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2. A Critical Review of the Impact of Candidate Copy Number Variants on Autism Spectrum Disorders

Author

Abedini, Seyedeh Sedigheh, Akhavan, Shiva, Heng, Julian, Alizadehsani, Roohallah, Dehzangi, Iman, Bauer, Denis C., and Rokny, Hamid

Subjects
Quantitative Biology - Genomics, Quantitative Biology - Neurons and Cognition
Abstract

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) that is caused by genetic, epigenetic, and environmental factors. Recent advances in genomic analysis have uncovered numerous candidate genes with common and/or rare mutations that increase susceptibility to ASD. In addition, there is increasing evidence that copy number variations (CNVs), single nucleotide polymorphisms (SNPs), and unusual de novo variants negatively affect neurodevelopment pathways in various ways. The overall rate of copy number variants found in patients with autism is 10%-20%, of which 3%-7% can be detected cytogenetically. Although the role of submicroscopic CNVs in ASD has been studied recently, their association with genomic loci and genes has not been properly studied. In this review, we focus on 47 ASD-associated CNV regions and their related genes. Here, we identify 1,632 protein-coding genes and long non-coding RNAs (lncRNAs) within these regions. Among them, 552 are significantly expressed in the brain. Using a list of ASD-associated genes from SFARI, we detect 17 regions containing at least one known ASD-associated protein-coding genes. Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding genes with brain-enriched expression and nervous system phenotype in mouse mutant and one lncRNAs with both brain-enriched expression and upregulation in iPSC to neuron differentiation. Our analyses highlight the diversity of genetic lesions of CNV regions that contribute to ASD and provide new genetic evidence that lncRNA genes may contribute to etiology of ASD. In addition, the discovered CNVs will be a valuable resource for diagnostic facilities, therapeutic strategies, and research in terms of variation priority.

Published
2023

3. Applying data technologies to combat AMR: current status, challenges, and opportunities on the way forward

Author

Chindelevitch, Leonid, Jauneikaite, Elita, Wheeler, Nicole E., Allel, Kasim, Ansiri-Asafoakaa, Bede Yaw, Awuah, Wireko A., Bauer, Denis C., Beisken, Stephan, Fan, Kara, Grant, Gary, Graz, Michael, Khalaf, Yara, Liyanapathirana, Veranja, Montefusco-Pereira, Carlos, Mugisha, Lawrence, Naik, Atharv, Nanono, Sylvia, Nguyen, Anthony, Rawson, Timothy, Reddy, Kessendri, Ruzante, Juliana M., Schmider, Anneke, Stocker, Roman, Unruh, Leonhardt, Waruingi, Daniel, Graz, Heather, and van Dongen, Maarten

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications, I.2.1, J.3
Abstract

Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of quality of life attributed to AMR. Numerous interventions have been proposed to control the development of AMR and mitigate the risks posed by its spread. This paper reviews key aspects of bacterial AMR management and control which make essential use of data technologies such as artificial intelligence, machine learning, and mathematical and statistical modelling, fields that have seen rapid developments in this century. Although data technologies have become an integral part of biomedical research, their impact on AMR management has remained modest. We outline the use of data technologies to combat AMR, detailing recent advancements in four complementary categories: surveillance, prevention, diagnosis, and treatment. We provide an overview on current AMR control approaches using data technologies within biomedical research, clinical practice, and in the "One Health" context. We discuss the potential impact and challenges wider implementation of data technologies is facing in high-income as well as in low- and middle-income countries, and recommend concrete actions needed to allow these technologies to be more readily integrated within the healthcare and public health sectors., Comment: 65 pages, 3 figures

Published
2022

4. Scalable genomic data exchange and analytics with sBeacon

Author

Wickramarachchi, Anuradha, Hosking, Brendan, Jain, Yatish, Grimes, John, O’Brien, Mitchell J., Wright, Tracey, Burgess, Mark A., Lin, Victor San Kho, Reisinger, Florian, Hofmann, Oliver, Lawley, Michael, Wilson, Laurence O. W., Twine, Natalie A., and Bauer, Denis C.

Published
2023
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11. Future-proofing genomic data and consent management: a comprehensive review of technology innovations

Author

Oliva, Adrien, primary, Kaphle, Anubhav, additional, Reguant, Roc, additional, Sng, Letitia M F, additional, Twine, Natalie A, additional, Malakar, Yuwan, additional, Wickramarachchi, Anuradha, additional, Keller, Marcel, additional, Ranbaduge, Thilina, additional, Chan, Eva K F, additional, Breen, James, additional, Buckberry, Sam, additional, Guennewig, Boris, additional, Haas, Matilda, additional, Brown, Alex, additional, Cowley, Mark J, additional, Thorne, Natalie, additional, Jain, Yatish, additional, and Bauer, Denis C, additional

Published
2024
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15. Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

Author

Logullo, Francesco O., Simone, Isabella, Logroscino, Giancarlo, Salvi, Fabrizio, Bartolomei, Ilaria, Borghero, Giuseppe, Murru, Maria Rita, Costantino, Emanuela, Pani, Carla, Puddu, Roberta, Caredda, Carla, Piras, Valeria, Tranquilli, Stefania, Cuccu, Stefania, Corongiu, Daniela, Melis, Maurizio, Milia, Antonio, Marrosu, Francesco, Marrosu, Maria Giovanna, Floris, Gianluca, Cannas, Antonino, Capasso, Margherita, Caponnetto, Claudia, Mancardi, Gianluigi, Origone, Paola, Mandich, Paola, Conforti, Francesca L., Cavallaro, Sebastiano, Mora, Gabriele, Marinou, Kalliopi, Sideri, Riccardo, Penco, Silvana, Mosca, Lorena, Lunetta, Christian, Pinter, Giuseppe Lauria, Corbo, Massimo, Riva, Nilo, Carrera, Paola, Volanti, Paolo, Mandrioli, Jessica, Fini, Nicola, Fasano, Antonio, Tremolizzo, Lucio, Arosio, Alessandro, Ferrarese, Carlo, Trojsi, Francesca, Tedeschi, Gioacchino, Monsurrò, Maria Rosaria, Piccirillo, Giovanni, Femiano, Cinzia, Ticca, Anna, Ortu, Enzo, La Bella, Vincenzo, Spataro, Rossella, Colletti, Tiziana, Sabatelli, Mario, Zollino, Marcella, Conte, Amelia, Luigetti, Marco, Lattante, Serena, Marangi, Giuseppe, Santarelli, Marialuisa, Petrucci, Antonio, Pugliatti, Maura, Pirisi, Angelo, Parish, Leslie D., Occhineri, Patrizia, Giannini, Fabio, Battistini, Stefania, Ricci, Claudia, Benigni, Michele, Cau, Tea B., Loi, Daniela, Calvo, Andrea, Moglia, Cristina, Brunetti, Maura, Barberis, Marco, Restagno, Gabriella, Casale, Federico, Marrali, Giuseppe, Fuda, Giuseppe, Ossola, Irene, Cammarosano, Stefania, Canosa, Antonio, Ilardi, Antonio, Manera, Umberto, Grassano, Maurizio, Tanel, Raffaella, Pisano, Fabrizio, Harms, Matthew B., Goldstein, David B., Shneider, Neil A., Goutman, Stephen, Simmons, Zachary, Miller, Timothy M., Chandran, Siddharthan, Pal, Suvankar, Manousakis, Georgios, Appel, Stanley H., Simpson, Ericka, Wang, Leo, Baloh, Robert H., Gibson, Summer, Bedlack, Richard, Lacomis, David, Sareen, Dhruv, Sherman, Alexander, Bruijn, Lucie, Penny, Michelle, Allen, Andrew S., Appel, Stanley, Bedlack, Richard S., Boone, Braden E., Brown, Robert, Carulli, John P., Chesi, Alessandra, Chung, Wendy K., Cirulli, Elizabeth T., Cooper, Gregory M., Couthouis, Julien, Day-Williams, Aaron G., Dion, Patrick A., Gitler, Aaron D., Glass, Jonathan D., Han, Yujun, Harris, Tim, Hayes, Sebastian D., Jones, Angela L., Keebler, Jonathan, Krueger, Brian J., Lasseigne, Brittany N., Levy, Shawn E., Lu, Yi-Fan, Maniatis, Tom, McKenna-Yasek, Diane, Myers, Richard M., Petrovski, Slavé, Pulst, Stefan M., Raphael, Alya R., Ravits, John M., Ren, Zhong, Rouleau, Guy A., Sapp, Peter C., Sims, Katherine B., Staropoli, John F., Waite, Lindsay L., Wang, Quanli, Wimbish, Jack R., Xin, Winnie W., Phatnani, Hemali, Kwan, Justin, Broach, James R., Arcila-Londono, Ximena, Lee, Edward B., Van Deerlin, Vivianna M., Fraenkel, Ernest, Ostrow, Lyle W., Baas, Frank, Zaitlen, Noah, Berry, James D., Malaspina, Andrea, Fratta, Pietro, Cox, Gregory A., Thompson, Leslie M., Finkbeiner, Steve, Dardiotis, Efthimios, Hornstein, Eran, MacGowan, Daniel J., Heiman-Patterson, Terry, Hammell, Molly G., Patsopoulos, Nikolaos A., Dubnau, Joshua, Nath, Avindra, Kaye, Julia, Finkbeiner, Steven, Wyman, Stacia, LeNail, Alexander, Lima, Leandro, Rothstein, Jeffrey D., Svendsen, Clive N., Van Eyk, Jenny, Maragakis, Nicholas J., Kolb, Stephen J., Cudkowicz, Merit, Baxi, Emily, Benatar, Michael, Taylor, J. Paul, Wu, Gang, Rampersaud, Evadnie, Wuu, Joanne, Rademakers, Rosa, Züchner, Stephan, Schule, Rebecca, McCauley, Jacob, Hussain, Sumaira, Cooley, Anne, Wallace, Marielle, Clayman, Christine, Barohn, Richard, Statland, Jeffrey, Ravits, John, Swenson, Andrea, Jackson, Carlayne, Trivedi, Jaya, Khan, Shaida, Katz, Jonathan, Jenkins, Liberty, Burns, Ted, Gwathmey, Kelly, Caress, James, McMillan, Corey, Elman, Lauren, Pioro, Erik, Heckmann, Jeannine, So, Yuen, Walk, David, Maiser, Samuel, Zhang, Jinghui, Silani, Vincenzo, Ticozzi, Nicola, Gellera, Cinzia, Ratti, Antonia, Taroni, Franco, Lauria, Giuseppe, Verde, Federico, Fogh, Isabella, Tiloca, Cinzia, Comi, Giacomo P., Sorarù, Gianni, Cereda, Cristina, D’Alfonso, Sandra, Corrado, Lucia, De Marchi, Fabiola, Corti, Stefania, Ceroni, Mauro, Mazzini, Letizia, Siciliano, Gabriele, Filosto, Massimiliano, Inghilleri, Maurizio, Peverelli, Silvia, Colombrita, Claudia, Poletti, Barbara, Maderna, Luca, Del Bo, Roberto, Gagliardi, Stella, Querin, Giorgia, Bertolin, Cinzia, Pensato, Viviana, Castellotti, Barbara, Camu, William, Mouzat, Kevin, Lumbroso, Serge, Corcia, Philippe, Meininger, Vincent, Besson, Gérard, Lagrange, Emmeline, Clavelou, Pierre, Guy, Nathalie, Couratier, Philippe, Vourch, Patrick, Danel, Véronique, Bernard, Emilien, Lemasson, Gwendal, Al Kheifat, Ahmad, Al-Chalabi, Ammar, Andersen, Peter, Basak, A. Nazli, Blair, Ian P., Chio, Adriano, Cooper-Knock, Jonathan, de Carvalho, Mamede, Dekker, Annelot, Drory, Vivian, Redondo, Alberto Garcia, Gotkine, Marc, Hardiman, Orla, Hide, Winston, Iacoangeli, Alfredo, Glass, Jonathan, Kenna, Kevin, Kiernan, Matthew, Kooyman, Maarten, Landers, John, McLaughlin, Russell, Middelkoop, Bas, Mill, Jonathan, Neto, Miguel Mitne, Moisse, Mattieu, Pardina, Jesus Mora, Morrison, Karen, Newhouse, Stephen, Pinto, Susana, Pulit, Sara, Robberecht, Wim, Shatunov, Aleksey, Shaw, Pamela, Shaw, Chris, Sproviero, William, Tazelaar, Gijs, van Damme, Philip, van den Berg, Leonard, van der Spek, Rick, van Eijk, Kristel, van Es, Michael, van Rheenen, Wouter, van Vugt, Joke, Veldink, Jan, Weber, Markus, Williams, Kelly L., Zatz, Mayana, Bauer, Denis C., Twine, Natalie A., Nicolas, Aude, Kenna, Kevin P., Renton, Alan E., Faghri, Faraz, Chia, Ruth, Dominov, Janice A., Kenna, Brendan J., Nalls, Mike A., Keagle, Pamela, Rivera, Alberto M., Murphy, Natalie A., van Vugt, Joke J.F.A., Geiger, Joshua T., Van der Spek, Rick A., Pliner, Hannah A., Shankaracharya, Smith, Bradley N., Topp, Simon D., Abramzon, Yevgeniya, Gkazi, Athina Soragia, Eicher, John D., Kenna, Aoife, Messina, Sonia, Simone, Isabella L., Ferrucci, Luigi, Moreno, Cristiane de Araujo Martins, Kamalakaran, Sitharthan, Musunuri, Rajeeva Lochan, Evani, Uday Shankar, Abhyankar, Avinash, Zody, Michael C., Wyman, Stacia K., LeNail, Alex, Van Eyk, Jennifer E., Laaksovirta, Hannu, Myllykangas, Liisa, Jansson, Lilja, Valori, Miko, Ealing, John, Hamdalla, Hisham, Rollinson, Sara, Pickering-Brown, Stuart, Orrell, Richard W., Sidle, Katie C., Hardy, John, Singleton, Andrew B., Johnson, Janel O., Arepalli, Sampath, Polak, Meraida, Asress, Seneshaw, Al-Sarraj, Safa, King, Andrew, Troakes, Claire, Vance, Caroline, de Belleroche, Jacqueline, ten Asbroek, Anneloor L.M.A., Muñoz-Blanco, José Luis, Hernandez, Dena G., Ding, Jinhui, Gibbs, J. Raphael, Scholz, Sonja W., Floeter, Mary Kay, Campbell, Roy H., Landi, Francesco, Bowser, Robert, MacGowan, Daniel J.L., Kirby, Janine, Pioro, Erik P., Pamphlett, Roger, Broach, James, Gerhard, Glenn, Dunckley, Travis L., Brady, Christopher B., Kowall, Neil W., Troncoso, Juan C., Le Ber, Isabelle, Heiman-Patterson, Terry D., Kamel, Freya, Van Den Bosch, Ludo, Strom, Tim M., Meitinger, Thomas, Van Eijk, Kristel R., Moisse, Matthieu, McLaughlin, Russell L., Van Es, Michael A., Boylan, Kevin B., Van Blitterswijk, Marka, Morrison, Karen E., Mora, Jesús S., Drory, Vivian E., Shaw, Pamela J., Turner, Martin R., Talbot, Kevin, Fifita, Jennifer A., Nicholson, Garth A., Esteban-Pérez, Jesús, García-Redondo, Alberto, Rogaeva, Ekaterina, Zinman, Lorne, Cooper-Knock, Johnathan, Brice, Alexis, Goutman, Stephen A., Feldman, Eva L., Gibson, Summer B., Van Damme, Philip, Ludolph, Albert C., Andersen, Peter M., Weishaupt, Jochen H., Trojanowski, John Q., Brown, Robert H., Jr., van den Berg, Leonard H., Veldink, Jan H., Stone, David J., Tienari, Pentti, Chiò, Adriano, Shaw, Christopher E., Traynor, Bryan J., and Landers, John E.

Published
2018
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17. Predicting CRISPR-Cas12a guide efficiency for targeting using machine learning.

Author

O'Brien, Aidan, Bauer, Denis C., and Burgio, Gaetan

Subjects
*MACHINE learning, *CRISPRS, *GENOME editing, *RANDOM forest algorithms, *SYNTHETIC biology
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. [ABSTRACT FROM AUTHOR]

Published
2023
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20. Methylome and transcriptome maps of human visceral and subcutaneous adipocytes reveal key epigenetic differences at developmental genes

Author

Bradford, Stephen T., Nair, Shalima S., Statham, Aaron L., van Dijk, Susan J., Peters, Timothy J., Anwar, Firoz, French, Hugh J., von Martels, Julius Z. H., Sutcliffe, Brodie, Maddugoda, Madhavi P., Peranec, Michelle, Varinli, Hilal, Arnoldy, Rosanna, Buckley, Michael, Ross, Jason P., Zotenko, Elena, Song, Jenny Z., Stirzaker, Clare, Bauer, Denis C., Qu, Wenjia, Swarbrick, Michael M., Lutgers, Helen L., Lord, Reginald V., Samaras, Katherine, Molloy, Peter L., and Clark, Susan J.

Published
2019
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23. Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Author

SNG, Letitia M. F., SHARMA, Reevanshi, BAGOT, Sam, BAUER, Denis C., and TWINE, Natalie A.

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. [ABSTRACT FROM AUTHOR]

Published
2023
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24. An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Author

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, and REGUANT, Roc

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. [ABSTRACT FROM AUTHOR]

Published
2023
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25. PEPS: Polygenic Epistatic Phenotype Simulation.

Author

REGUANT, Roc, O'BRIEN, Mitchell J., BAYAT, Arash, HOSKING, Brendan, JAIN, Yatish, TWINE, Natalie A., and BAUER, Denis C.

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. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

Author

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

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. [ABSTRACT FROM AUTHOR]

Published
2023
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29. Predicting SUMOylation Sites

Author

Bauer, Denis C., Buske, Fabian A., Bodén, Mikael, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Chetty, Madhu, editor, Ngom, Alioune, editor, and Ahmad, Shandar, editor

Published
2008
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33. Applying a risk governance approach to examine how professionals perceive the benefits and risks of clinical genomics in Australian healthcare.

Author

Malakar, Yuwan, Lacey, Justine, Twine, Natalie A., and Bauer, Denis C.

Subjects
GENOMICS, RESEARCH personnel, RISK perception, PROFESSIONAL employees, SEMI-structured interviews
Abstract

Clinical genomics is a system of multiple stakeholders and institutions. Yet, studies focusing on the comparative perspectives of these stakeholders are limited. This study engages four groups of professionals (clinical geneticists, genetic counselors, laboratory professionals, and researchers) working in clinical genomics to investigate their perceptions of the benefits and risks of using genomics in Australian healthcare. The study is underpinned by a risk governance approach. For data collection, qualitative semi-structured interviews were used. Our results show that all professionals unanimously identified that the benefit of clinical genomics lies in improving health outcomes for patients. However, the risks associated with delivering this benefit differed by professional category. We found that the further the profession was from the patient (e.g. researcher) the narrower the perceived risks were amongst the individuals interviewed. However, "privacy" as a perceived risk was ranked highly by all professions indicating a shared desire for responsible data governance practices. [ABSTRACT FROM AUTHOR]

Published
2023
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34. The inequity of targeted cystic fibrosis reproductive carrier screening tests in Australia.

Author

Shum, Bennett O. V., Sng, Letitia M. F., Ruseckaite, Rasa, Henner, Ilya, Twine, Natalie, Bauer, Denis C., Wilgen, Urs, Pretorius, Carel, Barahona, Paulette, Ungerer, Jacobus P. J., and Bennett, Glenn

Abstract

Objective: European and Australian guidelines for cystic fibrosis (CF) reproductive carrier screening recommend testing a small number of high frequency CF causing variants, rather than comprehensive CFTR sequencing. The study objective was to determine variant detection rates of commercially available targeted reproductive carrier screening tests in Australia. Methods: Next‐generation DNA sequencing of the CFTR gene was performed on 2552 individuals from a whole population sample to identify CF causing variants. The variant detection rates of two commercially available Australian reproductive carrier screening tests, which target 50 or 175 CF causing variants, in this population were calculated. The ethnicity of individuals was determined using principal component analysis. Results: Variant detection rates of the tests for 50 and 175 CF causing variants were 88.2% and 90.8%, respectively. No CF causing variants in individuals of East Asian ethnicity (n = 3) were detected by either test, while >86.6% (n = 69) of CF causing variants in Europeans would be identified by either test. Conclusions: Reproductive carrier screening tests for a targeted set of high frequency CF variants are unable to detect approximately 10% of CF variants in a multiethnic Australian population, and individuals of East Asian ethnicity are disproportionally affected by this test limitation. Key points: What's already known about this topic? Reproductive carrier screening for cystic fibrosis (CF) is becoming increasingly common in Europe and Australia. European and Australian guidelines recommend testing for a targeted set of the most common CF causing variants. What does this study add? Reproductive carrier screening tests for a targeted set of CF causing variants are unable to detect approximately 10% of CF variants in a multiethnic Australian population and individuals of East Asian ethnicity are disproportionally affected by this limitation of the test. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Optimized nickase- and nuclease-based prime editing in human and mouse cells

Author

Adikusuma, Fatwa, primary, Lushington, Caleb, additional, Arudkumar, Jayshen, additional, Godahewa, Gelshan I, additional, Chey, Yu C J, additional, Gierus, Luke, additional, Piltz, Sandra, additional, Geiger, Ashleigh, additional, Jain, Yatish, additional, Reti, Daniel, additional, Wilson, Laurence O W, additional, Bauer, Denis C, additional, and Thomas, Paul Q, additional

Published
2021
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37. Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing

Author

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

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.

Published
2021

38. Genetic Analysis of Tryptophan Metabolism Genes in Sporadic Amyotrophic Lateral Sclerosis

Author

Fifita, Jennifer A., primary, Chan Moi Fat, Sandrine, additional, McCann, Emily P., additional, Williams, Kelly L., additional, Twine, Natalie A., additional, Bauer, Denis C., additional, Rowe, Dominic B., additional, Pamphlett, Roger, additional, Kiernan, Matthew C., additional, Tan, Vanessa X., additional, Blair, Ian P., additional, and Guillemin, Gilles J., additional

Published
2021
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39. Genetic analysis of GLT8D1 and ARPP21 in Australian familial and sporadic amyotrophic lateral sclerosis

Author

Chan Moi Fat, Sandrine, primary, McCann, Emily P., additional, Williams, Kelly L., additional, Henden, Lyndal, additional, Twine, Natalie A., additional, Bauer, Denis C., additional, Pamphlett, Roger, additional, Kiernan, Matthew C., additional, Rowe, Dominic B., additional, Nicholson, Garth A., additional, Fifita, Jennifer A., additional, and Blair, Ian P., additional

Published
2021
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44. How COVID-19 has accelerated the journey to data-driven health decisions

Author

Bauer, Denis C.

Subjects
Uncategorized
Abstract

COVID-19 has highlighted the need to substantially improve disease preparedness to resolve this pandemic and avoid future ones. Digital health and the use of technology at large will be a key contributor to this preparedness. This session talks about how CSIRO has leveraged cloud-native technologies to advance three areas of the COVID-19 response: firstly we worked with GISAID, the largest data resource for the virus causing COVID-19, and use standard health terminologies (FHIR) to help collect clinical patient data. This feeds into a docker-based workflow that creates identifying “fingerprints” of the virus for guiding vaccine developments and investigating whether there are more pathogenic versions of the virus. Secondly, we developed a fully serverless web-service for tailoring diagnostics efforts, capable of differentiating between strains. Thirdly, we are creating a serverless COVID-19 analysis platform that allows distributed genomics and patient data to be shared and analysed in a privacy- and ownership-preserving manner and functioning as an surveillance system for detecting more virulent strains early. ABOUT THE AUTHOR(S)Dr Denis Bauer is CSIRO’s Principal Research Scientist leading the Transformational Bioinformatics group. She is an internationally recognised expert in machine learning and cloud-based genomics, having keynoted AWS Summits (’18), Open Data Science Conference (India, ’18) and International conference on Bioinformatics (Indonesia ‘19). Her achievements include developing open-source machine-learning cloud services that are used by 10,000 researchers and contributing to Australia’s COVID-19 response.

Published
2020
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45. BitEpi: A Fast and Accurate Exhaustive Higher-Order Epistasis Search

Author

Bayat, Arash, Hosking, Brendan, Jain, Yatish, Hosking, Cameron, Twine, Natalie, and Bauer, Denis C.

Abstract

Motivation Higher-order epistatic interactions can be the driver for complex genetic diseases. An exhaustive search is the most accurate method for identifying interactive SNPs. While there is a fast bitwise algorithm for pairwise exhaustive searching (BOOST), higher-order exhaustive searching has yet to be efficiently optimized. Results In this paper, we introduce BitEpi, a program to detect and visualize higher-order epistatic interactions using an exhaustive search. BitEpi introduces a novel bitwise algorithm that can perform higher-order analysis more quickly and is the first bitwise algorithm to search for 4-SNP interactions. Furthermore, BitEpi increases detection accuracy by using a novel entropy-based power analysis. BitEpi visualizes significant interactions in a publication-ready interactive graph. BitEpi is 56 times faster than MDR for 4-SNP searching and is up to 1.33 and 2.09 times more accurate than BOOST and MPI3SNP respectively. Availability Codes and data are publicly available on GitHub https://github.com/aehrc/BitEpi . BitEpi is also available on CodeOcean https://doi.org/10.24433/CO.3671084.v1 . Contact: Arash.Bayat@csiro.au - Denis.Bauer@csiro.au Supplementary information Supplementary data are available at bioRxiv online.

Published
2019
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50. Host Porphobilinogen Deaminase Deficiency Confers Malaria Resistance in Plasmodium chabaudi but Not in Plasmodium berghei or Plasmodium falciparum During Intraerythrocytic Growth

Author

Schnider, Cilly Bernardette, primary, Yang, Hao, additional, Starrs, Lora, additional, Ehmann, Anna, additional, Rahimi, Farid, additional, Di Pierro, Elena, additional, Graziadei, Giovanna, additional, Matthews, Kathryn, additional, De Koning-Ward, Tania, additional, Bauer, Denis C., additional, Foote, Simon J., additional, Burgio, Gaetan, additional, and McMorran, Brendan J., additional

Published
2020
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