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Releases: molbio-dresden/flexidot

Official release of FlexiDot (v1)

27 Feb 18:22
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Official release of FlexiDot (v1)

This is the official release of FlexiDot (v1). This release serves primarily as an archived version corresponding to our publication (see citation), as we are on the verge of updating the code to Python 3, thanks to the great help of Adam Taranto.
We would like to take this opportunity to thank everyone who contributed to the development and testing of FlexiDot (v1): T. Heitkam, T. Schmidt, K. Seibt, and B. Weber (listed alphabetically).
As many of the initial developers are not available anymore, this release is currently maintained by L. Mann.

Functions and features

FlexiDot is a versatile, cross-platform dotplot suite designed for comprehensive visual sequence analysis. It offers a range of key features:

  • Generates high-quality self, pairwise, and all-against-all dotplot visualizations, accommodating various analytical needs.
  • Incorporates routines for both strict and relaxed handling of mismatches and ambiguous residues, enhancing the comparison of consensus and error-prone sequences.
  • Visualizes reverse complementary sequence similarities, enabling detection of inverted repeats and palindromic sequences.
  • Allows the addition of sequence annotations (e.g., GFF3-type structural annotations) to dotplots, enriching the interpretative value of visualizations.
  • Provides custom shading options based on sequence similarities, facilitating motif identification and dotplot interpretation and offers extensive flexibility for customization.
  • Supports simultaneous visual screening of large sequence sets through collage-style outputs, enabling efficient routine analyses.
  • Generates both vector and raster graphics, providing high-quality visual outputs suitable for various applications.

Citation

If you find this tool helpful, please cite:
Kathrin M. Seibt, Thomas Schmidt, and Tony Heitkam (2018) "FlexiDot: Highly customizable, ambiguity-aware dotplots for visual sequence analyses". Bioinformatics 34 (20), 3575–3577, doi: 10.1093/bioinformatics/bty395 - Read article - Preprint