Research
His research group focuses on the development of novel and cutting-edge computational methods for analysing high throughput experimental data. He pioneered the development of methods for accurately measuring gene expression from plant RNA-seq experiments by developing the quality control pipelines that were essential for constructing a comprehensive Reference Transcript Dataset in Arabidopsis (AtRTD2). AtRTD2 allows rapid and accurate quantification of differential expression and alternative splicing (AS) analysis. This method has been translated into barley, potato and other plant species. His group also developed novel analytical tools for time-series RNA-seq data that capture the dynamics of expression and AS changes, including the first tool (R package TSIS) for characterising transcript isoform switches in a time series. These methods have now been incorporated into an easy-to-use tool for rapid and accurate RNA-seq and alternative splicing analysis (3D RNA-seq) which has been taken up by >6,000 users from ~60 countries. His group recently developed Protview, which optimizes the enzyme scheme to increase the protein coverage for proteomics experiments.
Current research projects
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BBSRC BBR BB/S020160/1 ‘PlantRTD’ 2019-2022 (£391,401) (PI)
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BBSRC Response Mode ‘The Generation Gap – Mechanisms of maternal control on grain’ BB/W002590/1 2022-2025 (£744,015) (Co-I)
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BBSRC International Partnering Award ‘BarleyEUNetwork’ BB/V018906/1 2021-2024 (£30,000) (Co-I)
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BBSRC International Partnering Award ‘UK Australia’ BB/V018299/1 2021-2024 (£30,000) (Co-I)
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BBSRC ERA-CAPS BB/S004610/1 ‘BARN’ 2018-2022 (£649,658) (Co-I)
Publications
The following Publications have not yet been migrated to the James Hutton Institute's Pure service and relate to the research outputs from the two legacy organisations: The Macaulay Land Use Research Institute and The Scottish Crop Research Institute.
Journals
- Zhang, R.; Barton, A.; Brittenden, J.; Huang, J.T.J.; Crowther, D. (2010) Evaluation for computational platforms of LC-MS based label-free quantitative proteomics: a global view., Journal of Proteomics & Bioinformatics, 3, 260-265.
- Grobei, M.A.; Qeli, E.; Brunner, E.; Rehrauer, H.; Zhang, R.; Roschitzki, B.; Basler, K.; Ahrens, C.H.; Grossniklaus, U. (2009) Determinisitic protein inference for shotgun proteomics data provides new insights into Arabidopsis pollen development and function., Genome Research, 19, 1786-1800.
- Tafelmeyer, P.; Laurent, C.; Lenormand, P.; Rouselle, J.C.; Marsollier, L.; Reysset, G.; Zhang, R.; Sickmann, A.; Namane, A.; Cole, S. (2008) Comprehensive proteome analysis of Mycobacterium ulcerans and quantitative comparison of wild-type with a mycolactone-deficient mutant., Proteomics, 8, 3214-3138.
- Vandenbogaert, M.; Li-Thiao-Te, S.; Kaltenback, H.; Zhang, R.; Aittokallio, T.; Schwikowski, B. (2008) Alignment of LCMS images: applications to biomarker discovery and protein identification., Proteomics, 8, 650-672.
- Zhang, R.; Huang, G.; Sundararajan, N.; Saratchandran, P. (2007) Improved GAP-RBF network for classification problems., Neurocomputing, 70, 3011-3018.
- Zhang, R.; Huang, G.; Sundararajan, N.; Saratchandran, P. (2007) Multi-category classification using extreme learning machine for microarray gene expression cancer diagnosis., IEEE Transactions on Computational Biology and Bioinformatics, 4, 485-495.
