pipeline-umi-amplicon

Overview

pipeline-umi-amplicon is a pipeline for generating high accuracy single molecule reads using unique molecular identifiers (UMIs) from amplicon data. The pipeline accepts FASTQ-format sequence files as input and outputs both aligned reads and QC stats.

Features

The pipeline performs the following steps:

Reads are mapped to reference genome using minimap2
Separate into amplicons
Extract UMI sequences for all reads
Cluster UMI sequences per amplicon using vsearch and compute high accuracy consensus reads
Align high accuracy conesnsus reads and perform simple variant calling (optional)

Getting Started

Requirements

The following software packages must be installed prior to running:

miniconda3 - please refer to installation instructions.

Installation

After installing miniconda3, install the pipeline as follows:

# Get pipeline
git clone https://github.com/nanoporetech/pipeline-umi-amplicon.git 
# Change to directory
cd pipeline-umi-amplicon
# Create conda environment with all dependencies
conda env create -f environment.yml
# Activate environment
conda activate pipeline-umi-amplicon
# Install python packages provided by pipeline-umi-amplicon
cd lib && pip install . && cd ..

# To test if the installation was successful run
snakemake -j 1 -pr --configfile config.yml
# Deactivate environment
conda deactivate

Input

To run the pipeline the following input files are required:

Input	Description
Reference genome	FASTA file containing the reference genome (e.g. GRCh38 for human)
Nanopore reads	Folder containing FASTQ files or a single concatenated FASTQ file.
Targets / Amplicons	A BED file containing the chromosome, start and end coordinate and the name of all amplicons

BED format

Tab separated and needs a unique name:

chr1    107167322       107168239       target_a_chr1_107167756_T_C

Output

The main output files created by the pipeline are:

Output	Description
Aligned reads	Aligned reads in indexed and sorted BAM format
Variant calls (optional)	Called variants in VCF format

After the a pipeline analysis has completed, the aligned reads can be found at {output_folder}/{run_name}/align/{amplicon_name}_final.bam e.g. example_egfr_single_read_run/align/EGFR_917_final.bam.

Usage:

To run the pipeline with default settings invoke snakemake as follows.

$ snakemake -j 30 reads --configfile config.yml

-j specifies how many CPU cores will be used by the pipeline. reads is the default target (see Targets); this will run all steps required to produce aligned high accuracy consensus reads. Please see the example config files for the required parameters.

Targets

Name	Description
reads	Only prodcues high accuracy consensus and align them to the reference
variants	Same as `reads` + calls variants using `varscan2`

Options

The pipeline accepts several input parameters. They can either be changed in the config.yml file or specified when running snakemake.

For example:

snakemake -j 30 reads --config input_fastq=data reference_fasta=data/example_egfr_reference.fasta targets_bed=data/example_egfr_amplicon.bed

Required parameters

These parameters have to be specified to run the pipeline.

Parameter	Allowed	Description
sample_name	String	Name of the output folder
input_fastq	Absolute file path	FASTQ file or folder containing FASTQ files
reference_fasta	Absolute file path	FASTA file containing the reference genome
targets_bed	Absolute file path	BED file containing amplicon coordinates and names

Optional parameters

See config.yml

Help

Licence and Copyright

This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

References and Supporting Information

If you use this pipeline please cite:

Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34(18), 3094–3100. https://doi.org/10.1093/bioinformatics/bty191
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and 1000 Genome Project Data Processing Subgroup, The Sequence alignment/map (SAM) format and SAMtools, Bioinformatics (2009) 25(16) 2078-9 [19505943]
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. doi: 10.7717/peerj.2584
For optional variant calling: Koboldt, D., Zhang, Q., Larson, D., Shen, D., McLellan, M., Lin, L., Miller, C., Mardis, E., Ding, L., & Wilson, R. (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing Genome Research DOI: 10.1101/gr.129684.111

Research Release

Research releases are provided as technology demonstrators to provide early access to features or stimulate Community development of tools. Support for this software will be minimal and is only provided directly by the developers. Feature requests, improvements, and discussions are welcome and can be implemented by forking and pull requests. However much as we would like to rectify every issue and piece of feedback users may have, the developers may have limited resource for support of this software. Research releases may be unstable and subject to rapid iteration by Oxford Nanopore Technologies.

TODO

Number of reads collapsed in the header of the bam file
Check that balance reads is working as its meant to
Add handling of errors if no reads aligned to a reference
Add stats output and plots, number of umis, umi distribution, snps plot umi vs raw, read alignment stats

Done

I added a fastq filter options (min read length and average quality) using filtlong
The user can also now pass options to the variant calling using the config file
It runs as before when in defaults mode

nanoporetech / pipeline-umi-amplicon

readme