CRISPR-HAWK: Haplotype- and Variant-Aware Guide Design Toolkit for CRISPR-Cas

MOTIVATION: Current CRISPR guide RNA design tools rely on reference genomes, overlooking how genetic variation impacts editing outcomes. As genome editing advances toward clinical applications, incorporating population diversity becomes essential for ensuring therapeutic efficacy across diverse populations. RESULTS: We present CRISPR-HAWK, a framework integrating individual- and population-scale variants and haplotypes into gRNA design. Analyzing therapeutic targets across 79,648 genomes reveals that genetic variants substantially alter guide performance. For the clinically approved sickle cell disease therapeutic guide targeting BCL11A, we identify haplotypes that completely abolish predicted cutting activity. Across seven therapeutic loci, 82.5% of guides contain variants modifying on-target activity. Variants also create novel protospacer adjacent motif sites generating individual-specific guides invisible to reference-based design. These findings demonstrate that variant-aware selection is critical for equitable genome editing. AVAILABILITY: CRISPR-HAWK is available at https://github.com/pinellolab/CRISPR-HAWK and https://github.com/InfOmics/CRISPR-HAWK. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors

Institutions

Publication Details

Journal
Bioinformatics
Published
2026-10-14
DOI
https://doi.org/10.1093/bioinformatics/btag708
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CRISPR-HAWK: Haplotype- and Variant-Aware Guide Design Toolkit for CRISPR-Cas

Rosalba Giugno, Manuel Tognon, Luca Pinello, Nicola Bombieri et al.
Bioinformatics
CRISPR and Genetic Engineering
article

CRISPR-HAWK: Haplotype- and Variant-Aware Guide Design Toolkit for CRISPR-Cas

Rosalba Giugno, Manuel Tognon, Luca Pinello, Nicola Bombieri, Alisa Kumbara, Arianna Fontanesi, Giulia Carone
article en

Abstract

MOTIVATION: Current CRISPR guide RNA design tools rely on reference genomes, overlooking how genetic variation impacts editing outcomes. As genome editing advances toward clinical applications, incorporating population diversity becomes essential for ensuring therapeutic efficacy across diverse populations. RESULTS: We present CRISPR-HAWK, a framework integrating individual- and population-scale variants and haplotypes into gRNA design. Analyzing therapeutic targets across 79,648 genomes reveals that genetic variants substantially alter guide performance. For the clinically approved sickle cell disease therapeutic guide targeting BCL11A, we identify haplotypes that completely abolish predicted cutting activity. Across seven therapeutic loci, 82.5% of guides contain variants modifying on-target activity. Variants also create novel protospacer adjacent motif sites generating individual-specific guides invisible to reference-based design. These findings demonstrate that variant-aware selection is critical for equitable genome editing. AVAILABILITY: CRISPR-HAWK is available at https://github.com/pinellolab/CRISPR-HAWK and https://github.com/InfOmics/CRISPR-HAWK. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bioinformatics
Broad Institute (US), University of Verona (IT), Harvard University (US), Center for Cancer Research (US)
Massachusetts General Hospital, Università degli Studi di Verona, National Institutes of Health
Good health and well-being
Openalex Percentile: Top 24%
CRISPR and Genetic Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.