bioRxivBioinformaticsPreprint
Improved Python Package for DNA Sequence Encoding using Frequency Chaos Game Representation
Abhishek HalderiD, Piyush PiyushiD, Bernadette MathewiD, Debarka SenguptaiD
Abstract
SummaryFrequency Chaos Game Representation (FCGR), an extended version of Chaos Game Representation (CGR), emerges as a robust strategy for DNA sequence encoding. The core principle of the CGR algorithm involves mapping a onedimensional sequence representation into a higher-dimensional space, typically in the two-dimensional spatial domain. This paper introduces a use case wherein FCGR serves as a kmer frequency-based encoding method for motif classification using a publicly available dataset. Availability and implementationThe FCGR python package, use case, along with additional functionalities, is available in the GitHub. Our FCGR package demonstrates superior accuracy and computational efficiency compared to a leading R-based FCGR library [1], which is designed for versatile tasks, including proteins, letters, and amino acids with user-defined resolution. Nevertheless, it is important to note that our Python package is specifically designed for DNA sequence encoding, where the resolution is predetermined based on the kmer length. It is a drawback of our current package compared to the state-of-the-art R-based kaos package [1].
- Citations
- 3
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Deep Learning on Chaos Game Representation for Proteins
Hannah Franziska Löchel, Dominic Eger, … Dominik Heider·BioinformaticsPeer-reviewed·Jun 14, 2019
- Chamaeleo: a robust library for DNA storage coding schemes
Zhi Ping, Haoling Zhang, … Yue Shen·bioRxiv·Jan 2, 2020
- Complexity And Ergodicity In Chaos Game Representation Of Genomic Sequences
Adel Mehrpooya, Iman Tavassoly·bioRxiv·Dec 30, 2023
- dna-parser: a Python library written in Rust for fast encoding of DNA and RNA sequences
Matthieu Vilain, Stéphane Aris‐Brosou·bioRxiv·Jan 21, 2026
- Evaluation of Convolutionary Neural Networks Modeling of DNA Sequences using Ordinal versus one-hot Encoding Method
Allen Chieng Hoon Choong, Nung Kion Lee·bioRxiv·Oct 25, 2017
- CRAFT: Compact genome Representation towards largescale Alignment-Free daTabase
Yang Young Lu, Jiaxing Bai, … Fengzhu Sun·BioinformaticsPeer-reviewed·Aug 4, 2020