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Sequence Logo - relative style is an alternative that provides full control over how the graphics of a Sequence Logo should look like, and is an alternative to an application called WebLogo. All the inner workings of this open source application are written in native javascript.

License: MIT License

HTML 100.00%
sequence-logo motif js javascript html5 dna dna-processing rna-sequences proteomics html-css-javascript

sequence-logo-relative-style's Introduction

Sequence Logo (relative style)

Sequence Logo - relative style is an alternative that provides full control over how the graphics of a Sequence Logo should look like, and is an alternative to an application called WebLogo. All the inner workings of this open source application are written in native javascript. The application is independent of the internet once it is saved as a html file on the PC/Mobile phone. For the input it requires a set of motif sequences, whereas the output is an image that represents the Sequence Logo. The total height of a logo position depends on the degree of conservation detected in the corresponding alignment column of the motif set. Thus, highly conserved alignment columns produce tall logo positions. The height of each letter on a logo position is proportional to the observed frequency of the corresponding letter/symbol in the alignment column from the motif set. The letter of each stack is ordered from most to least frequent. Thus, the consensus sequence can be read from the top of the stacks. A Sequence Logo may reveal significant features of the motif set alignment that could otherwise be difficult to perceive. As such, a Sequence Logo is important for the human reader. For more detailed information, note that these native Sequence Logos in Javascript, were published in the supplementary materials of the book entitled Algorithms in Bioinformatics: Theory and Implementation. The explanations on how the code works can be found in the book's Appendix.

Live demo: https://gagniuc.github.io/Sequence-Logo-relative-style/

References

  • Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, Hoboken, NJ, USA, 2021, ISBN: 9781119697961.

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