v0.2.0

CRISPR-HAWK v0.1.2 – Release Notes

Date: November 26, 2025

This major update transforms the framework into a comprehensive scoring powerhouse by integrating three state-of-the-art efficiency models and introducing an automated environment and model management system.

What's Changed

• Advanced Scoring Integration: We have expanded our predictive capabilities by integrating sgDesigner, PLM-CRISPR, and CRISPRon. These models provide high-confidence efficiency metrics for your guide designs. (#8)

• Automated Model Catalogue: No more manual downloads. CRISPR-HAWK now automatically detects missing model weights, downloads them with built-in retry logic, and manages the extraction process. (#10)

• Intelligent Environment Management: Using the new ScoringEnvs system, the tool now handles complex dependencies (like Perl/Python wrappers for sgDesigner) via automated Conda/Mamba execution. (#10)

• Codebase Hardening: We've streamlined the internal API, enforced strict sequence padding for deep-learning compatibility, and removed legacy modules (bitset.py, microhomology.py) to improve maintainability. (#10)

Backwards Compatibility / Migration Notes

• Output Format: Three new columns (score_sgdesigner, score_plmcrispr, and score_crispron) have been added to the output TSV. This shifts the index positions of subsequent columns. If you use downstream scripts, please switch from hardcoded column indices to header-name parsing. (#10)

• Sequence Padding: A strict GUIDESEQPAD = 10 is now enforced in the Guide class to satisfy the input requirements of the new deep-learning models. (#8)

New Contributors

• @alisakumbara

Changelog

Full Changelog: v0.1.2...v0.2.0

v0.1.2

CRISPR-HAWK v0.1.2 – Release Notes

Date: November 26, 2025

What's New / Fixed

• Corrected the formatting and ordering of report columns in off-target estimation output for each reported guide.
  This fix ensures consistent column alignment across guides, resolves mismatches between headers and values, and improves compatibility with downstream parsing/analysis tools. (PR #5)

Backwards Compatibility / Migration Notes

• Existing workflows remain supported and functional, but we reccomend verifying your pipelines after the update to ensure consistency with the new report column formatting.

Changelog

Full Changelog: v0.1.1...v0.1.2

v0.1.1

CRISPR-HAWK v0.1.1 – Release Notes

Date: November 2025

What's New

• Added support for off-target annotation via CLI and core logic – users can now supply BED files and custom column names for enriched off-target reporting. (PR #3)

• Refactored the workflow into discrete modules (annotation.py, scoring.py, search_offtargets.py) with the main driver (crisprhawk.py) updated accordingly for better readability, testability and maintainability. (PR #3)

• Enhanced CLI argument parsing/validation in crisprhawk_argparse.py, including a new flag --candidate-guides for detailed guide analysis and graphical reporting. (PR #3)

• Introduced a CandidateGuide class and associated logic in candidate_guides.py; provided new report generation features (dot plot & scatter plot) for variant effect analysis of candidate guides. (PR #3)

• Adopted a unified CrisprHawkSearchInputArgs object for major modules to streamline parameter passing and reduce redundancy. (PR #3)

• Removed the need for the user to supply a FASTA index file (FAI) – index handling is now automatic within the Fasta class. (PR #3)

• Improved handling of IUPAC nucleotide codes in micro-homology scoring and guide search routines (supporting ambiguous bases better). (PR #3)

• Documentation improvements: updated docstrings and formatting in modules such as microhomology.py, crisprhawk_scores.py, search_guides.py. (PR #3)

Bug Fixes & Enhancements

• Added parallel computation for DeepCpf1 scoring (chunking guide sequences + ProcessPoolExecutor) to improve performance on large guide sets. (PR #4)

• Refactored candidate-guide handling and graphical reports: updated coordinate parsing to require strand information, improved helper functions for guide ID assignment, delta calculations and color palette generation. (PR #4)

• Fixed IUPAC encoding logic in haplotypes.py (use of IUPACTABLE for reference base conversion) and cleaned up variant addition logic. (PR #4)

• Fixed bugs in variant annotation and guide mapping logic: guide retrieval and annotation functions now use position‐maps and variant normalization; multiallelic variant handling improved. (PR #4)

• Improved formatting for allele frequencies (scientific notation) and pie‐chart representation in graphical reports; corrected parsing of AF field when no additional info present. (PR #4)

• Refactored CLI argument groups: made --outdir optional in search & converter subcommands; updated README logo file. (PR #4)

• Adjusted test assertions to reflect changes in allele frequency formatting and scoring logic. (PR #4)

Backwards Compatibility / Migration Notes

• If you were previously passing a FASTA index file via the CLI (FAI), you no longer need to; the index is handled automatically.

• Some function signatures changed (because of the move to CrisprHawkSearchInputArgs), so if you have custom downstream code extending CRISPR-HAWK modules, you may need to update accordingly.

• Existing workflows remain supported and will continue to run as before; the new CLI flags and reporting modules are additive.

Changelog

Full Changelog: v0.1.0...v0.1.1

v0.1.0

CRISPR-HAWK v0.1.0 - Initial Release

This is the first stable release of CRISPR-HAWK, a comprehensive and scalable toolkit for variant- and haplotype-aware CRISPR guide RNA design.

Key Features

Variant-Aware Guide Design

• Integrates major genetic variation datasets (1000 Genomes Project, HGDP, gnomAD)
• Handles both SNVs and indels
• Supports individual- and population-specific haplotypes
• Accounts for genetic diversity impact on guide efficiency

CRISPR System Flexibility

• Cas system-independent: Supports Cas9, SaCas9, Cpf1 (Cas12a), and custom nucleases
• Flexible PAM sequences: Define custom PAM motifs for emerging CRISPR technologies
• Variable guide lengths: Customize guide RNA length for specific experimental requirements
• Directional control: Support for upstream/downstream guide extraction

Comprehensive Scoring & Annotation

• Multiple efficiency predictors: Azimuth, RS3, CFDon, Elevation-on, DeepCpf1
• Functional annotation: Custom BED file integration for regulatory elements
• Gene annotation: GENCODE-style gene feature annotation
• Haplotype tracking: Detailed variant-guide association tables

Off-Target Analysis Integration

• CRISPRitz integration: Genome-wide off-target nomination (Linux only)
• Configurable parameters: Customizable mismatch and bulge tolerance
• Structured reporting: Detailed off-target tables per guide
• CRISPRme compatibility: Seamless transition to variant-aware off-target analysis

Production-Ready Workflow

• Automated pipeline: End-to-end processing from preprocessing to visualization
• Parallel processing: Multi-threaded execution for large-scale analyses
• Multiple output formats: TSV reports, annotated sequences, publication-ready figures
• Modular design: Easy integration with existing CRISPR analysis pipelines

Integration Ecosystem

CRISPR-HAWK is designed to work seamlessly with:

• [CRISPRme](https://github.com/pinellolab/CRISPRme): Variant-aware off-target prediction
• [CRISPRitz](https://github.com/pinellolab/CRISPRitz): High-throughput off-target nomination
• Standard bioinformatics formats: VCF, BED, FASTA

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We welcome community feedback, contributions, and feature requests to help evolve CRISPR-HAWK into an even more powerful tool.