Useful scripts for dealing with sequencing data from insertional and deletional mutational scanning experiments.
- stickleback: A python script for mapping long-reads sequencing data with engineered insertion sequences.
- smelt: A python script for parsing and counting insertions and deletions of defined size in sam files.
-
Python 3.0+
-
And python packages, via pip:
pip install numpy
pip install pandas
pip install multiprocessing
pip install Bio
pip install LevenshteinModule Versions Tested:
- numpy: '1.26.4' * Note: this was developed and run prior to release of numpy 2.+, however no conflicts are expected. Please notify us of any usage issues.
- pandas: '2.2.2'
- multiprocessing: '2.6.2.1'
- Bio: '1.7.1'
- levenshtein: '0.25.1'
To install these tested module versions, via pip:
pip install numpy==1.26.4
pip install pandas==2.2.2
pip install multiprocessing==2.6.2.1
pip install Bio==1.7.1
pip install Levenshtein==0.25.1- Clone the repository:
git clone https://github.com/QVEU/InDel_Toolkit.git
- Navigate to the project directory:
cd InDel_Toolkit
stickleback was design to map long-read sequencing libraries from nanopore or pacbio containing engineered libraries containing any defined insertion introduced via the SPINE pipeline.
python stickleback.py <pathto/input.sam (str)> <query sequence (str)> </path/to/templateFasta (str)> [Min Read Length (int)] [Max Read Length (int)]
# <> = req'd argument, []= optional argument
To identify reads with insertions from an example SAM:
> cd InDel_Toolkit/
> python lib/stickleback.py test/stickleback_test.sam agcgggagaccggggtctctgagcg lib/templates/puc19-ev71-twtainan1998_4643-bsmbi-and-bsai-free-deleted-1-annotations-1-7471.fasta
----------------=============-----------------
--==--==--==--== ><```º> ==--==--==--==--=
==--==--==--==-- stickleback --==--==--==--==-
----------------=============-----------------
Query Length: 25
Template Length: 7471
Template-Query Distance: 9
Mapping Reads of Size 25 to 7471 with a cutoff of 7
Loading SAM: test/stickleback_test.sam
Total Candidate Reads: 250
Outfile: test/stickleback_test_stickleback.csv
1. Computing minimum distance hit position for 247 reads.
Query: AGCGGGAGACCGGGGTCTCTGAGCG
2. Mapping minimum distance site on template sequence...
done.
Mapped hits in 36 reads.
Done in 0.03501284917195638 minutes.
Wrote test/stickleback_test_stickleback.csv.
smelt compliments Stickleback, and is used to tabulate deletions across coding sequences. It takes an SAM file (from Illumina Sequencing, or other high-accuracy NGS methods), reads the cigar strings, and identifies reads with deletions of a given size relative to the reference. It identifies these deletions on the translated sequence relative to a translated nucelotide reference to resolve ambiguities, and is therefore specifically designed for engineered libraries where specific codons are deleted.
python smelt.py <input_file.sam (str)> <Deletion Size (int)> <output.csv (str)>
# <> = req'd argument
To identify reads with 3 bp deletions from an example SAM:
cd InDel_Toolkit/lib/
python smelt.py ../test/smelt_test.sam 3 ../test/smelt_test.csvPlease cite: Bakhache, W., Orr, W., McCormick, L. & Dolan, P., Uncovering Structural Plasticity of Enterovirus A through Deep Insertional and Deletional Scanning. Research Square (Preprint) (2024).
Steps to contribute:
- Fork the repository.
- Create a new branch (git checkout -b feature-branch).
- Commit your changes (git commit -am 'Add new feature').
- Push to the branch (git push origin feature-branch).
- Open a pull request.
This project is licensed under the MIT License - see the LICENSE.md file for details.
Email: [email protected]
Twitter: @drptdolan
GitHub Issues: Submit an issue
The authors are thankful for minimap2.