HamHeat: A fast and simple package for calculating Hamming distance from multiple sequence data for heatmap visualization
Alexey V. RakoviD, Dieter M. SchifferliiD, Shu-Lin LiuiD, Emilio MastrianiiD
Abstract
The problem of fast calculation of Hamming distance inferred from many sequence datasets is still not a trivial task. Here, we present HamHeat, as a new software package to efficiently calculate Hamming distance for hundreds of aligned protein or DNA sequences of a large number of residues or nucleotides, respectively. HamHeat uses a unique algorithm with many advantages, including its ease of use and the execution of fast runs for large amounts of data. The package consists of three consecutive modules. In the first module, the software ranks the sequences from the most to the least frequent variant. The second module uses the most common variant as the reference sequence to calculate the Hamming distance of each additional sequence based on the number of residue or nucleotide changes. A final module formats all the results in a comprehensive table that displays the sequence ranks and Hamming distances. Availability and implementationHamHeat is based on Python 3 and AWK, runs on Linux system and is available under the MIT License at: https://github.com/alexeyrakov/HamHeat. Contactrakovalexey@gmail.com
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 1
- Assay
- Research Focus
- Approach
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
- MMseqs2 desktop and local web server app for fast, interactive sequence searches
Milot Mirdita, Martin Steinegger, Johannes Söding·BioinformaticsPeer-reviewed·Jan 4, 2019
- Edlib: A C/C++ library for fast, exact sequence alignment using edit distance
Martin Sosic, Mile Sikic·BioinformaticsPeer-reviewed·Jan 31, 2017
- New distance measure for comparing protein using cellular automata image
Luryane F. Souza, Hernane Borges de Barros Pereira, … Marcelo Albano Moret·PLoS ONEPeer-reviewed·Oct 5, 2023
- aaHash: recursive amino acid sequence hashing
Johnathan Wong, Parham Kazemi, … İnanç Birol·Bioinformatics AdvancesPeer-reviewed·Jan 1, 2023
- HTSeq - A Python framework to work with high-throughput sequencing data
Simon Anders, Paul Theodor Pyl, Wolfgang Huber·BioinformaticsPeer-reviewed·Sep 25, 2014
- Similarity Estimation Between DNA Sequences Based on Local Pattern Histograms of Binary Images
Yusei Kobori, Satoshi Mizuta·Genomics, Proteomics & BioinformaticsPeer-reviewed·Apr 27, 2016