# nf-core/bamtofastq: Usage

## :warning: Please read this documentation on the nf-core website: [https://nf-co.re/bamtofastq/usage](https://nf-co.re/bamtofastq/usage)

> _Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files._

## Introduction

Bamtofastq is a workflow designed to convert one or multiple bam/cram files into fastq format.

## Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

```bash
--input '[path to samplesheet file]'
```

### Full samplesheet

The pipeline will auto-detect whether a sample is single- or paired-end. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below. If the index files are not available, the files will be automatically indexed during the pipeline run which can have an effect on the runtime.

```console
sample_id,mapped,index,file_type
test1,test1.cram,test1.cram.crai,cram
test2,test2.cram,test2.cram.crai,cram
```

| Column     | Description                                                                                                       |
| ---------- | ----------------------------------------------------------------------------------------------------------------- |
| `sample`   | Custom sample name.                                                                                               |
| `mapped`   | Absolute path to input BAM/CRAM file. Allowed file extensions: ".bam" or ".cram".                                 |
| `index`    | If available, provide full path to input BAI/CRAI index file. File extensions must be ".bam.bai" or ".cram.crai". |
| `filetype` | Type of input file. Options: "bam" or "cram".                                                                     |

An [example samplesheet](../assets/samplesheet.csv) has been provided with the pipeline.

## Running the pipeline

The typical command for running the pipeline is as follows:

```bash
nextflow run nf-core/bamtofastq --input 'samplesheet.csv' --outdir <OUTDIR>  -profile docker
```

This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

```bash
work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.
```

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a `yaml` or `json` file via `-params-file <file>`.

> ⚠️ Do not use `-c <file>` to specify parameters as this will result in errors. Custom config files specified with `-c` must only be used for [tuning process resource specifications](https://nf-co.re/docs/usage/configuration#tuning-workflow-resources), other infrastructural tweaks (such as output directories), or module arguments (args).
> The above pipeline run specified with a params file in yaml format:

```bash
nextflow run nf-core/bamtofastq -profile docker -params-file params.yaml
```

with `params.yaml` containing:

```yaml
input:  './samplesheet.csv'
outdir: './results/'
fasta:  './reference.fasta'
<...>
```

You can also generate such `YAML`/`JSON` files via [nf-core/launch](https://nf-co.re/launch).

### Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

```bash
nextflow pull nf-core/bamtofastq
```

### Reproducibility

It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the [nf-core/bamtofastq releases page](https://github.com/nf-core/bamtofastq/releases) and find the latest pipeline version - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. Of course, you can switch to another version by changing the number after the `-r` flag.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducbility, you can use share and re-use [parameter files](#running-the-pipeline) to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

> 💡 If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

## Core Nextflow arguments

> **NB:** These options are part of Nextflow and use a _single_ hyphen (pipeline parameters use a double-hyphen).

### `-profile`

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

> We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation).

Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important!
They are loaded in sequence, so later profiles can overwrite earlier profiles.

If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer enviroment.

- `test`
  - A profile with a complete configuration for automated testing
  - Includes links to test data so needs no other parameters
- `docker`
  - A generic configuration profile to be used with [Docker](https://docker.com/)
- `singularity`
  - A generic configuration profile to be used with [Singularity](https://sylabs.io/docs/)
- `podman`
  - A generic configuration profile to be used with [Podman](https://podman.io/)
- `shifter`
  - A generic configuration profile to be used with [Shifter](https://nersc.gitlab.io/development/shifter/how-to-use/)
- `charliecloud`
  - A generic configuration profile to be used with [Charliecloud](https://hpc.github.io/charliecloud/)
- `apptainer`
  - A generic configuration profile to be used with [Apptainer](https://apptainer.org/)
- `conda`
  - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter or Charliecloud.

### `--input`

Use this to specify the location of your input BAM/CRAM files. For example:

```bash
--input 'path/to/samplesheet.csv'
```

### `--fasta`

When converting a CRAM file the fasta file specified in the CRAM header should be used to decompress the file. If that file is not available, you will need to specify an alternative path using the [`--fasta`](#--fasta) option.
You can check which reference FASTA file should be used by inspecting the CRAM file with the following command:

```bash
samtools view -H path/to/sample.cram | grep '@SQ'.
```

To specify a reference genome FASTA you can follow the command below:

```bash
--input 'path/to/samplesheet.csv' --fasta 'ftp://ftp.broadinstitute.org/pub/seq/references/Homo_sapiens_assembly19.fasta'
```

### `--chr` (optional)

Use to only obtain reads mapping to a specific chromosome or region.

> It is important to specify the chromosome or region name **exactly** as set in the bam/cram file. Otherwise no reads may be extracted!

For example:

```bash
--chr 'X chrX'
```

This extracts reads mapping to `X` as well as `chrX`.
To check beforehand which chromosome notation is used in your bam/cram file you can use samtools.

```bash
samtools idxstats your_input.[bam|cram] | head -n 25
```

### `--no_read_QC` (optional)

Use to skip `FastQC` on obtained reads. This is useful, when the reads are used as input in another pipeline, which runs `QC` on its input data as well.

```bash
--no_read_QC
```

### `--samtools_collate_fast` (optional)

Use to specify the fast mode for the `samtools collate` command in the processes `COLLATE_FASTQ_MAP`, `COLLATE_FASTQ_UNMAP` and `SAMTOOLS_COLLATEFASTQ_SINGLE_END`. This option relies on the samtools command line flags `-f -r INT` and will output primary alignments only. For full documentation of this mode please refer to the [samtools documentation](http://www.htslib.org/doc/samtools-collate.html#OPTIONS).

### `--reads_in_memory` (optional)

Only relevant in combination with `--samtools_collate_fast`. It specifies how many alignment reads are kept in memory [default = '100000']. This is useful for speeding up the processes `COLLATE_FASTQ_MAP`, `COLLATE_FASTQ_UNMAP` and `SAMTOOLS_COLLATEFASTQ_SINGLE_END`.

Example:

```bash
--samtools_collate_fast --reads_in_memory '1000000'
```

### `--no_stats` (optional)

Use to skip `FastQC` on both input bam/cram and output reads, as well as all processes that compute statistics `samtools flagstat`, `samtools idxstats`, and `samtools stats`. This is useful for large datasets, since the quality metrics processes require a significant amount of time and resources.

:exclamation: Use this at own risk. You won't be able to quickly sanity check the results.

```bash
--no_stats
```

## Job resources

### Automatic resubmission

Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with an error code of `143` (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

### Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at [`nf-core/configs`](https://github.com/nf-core/configs/tree/master/conf) for examples.

If you are likely to be running `nf-core` pipelines regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter (see definition below). You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile.

If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack/).

## AWS Batch specific parameters

Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use the `-awsbatch` profile and then specify all of the following parameters.

### `--awsqueue`

The JobQueue that you intend to use on AWS Batch.

### `--awsregion`

The AWS region to run your job in. Default is set to `eu-west-1` but can be adjusted to your needs.

Please make sure to also set the `-w/--work-dir` and `--outdir` parameters to a S3 storage bucket of your choice - you'll get an error message notifying you if you didn't.

## Other command line parameters

### `--outdir`

The output directory where the results will be saved.

### `--email`

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (`~/.nextflow/config`) then you don't need to specify this on the command line for every run.

### `--email_on_fail`

This works exactly as with `--email`, except emails are only sent if the workflow is not successful.

### `-name`

Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always).

**NB:** Single hyphen (core Nextflow option)

### `-resume`

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html).

You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names.

### `-c`

Specify the path to a specific config file (this is a core Nextflow command). See the [nf-core website documentation](https://nf-co.re/usage/configuration) for more information.

## Custom configuration

### Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified [here](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L18) it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the [max resources](https://nf-co.re/docs/usage/configuration#max-resources) and [tuning workflow resources](https://nf-co.re/docs/usage/configuration#tuning-workflow-resources) section of the nf-core website.

### Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses for a particular tool. By default nf-core pipelines use containers and software from the [biocontainers](https://biocontainers.pro/) or [bioconda](https://bioconda.github.io/) projects. However in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the [updating tool versions](https://nf-co.re/docs/usage/configuration#updating-tool-versions) section of the nf-core website.

### Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the [customising tool arguments](https://nf-co.re/docs/usage/configuration#customising-tool-arguments) section of the nf-core website.

### nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter. You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile.

See the main [Nextflow documentation](https://www.nextflow.io/docs/latest/config.html) for more information about creating your own configuration files.

If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack) on the [`#configs` channel](https://nfcore.slack.com/channels/configs).

## Azure Resource Requests

To be used with the `azurebatch` profile by specifying the `-profile azurebatch`.
We recommend providing a compute `params.vm_type` of `Standard_D16_v3` VMs by default but these options can be changed if required.

Note that the choice of VM size depends on your quota and the overall workload during the analysis.
For a thorough list, please refer the [Azure Sizes for virtual machines in Azure](https://docs.microsoft.com/en-us/azure/virtual-machines/sizes).

## Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow `-bg` flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use `screen` / `tmux` or similar tool to create a detached session which you can log back into at a later time.
Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

## Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory.
We recommend adding the following line to your environment to limit this (typically in `~/.bashrc` or `~./bash_profile`):

```bash
NXF_OPTS='-Xms1g -Xmx4g'
```
