Bi-alignment of RNAs and Proteins with affine gap cost

0.3 (Nov 2021--May 2022)

• Implement efficient affine gap cost alignment
  with O(n^2 max_shift^2) time complexity, 9 states and 15 cases per state
• Preserve implementation of non-affine algorithm
  (it is automatically chosen for gap_opening_cost 0)
• Use sparse DP matrix O(n^2 max_shift^2)
• Support similarity matrix for protein comparison, include BLOSUM62
• Rewrite code for compilation with Cython and optimize due to static typing
• Support input from files
• Graphic representation of protein alignments
• Smart trace back preferring more natural shifts (in case of
  ambiguity)
• add pip and conda packaging
• cleanup
• improve visualization: add shift lines
• improve/update documentation
• minor fixes

Bi-alignment of RNAs and Proteins with affine gap cost

0.3b0x (Nov 2021--Mar 2022)
* Implement efficient affine gap cost alignment
with O(n^2 max_shift^2) time complexity, 9 states and 15 cases per state
* Preserve implementation of non-affine algorithm
(it is automatically chosen for gap_opening_cost 0)
* Use sparse DP matrix O(n^2 max_shift^2)
* Support similarity matrix for protein comparison, include BLOSUM62
* Rewrite code for compilation with Cython and optimize due to static typing
* Support input from files
* Graphic representation of protein alignments
* Smart trace back preferring more natural shifts (in case of
ambiguity)
* add pip and conda packaging
* cleanup
* improve visualization: add shift lines
* improve/update documentation
* minor fixes

Bi-alignment of RNAs

This release corresponds to the CIBB19 post-proceedings paper

Waldl M., Will S., Wolfinger M.T., Hofacker I.L., Stadler P.F. (2020)
Bi-alignments as Models of Incongruent Evolution of RNA Sequence and
Secondary Structure. In: Cazzaniga P., Besozzi D., Merelli I., Manzoni L.
(eds) Computational Intelligence Methods for Bioinformatics and
Biostatistics. CIBB 2019. Lecture Notes in Computer Science, vol 12313.
Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_15