About the connected Data sets

Currently, 5 very distinct and individual data collections have been linked to the ArgiConnect search portal. The AgriConnect portal uses existing BLAST and Text-mining tools to link together identifiers and experimental parameters from separate species, experimentation and data types. This directly links data available in separate data sets/ search portals and increases awareness of what information is attached to matched identifiers while searching.
The main goal of AgriConnect is to discover links between distinct species, data types and to save the user time compared to assessing suitability of individual data sets separately. The immediate identification of hits in several data sets will identify new linking strategies for data sets that would normally not fall into the users search criteria. The return offers a summary of the data hit and links for in depth data query or download.


AgriBio data contains several sets of data that focus on changes during germination. Currently there are two data sets connected to AgriConnect, one for rice and one for barley.

The rice dataset originates from research carried out and published in 2016. The RNA-seq data generated provides a detailed molecular profile of Oryza sativa enabling the identification of differentially expressed genes and uncovers the essential features for germination and early seedling growth under anoxic conditions. An extensive time course was examined, from dry seeds to germinating embryos (24 h) and up to 4-day-old rice coleoptiles, as well as coleoptiles collected from seedlings grown for 3 days under anoxic conditions and transferred to air for one day. RNA-seq analyses were undertaken which resulted in data creation from several time points during germination and in the rice coleoptile.

The barley data set contains developmental profiles of 19,611 barley gene transcripts that were precisely defined in the purified tissues and in whole grain during the first 24 hours of germination by RNA-seq. Spatial and temporal patterns of transcription were validated against well-defined data on enzyme activities both in whole grain and in isolated tissues. Transcript profiles of genes involved in mitochondrial assembly and function were used to validate the very early stages of germination. The data will be broadly applicable for the interrogation of co-expression and differential expression patterns and for the identification of transcription factors that are important in the early stages of grain and seed germination using the RNAseq Database interrogation platform.


The compendium of crop Proteins with Annotated Locations (cropPAL) collates more than 648 data sets from previously published fluorescent tagging or mass spectrometry studies and eight pre-computed subcellular predictions for 11 different crop proteomes (banana (musa acuminata), barley (hordeum vulgare), canola (brassica napus), maize (zea mays), potato (solanum tuberosum), rice (oryza sativa), sorghum (sorghum bicolor), soybean (glycine max), tomato (solanum lycopersicum), wheat (triticum aestivum), wine grape (vitis Vinifera). The crop data is linked to experimental localisation and protein-protein interaction data in Arabidopsis from SUBA4


Approximately 25% of all plant genes encode proteins that transport substances across membranes, and a large proportion of energy available from photosynthesis is required to transport the nutrients required for growth and grain filling. Plant membrane transporters can be manipulated to enhance crop yields and cultivatable land, by increasing nutrient content and resistance to key stresses (salinity, drought, pathogens, extreme soil pH, etc). CropTiPS (Crop Transport Information, Physiology and Signalling Database) is a comprehensive knowledgebase of membrane transport and signalling systems classified under each substrate, including the gene families for wheat, barley, rice and maize. The source of the data is obtained from published membrane transporter studies and linked to reference transport proteins in the four crop species.

Next Gen Mungbeans

Mungbean, Vigna radiata (L.) Wilczek var. radiata, is established as the key rotation in tropical Australia’s cereal-based cropping systems. It has a short duration, wide sowing window, rotation benefits and is established as a high value product for discerning international markets. Annual production increased to 70,000 tonnes since 2003 with a new production target of 170,000 tonnes by 2020. To achieve this, adoption of new breeding technologies and an understanding of traits and the physiological processes determining yield and response to biotic and abiotic stress is critical. The data of this project is delivering new genetic knowledge directly assisting the breeding of better mungbean varieties for Australian growers. The NAM framework has been used to introduce genetic diversity, including disease resistance and new adaptive traits into elite mungbean germplasm using bi-parental and backcross breeding. The data portal can be accessed through our interacting Next Gen Mungbeans portal


Plant phenomics data in TraitCapture consists of time-series images of plant growth trials (Arabidopsis, Brachypodium, Wheat, Eucalyptus and others). Image data is available in jpg or RAW format. Most Arabidopsis trials have time-series phenotype data available in CSV format (projected leaf area, compactness, eccentricity, green chromatic coordinate, perimeter and roundness). Most trials after mid 2017 will also have extensive metadata on climate chamber conditions including the environmental and lighting control files and actual growth conditions. Genotypic data for Arabidopsis accessions is available from 1001 Genomes

The TraitCapture phenomics data portal is a joint collaboration between the Borevtiz Lab at the Australian National University (ANU) and the ANU nodes of the Centre of Excellence in Plant Energy Biology (PEB) and the Australian Plant Phenomics Facility (APPF) TraitCapture is an open source hardware and software pipeline for enabling low cost high throughput phenotyping for Australian and International researchers.

TraitCapture was developed with funding from the ARC (LE130100081, LP140100572) with additional financial and in-kind support from ANU, APPF, ANDS and PEB.