The Transformational Bioinformatics Group published a new software (GOANA [1]) to score genome editing approaches: It helps make Gene Therapy become a reality, by detecting and comparing rare mutations that may be accidentally introduced by the different editing approaches.

Since its discovery in 2012, CRISPR-based genome engineering has been used in a variety of applications; from cancer treatments to diagnostics; from improving crop yield to eradicating disease vectors. While the underlying aim of installing genetic changes into the genome are the same in all applications, different domain-specific approaches are needed to cover such a broad spectrum.

All genome engineering methods behave differently

Consequently, there are a plethora of different approaches (nucleases, base editors, transposases/recombinases and prime editors), each with their own strength and weaknesses [2]. For example, while nucleases are an all-purpose editing approach, Prime editors are better at installing specific mutations but have a lower efficiency. Expanding the application space further are different enzymes that interact with the genome (Cas9, Cas12a, and Cas13) [3]. For example, Cas12a has higher specificity than the typically used Cas9, but a lower overall binding capability; while Cas13 preferentially binds RNA instead of DNA. On top of that, new application parameters (e.g. different organisms or delivery method) can affect known strengths and introduce new weaknesses.

Generating a score card for each method helps to compare the different approaches. Specifically, choosing the optimal strategy requires the ability to score editing success in the intended set-up and compare outcomes quantitatively between different approaches.

As published in The CRISPR Journal [1], CSIRO has developed a new software, GOANA, able to identify and catalogue changes (SNP, insertion, deletions) introduced by genome editing technology. This is especially crucial for medical applications, such as gene therapy, where characterizing even rare outcomes is crucial to reaching the precision and control needed for this domain.

Cloud-based and editor-agnostic, GOANA is future ready

GOANA is the first tool able to tease apart the contribution of each individual mutation. While most tools simply compare the overall number of mutations before and after CRISPR application and report the observed difference, GOANA provides a more accurate measurement, which distinguishes a modification from natural occurring variability.

GOANA is also applicable beyond CRISPR. It measures the effects of any mutation-producing method. This includes new technologies such as base-editors and gene therapy applications, where entire genes need to be inserted.

GOANA is sensitive to SNPs, deletions, and insertions of any size. It processes large datasets (from high-throughput experiments) and is fast (4000% faster than the closest competitor).

GOANA is available as a webapp ( This makes analysis convenient and because the computation is done client-side the webpage does not require private/sensitive data be uploaded.

Ultimately, GOANA will help create the dataset for Artificial Intelligence tools, such as Be-HIVE [4], to learn the situation-specific behaviour of the different nucleases and editing technologies, creating a recommender system for the future.

[1] Daniel Reti, Aidan O'Brien, Pascal Wetzel, Aidan Tay, Denis C. Bauer, Laurence O.W. Wilson, GOANA: A Universal High-Throughput Web Service for Assessing and Comparing the Outcome and Efficiency of Genome Editing Experiments The CRISPR Journal April 2021 DOI:
[2] Andrew V. Anzalone, Luke W. Koblan, David R. Liu, Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors Nature Biotechnology, 2020 DOI:
[3] Fancheng Yan, William Wang, Jiaqiang Zhang, CRISPR-Cas12 and Cas13: the lesser known siblings of CRISPR-Cas9 Cell Biology and Toxicology, August 2019 DOI:
[4] Mandana Arbab, Max W. Shen, Beverly Mok, Christopher Wilson, Żaneta Matuszek, Christopher A. Cassa, David R. Liu, Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning Cell, July 2020 DOI: