Laboratory information management software for genotyping workflows: Applications in high throughput crop genotyping

Background: With the advances in DNA sequencer-based technologies, it has become possible to automate several steps of the genotyping process leading to increased throughput. To efficiently handle the large amounts of genotypic data generated and help with quality control, there is a strong need for a software system that can help with the tracking of samples and capture and management of data at different steps of the process. Such systems, while serving to manage the workflow precisely, also encourage good laboratory practice by standardizing protocols, recording and annotating data from every step of the workflow Results: A laboratory information management system (LIMS) has been designed and implemented at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) that meets the requirements of a moderately high throughput molecular genotyping facility. The application is designed as modules and is simple to learn and use. The application leads the user through each step of the process from starting an experiment to the storing of output data from the genotype detection step with auto-binning of alleles; thus ensuring that every DNA sample is handled in an identical manner and all the necessary data are captured. The application keeps track of DNA samples and generated data. Data entry into the system is through the use of forms for file uploads. The LIMS provides functions to trace back to the electrophoresis gel files or sample source for any genotypic data and for repeating experiments. The LIMS is being presently used for the capture of high throughput SSR (simple-sequence repeat) genotyping data from the legume (chickpea, groundnut and pigeonpea) and cereal (sorghum and millets) crops of importance in the semi-arid tropics. Conclusions: A laboratory information management system is available that has been found useful in the management of microsatellite genotype data in a moderately high throughput genotyping laboratory. The application with source code is freely available for academic users and can be downloaded from http://www.icrisat.org/bt-software-d-lims.htm

Best use modelling for sustainable Australia sports field surfaces

The Best Use Modelling for Sustainable Australian Sports Field Surfaces project has achieved significant success. The project has attracted participation from councils throughout Australia, with in excess of 300 sports fields evaluated from 18 councils to date. An important project component is the derivation of a recommended standard procedure for specifying the performance of playing surfaces. An associated step has been to establish recommended playing surface performance standards for community level sports fields. The derived modelling also provides information on the expected usage and associated costs of different sports surface development options. This is expected to assist the Australian turf production industry through demonstrating to councils that cost effective natural turf options exist that can meet higher usage expectation (as a viable alternative to synthetic turf). A web-accessed data base system will be made available to councils from January 2010 on (reference to www.passturf.com). This system will enable participating councils to record and analyse field performance over time. The system is considered world-leading, and will help keep the Australian parks industry to the international forefront. Tools developed as part of the project offer councils the opportunity to internally assess the performance of their current sports field provision, to identify any deficiencies and to determine the best corrective measure if any deficiency is identified. This is expected to offer community benefits to both sports facility providers and facility user groups. In turn this will aid the provision of affordable community access to safe and good quality playing surfaces. Tools and associated information material will be made available to councils throughout Australia by the end of this year, via the Parks and Leisure Aust. web site. The Best Use Modelling Project is work in progress. On-going input will be needed to ensure the web-accessed database software is as user friendly as possible, new performance testing data will need to be inputted, and tools provided to participating councils updated. Through the support of HAL there is now a well-structured, nationally-supported system in place for benchmarking playing surfaces and for assisting councils to optimise their resource allocation to sports field upgrade or maintenance work.

Simple software processes and tests improve the reliability and usefulness of a model.

Models are abstractions of reality that have predetermined limits (often not consciously thought through) on what problem domains the models can be used to explore. These limits are determined by the range of observed data used to construct and validate the model. However, it is important to remember that operating the model beyond these limits, one of the reasons for building the model in the first place, potentially brings unwanted behaviour and thus reduces the usefulness of the model. Our experience with the Agricultural Production Systems Simulator (APSIM), a farming systems model, has led us to adapt techniques from the disciplines of modelling and software development to create a model development process. This process is simple, easy to follow, and brings a much higher level of stability to the development effort, which then delivers a much more useful model. A major part of the process relies on having a range of detailed model tests (unit, simulation, sensibility, validation) that exercise a model at various levels (sub-model, model and simulation). To underline the usefulness of testing, we examine several case studies where simulated output can be compared with simple relationships. For example, output is compared with crop water use efficiency relationships gleaned from the literature to check that the model reproduces the expected function. Similarly, another case study attempts to reproduce generalised hydrological relationships found in the literature. This paper then describes a simple model development process (using version control, automated testing and differencing tools), that will enhance the reliability and usefulness of a model.

Status assessment of water use research in turf growth and maintenance.

The strategic objectives of Turf Australia (formerly the Turf Producers Association (TPA)) relating to water use in turf are to: • Source and collate information to support the case for adequate access to water for the Turf production and maintenance sectors and • Compile information generated into a convincing communication package that can be readily used by the industry in its advocacy programs (to government, regulators, media etc) More specifically, the turfgrass industry needs unbiased scientific evidence of the value of healthy grass in our environment. It needs to promote the use of adequate water even during drought periods to maintain quality turfgrass, which provides many benefits to the broader community including cooling the environment, saving energy and encouraging healthy lifestyles. The many environmental, social and health benefits of living turfgrass have been the subject of numerous investigations beyond the scope of this review. However further research is needed to fully understand the economic returns achievable by the judicious use of water for the maintenance of healthy turfgrass. Consumer education, backed by scientific evidence will highlight the “false economy” in allowing turfgrass to wither and die during conditions which require high level water restrictions. This report presents a review of the literature pertaining to research in the field of turf water use. The purpose of the review was to better understand the scope and nature of existing research results on turf water relations so that knowledge gaps could be identified in achieving the above strategic objectives of the TPA. Research to date has been found to be insufficient to compile a convincing communication package as described. However, identified knowledge gaps can now be addressed through targeted research. Information derived from targeted research will provide valuable material for education of the end user of turfgrass. Recommendations have been developed, based on the results of this desktop review. It was determined that future research in the field of turf irrigation needs to focus on a number of key factors which directly or indirectly affect the relationship between turfgrass and water use. These factors are: • Climate • Cultivar • Quality • Site use requirements • Establishment and management The overarching recommendation is to develop a strategic plan for turfgrass water relations research based around the five determinants of turf water use listed above. This plan should ensure research under these five categories is integrated into a holistic approach by which the consumer can be guided in species and/or cultivar choices as well as best management practices with respect to turfgrass water relations. Worsening drought cycles and limited supply of water for irrigation were the key factors driving every research project reviewed in this report. Subsidence of the most recent (or current) drought conditions in Australia should not be viewed by the turf industry as a reason to withdraw support or funding for research in this area. Drought conditions, limited domestic water availability and urban water restrictions will return in Australia albeit in 5, 10 or 20 years time and the turf industry has an opportunity to prepare for that time.

Breedcow and Dynama software redevelopment

The project renewed the Breedcow and Dynama software making it compatible with modern computer operating systems and platforms. Enhancements were also made to the linkages between the individual programs and their operation. The suite of programs is a critical component of the skill set required to make soundly based plans and production choices in the north Australian beef industry.

Improvement and Maintenance of Turf Demonstration Plots

DEEDI have built and maintained a living library of turf grass selections of 138 different turf cultivars. Material is also preserved in tubs in a protected growing environment. The maintenance and ongoing upgrading of these plots requires significant resources to maintain their integrity. As the plots have been in place since 2000, they are currently in need of significant repair. This project will assist in the improvement and maintenance of the turf library for the benefit of the turf industry producers and Australian turf research more generally.

Australian Tropical Grains Germplasm Centre

DAQ00131 project activities aimed to: conserve tropical grains germplasm under long-term storage conditions; acquire new germplasm with unique traits of interest to clients (particularly to breeding programs); the maintenance of germplasm through viability testing and regeneration; and to increase awareness of the availability of tropical grains germplasm to clients. New project goals were to facilitate the creation of the national grains Genetic Resources Centre (GRC) and included training GRC staff in the use of GRIN-Global (GG), a software management system, so that grains data can be nationalised across Australia; and contribute to the action plan development for the relocation of tropical grains germplasm to both Tamworth and Horsham.

NEESTIMATOR v2: re-implementation of software for the estimation of contemporary effective population size (N-e) from genetic data

NeEstimator v2 is a completely revised and updated implementation of software that produces estimates of contemporary effective population size, using several different methods and a single input file. NeEstimator v2 includes three single-sample estimators (updated versions of the linkage disequilibrium and heterozygote-excess methods, and a new method based on molecular coancestry), as well as the two-sample (moment-based temporal) method. New features include the following: (i) an improved method for accounting for missing data; (ii) options for screening out rare alleles; (iii) confidence intervals for all methods; (iv) the ability to analyse data sets with large numbers of genetic markers (10000 or more); (v) options for batch processing large numbers of different data sets, which will facilitate cross-method comparisons using simulated data; and (vi) correction for temporal estimates when individuals sampled are not removed from the population (Plan I sampling). The user is given considerable control over input data and composition, and format of output files. The freely available software has a new JAVA interface and runs under MacOS, Linux and Windows.