Runxuan Zhang
Dr Runxuan Zhang is a Quantitative Geneticist/Computational biologist in the Information and Computational Sciences group.
He has gained extensive experience in computational biology by taking up postdoc positions in Systems Biology Group of Institut Pasteur, Paris, Center for Model Organisms Proteome, University of Zurich, Zurich and Translational Medicine Research Collaboration lab, Dundee, where his research focuses on the development of novel and cutting-edge computational methods for high throughput experimental data, especially for transcriptomics and proteomics experiments..
Current research interests
There are three areas of special interest in 1) network construction and analysis for biological networks and 2) microRNA identification and regulation using computational methods. 3) accurate and fast quantification of alternative splicing in plants using RNA-seq data
NETWORK CONSTRUCTION AND ANALYSIS FOR BIOLOGICAL NETWORKS
At a cellular and molecular level, biology functions as a unified system of interacting and/or independent groups of molecules. Extensive knowledge about individual cellular components often cannot explain how a system operates at the level of the system as a whole, and thus fails to deliver successful solutions to many biological problems. With the availability of both genome sequences and high-throughput technologies such as RNA-seq and various flavours of mass spectrometry, cellular components can be detected and quantified simultaneously providing opportunities to gather accurate biological information at a range of different levels (DNA, RNA, proteins, metabolites, traits and phenotypes). Network analysis methods can integrate and interpret these data in a meaningful way and are therefore powerful tools for identifying known and unknown links between components, in turn helping to identify genetic and molecular mechanisms that provide a more holistic understanding of the process under investigation. I have worked on several projects of this nature: Construction of transcriptional interaction networks and identification of functional modules for transcriptional changes in barley seed development using network analysis methods; Transcriptional and metabolic co-expression network construction and motif extraction for potato leaf and tuber tissues under heat stress; Identification of the regulations/co-regulations for alternative splicing in the plant circadian clock; Transcriptional and metabolic co-expression network analysis to identify the genes involved in the desired traits for Raspberry variety development.
MicroRNA IDENTIFICATION AND REGULATION IN PLANTS
Micro RNAs (miRNA) are a class of non-coding small RNAs which play key roles in plant biological processes such as development, signal transduction and environmental stress response. miRNAs have been known to play important roles in plants, such as shoot morphogenesis, vegetative to a reproductive phase transition, floral differentiation and development, root initiation, vascular development as well as hormone signalling and homeostasis. They also regulate gene expression in response to environmental stresses, such as pathogen attack, oxidative stress, dehydration and phosphate and sulphate limitation. My research interest mainly focuses on developing computational methods for characterisation, detection, quantification of microRNAs and their target predictions in plants. We have carried out studies for identification and characterization of miRNA transcriptome in potato and in silico analysis of miRNAs in barley.
ACCURATE AND FAST QUANTIFICATION OF ALTERNATIVE SPLICING IN PLANT USING RNA-SEQ DATA
Currently, the standard way of RNA-seq analysis is to assemble the transcripts from the samples first and then quantification carried out based on the transcripts assembled. Many studies (Steijger et al, Assessment of transcript reconstruction methods for RNA-seq, Nature methods, 2013; Pertea et al, StringTie enables improved construction of a transcriptome from RNA-seq reads, Nature Biotechnology 2015; Hayer et al, Benchmark Analysis of Algorithms for Determining and Quantifying Full-length mRNA Splice Forms from RNA-Seq Data, Bioinformatics, 2015) have shown that even the best performing assembly methods (cufflinks, StringTie etc) have a sensitivity of <50% and precision of <50% even in a simulated dataset generated in an ideal scenario (e.g. every transcript is highly expressed). In other words, half of the transcripts in the sample are not assembled at all and among all the assembled transcripts half of them are actually mis-assembled. By assigning reads to such poor quality assembled transcripts to obtain quantification will lead to inaccurate results. We have found out in Arabidopsis studies, by constructing a high quality, comprehensive, non-redundant reference transcript dataset, we are able to improve the quantification accuracy significantly (http://onlinelibrary.wiley.com/doi/10.1111/nph.13545/full). Construction of Reference Transcript datasets addresses an important question in computational technology that will impact every RNA-seq experiment on the species under study.
Bibliography
- Kuo, R.I.; Cheng, Y.; Zhang, R.; Brown, J.W.S.; Smith, J.; Archibald, A.L.; Burt, D.W. (2020) Illuminating the dark side of the human transcriptome with long read transcript sequencing., BMC Genomics, 21, Article No. 751.
- Raxwal, V.K.; Simpson, C.G.; Gloggnitzer, J.; Entinze, J.C.; Guo, W.; Zhang, R.; Brown, J.W.S.; Riha, K. (2020) Nonsense-mediated RNA decay factor UPF1 is critical for posttranscriptional and translational gene regulation in arabidopsis., The Plant Cell, 32, 2725-2741.
- Lewandowska, D.; Zhang, R.; Colas, I.; Uzrek, N.; Waugh, R. (2019) Application of a sensitive and reproducible label-free proteomic approach to explore the proteome of individual meiotic-phase barley anthers., Frontiers in Plant Science, 10, Article No. 393.
- Calixto, C.P.G.; Tzioutziou, N.A.; James, A.B.; Hornyik, C.; Guo, W.B.; Zhang, R.X.; Nimmo, H.G.; Brown, J.W.S. (2019) Cold-dependent expression and alternative splicing of arabidopsis long non-coding RNAs., Frontiers in Plant Science, 10, Article No. 235.
- Bonar, N.; Liney, M.; Zhang, R.X.; Austin, C.; Dessoly, J.; Davidson, D.; Stephens, J.; McDougall, G.; Taylor, M.; Bryan, G.J.; Hornyik, C. (2018) Potato miR828 is associated with purple tuber skin and flesh colour., Frontiers in Plant Sciences, 9, Article No. 1742.
- Calixto, C.P.G.; Guo, W.; James, A.B.; Tzioutziou, N.A.; Entizne, J.C.; Panter, P.E.; Knight, H.; Nimmo, H.G.; Zhang, R.X.; Brown, J.W.S. (2018) Rapid and dynamic alternative splicing impacts the arabidopsis cold response transcriptome., Plant Cell, 30, 1424-1444.
- James, A.B.; Calixto, C.P.G.; Tzioutziou, N.A.; Guo, W.; Zhang, R.X.; Simpson C.G.; Jiang. W.Y.; Nimmo, G.A.; Brown, J.W.S.; Nimmo, H.G. (2018) How does temperature affect splicing events? Isoform switching of splicing factors regulates splicing of LATE ELONGATED HYPOCOTYL (LHY)., Plant Cell and Environment, 41, 1539-1550.
- Simpson, C.G.; Barrero, R.A.; Bayer, M.; Flavell, A.; Rapazote-Flores, P.; Bellgard, M.I.; Hedley, P.E.; Zhang, R.; Waugh, R. (2018) The expressed portion of the barley genome., In: Stein, N. & Muehlbauer, G. (eds.). The Barley Genome, Compendium of Plant Genomes. Springer, Cham, Switzerland, Chapter 7, 789-107.
- Zhang, R.X.; Calixto, C.P.G.; Marquez, Y.; Venhuizen, P.; Tzioutziou, N.A.; Guo, W.B.; Spensley, M.; Entizne, J.C.; Lewandowska, D.; ten Have, S.; Frey, N.F.D.; Hirt, H.; James, A.B.; Nimmo, H.G.; Barta, A.; Kalyna, M.; Brown, J.W.S. (2017) A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing., Nucleic Acids Research, 45, 5061-5073.
- Guo, W.; Calixto, C.P.G.; Tzioutziou, N.; Lin, P.; Waugh, R.; Brown, J.W.S.; Zhang, R. (2017) Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size., BMC Systems Biology, 11, Article No. 62.
- Guo, W.B.; Calixto, C.P.G.; Brown, J.W.S.; Zhang, R.X. (2017) TSIS: an R package to infer alternative splicing isoform switches for time-series data., Bioinformatics, 33, 3308-3310.
- Gou, W.; Calixtio, C.P.G.; Tzioutziou, N.; Ping, L.; Waugh, R.; Brown, J.W.S.; Zhang, R. (2017) Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size., BMC Systems Biology, 11, Article No. 62.
- Brown, J.W.S.; Calixto, C.P.G.; Zhang, R.X. (2017) High quality reference transcript datasets hold the key to transcript-specific RNA-seq analysis in plants., New Phytologist, 213, 525-530.
- Bull, H.; Casao, M.C.; Zwirek, M.; Flavell, A.J.; Thomas, W.T.B.; Guo, W.B.; Zhang, R.X.; Rapazote-Flores, P.; Kyriakidis, S.; Russell, J.; Druka, A.; McKim, S.M.; Waugh, R. (2017) Barley SIX-ROWED SPIKE3 encodes a putative Jumonji C-type H3K9me2/me3 demethylase that represses lateral spikelet fertility., Nature Communications, 8, Article No. 936.
- Zhang, R.X.; Tucker, M.R.; Burton, R.A.; Shirley, N.J.; Little, A.; Morris, J.; Milne, L.; Houston, K.; Hedley, P.E.; Waugh, R.; Fincher, G.B. (2016) The dynamics of transcript abundance during cellularization of developing barley endosperm., Plant Physiology, 170, 1549-1565.
- Katsarou, K.; Wu, Y.; Zhang, R.X.; Bonar, N.; Morris, J.; Hedley, P.E.; Bryan, G.J.; Kalantidis, K.; Hornyik, C. (2016) Insight on genes affecting tuber development in potato upon Potato spindle tuber viroid (PSTVd) infection., PLoS ONE, 11, Article No. e0150711.
- Zhang, R.X.; Calixto, C.P.G.; Tzioutziou, N.A.; James, A.B.; Simpson, C.G.; Guo, W.B.; Marquez, Y.; Kalyna, M.; Patro, R.; Eyras, E.; Barta, A.; Nimmo, H.G.; Brown, J.W.S. (2015) AtRTD - a comprehensive reference transcript dataset resource for accurate quantification of transcript-specific expression in Arabidopsis thaliana., New Phytologist, 208, 96-101.
- Wu, X.; Hornyik, C.; Bayer, M.; Marshall, D.; Waugh, R.; Zhang, R.X. (2014) In silico identification and characterization of conserved plant microRNAs in barley., Central European Journal of Biology, 9, 841-852.
- Hancock, R.D.; Morris, W.L.; Ducreux, L.J.D.; Morris, J.A.; Usman, M.; Verrall, S.R.; Fuller, J.; Simpson, C.G.; Zhang, R.; Hedley, P.E.; Taylor, M.A. (2014) Physiological, biochemical and molecular responses of the potato (Solanum tuberosum L.) plant to moderately elevated temperature., Plant Cell and Environment, 37, 439-450.
- Jupe, J.; Stam R.; Howden A.J.M.; Morris J.A.; Zhang R.; Hedley P.E.; Huitema E. (2013) Phytophthora capsici-tomato interaction features dramatic shifts in gene expression associated with a hemi-biotrophic lifestyle., Genome Biology, 14, Article No. R63.
- Zhang, R.; Marshall, D.F.; Bryan, G.J.; Hornyik, C. (2013) Identification and characterization of miRNA transcriptome in potato by high-throughput sequencing., PLoS ONE, 8, Article No. e57233.
- 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.
- 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.
- 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.
- 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.
- Zhang, R.; Huang, G.; Sundararajan, N.; Saratchandran, P. (2007) Improved GAP-RBF network for classification problems., Neurocomputing, 70, 3011-3018.
- Kuo, R.I.; Cheng, Y.; Zhang, R.; Brown, J.W.S.; Smith, J.; Archibald, A.L.; Burt, D.W. (2020) Illuminating the dark side of the human transcriptome with long read transcript sequencing., BMC Genomics, 21, Article No. 751.
- Raxwal, V.K.; Simpson, C.G.; Gloggnitzer, J.; Entinze, J.C.; Guo, W.; Zhang, R.; Brown, J.W.S.; Riha, K. (2020) Nonsense-mediated RNA decay factor UPF1 is critical for posttranscriptional and translational gene regulation in arabidopsis., The Plant Cell, 32, 2725-2741.
- Lewandowska, D.; Zhang, R.; Colas, I.; Uzrek, N.; Waugh, R. (2019) Application of a sensitive and reproducible label-free proteomic approach to explore the proteome of individual meiotic-phase barley anthers., Frontiers in Plant Science, 10, Article No. 393.
- Calixto, C.P.G.; Tzioutziou, N.A.; James, A.B.; Hornyik, C.; Guo, W.B.; Zhang, R.X.; Nimmo, H.G.; Brown, J.W.S. (2019) Cold-dependent expression and alternative splicing of arabidopsis long non-coding RNAs., Frontiers in Plant Science, 10, Article No. 235.
- Bonar, N.; Liney, M.; Zhang, R.X.; Austin, C.; Dessoly, J.; Davidson, D.; Stephens, J.; McDougall, G.; Taylor, M.; Bryan, G.J.; Hornyik, C. (2018) Potato miR828 is associated with purple tuber skin and flesh colour., Frontiers in Plant Sciences, 9, Article No. 1742.
- Calixto, C.P.G.; Guo, W.; James, A.B.; Tzioutziou, N.A.; Entizne, J.C.; Panter, P.E.; Knight, H.; Nimmo, H.G.; Zhang, R.X.; Brown, J.W.S. (2018) Rapid and dynamic alternative splicing impacts the arabidopsis cold response transcriptome., Plant Cell, 30, 1424-1444.
- James, A.B.; Calixto, C.P.G.; Tzioutziou, N.A.; Guo, W.; Zhang, R.X.; Simpson C.G.; Jiang. W.Y.; Nimmo, G.A.; Brown, J.W.S.; Nimmo, H.G. (2018) How does temperature affect splicing events? Isoform switching of splicing factors regulates splicing of LATE ELONGATED HYPOCOTYL (LHY)., Plant Cell and Environment, 41, 1539-1550.
- Zhang, R.X.; Calixto, C.P.G.; Marquez, Y.; Venhuizen, P.; Tzioutziou, N.A.; Guo, W.B.; Spensley, M.; Entizne, J.C.; Lewandowska, D.; ten Have, S.; Frey, N.F.D.; Hirt, H.; James, A.B.; Nimmo, H.G.; Barta, A.; Kalyna, M.; Brown, J.W.S. (2017) A high quality Arabidopsis transcriptome for accurate transcript-level analysis of alternative splicing., Nucleic Acids Research, 45, 5061-5073.
- Guo, W.; Calixto, C.P.G.; Tzioutziou, N.; Lin, P.; Waugh, R.; Brown, J.W.S.; Zhang, R. (2017) Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size., BMC Systems Biology, 11, Article No. 62.
- Guo, W.B.; Calixto, C.P.G.; Brown, J.W.S.; Zhang, R.X. (2017) TSIS: an R package to infer alternative splicing isoform switches for time-series data., Bioinformatics, 33, 3308-3310.
- Gou, W.; Calixtio, C.P.G.; Tzioutziou, N.; Ping, L.; Waugh, R.; Brown, J.W.S.; Zhang, R. (2017) Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size., BMC Systems Biology, 11, Article No. 62.
- Brown, J.W.S.; Calixto, C.P.G.; Zhang, R.X. (2017) High quality reference transcript datasets hold the key to transcript-specific RNA-seq analysis in plants., New Phytologist, 213, 525-530.
- Bull, H.; Casao, M.C.; Zwirek, M.; Flavell, A.J.; Thomas, W.T.B.; Guo, W.B.; Zhang, R.X.; Rapazote-Flores, P.; Kyriakidis, S.; Russell, J.; Druka, A.; McKim, S.M.; Waugh, R. (2017) Barley SIX-ROWED SPIKE3 encodes a putative Jumonji C-type H3K9me2/me3 demethylase that represses lateral spikelet fertility., Nature Communications, 8, Article No. 936.
- Zhang, R.X.; Tucker, M.R.; Burton, R.A.; Shirley, N.J.; Little, A.; Morris, J.; Milne, L.; Houston, K.; Hedley, P.E.; Waugh, R.; Fincher, G.B. (2016) The dynamics of transcript abundance during cellularization of developing barley endosperm., Plant Physiology, 170, 1549-1565.
- Katsarou, K.; Wu, Y.; Zhang, R.X.; Bonar, N.; Morris, J.; Hedley, P.E.; Bryan, G.J.; Kalantidis, K.; Hornyik, C. (2016) Insight on genes affecting tuber development in potato upon Potato spindle tuber viroid (PSTVd) infection., PLoS ONE, 11, Article No. e0150711.
- Zhang, R.X.; Calixto, C.P.G.; Tzioutziou, N.A.; James, A.B.; Simpson, C.G.; Guo, W.B.; Marquez, Y.; Kalyna, M.; Patro, R.; Eyras, E.; Barta, A.; Nimmo, H.G.; Brown, J.W.S. (2015) AtRTD - a comprehensive reference transcript dataset resource for accurate quantification of transcript-specific expression in Arabidopsis thaliana., New Phytologist, 208, 96-101.
- Wu, X.; Hornyik, C.; Bayer, M.; Marshall, D.; Waugh, R.; Zhang, R.X. (2014) In silico identification and characterization of conserved plant microRNAs in barley., Central European Journal of Biology, 9, 841-852.
- Hancock, R.D.; Morris, W.L.; Ducreux, L.J.D.; Morris, J.A.; Usman, M.; Verrall, S.R.; Fuller, J.; Simpson, C.G.; Zhang, R.; Hedley, P.E.; Taylor, M.A. (2014) Physiological, biochemical and molecular responses of the potato (Solanum tuberosum L.) plant to moderately elevated temperature., Plant Cell and Environment, 37, 439-450.
- Jupe, J.; Stam R.; Howden A.J.M.; Morris J.A.; Zhang R.; Hedley P.E.; Huitema E. (2013) Phytophthora capsici-tomato interaction features dramatic shifts in gene expression associated with a hemi-biotrophic lifestyle., Genome Biology, 14, Article No. R63.
- Zhang, R.; Marshall, D.F.; Bryan, G.J.; Hornyik, C. (2013) Identification and characterization of miRNA transcriptome in potato by high-throughput sequencing., PLoS ONE, 8, Article No. e57233.
- 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.
- 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.
- 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.
- 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.
- Zhang, R.; Huang, G.; Sundararajan, N.; Saratchandran, P. (2007) Improved GAP-RBF network for classification problems., Neurocomputing, 70, 3011-3018.
- Simpson, C.G.; Barrero, R.A.; Bayer, M.; Flavell, A.; Rapazote-Flores, P.; Bellgard, M.I.; Hedley, P.E.; Zhang, R.; Waugh, R. (2018) The expressed portion of the barley genome., In: Stein, N. & Muehlbauer, G. (eds.). The Barley Genome, Compendium of Plant Genomes. Springer, Cham, Switzerland, Chapter 7, 789-107.