RL-CSA

RL-CSA performs pairwise global sequence alignment under constraints defined by regular languages to incorporate a priori biological knowledge into alignments.

Key Features:

• Constraint Integration: Uses finite automata or regular expressions as constraints to incorporate known structural motifs or functional domains into the alignment process.
• Dynamic Programming Framework: Employs dynamic programming to compute pairwise global alignments subject to regular language constraints.
• Algorithmic Complexity: Initial algorithms had time complexity O(n^2 t^4) and space complexity O(n^2 t^2), where n is input string length and t is the number of states in the non-deterministic automaton, and were subsequently improved to time O(n^2 t^3).
• Further Optimization via Straight-Line Programs: Latest improvements reduce worst-case time to O(n^2 t^3)/log t by optimizing the size of Straight-Line Programs used to solve the maxima computation subproblem.
• Alternative Steiner Tree Approach: Explores Steiner Tree-based solutions that do not improve worst-case bounds but show empirical efficiency in practice, particularly for dense input automata.

Scientific Applications:

• Incorporating Biological Knowledge: Aligns sequences while enforcing known biological features such as conserved regions or specific sequence motifs via regular language constraints.
• Enhanced Alignment Accuracy: Produces alignments that reflect additional a priori knowledge, aiding interpretation of gene function and evolutionary relationships.
• Efficient Handling of Complex Alignments: Processes large or complex constrained alignment problems efficiently through algorithmic and data-structure optimizations.

Methodology:

Uses dynamic programming constrained by regular languages implemented via finite automata or regular expressions; optimizes Straight-Line Program size for the maxima computation subproblem; and explores Steiner Tree computations as an alternative approach.