Ajout d'une documentation détaillée sur l'architecture des commandes OBITools, incluant la structure modulaire, les patterns architecturaux et les bonnes pratiques pour la création de nouvelles commandes.
This commit refactors the SuperKmer extraction functionality to use Go's new iterator pattern. The ExtractSuperKmers function is now implemented as a wrapper around a new IterSuperKmers iterator function, which yields results one at a time instead of building a complete slice. This change provides better memory efficiency and more flexible consumption of super k-mers. The functionality remains the same, but the interface is now more idiomatic and efficient for large datasets.
Mise à jour du Makefile pour améliorer le processus de version bump et de création de tag.
- Utilisation de variables pour stocker les versions précédente et actuelle
- Ajout de la génération automatique des notes de version à partir des commits entre les tags
- Intégration d'une logique de fallback si orla n'est pas disponible
- Amélioration de la documentation des étapes du processus de release
- Mise à jour de la commande de création du tag avec le message généré
Update installation script to support specific version installation, list available versions, and improve documentation.
- Add support for installing specific versions with -v/--version flag
- Add -l/--list flag to list all available versions
- Improve help message with examples
- Update README.md to reflect new installation options and examples
- Add note on version compatibility between OBITools2 and OBITools4
- Remove ecoprimers directory
- Improve error handling and user feedback during installation
- Add version detection and download logic from GitHub releases
- Update installation process to use tagged releases instead of master branch
This commit simplifies the artifact packaging process by creating a single tar.gz file containing all binaries for each platform, instead of individual files. It also updates the release notes to reflect the new packaging approach and corrects the documentation to use the new naming convention 'obitools4' instead of '<tool>'.
Mise à jour du workflow de release pour utiliser ubuntu-24.04-arm au lieu de ubuntu-latest pour ARM64, et macos-15-intel au lieu de macos-latest pour macOS. Suppression de la compilation croisée pour ARM64 et ajustement de l'installation des outils de build pour macOS.
This commit introduces a new build job that compiles binaries for multiple platforms (Linux, macOS) and architectures (amd64, arm64). It also refactors the release process to download pre-built artifacts and simplify the release directory preparation. The workflow now uses matrix strategy for building binaries and downloads all artifacts for the final release, removing the previous manual build steps for each platform.
Modification du fichier de workflow de release pour compiler uniquement les outils obitools lors de la construction des binaires pour chaque plateforme (Linux AMD64, Linux ARM64, macOS AMD64, macOS ARM64, Windows AMD64). Cela permet d'optimiser le processus de build en ne générant que les binaires nécessaires.
This commit introduces a new GitHub Actions workflow to automatically create releases when tags matching the pattern 'Release_*' are pushed. It also updates the Makefile to use the new tag format 'Release_<version>' for tagging commits, ensuring consistency with the new release automation.
Add Jaccard distance and similarity computations for KmerSet and KmerSetGroup
This commit introduces Jaccard distance and similarity methods for KmerSet and KmerSetGroup.
For KmerSet:
- Added JaccardDistance method to compute the Jaccard distance between two KmerSets
- Added JaccardSimilarity method to compute the Jaccard similarity between two KmerSets
For KmerSetGroup:
- Added JaccardDistanceMatrix method to compute a pairwise Jaccard distance matrix
- Added JaccardSimilarityMatrix method to compute a pairwise Jaccard similarity matrix
Also includes:
- New DistMatrix implementation in pkg/obidist for storing and computing distance/similarity matrices
- Updated version handling with bump-version target in Makefile
- Added tests for all new methods
This commit refactors the k-mer normalization functions, renaming them from 'NormalizeKmer' to 'CanonicalKmer' to better reflect their purpose of returning canonical k-mers. It also introduces new quorum operations (AtLeast, AtMost, Exactly) for k-mer set groups, along with comprehensive tests and benchmarks. The version commit hash has also been updated.
This commit translates all French comments in the kmer filtering and set management code to English, improving code readability and maintainability for international collaborators.
Ajout de la fonctionnalité de sauvegarde et de chargement pour FrequencyFilter en utilisant le KmerSetGroup sous-jacent.
- Nouvelle méthode Save() pour enregistrer le filtre dans un répertoire avec formatage des métadonnées
- Nouvelle méthode LoadFrequencyFilter() pour charger un filtre depuis un répertoire
- Initialisation des métadonnées lors de la création du filtre
- Optimisation des méthodes Union() et Intersect() du KmerSetGroup
- Mise à jour du commit hash
This commit refactors all k-mer encoding and normalization functions to consistently use 'canonical' instead of 'normalized' terminology. This includes renaming functions like EncodeNormalizedKmer to EncodeCanonicalKmer, IterNormalizedKmers to IterCanonicalKmers, and NormalizeKmer to CanonicalKmer. The change aligns the API with biological conventions where 'canonical' refers to the lexicographically smallest representation of a k-mer and its reverse complement. All related documentation and examples have been updated accordingly. The commit also updates the version file with a new commit hash.
This commit introduces new functions for encoding and decoding k-mers, including support for normalized k-mers. It also updates the frequency filter and k-mer set implementations to use the new encoding functions, providing zero-allocation encoding for better performance. The commit hash has been updated to reflect the latest changes.
This commit refactors the KmerSet and related structures to use an immutable K parameter and introduces consistent Copy methods instead of Clone. It also adds attribute API support for KmerSet and KmerSetGroup, and updates persistence logic to handle IDs and metadata correctly.
Cette modification ajoute la capacité de stocker et de persister des métadonnées utilisateur dans les structures KmerSet et KmerSetGroup. Les changements incluent l'ajout d'un champ Metadata dans KmerSet et KmerSetGroup, ainsi que la mise à jour des méthodes de clonage et de persistance pour gérer ces métadonnées. Cela permet de conserver des informations supplémentaires liées aux ensembles de k-mers tout en maintenant la compatibilité avec les opérations existantes.
This commit adds support for saving and loading KmerSet and KmerSetGroup structures using TOML, YAML, and JSON formats for metadata. It includes:
- Added github.com/pelletier/go-toml/v2 dependency
- Implemented Save and Load methods for KmerSet and KmerSetGroup
- Added metadata persistence with support for multiple formats (TOML, YAML, JSON)
- Added helper functions for format detection and metadata handling
- Updated version commit hash
Refactor FrequencyFilter to inherit from KmerSetGroup for better code organization and maintainability. This change replaces the direct bitmap management with a group-based approach, simplifying the implementation and improving readability.
This commit refactors the k-mer encoding logic to handle ambiguous bases more consistently and introduces a KmerSet type for better management of k-mer collections. The frequency filter now works with KmerSet instead of roaring bitmaps directly, and the API has been updated to support level-based frequency queries. Additionally, the commit updates the version and commit hash.
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.
