REDigest
REDigest: In-silico restriction digestion analysis of DNA sequences
REDigest performs in-silico restriction digestion of gene and complete genome DNA sequences to simulate restriction enzyme cleavage and generate restriction fragment profiles for downstream analysis.
Key Features:
- In-Silico Restriction Digestion: Simulates restriction enzyme cleavage at specific recognition sites within DNA sequences to generate predicted restriction fragments.
- Batch Processing: Processes large numbers of gene or genome sequences simultaneously for high-throughput analyses.
- Fragment Output and Profiling: Produces restriction fragment sequences and generates restriction digestion profile plots.
- T-RFLP Support: Supports Terminal Restriction Fragment Length Polymorphism (T-RFLP) analysis of marker gene amplicons including 16S rRNA and FTHFS.
Scientific Applications:
- Genome Mapping: Generates restriction fragment patterns for molecular characterization and genome mapping.
- Medical Genetics: Analyzes restriction fragment length polymorphisms for detection of genomic polymorphisms.
- Molecular Microbiology: Profiles microbial communities using T-RFLP of marker genes such as 16S rRNA and FTHFS.
- Forensics: Simulates restriction digestion patterns for DNA-based forensic analysis.
Methodology:
REDigest applies computational algorithms to identify restriction enzyme recognition sites within input DNA sequences and simulate enzymatic cleavage. Predicted fragments are computed in silico, compiled into restriction fragment datasets, and used to generate restriction digestion profiles comparable to laboratory-based Restriction Fragment Length Polymorphism (RFLP) analyses.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- library
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 3/29/2022
- Last Updated:
- 3/29/2022
Operations
Publications
Singh A. REDigest: a Python GUI for <i>In-Silico</i> Restriction Digestion Analysis of Genes or Complete Genome Sequences. Unknown Journal. 2021. doi:10.1101/2021.11.09.467873.