Zhen Su

Bio: Zhen Su is an academic researcher from University of Minnesota. The author has contributed to research in topics: Gene & Arabidopsis. The author has an hindex of 32, co-authored 93 publications receiving 7342 citations. Previous affiliations of Zhen Su include China Agricultural University & Chinese Academy of Sciences.

agriGO: a GO analysis toolkit for the agricultural community

Abstract: Gene Ontology (GO), the de facto standard in gene functionality description, is used widely in functional annotation and enrichment analysis. Here, we introduce agriGO, an integrated web-based GO analysis toolkit for the agricultural community, using the advantages of our previous GO enrichment tool (EasyGO), to meet analysis demands from new technologies and research objectives. EasyGO is valuable for its proficiency, and has proved useful in uncovering biological knowledge in massive data sets from high-throughput experiments. For agriGO, the system architecture and website interface were redesigned to improve performance and accessibility. The supported organisms and gene identifiers were substantially expanded (including 38 agricultural species composed of 274 data types). The requirement on user input is more flexible, in that user-defined reference and annotation are accepted. Moreover, a new analysis approach using Gene Set Enrichment Analysis strategy and customizable features is provided. Four tools, SEA (Singular enrichment analysis), PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA), are integrated as a toolkit to meet different demands. We also provide a cross-comparison service so that different data sets can be compared and explored in a visualized way. Lastly, agriGO functions as a GO data repository with search and download functions; agriGO is publicly accessible at http://bioinfo.cau.edu.cn/agriGO/.

agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update.

Abstract: The agriGO platform, which has been serving the scientific community for >10 years, specifically focuses on gene ontology (GO) enrichment analyses of plant and agricultural species. We continuously maintain and update the databases and accommodate the various requests of our global users. Here, we present our updated agriGO that has a largely expanded number of supporting species (394) and datatypes (865). In addition, a larger number of species have been classified into groups covering crops, vegetables, fish, birds and insects closely related to the agricultural community. We further improved the computational efficiency, including the batch analysis and P-value distribution (PVD), and the user-friendliness of the web pages. More visualization features were added to the platform, including SEACOMPARE (cross comparison of singular enrichment analysis), direct acyclic graph (DAG) and Scatter Plots, which can be merged by choosing any significant GO term. The updated platform agriGO v2.0 is now publicly accessible at http://systemsbiology.cau.edu.cn/agriGOv2/.

DWARF27, an Iron-Containing Protein Required for the Biosynthesis of Strigolactones, Regulates Rice Tiller Bud Outgrowth

Abstract: Tillering in rice (Oryza sativa) is one of the most important agronomic traits that determine grain yields. Previous studies on rice tillering mutants have shown that the outgrowth of tiller buds in rice is regulated by a carotenoid-derived MAX/RMS/D (more axillary branching) pathway, which may be conserved in higher plants. Strigolactones, a group of terpenoid lactones, have been recently identified as products of the MAX/RMS/D pathway that inhibits axillary bud outgrowth. We report here the molecular genetic characterization of d27, a classic rice mutant exhibiting increased tillers and reduced plant height. D27 encodes a novel iron-containing protein that localizes in chloroplasts and is expressed mainly in vascular cells of shoots and roots. The phenotype of d27 is correlated with enhanced polar auxin transport. The phenotypes of the d27 d10 double mutant are similar to those of d10, a mutant defective in the ortholog of MAX4/RMS1 in rice. In addition, 2'-epi-5-deoxystrigol, an identified strigolactone in root exudates of rice seedlings, was undetectable in d27, and the phenotypes of d27 could be rescued by supplementation with GR24, a synthetic strigolactone analog. Our results demonstrate that D27 is involved in the MAX/RMS/D pathway, in which D27 acts as a new member participating in the biosynthesis of strigolactones.

Integrative genomic analyses reveal clinically relevant long noncoding RNAs in human cancer.

Abstract: Despite growing appreciations of the importance of long non-coding RNA (lncRNA) in normal physiology and disease, our knowledge of cancer-related lncRNA remains limited. By repurposing microarray probes, we constructed the expression profile of 10,207 lncRNA genes in approximately 1,300 tumors over four different cancer types. Through integrative analysis of the lncRNA expression profiles with clinical outcome and somatic copy number alteration (SCNA), we identified lncRNA that are associated with cancer subtypes and clinical prognosis, and predicted those that are potential drivers of cancer progression. We validated our predictions by experimentally confirming prostate cancer cell growth dependence on two novel lncRNA. Our analysis provided a resource of clinically relevant lncRNA for development of lncRNA biomarkers and identification of lncRNA therapeutic targets. It also demonstrated the power of integrating publically available genomic datasets and clinical information for discovering disease associated lncRNA.

Transcriptome analyses show changes in gene expression to accompany pollen germination and tube growth in Arabidopsis.

Abstract: Pollen germination, along with pollen tube growth, is an essential process for the reproduction of flowering plants. The germinating pollen with tip-growth characteristics provides an ideal model system for the study of cell growth and morphogenesis. As an essential step toward a detailed understanding of this important process, the objective of this study was to comprehensively analyze the transcriptome changes during pollen germination and pollen tube growth. Using Affymetrix Arabidopsis (Arabidopsis thaliana) ATH1 Genome Arrays, this study is, to our knowledge, the first to show the changes in the transcriptome from desiccated mature pollen grains to hydrated pollen grains and then to pollen tubes of Arabidopsis. The number of expressed genes, either for total expressed genes or for specifically expressed genes, increased significantly from desiccated mature pollen to hydrated pollen and again to growing pollen tubes, which is consistent with the finding that pollen germination and tube growth were significantly inhibited in vitro by a transcriptional inhibitor. The results of Gene Ontology analyses showed that expression of genes related to cell rescue, transcription, signal transduction, and cellular transport was significantly changed, especially for up-regulation, during pollen germination and tube growth. In particular, genes of the calmodulin/calmodulin-like protein, cation/hydrogen exchanger, and heat shock protein families showed the most significant changes during pollen germination and tube growth. These results demonstrate that the overall transcription of genes, both in the number of expressed genes and in the levels of transcription, was increased. Furthermore, the appearance of many novel transcripts during pollen germination as well as tube growth indicates that these newly expressed genes may function in this complex process.

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.

Abstract: A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era

Gene ontology analysis for RNA-seq: accounting for selection bias

Abstract: We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.

REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Abstract: Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret. REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).

Abstract: Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.