Nanocompore pipeline

This repo contains the code for running the Nanocompore analysis workflow using Nextflow.

Pipeline steps

The pipeline runs the following steps:

• Basecalls the raw fast5 files using Guppy
• Run 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
• Realign the raw signal-level data to the kmers of the reference with 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.