https://github.com/gagniuc/information-content-vs-percentage
This is a scanner that produces two types of signals. The two signals are calculated from the content of a sliding window. One signal is represented by the self-sequence alignment values and the other is represented by the relative frequency values of Cplus G letters.
https://github.com/gagniuc/information-content-vs-percentage
bioinformatics biology dna information-content javascript js percentage relative rna
Last synced: 10 days ago
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This is a scanner that produces two types of signals. The two signals are calculated from the content of a sliding window. One signal is represented by the self-sequence alignment values and the other is represented by the relative frequency values of Cplus G letters.
- Host: GitHub
- URL: https://github.com/gagniuc/information-content-vs-percentage
- Owner: Gagniuc
- License: mit
- Created: 2021-10-28T21:11:35.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-21T11:04:53.000Z (over 3 years ago)
- Last Synced: 2025-03-04T22:25:11.391Z (8 months ago)
- Topics: bioinformatics, biology, dna, information-content, javascript, js, percentage, relative, rna
- Language: HTML
- Homepage:
- Size: 154 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md
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README
# Information content vs percentage
Information content vs percentage - is a demonstrative DNA scanner that produces two types of signals. The two signals are calculated from the contents of a user-defined sliding window. One signal is represented by the [self-sequence alignment](https://github.com/Gagniuc/Self-sequence-alignment) values and the other is represented by the relative frequency values of C plus G letters. Note that the construction and theory behind the chart of this application can be found [here](https://github.com/Gagniuc/World-smallest-js-chart-v1.0).
# Live demo
https://gagniuc.github.io/Information-content-vs-percentage/
# References
- Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, Hoboken, NJ, USA, 2021, ISBN: 9781119697961.