ONT-cappable-seq2

This document provides a comprehensive overview of the updated ONT-cappable-seq data analysis pipeline, now fully automated. For detailed information on individual steps, please refer to the old ONT-cappable-seq repository.

Installation

Note: This snakemake pipeline is designed for Linux systems! Windows users are advised to install Windows Subsystem for Linux (WSL) before proceeding with the installation.

1. Install Snakemake by following the instructions provided here.

2. Clone the ONT-cappable-seq2 repository to the desired location to obtain the Snakemake pipeline:

git clone https://github.com/LoGT-KULeuven/ONT-cappable-seq2.git

Usage

I. Data Navigation

Obtain high-quality FASTA files for the reference genomes of the organisms you are studying and store them in the input/genome_data directory. For better organization, it is advisable to rename the FASTA files using the respective IDs of the phage or host, referred to as sampleID. Additionally, download the FLuc Control Plasmid sequence used for in vitro transcription of the RNA control spike-in from the manufacturer's website. Save this sequence in an appropriate location.

ONT-cappable-seq2/
…
├── input/
|     └── genome_data/
|          ├── phageID.fasta
|          ├── hostID.fasta
|          └── in-vitro-RNA.fasta

Place the raw sequencing files in the input/fastq_data directory and rename them to incorporate information about sampleID and treatment (enriched or control sample). Ensure that each individual sample has a single decompressed .fastq file with an adequate number of reads and a suitable mean read length (> 200 bp). This naming convention and file organization will contribute to the clarity and efficiency of the analysis.

ONT-cappable-seq2/
…
├── input/
|     ├─ fastq_data/
|        ├── sampleID_enriched.fastq
|        └── sampleID_control.fastq
|     └── genome_data/
|        ├── phageID.fasta
|        ├── hostID.fasta
|        └── in-vitro-RNA.fasta

II. Setting up the Configuration File

1. Navigate to the config directory within the ONT-cappable-seq project folder.

2. Open the config.yaml file. This file contains paths to your input files and various parameters used for annotating transcriptional boundaries. A general example of the config.yaml file is displayed below:

Sample name: sampleID
fasta file: input/genome_data/sampleID.fasta
enriched fastq: input/fastq_data/sampleID_enriched.fastq
control fastq: input/fastq_data/sampleID_control.fastq
ID: group
termseq alpha: 0.001

cluster width:
  TSS: 15
  TTS: 30

minimum coverage:
  enriched:
    TSS: 25
    TTS: 25
  control:
    TSS: 2
    TTS: 2

peak alignment error: 2
TSS Threshold: 1.5
TTS threshold: 0.25

TSS sequence extraction:
  upstream: 40
  downstream: 0

TTS sequence extraction:
  upstream: 30
  downstream: 30

• Sample name: Contains the name of your organism of interest (e.g., sampleID, LUZ19).
• Fasta file: Path to your reference genome (e.g., input/genome_data/sampleID.fasta, input/genome_data/LUZ19.fasta).
• Enriched fastq: Path to the raw sequencing file of the enriched sample (e.g., input/fastq_data/sampleID_enriched.fastq, input/fastq_data/LUZ19_enriched.fastq).
• Control fastq: Path to the raw sequencing file of the control sample (e.g., input/fastq_data/sampleID_control.fastq, input/fastq_data/LUZ19_control.fastq).
• ID: Additional identifier for sample classification (e.g., timepoint, condition, replicate).
• Termseq alpha: Peak calling threshold for the termseq-peak algorithm.
• Cluster width: Distance within which peak positions are clustered.
• Minimum coverage: Minimum number of reads required for a candidate TSS/TTS position.
• Peak alignment error: Positional difference allowed between peak positions in enriched and control datasets.
• TSS/TTS threshold: Enrichment ratio and read reduction thresholds for TSS and TTS annotation.
• TSS/TTS sequence extraction: Upstream and downstream nucleotides relative to TSS and TTS for DNA sequence extraction.

III. Running the Pipeline

Before executing the Snakemake pipeline, perform a dry-run to ensure the workflow is correctly defined:

cd ONT-cappable-seq2
snakemake --dry-run

To run the Snakemake pipeline, use the following commands:

cd ONT-cappable-seq2
snakemake --use-conda --cores 8

During the initial run, the pipeline will install all necessary Conda packages, which may take some time. Subsequent runs will reuse this Conda environment.

Changes to the pipeline as of 3/07/2024

• updated conda environment env_read_mapping: cutadapt>=4.4, biopython
• removal of termseq-peaks as dependency
• cutadapt --poly-a to only remove polyA-stretches at the ends (and not in-sequence)
• cutadapt --max-aer 0.1: filter error rate that takes into account the different read lengths typical for ont data
• minimap2 -p 0.99
• compatibility to multiple nucleotide sequences in the fasta file in peak clustering and TSS/TTS annotation
• Additional scripts: remove_empty_seqs.py (removes empty reads after poly-A clipping with cutadapt, before filtering), peak_calling.py (simplified peak calling, replaces the termseq-peaks packages)