This commit introduces error handling for ambiguous DNA bases (N, R, Y, W, S, K, M, B, D, H, V) in k-mer encoding. It adds new functions IterNormalizedKmersWithErrors and EncodeNormalizedKmersWithErrors that track and encode the number of ambiguous bases in each k-mer using error markers in the top 2 bits. The commit also updates the version string to reflect the latest changes.
Implémentation complète du filtre de fréquence utilisant v niveaux de Roaring Bitmaps pour éliminer efficacement les erreurs de séquençage.
- Ajout de la logique de filtrage par fréquence avec v niveaux
- Intégration des bibliothèques RoaringBitmap et bitset
- Ajout d'exemples d'utilisation et de documentation
- Implémentation de l'itérateur de k-mers pour une utilisation mémoire efficace
- Optimisation pour les distributions skewed typiques du séquençage
Ce changement permet de filtrer les k-mers par fréquence minimale avec une utilisation mémoire optimale et une seule passe sur les données.
This commit introduces error marker functionality for k-mers with odd lengths up to 31. The top 2 bits of each k-mer are now reserved for error coding (0-3), allowing for error detection and correction capabilities. Key changes include:
- Added constants KmerErrorMask and KmerSequenceMask for bit manipulation
- Implemented SetKmerError, GetKmerError, and ClearKmerError functions
- Updated EncodeKmers, ExtractSuperKmers, EncodeNormalizedKmers functions to enforce k ≤ 31
- Enhanced ReverseComplement to preserve error bits during reverse complement operations
- Added comprehensive tests for error marker functionality including edge cases and integration tests
The maximum k-mer size is now capped at 31 to accommodate the error bits, ensuring that k-mers with odd lengths ≤ 31 utilize only 62 bits of the 64-bit uint64, leaving the top 2 bits available for error coding.
This commit introduces the ExtractSuperKmers function which identifies maximal subsequences where all consecutive k-mers share the same minimizer. It includes:
- SuperKmer struct to represent the maximal subsequences
- dequeItem struct for tracking minimizers in a sliding window
- Efficient algorithm using monotone deque for O(1) amortized minimizer tracking
- Comprehensive parameter validation
- Support for buffer reuse for performance optimization
- Extensive test cases covering basic functionality, edge cases, and performance benchmarks
The implementation uses simultaneous forward/reverse m-mer encoding for O(1) canonical m-mer computation and maintains a monotone deque to track minimizers efficiently.
