Runxuan Zhang

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.