Bioscripts

A collection of Python scripts (not only) for processing and analyzing bisulfite sequencing (Bismark) data.

Overview

BioScripts provides tools for annotating DNA methylation calls with sequence context and merging forward/reverse strand methylation data from Bismark coverage files.

Scripts

get_CpG_from_genome

Annotates Bismark coverage files with sequence context information (±1 base around methylated sites).

Usage:

python get_CpG_from_genome/get_CpG_from_genome.py -f <fasta_file> -b <bed_file> -o <output_bed> [-c <chunk_size>] [-m]

Arguments:

• -f, --fasta_file: Path to the input FASTA file (required)
• -b, --bed_file: Path to the input BED/coverage file (required)
• -o, --output_bed: Path to the output BED file (required)
• -c, --chunk_size: Chunk size for reading FASTA file
• -m, --methylkit: Input is methylKit format

Output: Generates an annotated BED file with additional columns:

• REF_-1+1: Sequence context (base -1 and +1 relative to the CpG site)

merge_reverse_strand_calls

Merges bookended forward and reverse strand methylation calls from the output of get_CpG_from_genome.

Usage:

python merge_reverse_strand_calls/merge_reverse_strand_calls.py -b <bed_file> -o <output_bed>

Arguments:

• -b, --bed_file: Path to the input BED file (required)
• -o, --output_bed: Path to the output BED file (required)

Output: BED file with merged methylation calls containing:

• seqname: Chromosome/sequence name
• start: Start position
• end: End position
• perc_mCpG: Percentage of methylated cytosines
• numCs: Number of methylated cytosines
• numTs: Number of unmethylated cytosines

split_rename_fasta

Splits a large FASTA file into individual sequence files based on regex pattern matching.

Usage:

python split_rename_fasta/split_rename_fasta.py -i <input_file> -p <pattern> -o <output_dir>

Arguments:

• -i, --input_file: Path to the input FASTA file
• -p, --pattern: Regex pattern to match sequence names
• -o, --output_dir: Directory for output files

nf-samplesheet

Generates a nextflow samplesheet from FASTQ files in a directory.

Usage:

python nf-samplesheet/get_samplesheet.py <sample1> [<sample2> ...] -d <fastq_dir> [--single]

Arguments:

• samples: One or more sample names (positional)
• -d, --dir: Directory with FASTQ files
• --single: Single-end reads flag

Output: CSV samplesheet with columns:

• sample: Sample name
• single: Single-end flag (true/false)
• fastq_1: Path to R1 FASTQ file
• fastq_2: Path to R2 FASTQ file (if paired-end)
• genome: Genome reference (empty by default)

Installation

This project uses conda for environment management:

conda env create -f environment.yaml

Or use the Apptainer container:

apptainer run bioscripts.sif <script> [arguments]

Requirements

• Python 3.x
• pandas
• numpy
• BioPython

License

MIT License - See LICENSE for details.

Copyright (c) 2025 Fritjof Lammers