R1_001\" in this case, will be the text to be passed to the command as an argument. \n- `50000000` is the number of rows-per-chunk (used when cores>1. It must be a number multiple of 4)\n\n\n#### The slow way (Linux & Mac OS)\nTo enable the use of preconfigured [pipelines](https://github.com/mazzalab/fastqwiper/tree/main/pipeline), you need to install **Snakemake**. The recommended way to install Snakemake is via Conda, because it enables **Snakemake** to [handle software dependencies of your workflow](https://snakemake.readthedocs.io/en/stable/snakefiles/deployment.html#integrated-package-management).\nHowever, the default conda solver is slow and often hangs. Therefore, we recommend installing [Mamba](https://github.com/mamba-org/mamba) as a drop-in replacement via\n\n`$ conda install -c conda-forge mamba`\n\nif you have anaconda/miniconda already installed, or directly installing `Mambaforge` as described [here](https://github.com/conda-forge/miniforge#mambaforge).\n\n\nThen, create and activate a clean environment as above:\n\n```\nmamba create -n fastqwiper python=3.10\nmamba activate fastqwiper\n```\nFinally, install a few dependencies:\n\n```\n$ mamba install -c bioconda snakemake\n$ mamba install colorama click\n```\n\n\n#### Usage\nClone the FastqWiper repository in a folder of your choice and enter it:\n\n```\ngit clone https://github.com/mazzalab/fastqwiper.git\ncd fastqwiper\n```\n\nIt contains, in particular, a folder `data` containing the fastq files to be processed, a folder `pipeline` containing the released pipelines and a folder `fastq_wiper` with the source files of `FastqWiper`. <br/>\nInput files to be processed should be copied into the **data** folder.\n\nCurrently, to run the `FastqWiper` pipelines, the following packages need to be installed manually:\n\n### required packages:\n[gzrt](https://github.com/arenn/gzrt) (Linux build fron source [instructions](https://github.com/arenn/gzrt/blob/master/README.build), Ubuntu install [instructions](https://howtoinstall.co/en/gzrt), Mac OS install [instructions](https://formulae.brew.sh/formula/gzrt))\n\n[BBTools](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/) (install [instructions](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/installation-guide/))\n\nIf installed from source, `gzrt` scripts need to be put on PATH. `bbmap` must be installed in the root folder of FastqWiper, as the image below\n\n\n\n### Commands:\nCopy the fastq files you want to fix in the `data` folder.\n**N.b.**: In all commands above, you will pass to the workflow the name of the sample to be analyzed through the config argument: `sample_name`. Remember that your fastq files' names must finish with `_R1.fastq.gz` and `_R2.fastq.gz`, for paired fastq files, and with `.fastq.gz`, for individual fastq files, and, therefore, the text to be assigned to the variable `sample_name` must be everything before them. E.g., if your files are `my_sample_R1.fastq.gz` and `my_sample_R2.fastq.gz`, then `--config sample_name=my_sample`.\n\n#### Paired-end files\n\n- **Get a dry run** of a pipeline (e.g., `fix_wipe_pairs_reads_sequential.smk`):<br />\n`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_pairs_reads_sequential.smk --use-conda --cores 4`\n\n- **Generate the planned DAG**:<br />\n`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_pairs_reads_sequential.smk --dag | dot -Tpdf > dag.pdf`<br /> <br />\n<img src=\"https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_pairs_reads.png?raw=true\" width=\"400\">\n\n- **Run the pipeline** (n.b., during the first execution, Snakemake will download and install some required remote packages and may take longer). The number of computing cores can be tuned accordingly:<br />\n`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2`\n\nFixed files will be copied in the `data` folder and will be suffixed with the string `_fixed_wiped_paired_interleaving`.\nWe remind that the `fix_wipe_pairs_reads_sequential.smk` and `fix_wipe_pairs_reads_parallel.smk` pipelines perform the following actions:\n- execute `gzrt` on corrupted fastq.gz files (i.e., that cannot be unzipped because of errors) and recover readable reads;\n- execute `FastqWiper` on recovered reads to make them compliant with the FASTQ format (source: [Wipipedia](https://en.wikipedia.org/wiki/FASTQ_format))\n- execute `Trimmomatic` on wiped reads to remove residual unpaired reads\n- execute `BBmap (repair.sh)` on paired reads to fix the correct interleaving and sort fastq files. \n\n#### Single-end files\n`fix_wipe_single_reads_parallel.smk` and `fix_wipe_single_reads_sequential.smk` will not execute `trimmomatic` and BBmap's `repair.sh`.\n\n- **Get a dry run** of a pipeline (e.g., `fix_wipe_single_reads_sequential.smk`):<br />\n`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2 -np`\n\n- **Generate the planned DAG**:<br />\n`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --dag | dot -Tpdf > dag.pdf`<br /><br />\n<img src=\"https://github.com/mazzalab/fastqwiper/blob/main/pipeline/fix_wipe_single_reads.png?raw=true\" width=\"200\">\n\n- **Run the pipeline** (n.b., The number of computing cores can be tuned accordingly):<br />\n`snakemake --config sample_name=my_sample -s pipeline/fix_wipe_single_reads_sequential.smk --use-conda --cores 2`\n\n# Author\n**Tommaso Mazza** \n[](https://twitter.com/irongraft)\n\nLaboratory of Bioinformatics</br>\nFondazione IRCCS Casa Sollievo della Sofferenza</br>\nViale Regina Margherita 261 - 00198 Roma IT</br>\nTel: +39 06 44160526 - Fax: +39 06 44160548</br>\nE-mail: t.mazza@css-mendel.it</br>\nWeb page: http://www.css-mendel.it</br>\nWeb page: http://bioinformatics.css-mendel.it</br>\n",
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"bitbucket": false,
"codeberg": false,
"github_user": "mazzalab",
"github_project": "fastqwiper",
"travis_ci": false,
"coveralls": true,
"github_actions": true,
"requirements": [],
"lcname": "fastqwiper"
}
JSON
