# nf-core/hic: Usage

## Running the pipeline

The typical command for running the pipeline is as follows:

```bash
nextflow run nf-core/hic --input '*_R{1,2}.fastq.gz' -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
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.
```

### 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/hic
```

### Reproducibility

It's 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.

It's 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/hic releases page](https://github.com/nf-core/hic/releases) and find
the latest version number - numeric only (eg. `1.3.1`).
Then specify this when running the pipeline with `-r` (one hyphen)
eg. `-r 1.3.1`.

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.

### 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.

## 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, 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.

* `docker`
  * A generic configuration profile to be used with [Docker](https://docker.com/)
  * Pulls software from Docker Hub: [`nfcore/hic`](https://hub.docker.com/r/nfcore/hic/)
* `singularity`
  * A generic configuration profile to be used with [Singularity](https://sylabs.io/docs/)
  * Pulls software from Docker Hub: [`nfcore/hic`](https://hub.docker.com/r/nfcore/hic/)
* `conda`
  * Please only use Conda as a last resort i.e. when it's not possible to run the
  pipeline with Docker or Singularity.
  * A generic configuration profile to be used with [Conda](https://conda.io/docs/)
  * Pulls most software from [Bioconda](https://bioconda.github.io/)
* `test`
  * A profile with a complete configuration for automated testing
  * Includes links to test data so needs no other parameters

### `-resume`

Specify this when restarting a pipeline. Nextflow will used cached results from
any pipeline steps where the inputs are the same, continuing from where it got
to previously.
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 resource 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
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.

Whilst these default requirements will hopefully work for most people with most data,
you may find that you want to customise the compute resources that the pipeline requests.
You can do this by creating a custom config file. For example, to give the workflow
process `star` 32GB of memory, you could use the following config:

```nextflow
process {
  withName: star {
    memory = 32.GB
  }
}
```

See the main [Nextflow documentation](https://www.nextflow.io/docs/latest/config.html)
for more information.

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) on the
[`#configs` channel](https://nfcore.slack.com/channels/configs).

### 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'
```

## Inputs

### `--input`

Use this to specify the location of your input FastQ files. For example:

```bash
--input 'path/to/data/sample_*_{1,2}.fastq'
```

Please note the following requirements:

1. The path must be enclosed in quotes
2. The path must have at least one `*` wildcard character
3. When using the pipeline with paired end data, the path must use `{1,2}`
notation to specify read pairs.

If left unspecified, a default pattern is used: `data/*{1,2}.fastq.gz`

By default, the pipeline expects paired-end data. If you have single-end data,
you need to specify `--single_end` on the command line when you launch the pipeline.
A normal glob pattern, enclosed in quotation marks, can then be used for `--input`.
For example:

```bash
--single_end --reads '*.fastq'
```

It is not possible to run a mixture of single-end and paired-end files in one run.

## Reference genomes

The pipeline config files come bundled with paths to the illumina iGenomes reference
index files. If running with docker or AWS, the configuration is set up to use the
[AWS-iGenomes](https://ewels.github.io/AWS-iGenomes/) resource.

### `--genome` (using iGenomes)

There are many different species supported in the iGenomes references. To run
the pipeline, you must specify which to use with the `--genome` flag.

You can find the keys to specify the genomes in the
[iGenomes config file](../conf/igenomes.config).

### `--fasta`

If you prefer, you can specify the full path to your reference genome when you
run the pipeline:

```bash
--fasta '[path to Fasta reference]'
```

### `--bwt2_index`

The bowtie2 indexes are required to run the Hi-C pipeline. If the
`--bwt2_index` is not specified, the pipeline will either use the igenome
bowtie2 indexes (see `--genome` option) or build the indexes on-the-fly
(see `--fasta` option)

```bash
--bwt2_index '[path to bowtie2 index (with basename)]'
```

### `--chromosome_size`

The Hi-C pipeline will also requires a two-columns text file with the
chromosome name and its size (tab separated).
If not specified, this file will be automatically created by the pipeline.
In the latter case, the `--fasta` reference genome has to be specified.

```bash
   chr1    249250621
   chr2    243199373
   chr3    198022430
   chr4    191154276
   chr5    180915260
   chr6    171115067
   chr7    159138663
   chr8    146364022
   chr9    141213431
   chr10   135534747
   (...)
```

```bash
--chromosome_size '[path to chromosome size file]'
```

### `--restriction_fragments`

Finally, Hi-C experiments based on restriction enzyme digestion requires a BED
file with coordinates of restriction fragments.

```bash
   chr1   0       16007   HIC_chr1_1    0   +
   chr1   16007   24571   HIC_chr1_2    0   +
   chr1   24571   27981   HIC_chr1_3    0   +
   chr1   27981   30429   HIC_chr1_4    0   +
   chr1   30429   32153   HIC_chr1_5    0   +
   chr1   32153   32774   HIC_chr1_6    0   +
   chr1   32774   37752   HIC_chr1_7    0   +
   chr1   37752   38369   HIC_chr1_8    0   +
   chr1   38369   38791   HIC_chr1_9    0   +
   chr1   38791   39255   HIC_chr1_10   0   +
   (...)
```

If not specified, this file will be automatically created by the pipline.
In this case, the `--fasta` reference genome will be used.
Note that the `--restriction_site` parameter is mandatory to create this file.

## Hi-C specific options

The following options are defined in the `hicpro.config` file, and can be
updated either using a custom configuration file (see `-c` option) or using
command line parameter.

### Reads mapping

The reads mapping is currently based on the two-steps strategy implemented in
the HiC-pro pipeline. The idea is to first align reads from end-to-end.
Reads that do not aligned are then trimmed at the ligation site, and their 5'
end is re-aligned to the reference genome.
Note that the default option are quite stringent, and can be updated according
to the reads quality or the reference genome.

#### `--bwt2_opts_end2end`

Bowtie2 alignment option for end-to-end mapping.
Default: '--very-sensitive -L 30 --score-min L,-0.6,-0.2 --end-to-end
--reorder'

```bash
--bwt2_opts_end2end '[Options for bowtie2 step1 mapping on full reads]'
```

#### `--bwt2_opts_trimmed`

Bowtie2 alignment option for trimmed reads mapping (step 2).
Default: '--very-sensitive -L 20 --score-min L,-0.6,-0.2 --end-to-end
--reorder'

```bash
--bwt2_opts_trimmed '[Options for bowtie2 step2 mapping on trimmed reads]'
```

#### `--min_mapq`

Minimum mapping quality. Reads with lower quality are discarded. Default: 10

```bash
--min_mapq '[Minimum quality value]'
```

### Digestion Hi-C

#### `--restriction_site`

Restriction motif(s) for Hi-C digestion protocol. The restriction motif(s)
is(are) used to generate the list of restriction fragments.
The precise cutting site of the restriction enzyme has to be specified using
the '^' character. Default: 'A^AGCTT'
Here are a few examples:

* MboI: ^GATC
* DpnII: ^GATC
* BglII: A^GATCT
* HindIII: A^AGCTT
* ARIMA kit: ^GATC,G^ANTC

Note that multiples restriction motifs can be provided (comma-separated) and
that 'N' base are supported.

```bash
--restriction_size '[Cutting motif]'
```

#### `--ligation_site`

Ligation motif after reads ligation. This motif is used for reads trimming and
depends on the fill in strategy.
Note that multiple ligation sites can be specified (comma separated) and that
'N' base is interpreted and replaced by 'A','C','G','T'.
Default: 'AAGCTAGCTT'

```bash
--ligation_site '[Ligation motif]'
```

Exemple of the ARIMA kit: GATCGATC,GANTGATC,GANTANTC,GATCANTC

#### `--min_restriction_fragment_size`

Minimum size of restriction fragments to consider for the Hi-C processing.
Default: ''

```bash
--min_restriction_fragment_size '[numeric]'
```

#### `--max_restriction_fragment_size`

Maximum size of restriction fragments to consider for the Hi-C processing.
Default: ''

```bash
--max_restriction_fragment_size '[numeric]'
```

#### `--min_insert_size`

Minimum reads insert size. Shorter 3C products are discarded.
Default: ''

```bash
--min_insert_size '[numeric]'
```

#### `--max_insert_size`

Maximum reads insert size. Longer 3C products are discarded.
Default: ''

```bash
--max_insert_size '[numeric]'
```

### DNAse Hi-C

#### `--dnase`

In DNAse Hi-C mode, all options related to digestion Hi-C
(see previous section) are ignored.
In this case, it is highly recommanded to use the `--min_cis_dist` parameter
to remove spurious ligation products.

```bash
--dnase'
```

### Hi-C processing

#### `--min_cis_dist`

Filter short range contact below the specified distance.
Mainly useful for DNase Hi-C. Default: ''

```bash
--min_cis_dist '[numeric]'
```

#### `--rm_singleton`

If specified, singleton reads are discarded at the mapping step.

```bash
--rm_singleton
```

#### `--rm_dup`

If specified, duplicates reads are discarded before building contact maps.

```bash
--rm_dup
```

#### `--rm_multi`

If specified, reads that aligned multiple times on the genome are discarded.
Note the default mapping options are based on random hit assignment, meaning
that only one position is kept per read.

```bash
--rm_multi
```

## Genome-wide contact maps

### `--bin_size`

Resolution of contact maps to generate (space separated).
Default:'1000000,500000'

```bash
--bins_size '[numeric]'
```

### `--ice_max_iter`

Maximum number of iteration for ICE normalization.
Default: 100

```bash
--ice_max_iter '[numeric]'
```

### `--ice_filer_low_count_perc`

Define which pourcentage of bins with low counts should be force to zero.
Default: 0.02

```bash
--ice_filter_low_count_perc '[numeric]'
```

### `--ice_filer_high_count_perc`

Define which pourcentage of bins with low counts should be discarded before
normalization. Default: 0

```bash
--ice_filter_high_count_perc '[numeric]'
```

### `--ice_eps`

The relative increment in the results before declaring convergence for ICE
normalization. Default: 0.1

```bash
--ice_eps '[numeric]'
```

## Inputs/Outputs

### `--splitFastq`

By default, the nf-core Hi-C pipeline expects one read pairs per sample.
However, for large Hi-C data processing single fastq files can be very
time consuming.
The `--splitFastq` option allows to automatically split input read pairs
into chunks of reads. In this case, all chunks will be processed in parallel
and merged before generating the contact maps, thus leading to a significant
increase of processing performance.

```bash
--splitFastq '[Number of reads per chunk]'
```

### `--saveReference`

If specified, annotation files automatically generated from the `--fasta` file
are exported in the results folder. Default: false

```bash
--saveReference
```

### `--saveAlignedIntermediates`

If specified, all intermediate mapping files are saved and exported in the
results folder. Default: false

```bash
--saveReference
```

### `--saveInteractionBAM`

If specified, write a BAM file with all classified reads (valid paires,
dangling end, self-circle, etc.) and its tags.

## Skip options

### `--skipMaps`

If defined, the workflow stops with the list of valid interactions, and the
genome-wide maps are not built. Usefult for capture-C analysis. Default: false

```bash
--skipMaps
```

### `--skipIce`

If defined, the ICE normalization is not run on the raw contact maps.
Default: false

```bash
--skipIce
```

### `--skipCool`

If defined, cooler files are not generated. Default: false

```bash
--skipCool
```

### `--skipMultiQC`

If defined, the MultiQC report is not generated. Default: false

```bash
--skipMultiQC
```