nanocompore_pipeline

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nanocompore_pipeline

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Nextflow pipeline for nanocompore analysis

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# Nanocompore pipeline
This repo contains the code for running the [Nanocompore](https://github.com/tleonardi/nancompore) analysis workflow using [Nextflow](https://www.nextflow.io/).

## Pipeline steps
The pipeline runs the following steps:

* Basecalls the raw fast5 files using Guppy
* Run [pycoQC](https://github.com/a-slide/pycoQC) on Guppy's output
* (Optionally) Prepare a fasta file for the reference transcriptome from the genome fasta and transcriptome annotation in GTF
* Map the basecalled data to the reference using [minimap2](https://github.com/lh3/minimap2)
* Realign the raw signal-level data to the kmers of the reference with [f5c](https://github.com/hasindu2008/f5c)
* Collapse Nanopolish output by kmer using Nanocompore's eventalign_collapse
* Run Nanocompore

All steps are executed in a Docker/Singularity containers.

## How to run

### Sample annotation
Prepare a tab-separated file that describes the samples:
```
SampleName  Condition DataPath
WT1         WT        /path/to/fast5dir
WT2         WT        /path/to/fast5dir
KD1         KD        /path/to/fast5dir
KD2         KD        /path/to/fast5dir
```

### Configure the pipeline
Configure the pipeline by editing the _nextflow.config_ file.

All parameters are described in the comments and should be self explanatory.

The only two options that deserve an explanation are `target_trancripts` and `input_is_basecalled`.
The former allows you to provide the path to a text file that lists Ensembl transcript IDs of interest. 
Any transcript not present in this list will be discarded from the reference.
`input_is_basecalled` allows you to start the pipeline __after__ the Albacore step. In order for this to work,
the paths in the sample annotation file must point to basecalled fast5 files.

### Run the pipeline
To run the pipeline just execute:
`nextflow run pipeline.nf`

### Profiles
The pipeline is shipped with 3 profiles: local (default), lsf and aws_batch, which respectively run the processing on the local
machine, an LSF cluster or an AWS Batch computer environment.
The local profile limits the number of cuncurrent tasks to 10, which is reduced to 2 and 3 for Guppy and f5c respectively.
The LSF profile if configured to use dynamic memory management.

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