Rakesh David

Dr Rakesh David

Data Architect

School of Agriculture, Food and Wine

College of Science

Research interests:

  • Plant Epigenetics
  • Machine Learning to accelerate biological discovery

My research is focussed on non-coding RNA-mediated gene regulatory mechanisms and how this contributes towards physiological adaption. I use a wide variety of approaches that include in planta experiments, deep sequencing and bioinformatic tools to identify novel epigenetic components and their physiological context. See details below for epigenetic related projects that I lead or am involved in.

A parallel research focus of mine is the use of Machine Learning models to analyse complex biological data to reveal new hidden layers of information. A particular focus area is facilitating the ‘upskilling’ of biological data in accordance with the FAIR data principles, so it is analytics ready and can be used (and re-used) for answering multiple research questions.

Research projects:

  1. Epitranscriptome modifications (In association with A/Prof. Iain Searle, School of Biological Sciences, UA): Molecular functions of post-transcriptional RNA modifications, 5-methylcytosine (m5C) in regulating gene expression and responses to the environment. 
  2. RNA transport and signalling: Investigate the role of cell-to-cell and systemic transport of RNA transcripts in response to developmental and environmental cues using grafting techniques in combination with high-throughput RNA sequencing. Identifying molecular signatures of mobile RNA transcripts in plants using bioinformatic approaches. GitHub repository: https://github.com/CharlotteSai/DiRT
  3. Machine Learning based Text Analytics: Natural Language Processing and Machine Learning models for automatic extraction of protein features from unstructured biomedical text. GitHub repository: https://github.com/RhysMenezes/find-a-protein
  4. Understanding the drivers of crop yield variability: Yields of major crops in Australia are often below their water-limited potential. Genotype x Environment x Management complexity results in crop growth with spatial and temporal variability. The GRDC funded project will use Machine Learning-based methods to discover underlying relationships between climate, crop and soil variables that cause variable crop growth and yields to inform paddock management decisions.

Date Position Institution name
2022 - ongoing Data Architect Australian Plant Phenomics Facility
2016 - 2022 Postdoctoral Research Fellow University of Adelaide, Adelaide
2013 - 2015 Postdoctoral Research Fellow University of Adelaide, Adelaide
2012 - 2013 Postdoctoral Research Fellow The Australian National University

Date Title Institution Country
PhD The Australian National University Australia
Masters of Biotechnology The University of Queensland Australia

Year Citation
2026 Zheng, F., Anand, L., Magnani, R., Rodríguez López, C. M., & David, R. (2026). DiRT v2.0: An Optimized Pipeline for Detecting Dicistronic tRNA-mRNA Transcripts in Plants. Bio Protocol, 16(12), 20 pages.
DOI Scopus1 WoS1
2025 David, R., Situmorang, A., Tran, N. N., Maythwe, T., Hessel, V., & Brewer, P. B. (2025). Light is sufficient to compensate for random positioning machine-simulated microgravity in plant roots. npj Microgravity, 11(1), 28-1-28-10.
DOI Scopus2 WoS1
2022 Qu, Y., Guan, R., Yu, L., Berkowitz, O., David, R., Whelan, J., . . . Gilliham, M. (2022). Enhanced reactive oxygen detoxification occurs in salt-stressed soybean roots expressing GmSALT3. Physiologia Plantarum, 174(3), 1-16.
DOI Scopus25 WoS24 Europe PMC19
2022 Wong, M., Levett, K., Lee, A., Box, P., Simons, B., David, R., . . . Thompson, H. (2022). Development and Governance of FAIR Thresholds for a Data Federation. Data Science Journal, 21, 1-12.
DOI Scopus5
2021 David, R., Menezes, R. -J. D., De Klerk, J., Castleden, I. R., Hooper, C. M., Carneiro, G., & Gilliham, M. (2021). Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network. Scientific Reports, 11(1), 1-11.
DOI Scopus5 WoS2 Europe PMC4
2021 Fabres, P. J., Anand, L., Sai, N., Pederson, S., Zheng, F., Stewart, A. A., . . . David, R. (2021). Tissue and regional expression patterns of dicistronic tRNA-mRNA transcripts in grapevine (Vitis vinifera) and their evolutionary co-appearance with vasculature in land plants. Horticultural Reviews, 8(1), 137-1-137-15.
DOI Scopus4 WoS4 Europe PMC3
2021 David, R., Ng, P. Q., Smith, L. M., & Searle, I. R. (2021). Novel allele <i>elh</i> of the <i>UBP14</i> gene affects plant organ size via cell expansion in <i>Arabidopsis thaliana.</i>. microPublication biology, 2021.
DOI Europe PMC3
2020 David, R., Menezes, R. -J., De Klerk, J., Castleden, I., Hooper, C., Carneiro, G., & Gilliham, M. (2020). Identifying protein subcellular localisation in scientific literature using bidirectional deep recurrent neural network.
DOI Europe PMC1
2019 David, R., Byrt, C. S., Tyerman, S. D., Gilliham, M., & Wege, S. (2019). Roles of membrane transporters: connecting the dots from sequence to phenotype. Annals of Botany, 124(2), 201-208.
DOI Scopus13 WoS13 Europe PMC10
2019 Guo, Q., Ng, P. Q., Shi, S., Fan, D., Li, J., Zhao, J., . . . Searle, I. R. (2019). Arabidopsis TRM5 encodes a nuclear-localised bifunctional tRNA guanine and inosine-N1-methyltransferase that is important for growth. PLoS ONE, 14(11), 1-26.
DOI Scopus24 WoS23 Europe PMC23
2019 David, R., Kortschak, R. D., & Searle, I. R. (2019). The root hair defective phenotype of Arabidopsis thaliana Pol IV subunit mutant nrpd1a-3 is associated with a deletion in RHD6. microPublication Biology, 2019, 1-4.
DOI Europe PMC3
2018 Wang, D., Gu, J., David, R., Wang, Z., Yang, S., Searle, I., . . . Timmis, J. (2018). Experimental reconstruction of double-stranded break repair-mediated plastid DNA insertion into the tobacco nucleus. The Plant Journal, 93(2), 227-234.
DOI Scopus10 WoS10 Europe PMC11
2017 David, R., Burgess, A., Parker, B., Li, J., Pulsford, K., Sibbritt, T., . . . Searle, I. (2017). Transcriptome-wide Mapping of RNA 5-Methylcytosine in Arabidopsis mRNAs and non-coding RNAs. The Plant cell, 29(3), 16.00751-1-16.0075-30.
DOI Scopus227 WoS214 Europe PMC209
2016 Burgess, A., David, R., & Searle, I. (2016). Deciphering the epitranscriptome: a green perspective. Journal of Integrative Plant Biology, 58(10), 822-835.
DOI Scopus32 WoS32 Europe PMC29
2015 Burgess, A., David, R., & Searle, I. (2015). Conservation of tRNA and rRNA 5-methylcytosine in the kingdom Plantae. BMC Plant Biology, 15(1), 199-1-199-17.
DOI Scopus107 WoS109 Europe PMC103
2007 Keys, J. R., Tallack, M. R., Hodge, D. J., Cridland, S. O., David, R., & Perkins, A. C. (2007). Genomic organisation and regulation of murine alpha haemoglobin stabilising protein by erythroid Kruppel-like factor. British Journal of Haematology, 136(1), 150-157.
DOI Scopus30 WoS28 Europe PMC22

Year Citation
2017 Burgess, A., David, R., Sibbritt, T., Jones, A., Preiss, T., Searle, I. R., & David, R. (2017). RNA 5-methylcytosine is required for oxidative stress tolerance in Arabidopsis thaliana. Poster session presented at the meeting of COMBIO.
2017 David, R. (2017). CropTiPS database information poster presented at ComBio2017 (Adelaide). Poster session presented at the meeting of Unknown Conference.
DOI
2015 David, R., Lim, H. M., & Searle, I. R. (2015). Identification of pla/ubp14, an Arabidopsis mutant that displays lengthened plastochron and larger organs. Poster session presented at the meeting of Annual Genetics Society of Australasia conference.

Year Citation
2025 Hobern, D., David, R., Aneja, A., Le, H. S., & Hernandez, L. A. (2025). MCCN Case Study 1 - Evaluate impact from environmental events/pressures.
DOI
2025 Hobern, D., Aneja, A., Le, H. S., Hernandez, L. A., & David, R. (2025). MCCN Case Study 2 - Spatial projection via modelled data.
DOI
2025 Hobern, D., Aneja, A., Le, H. S., David, R., & Hernandez, L. A. (2025). MCCN Case Study 3 - Select optimal survey locality.
DOI
2025 Hobern, D., Aneja, A., Le, H. S., Hernandez, L. A., & David, R. (2025). MCCN Case Study 4 - Validating gridded data products.
DOI
2025 Hobern, D., Le, H. S., Aneja, A., David, R., & Hernandez, L. A. (2025). MCCN Case Study 5 - Produce farm zone map.
DOI
2025 Hobern, D., Le, H. S., Aneja, A., Hernandez, L. A., & David, R. (2025). MCCN Case Study 6 - Environmental Correlates for Productivity.
DOI
2024 David, R., Schilling, R., & McDonald, G. (2024). Machine learning to extract maximum value from soil and crop variability, Paddocks pre-processed ML input datasets.
DOI
2024 David, R., Schilling, R., & McDonald, G. (2024). Machine learning to extract maximum value from soil and crop variability, Raw datasets.
DOI
2024 David, R., & Schilling, R. (2024). University of Adelaide National Sodic field trial reference dataset for GRDC Machine Learning Project- UOA2002-007RTX..
DOI
2024 Roy, S. (2024). APPF TPA phenotyping dataset: UA ACPFG (Roy, Schilling) - Barley.
DOI
2024 David, R., Brewer, P., Tran, N. N., & Hessel, V. (2024). Images of Arabidopsis and lettuce seedings grown in mircogravity conditions using the RPM Mini-phytotron experimental setup..
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA (Berger) - Various (Student).
DOI
2023 Berger, B., & Wilkinson, M. (2023). APPF TPA phenotyping dataset: UA AFW (Konate, Wilkinson) - Barley.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Quinoa (trial).
DOI
2023 Atieno, J., & Sutton, T. (2023). APPF TPA phenotyping dataset: UA (Atieno) - Chickpea.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: Ecovortek (Berger) - Various.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: EPPN (Berger) - Canola.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: EPPN (Berger) - Maize (1).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: EPPN (Berger) - Maize (2).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: TU Muenchen (Kipp, Berger) - Wheat.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Berger) - Chickpea (Wheat).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA AFW (Konate, Wilkinson) - Barley.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA (Hansen) - Wheat.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger, Burton) - Barley.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Fake (EPPN N).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Fake (EPPN S).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Maize (leaf rolling).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Quinoa (trial 2).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Rice.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Rice.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Tomato.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger, UGradPrj) - Tomato.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger, VanDenHengel) - Wheat (3D).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Various (camera calibration).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Various (evap 2014).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Various (leaf tracking).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Various (shadow 2014).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Wheat.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Wheat (2010 salt).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Wheat (root 2013).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Berger) - Wheat (UniformityTrial).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Bruning) - Wheat.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (MacAlpine, Berger) - Lantana.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (MacAlpine, Berger) - Various.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Mischis, Berger) - Various.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Mischis, Berger) - Various (N July).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Mischis, Berger) - Various (N June).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Mischis, Berger) - Various (S July).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Mischis, Berger) - Various (S June).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Mischis, KAUST) - Quinoa (pilot).
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Tanner) - Chickpea.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Tanner) - Chickpea.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UA TPA (Tester, Berger) - Wheat.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UCopenhagen (Jall) - Maize.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: Uni Sask (Beattie, Murrell) - Barley.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: Uni Sask (Taran) - Chickpea.
DOI
2023 Berger, B. (2023). APPF TPA phenotyping dataset: UniSA (Seiffert, Berger) - Various.
DOI
2023 Cai, J. (2023). APPF TPA phenotyping dataset: UniSA (Cai) - Wheat.
DOI
2023 Cavagnaro, T., & Watts-Williams, S. (2023). APPF TPA phenotyping dataset: UA (Cavagnaro, Cousins) - Wheat.
DOI
2023 Cavagnaro, T., & Watts-Williams, S. (2023). APPF TPA phenotyping dataset: UA (Cavagnaro, Hue) - Tomato.
DOI
2023 Cavagnaro, T., & Watts-Williams, S. (2023). APPF TPA phenotyping dataset: UA (Cavagnaro) - Tomato.
DOI
2023 Cavagnaro, T., & Watts-Williams, S. (2023). APPF TPA phenotyping dataset: UA (Cavagnaro, Watts-Williams) - Medic.
DOI
2023 Cavagnaro, T., & Watts-Williams, S. (2023). APPF TPA phenotyping dataset: UA (Cavagnaro, Watts-Williams) - Medic (calibration).
DOI
2023 Cavagnaro, T., & Watts-Williams, S. (2023). APPF TPA phenotyping dataset: UA (Solomon) - Maize.
DOI
2023 Collins, N. (2023). APPF TPA phenotyping dataset: UA ACPFG (Collins) - Wheat.
DOI
2023 Collins, N. (2023). APPF TPA phenotyping dataset: UA ACPFG (Collins) - Wheat.
DOI
2023 Collins, N. (2023). APPF TPA phenotyping dataset: UA ACPFG (Collins) - Wheat.
DOI
2023 Collins, N. (2023). APPF TPA phenotyping dataset: UA ACPFG (Collins) - Wheat.
DOI
2023 Collins, N. (2023). APPF TPA phenotyping dataset: UA ACPFG (Collins) - Wheat.
DOI
2023 Collins, N. (2023). APPF TPA phenotyping dataset: UA ACPFG (Collins) - Wheat.
DOI
2023 Denton, M., & Humphries, A. (2023). APPF TPA phenotyping dataset: UA (Denton, Innes) - Medics.
DOI
2023 Harbard, J. (2023). APPF TPA phenotyping dataset: UTAS (Griffin) - Acacia.
DOI
2023 Harbard, J. (2023). APPF TPA phenotyping dataset: UTAS (Griffin) - Acacia.
DOI
2023 Liu, H. (2023). APPF TPA phenotyping dataset: UA TPA (Liu) - Wheat.
DOI
2023 James, R. (2023). APPF TPA phenotyping dataset: CSIRO (James) - Wheat.
DOI
2023 Matros, A. (2023). APPF TPA phenotyping dataset: UA (Matros) - Barley.
DOI
2023 Matros, A. (2023). APPF TPA phenotyping dataset: UA PEB (Matros) - Barley (PIEPS).
DOI
2023 Okamoto, M. (2023). APPF TPA phenotyping dataset: UA ACPFG (Okamoto) - Wheat.
DOI
2023 Okamoto, M. (2023). APPF TPA phenotyping dataset: UA ACPFG (Okamoto) - Wheat.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Barley.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Barley (calibration).
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Durum.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Wheat.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Wheat.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Wheat.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Wheat.
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Wheat (1).
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA ACPFG (Langridge, Parent) - Wheat (2).
DOI
2023 Parent, B. (2023). APPF TPA phenotyping dataset: UA TPA (Parent, Berger) - Various.
DOI
2023 Pegler, J. (2023). APPF TPA phenotyping dataset: UON (Pegler) - Setaria.
DOI
2023 Plett, D., & Roy, S. (2023). APPF TPA phenotyping dataset: UA (Plett) - Rice.
DOI
2023 Plett, D., & Roy, S. (2023). APPF TPA phenotyping dataset: UA (Plett) - Wheat.
DOI
2023 Plett, D., & Roy, S. (2023). APPF TPA phenotyping dataset: UA (Plett) - Wheat (salt).
DOI
2023 Plett, D., & Phillips, A. (2023). APPF TPA phenotyping dataset: Uni Melb (Plett) - Rice.
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Asif) - Wheat.
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2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy) - Barley (MxK Rerun).
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Hairmansis) - Rice.
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Hairmansis) - Rice.
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Hairmansis) - Rice (1).
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Hairmansis) - Rice (2).
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Schilling) - Barley.
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Schilling) - Barley.
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Schilling) - Barley (Wheat).
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Schilling) - Wheat.
DOI
2023 Roy, S. (2023). APPF TPA phenotyping dataset: UA ACPFG (Roy, Schilling) - Wheat.
DOI
2019 Adelaide, T. U. O., David, R., Tyerman, S., Gilliham, M., Hooper, C., & Castleden, I. (2019). Knowledgebase of Crop Transport information, Physiology and Signalling (CropTiPS) version 1.0.
DOI

Year Citation
- Sai, N., Rodriguez Lopez, C. M., Bogias, K., Pederson, S., Burgess, A., Breen, J., . . . David, R. (n.d.). DiRT: Dicistronic RNA Transcripts - A bioinformatic pipeline to detect di-cistronic tRNA-mRNA transcripts from short-read RNA-sequencing data [Computer Software].
- David, R., Hooper, C. M., Castleden, I. R., Gilliham, M., & Tyerman, S. (n.d.). CropTiPS database (Crop Transport information, Physiology and Signalling) - www.croptips.org [Computer Software].

2018 Co-investigator on Interdisciplinary Research Fund, University of Adelaide, Accelerating biological discovery through Artificial Intelligence, Prof Mathew Gilliham and Dr Rakesh David. $90k

2019 Co-investigator on NCRIS funded Agriculture data sharing platform, Agriculture data sharing platform (18NCRIS RDP-62), Prof Mathew Gilliham, A/Prof Bettina Berger, Dr Rakesh David, $155k

2021 Co-investigator on Agrifood and Wine FAME grant, Optimising plant growth in simulated microgravity, led by Dr Philip Brewer and Prof Volker Hessel, $20k

 

  • 2018-2019: Invited Guest Lecture for 'Research Skills for Applied Biology II' (APP BIOL 2500WT), Lecture title: Plants as Vaccine Biofactories, Undergraduate, Year 2, University of Adelaide
  • 2015: Design and implementation of practical component for GENETICS3211 – Plant Transgene Expression and RNA Modifications, Undergraduate, Year 3, University of Adelaide.
  • 2004-2005: Practical demonstrating experience: BOTN3006 - Plant Molecular Biology and Biotechnology. Undergraduate, Year 3, University of Queensland. BIOT7011 - DNA and Protein Technology, Postgraduate, University of Queensland. BIOC7009 – Emerging Biotechnologies II, Postgraduate, University of Queensland.

Date Role Research Topic Program Degree Type Student Load Student Name
2022 Principal Supervisor The role of dicistronic tRNA-mRNA transcripts in plant systemic signalling and its application as a mobile genome-editing system for grapevine functional genomics Doctor of Philosophy Doctorate Full Time Mr Fei Zheng

Date Role Research Topic Program Degree Type Student Load Student Name
2018 - 2020 Co-Supervisor A Multiple 'Omics' Approach to Study the Interaction between the Vitis Vinifera Transcriptome and Epigenome and the Barossa Valley Terroir Doctor of Philosophy Doctorate Full Time Mr Pastor Jullian Fabres
2015 - 2018 Co-Supervisor Identification and Functional Characterization of Long Noncoding RNAs Involved in Endosperm Development of Arabidopsis thaliana Doctor of Philosophy Doctorate Full Time Mr Quang Trung Do
2014 - 2016 Co-Supervisor Conservation and Function of RNA 5-methylcytosine in Plants Doctor of Philosophy Doctorate Full Time Miss Alice Louise Burgess

Date Role Research Topic Location Program Supervision Type Student Load Student Name
2018 - 2018 Principal Supervisor tRNA:mRNA dicistronic transcripts are a conserved feature in flowering plants transcriptomes The University of Adelaide, School of Agriculture, Food & Wine Master of Biotechnology (Plant Biotechnology) Master Full Time Fei Zheng

Date Role Membership Country
2019 - ongoing Member RNA society United States
2015 - ongoing Member Epigenetics Consortium of South Australia Australia
2010 - ongoing Member Australian Society of Plant Scientists Australia

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