daspacio9/pooledpremiumpcrpipeline2

A snakemake pipeline for multiplex sequencing using the Plasmidsaurus Premium PCR service

Overview

Latest release: Downsample, Last update: 2026-05-09

Share link: https://snakemake.github.io/snakemake-workflow-catalog?wf=daspacio9/pooledpremiumpcrpipeline2

Quality control: linting: passed formatting: failed

Deployment

Step 1: Install Snakemake and Snakedeploy

Snakemake and Snakedeploy are best installed via the Conda package manager. It is recommended to install conda via Miniforge. Run

conda create -c conda-forge -c bioconda -c nodefaults --name snakemake snakemake snakedeploy

to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via

conda activate snakemake

For other installation methods, refer to the Snakemake and Snakedeploy documentation.

Step 2: Deploy workflow

With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:

mkdir -p path/to/project-workdir
cd path/to/project-workdir

In all following steps, we will assume that you are inside of that directory. Then run

snakedeploy deploy-workflow https://github.com/daspacio9/pooledpremiumpcrpipeline2 . --tag Downsample

Snakedeploy will create two folders, workflow and config. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.

Step 3: Configure workflow

To configure the workflow, adapt config/config.yml to your needs following the instructions below.

Step 4: Run workflow

The deployment method is controlled using the --software-deployment-method (short --sdm) argument.

Snakemake will automatically detect the main Snakefile in the workflow subfolder and execute the workflow module that has been defined by the deployment in step 2.

For further options such as cluster and cloud execution, see the docs.

Step 5: Generate report

After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using

snakemake --report report.zip

Configuration

The following section is imported from the workflow’s config/README.md.

Configuration Overview

The Pooled Premium PCR Pipeline 2 is a Snakemake workflow designed for demultiplexing and consensus sequence generation from pooled barcoded samples submitted to the Plasmidsaurus Premium PCR service. Configuration is managed through two main files:

  • config.yaml - Main workflow parameters and settings

  • ref/*

The workflow performs the following key steps:

  1. Prepare reference barcode-pairs file from supplied primer and barcode data (when prepare_barcode_reference with –force flag is provided)

  2. Demultiplex pooled sequencing data using barcode pairs

  3. Generate consensus sequences for each barcode group

  4. Produce synthetic AB1 trace files and alignment reports for each barcode group

  5. Generate quality statistics and coverage plots

Configuration Parameters

Main Pipeline Settings (config.yaml)

Parameter

Type

Description

Example

min_depth

integer

Minimum read depth for consensus generation

10

input_fastq

string

Path to input FASTQ file in sequences/ folder

26_03_05_DA487-492_S2R3LF_1_1.fastq.gz

primer_consensus

string

FASTA file with primer sequences in ref/ folder

cole1-primer-consensus.fasta

barcode_groups

string

CSV file defining barcode groups in ref/ folder

cole1-barcode-groups-48.csv

barcodes

string

FASTA file with barcode sequences in ref/ folder

cole1-barcodes-48.fasta

filter_size

integer

Minimum read length (bp) after adapter trimming

1500

cutadapt_min_overlap

integer

Minimum matching bases between read and adapter

15

cutadapt_error_rate

float

Maximum allowed error rate for adapter matching (0-1)

0.1

medaka_model

string

Medaka consensus polishing model

r1041_e82_400bps_hac_v5.0.0

medaka_spoa_threads

integer

Number of parallel threads for consensus generation

8

debug

boolean

Enable debug mode for verbose logging

False

Input Data Structure

Reference Files (in ref/ folder)

The pipeline requires three reference files for barcode-based demultiplexing:

  • cole1-primer-consensus.fasta - Consensus sequences for primer sets

  • cole1-barcode-groups-48.csv - Mapping of barcodes to sample groups

  • cole1-barcodes-48.fasta - FASTA sequences of all barcodes

For different barcode systems (e.g., non-colE1 plasmids), create a new working directory and prepare different reference files using the prepare_barcode_reference rule.

Sequencing Data (in sequences/ folder)

Place your demultiplexed or pooled FASTQ files (gzipped):

sequences/
├── sample_batch_1.fastq.gz
├── sample_batch_2.fastq.gz
└── ...

Advanced Parameters

Parameter

Type

Default

Notes

cutadapt_min_overlap

integer

15

Affects barcode matching stringency. Lower values are more permissive.

cutadapt_error_rate

float

0.1

Allows ~1.2 mismatches in 6bp stretch, ~3 mismatches in 15bp

filter_size

integer

1500

Filters out adapter dimers and very short reads

medaka_model

string

r1041_e82_400bps_hac_v5.0.0

Must match your sequencing device; this is optimized for oxford nanopore r1041

medaka_spoa_threads

integer

8

Adjust based on available CPU resources

Output Structure

The workflow generates outputs in the following directory structure:

├── ab1/                           # Synthetic AB1 trace files (ABIF format, max 5kb chunks)
│   ├── group_1_0.ab1
│   └── ...
├── consensus/                     # Consensus sequences
│   ├── group_1_consensus.fastq
│   └── ...
├── aln/                           # Alignment files
│   └── *.bam
├── consensus_split/               # Split consensus sequences by length
├── demux/                         # Demultiplexed reads
├── logs/                          # Processing logs
├── report/                        # Analysis reports
│   ├── coverage.pdf
│   ├── mismatch_freq.pdf
│   └── ...
├── sequences/                     # Processed sequences
├── demux_stats.csv                # Summary statistics table
└── consensus_summary.csv          # Consensus sequence metadata

Key Output Files

File

Description

Usage

*.ab1

Synthetic chromatogram trace files

Open in Benchling, SnapGene, APE for alignment and confidence checking

group_*_consensus.fastq

Consensus sequences in FASTQ format

Alignment to reference plasmid

consensus_summary.csv

Metadata for all consensus sequences

Summary of results and confidence metrics

coverage.pdf

Coverage depth plot

Visualize read mapping across consensus

mismatch_freq.pdf

Mismatch frequency plot

Identify problematic regions

demux_stats.csv

Barcode demultiplexing statistics

Track read counts per barcode group

Usage Examples

Running the Workflow

First, prepare the reference files (if using a new barcode system):

snakemake -s ../workflow/Snakefile -j 4 prepare_barcode_reference --use-conda --force

Then run the main demultiplexing and consensus workflow:

# Dry run to check for errors
snakemake -s ../workflow/Snakefile -j 4 --use-conda -np

# Execute the workflow
snakemake -s ../workflow/Snakefile -j 4 --use-conda -p

Reusing a Working Directory

To restart with new input data while keeping the directory structure:

snakemake -s ../workflow/Snakefile -j 4 --use-conda clean

This archives the previous run with a timestamp and prepares for a new run.

Reference Files Preparation

To use different barcode systems or primer sets, you can create custom reference files:

  1. Create a new working directory:

    mkdir working_directory_custom_locus
cd working_directory_custom_locus

  2. Place your reference files in ref/ folder and update config.yaml with new filenames

  3. Run the prepare_barcode_reference step with your new files

See the main README.md for more detailed workflow information.

Workflow parameters

The following table is automatically parsed from the workflow’s config.schema.y(a)ml file.

Parameter

Type

Description

Required

Default

min_depth

number

minimum read depth threshold for consensus generation

yes

primer_consensus

string

path to primer consensus FASTA file (placed in ref/)

yes

barcode_groups

string

path to barcode groups CSV file (placed in ref/)

yes

barcodes

string

path to barcodes FASTA file (placed in ref/)

yes

input_fastq

string

path to input FASTQ file for processing (placed in sequences/)

yes

medaka_spoa_threads

number

number of parallel threads for medaka consensus generation

yes

debug

boolean

enable debug mode

yes

filter-size

number

minimum read length filter (in bp) to remove short reads and adapter dimers

yes

downsample_reads

number

number of reads to downsample each demuxed sample to before consensus generation

yes

cutadapt_min_overlap

number

minimum overlap parameter for cutadapt adapter matching (in bp)

yes

cutadapt_error_rate

number

maximum allowed error rate for cutadapt adapter matching (0-1)

yes

medaka_model

string

medaka consensus polishing model identifier

yes

Linting and formatting

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