Prof. Zohar Yakhini's Lab


The Yakhini research group is based at the School of Computer Science in the Interdisciplinary Center in Herzliya and in the Faculty of Computer Science at the Technion in Haifa. It is an active bioinformatics and data science research group with scientists affiliated with both institutes. Yakhini’s group research spans several domains of interest, including data science, machine learning, statistics and algorithmics with applications in molecular biology and medical science. A special focus is data analysis for cancer research, image analysis for digital pathology, and computational aspects of synthetic biology and genome editing.

Partner's Genome Editing vision​

The CRISPR technology is widely used for genome editing, both in biotechnology and therapeutic settings. To bring CRISPR to the clinic as well as to improve its performance as a tool in experimental and synthetic biology, in a diverse set of organisms and systems, CRISPR guide RNAs (gRNAs) must have high on-target editing rates, with minimal off-target activity. One of the important challenges in evaluating the specificity and safety of CRISPR genome editing is that there is no standard pipeline for measuring these outcomes. Our vision is to develop and optimize robust and standardized pipelines and analysis tools for accurately quantify CRISPR on- and off-target activities, to enable the implementation of this genome editing technology in a wide range of biological applications, ranging from research to development of new therapies, and beyond.

Partner's activity within the consortium

The Yakhini group is an integral part of work group 2 of the consortium, working with the Hendel Lab to facilitate, optimize and develop on- and off-target quantification protocols for CRISPR-IL consortium members. Specifically, the Yakhini group develops, and design analysis tools required for the analysis measurement assays developed by the Hendel lab. These tools are based on refining and optimizing our existing tools that utilize machine learning and statistics to evaluate off- and on- target activities from experimental data. The Yakhini group develops a robust and standardized pipeline for quantifying on-target and off-target estimates based on our existing process which utilizes GuideSeq and rhAmpSeq analytics, combined with CRISPECTOR analysis tool (jointly developed with the Hendel lab; Amit et. al., Nat Commun. 2021).