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BioKIT, a versatile toolkit for processing or conducting analyses on alignments, coding sequences, fastq files, and genome files.

If you found BioKIT useful, please cite BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data. Genetics. doi: 10.1093/genetics/iyac079.

Quick Start

1) Installation

To install using pip, we strongly recommend building a virtual environment to avoid software dependency issues. To do so, execute the following commands:

# create virtual environment
python -m venv .venv
# activate virtual environment
source .venv/bin/activate
# install biokit
pip install jlsteenwyk-biokit

Note, the virtual environment must be activated to use biokit.

After using BioKIT, you may wish to deactivate your virtual environment and can do so using the following command:

# deactivate virtual environment
deactivate

Similarly, to install from source, we strongly recommend using a virtual environment. To do so, use the following commands:

# download
git clone https://github.com/JLSteenwyk/BioKIT.git
cd BioKIT/
# create virtual environment
python -m venv .venv
# activate virtual environment
source .venv/bin/activate
# install
make install

To deactivate your virtual environment, use the following command:

# deactivate virtual environment
deactivate

Note, the virtual environment must be activated to use biokit.

To install via anaconda, execute the following command:

conda install -c jlsteenwyk jlsteenwyk-biokit

Visit here for more information: https://anaconda.org/JLSteenwyk/jlsteenwyk-biokit

2) Usage

Get the help message from BioKIT:

biokit -h

After successful installation, BioKIT can be used in diverse applications. In this figure, we showcase three examples, which cover a small number of total possibilities, where BioKIT can be used. For each example, steps that can be executed by BioKIT are shown in bold.

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• About
  • The Developers
  • More Team Members
• Usage
  • General usage
    • Calling functions
    • Function aliases
    • Command line interfaces
  • Alignment-based functions
    • Alignment length
    • Alignment recoding
    • Alignment summary
    • Consensus sequence
    • Constant sites
    • Parsimony informative sites
    • Position specific score matrix
    • Variable sites
  • Coding sequence-based functions
    • GC content first codon position
    • GC content second codon position
    • GC content third codon position
    • Gene-wise relative synonymous codon usage (gRSCU)
    • Relative synonymous codon usage
    • Translate sequence
  • FASTQ file functions
    • FASTQ read lengths
    • Subset PE FASTQ reads
    • Subset SE FASTQ reads
    • Trim PE adapters FASTQ reads
    • Trim PE FASTQ reads
    • Trim SE adapters FASTQ reads
    • Trim SE FASTQ reads
  • Genome functions
    • GC content
    • Genome assembly metrics
    • L50
    • L90
    • Longest scaffold
    • N50
    • N90
    • Number of large scaffolds
    • Number of scaffolds
    • Sum of scaffold lengths
  • Sequence summary and processing functions
    • Character frequency
    • Get FASTA entry (faidx)
    • File format converter
    • Multiple line to single line FASTA
    • Remove FASTA entry
    • Remove short sequences
    • Rename FASTA entries
    • Reorder by sequence length
    • Sequence complement
    • Sequence length
    • Single line to multiple line fasta
• Tutorials
  • 1. Summarize genome assembly metrics
  • 2. Summarize multiple sequence alignment properties
  • 3. Calculate relative synonymous codon usage bias
  • 4. Estimating codon optimization
• Change log
• Other software
  • ClipKIT
  • PhyKIT
  • orthofisher
  • treehouse
  • ggpubfigs
• FAQ

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