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/.

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REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Whole-genome duplication (WGD), or polyploidy, followed by gene loss and diploidization has long been recognized as an important evolutionary force in animals, fungi and other organisms, especially plants. The success of angiosperms has been attributed, in part, to innovations associated with gene or whole-genome duplications, but evidence for proposed ancient genome duplications pre-dating the divergence of monocots and eudicots remains equivocal in analyses of conserved gene order. Here we use comprehensive phylogenomic analyses of sequenced plant genomes and more than 12.6 million new expressed-sequence-tag sequences from phylogenetically pivotal lineages to elucidate two groups of ancient gene duplications-one in the common ancestor of extant seed plants and the other in the common ancestor of extant angiosperms. Gene duplication events were intensely concentrated around 319 and 192 million years ago, implicating two WGDs in ancestral lineages shortly before the diversification of extant seed plants and extant angiosperms, respectively. Significantly, these ancestral WGDs resulted in the diversification of regulatory genes important to seed and flower development, suggesting that they were involved in major innovations that ultimately contributed to the rise and eventual dominance of seed plants and angiosperms.

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Ancestral polyploidy in seed plants and angiosperms

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/.

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

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base-driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis.

/pdf/ten-years-of-pathway-analysis-current-approaches-and-jkp29zcnl8.pdf

Ten years of pathway analysis: current approaches and outstanding challenges.

Amborella trichopoda is strongly supported as the single living species of the sister lineage to all other extant flowering plants, providing a unique reference for inferring the genome content and structure of the most recent common ancestor (MRCA) of living angiosperms. Sequencing the Amborella genome, we identified an ancient genome duplication predating angiosperm diversification, without evidence of subsequent, lineage-specific genome duplications. Comparisons between Amborella and other angiosperms facilitated reconstruction of the ancestral angiosperm gene content and gene order in the MRCA of core eudicots. We identify new gene families, gene duplications, and floral protein-protein interactions that first appeared in the ancestral angiosperm. Transposable elements in Amborella are ancient and highly divergent, with no recent transposon radiations. Population genomic analysis across Amborella's native range in New Caledonia reveals a recent genetic bottleneck and geographic structure with conservation implications.

/pdf/the-amborella-genome-and-the-evolution-of-flowering-plants-xzngxgl6jp.pdf

The Amborella Genome and the Evolution of Flowering Plants

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/.

/pdf/agrigo-a-go-analysis-toolkit-for-the-agricultural-community-2l6204nluq.pdf

agriGO: a GO analysis toolkit for the agricultural community

MicroRNAs (miRNA) are approximately 21 nucleotide-long non-coding small RNAs, which function as post-transcriptional regulators in eukaryotes. miRNAs play essential roles in regulating plant growth and development. In recent years, research into the mechanism and consequences of miRNA action has made great progress. With whole genome sequence available in such plants as Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Glycine max, etc., it is desirable to develop a plant miRNA database through the integration of large amounts of information about publicly deposited miRNA data. The plant miRNA database (PMRD) integrates available plant miRNA data deposited in public databases, gleaned from the recent literature, and data generated in-house. This database contains sequence information, secondary structure, target genes, expression profiles and a genome browser. In total, there are 8433 miRNAs collected from 121 plant species in PMRD, including model plants and major crops such as Arabidopsis, rice, wheat, soybean, maize, sorghum, barley, etc. For Arabidopsis, rice, poplar, soybean, cotton, medicago and maize, we included the possible target genes for each miRNA with a predicted interaction site in the database. Furthermore, we provided miRNA expression profiles in the PMRD, including our local rice oxidative stress related microarray data (LC Sciences miRPlants_10.1) and the recently published microarray data for poplar, Arabidopsis, tomato, maize and rice. The PMRD database was constructed by open source technology utilizing a user-friendly web interface, and multiple search tools. The PMRD is freely available at http://bioinformatics.cau.edu.cn/PMRD. We expect PMRD to be a useful tool for scientists in the miRNA field in order to study the function of miRNAs and their target genes, especially in model plants and major crops.

/pdf/pmrd-plant-microrna-database-4lwfqwbvgf.pdf

PMRD: plant microRNA database

The first ncRNA found was an alanine tRNA in baker's yeast, and the first detected microRNAs (miRNAs) promoted ncRNA research to a whole new level. Research on ncRNAs in animals has focused on the medical field, while in plant scientists are more concerned with improving agronomic traits. In 2010, we constructed a plant miRNA database named PMRD to meet the demand for miRNA research in plants. To provide a way to do fundamental research on plant ncRNAs and take full advantage of tremendous public resources, we designed an updated platform called plant ncRNA database (PNRD) based on its predecessor PMRD, which is accessible at http://structuralbiology.cau.edu.cn/PNRD. We collected a total of 25739 entries of 11 different types of ncRNAs from 150 plant species. Targets of miRNAs were extended to 178138 pairs in 46 species, while the number of miRNA expression profiles reached 35. Improvements in PNRD are not only the larger amounts of data, but also better service, such as a more user-friendly interface, more multifunctional and browsing options and more background data for users to download. We also integrated currently prevalent technologies and toolkits to strengthen the capability of the database and provide a one-stop service for scientific users.

/pdf/pnrd-a-plant-non-coding-rna-database-3p0dg18zxj.pdf

PNRD: a plant non-coding RNA database

Soybean (Glycine max L.) is one of the world’s most important leguminous crops producing high-quality protein and oil. Increasing the relative oil concentration in soybean seeds is many researchers’ goal, but a complete analysis platform of functional annotation for the genes involved in the soybean acyl-lipid pathway is still lacking. Following the success of soybean whole-genome sequencing, functional annotation has become a major challenge for the scientific community. Whole-genome transcriptome analysis is a powerful way to predict genes with biological functions. It is essential to build a comprehensive analysis platform for integrating soybean whole-genome sequencing data, the available transcriptome data and protein information. This platform could also be used to identify acyl-lipid metabolism pathways. In this study, we describe our construction of the Soybean Functional Genomics Database (SFGD) using Generic Genome Browser (Gbrowse) as the core platform. We integrated microarray expression profiling with 255 samples from 14 groups’ experiments and mRNA-seq data with 30 samples from four groups’ experiments, including spatial and temporal transcriptome data for different soybean development stages and environmental stresses. The SFGD includes a gene co-expression regulatory network containing 23,267 genes and 1873 miRNA-target pairs, and a group of acyl-lipid pathways containing 221 enzymes and more than 1550 genes. The SFGD also provides some key analysis tools, i.e. BLAST search, expression pattern search and cis-element significance analysis, as well as gene ontology information search and single nucleotide polymorphism display. The SFGD is a comprehensive database integrating genome and transcriptome data, and also for soybean acyl-lipid metabolism pathways. It provides useful toolboxes for biologists to improve the accuracy and robustness of soybean functional genomics analysis, further improving understanding of gene regulatory networks for effective crop improvement. The SFGD is publically accessible at http://bioinformatics.cau.edu.cn/SFGD/
 , with all data available for downloading.

/pdf/sfgd-a-comprehensive-platform-for-mining-functional-463ctavgek.pdf

SFGD: a comprehensive platform for mining functional information from soybean transcriptome data and its use in identifying acyl-lipid metabolism pathways

Plant diurnal oscillation is a 24-hour period based variation. The correlation between diurnal genes and biological pathways was widely revealed by microarray analysis in different species. Rice (Oryza sativa) is the major food staple for about half of the world's population. The rice flag leaf is essential in providing photosynthates to the grain filling. However, there is still no comprehensive view about the diurnal transcriptome for rice leaves. In this study, we applied rice microarray to monitor the rhythmically expressed genes in rice seedling and flag leaves. We developed a new computational analysis approach and identified 6,266 (10.96%) diurnal probe sets in seedling leaves, 13,773 (24.08%) diurnal probe sets in flag leaves. About 65% of overall transcription factors were identified as flag leaf preferred. In seedling leaves, the peak of phase distribution was from 2:00am to 4:00am, whereas in flag leaves, the peak was from 8:00pm to 2:00am. The diurnal phase distribution analysis of gene ontology (GO) and cis-element enrichment indicated that, some important processes were waken by the light, such as photosynthesis and abiotic stimulus, while some genes related to the nuclear and ribosome involved processes were active mostly during the switch time of light to dark. The starch and sucrose metabolism pathway genes also showed diurnal phase. We conducted comparison analysis between Arabidopsis and rice leaf transcriptome throughout the diurnal cycle. In summary, our analysis approach is feasible for relatively unbiased identification of diurnal transcripts, efficiently detecting some special periodic patterns with non-sinusoidal periodic patterns. Compared to the rice flag leaves, the gene transcription levels of seedling leaves were relatively limited to the diurnal rhythm. Our comprehensive microarray analysis of seedling and flag leaves of rice provided an overview of the rice diurnal transcriptome and indicated some diurnal regulated biological processes and key functional pathways in rice.

/pdf/transcriptome-phase-distribution-analysis-reveals-diurnal-59uuxoph0y.pdf

Zhenhai Zhang

Papers

agriGO: a GO analysis toolkit for the agricultural community

PMRD: plant microRNA database

PNRD: a plant non-coding RNA database

SFGD: a comprehensive platform for mining functional information from soybean transcriptome data and its use in identifying acyl-lipid metabolism pathways

Transcriptome phase distribution analysis reveals diurnal regulated biological processes and key pathways in rice flag leaves and seedling leaves.