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Article ; Online: Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning.

O'Brien, Aidan / Bauer, Denis C / Burgio, Gaetan

2023 Volume 18, Issue 10, Page(s) e0292924

Abstract: Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)-Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to ... ...

Abstract	Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)-Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.
MeSH term(s)	CRISPR-Cas Systems/genetics ; Gene Editing ; Endonucleases/genetics ; RNA ; Nucleotides
Chemical Substances	Endonucleases (EC 3.1.-) ; RNA (63231-63-0) ; Nucleotides
Language	English
Publishing date	2023-10-17
Publishing country	United States
Document type	Meta-Analysis ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0292924
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Sng, Letitia M F / Sharma, Reevanshi / Bagot, Sam / Bauer, Denis C / Twine, Natalie A

Studies in health technology and informatics

2024 Volume 310, Page(s) 1021–1025

Abstract: Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, ... ...

Abstract	Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.
MeSH term(s)	Humans ; Coronary Artery Disease/diagnostic imaging ; Coronary Artery Disease/genetics ; Carotid Intima-Media Thickness ; Phenotype ; Genotype ; Machine Learning
Language	English
Publishing date	2024-01-25
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI231119
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Article ; Online: Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

Jain, Yatish / Izzath, Fathima Afra Mohamed / Wilson, Laurence O W / Bauer, Denis C

Studies in health technology and informatics

2024 Volume 310, Page(s) 770–774

Abstract: With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and ... ...

Abstract	With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.
MeSH term(s)	Data Visualization ; CRISPR-Cas Systems ; RNA, Guide, CRISPR-Cas Systems ; Engineering ; Genomics
Chemical Substances	RNA, Guide, CRISPR-Cas Systems
Language	English
Publishing date	2024-01-25
Publishing country	Netherlands
Document type	Review ; Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI231069
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Article ; Online: A Navigation System for Base Editing: Are We There Yet?

Bauer, Denis C / Wilson, Laurence O W

The CRISPR journal

2020 Volume 3, Issue 4, Page(s) 224–225

Language	English
Publishing date	2020-08-24
Publishing country	United States
Document type	Journal Article ; Comment
ZDB-ID	3017891-5
ISSN	2573-1602 ; 2573-1599
ISSN (online)	2573-1602
ISSN	2573-1599
DOI	10.1089/crispr.2020.29097.dcb
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Article ; Online: PEPS: Polygenic Epistatic Phenotype Simulation.

Reguant, Roc / O'Brien, Mitchell J / Bayat, Arash / Hosking, Brendan / Jain, Yatish / Twine, Natalie A / Bauer, Denis C

Studies in health technology and informatics

2024 Volume 310, Page(s) 810–814

Abstract: Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, ... ...

Abstract	Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.
MeSH term(s)	Humans ; Computer Simulation ; Phenotype ; Genotype ; Models, Statistical ; Biotechnology
Language	English
Publishing date	2024-01-25
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI231077
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Article ; Online: Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia.

Malakar, Yuwan / Lacey, Justine / Twine, Natalie A / McCrea, Rod / Bauer, Denis C

European journal of human genetics : EJHG

2023 Volume 32, Issue 5, Page(s) 506–512

Abstract: There are inherent complexities and tensions in achieving a responsible balance between safeguarding patients' privacy and sharing genomic data for advancing health and medical science. A growing body of literature suggests establishing patient genomic ... ...

Abstract	There are inherent complexities and tensions in achieving a responsible balance between safeguarding patients' privacy and sharing genomic data for advancing health and medical science. A growing body of literature suggests establishing patient genomic data ownership, enabled by blockchain technology, as one approach for managing these priorities. We conducted an online survey, applying a mixed methods approach to collect quantitative (using scale questions) and qualitative data (using open-ended questions). We explored the views of 117 genomic professionals (clinical geneticists, genetic counsellors, bioinformaticians, and researchers) towards patient data ownership in Australia. Data analysis revealed most professionals agreed that patients have rights to data ownership. However, there is a need for a clearer understanding of the nature and implications of data ownership in this context as genomic data often is subject to collective ownership (e.g., with family members and laboratories). This research finds that while the majority of genomic professionals acknowledge the desire for patient data ownership, bioinformaticians and researchers expressed more favourable views than clinical geneticists and genetic counsellors, suggesting that their views on this issue may be shaped by how closely they interact with patients as part of their professional duties. This research also confirms that stronger health system infrastructure is a prerequisite for enabling patient data ownership, which needs to be underpinned by appropriate digital infrastructure (e.g., central vs. decentralised data storage), patient identity ownership (e.g., limited vs. self-sovereign identity), and policy at both federal and state levels.
MeSH term(s)	Humans ; Australia ; Genetic Privacy/standards ; Ownership ; Information Dissemination/ethics ; Genomics/methods ; Male ; Female ; Attitude of Health Personnel
Language	English
Publishing date	2023-01-11
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1141470-4
ISSN	1476-5438 ; 1018-4813
ISSN (online)	1476-5438
ISSN	1018-4813
DOI	10.1038/s41431-022-01273-w
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Article ; Online: An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Diouf, Ibrahima / Grimes, John / O'Brien, Mitchell J / Hassanzadeh, Hamed / Truran, Donna / Ngo, Hoa / Raniga, Parnesh / Lawley, Michael / Bauer, Denis C / Hansen, David / Khanna, Sankalp / Reguant, Roc

Studies in health technology and informatics

2024 Volume 310, Page(s) 820–824

Abstract: Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by ... ...

Abstract	Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.
MeSH term(s)	Humans ; Australia ; Queensland ; Disease Progression ; Health Facilities ; Privacy
Language	English
Publishing date	2024-01-25
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI231079
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Article: Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.

Dunne, Robert / Reguant, Roc / Ramarao-Milne, Priya / Szul, Piotr / Sng, Letitia M F / Lundberg, Mischa / Twine, Natalie A / Bauer, Denis C

Computational and structural biotechnology journal

2023 Volume 21, Page(s) 4354–4360

Abstract: Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of ... ...

Abstract	Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 10
Language	English
Publishing date	2023-09-01
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2023.08.033
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Article ; Online: Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform.

Lundberg, Mischa / Sng, Letitia M F / Szul, Piotr / Dunne, Rob / Bayat, Arash / Burnham, Samantha C / Bauer, Denis C / Twine, Natalie A

Scientific reports

2023 Volume 13, Issue 1, Page(s) 17662

Abstract: Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', ... ...

Abstract	Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.
MeSH term(s)	Humans ; Alzheimer Disease/genetics ; Genome-Wide Association Study ; Epistasis, Genetic ; Machine Learning ; Polymorphism, Single Nucleotide ; Apolipoproteins E/genetics ; Genetic Predisposition to Disease ; Adaptor Proteins, Signal Transducing/genetics
Chemical Substances	Apolipoproteins E ; SH3BP4 protein, human ; Adaptor Proteins, Signal Transducing
Language	English
Publishing date	2023-10-17
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-44378-y
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Article ; Online: Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.

O'Brien, Aidan R / Burgio, Gaetan / Bauer, Denis C

Briefings in bioinformatics

2020 Volume 22, Issue 1, Page(s) 308–314

Abstract: The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have ... ...

Abstract	The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures have made it hard to assess the validity of individual approaches, potentially leading to misinterpretation of ML results. This review aims to close the gap by discussing ML approaches and pitfalls in the context of CRISPR gene-editing applications. Specifically, we address common considerations, such as algorithm choice, as well as problems, such as overestimating accuracy and data interoperability, by providing tangible examples from the genome-engineering domain. Equipping researchers with the knowledge to effectively use ML to better design gene-editing experiments and predict experimental outcomes will help advance the field more rapidly.
MeSH term(s)	Animals ; CRISPR-Cas Systems ; Gene Editing/methods ; Gene Editing/standards ; Genomics/methods ; Genomics/standards ; Humans ; Machine Learning
Language	English
Publishing date	2020-01-29
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbz145
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