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

Seabed discrimination data from SeaScan™

Raw data from SeaScan™ transects off Wide Bay (south Queensland) taken in August 2007 as part of a study of ecological factors influencing the distribution of spanner crabs (Ranina ranina). The dataset (comma-delimited ascii file) comprises the following fields: 1. record number 2. date-time (GMT) 3. date-time (AEST) 4. latitude (signed decimal degrees) 5. longitude (decimal degrees) 6. speed over ground (knots) 7. depth (m) 8. seabed roughness (v) 9. hardness (v) Indices of roughness and hardness (from the first and second echoes respectively) were obtained using a SeaScan™ 100 system (un-referenced) on board the Research Vessel Tom Marshall, with the ship’s Furuno FCV 1100 echo sounder and 1 kW, 50 kHz transducer. Generally vessel speed was kept below about 14 kt (typically ~12 kt), and the echo-sounder range set to 80 m. The data were filtered to remove errors due to data drop-out, straying beyond system depth limits (min. 10 m), or transducer interference.

Great Barrier Reef Fish Biological Data

Biological measurements on fish sampled during the course of FRDC funded project Growth, Reproduction and Recruitment of Great Barrier Reef Food Fish Stocks (FRDC 90/18). The comma-delimited ascii file comprises the following fields: 1. Cruise number 2. Date (d-m-y) 3, Region (descriptor of part of Queensland coast or Great Barrier Reef system) 4. Reef (name or number) 5. Data source (Res=research, Rec=recreational fisher, Com=commercial fisher) 6. Capture method 7. Trap number (where appropriate) 8. Species name 9. LthStd (standard length, cm) 10. LthFrk (fork length, cm) 11. LthTot (total length, cm) 12. WtTot (approx total weight, g; weighed at sea) 13. FrameWt (weight of frame [after filleting, with viscera], g; weighed in lab) 14. Sex (macroscopic examination only) 15. GonadWt (g) Data obtained by the Department Employment, Economic Development and Innovation (formerly Primary Industries and Fisheries) between 1988 and 1993, primarily in the southern Great Barrier Reef (Capricorn-Bunker and Swain Groups), with fish traps and handlining.

Raw data for project "Development of a DNA based aging technique for use in fisheries assessments" Fisheries and Development Corporation project 2007/033

The primary aim of this study was to determine the relationship between telomere length and age in a range of marine invertebrates including abalone (Haliotis spp) oysters (Saccostrea glomerata), spiny lobsters (Sagmariasus verreauxi formerly Jasus verreauxi and Jasus edwardsii) and school prawns (Metapenaeus macleayi). Additionally, this relationship was studied in a vertebrate organism using the freshwater fish Silver perch (Bidyanus bidyanus). Telomere length differences between tissues were also examined in some species such as Saccostrea glomerata, Sagmariasus verreauxi and Bidyanus bidyanus. In some cases cultured specimens of known age were used and this is quoted in the spreadsheets. For other wild-caught specimens where age was not known, size was used as a proxy for age. This may be a broad size class, or be determined by shell size or carapace length depending on the organism. Each spreadsheet contains raw data of telomere length estimates from Terminal Restriction Fragment Assays (TRF) for various individuals of each species including appropriate details such as age or size and tissue. Telomere length estimates are given in base pairs (bp). In most cases replicate experiments were conducted on groups of samples three times but on a small number of occasions only two replicate experiments were conducted. Further description of the samples can be found in final report of FRDC 2007/033. The arithmetic average for each individual (sample ID) across the two or three replicate experiments is also given. Bidyanus bidyanus (SilverPerch) Two sheets are contained within. a) Comparison of telomere length between different tissues (heart, liver and muscle) within the three year old age class - two replicate experiments were conducted. b) Comparison of telomere length between fish of different but known ages (0.25, 1, 2, and 3 years old) in each of three tissues, heart, liver and muscle – three replicate experiments were conducted per tissue. Haliotis spp (Abalone species) Three species were tested. H. asinina Telomere length was compared in two age classes-11 month and 18 month old abalone using muscle tissue from the foot. Within gel-variation was also estimated using a single sample run three times on one gel (replicate experiment). H. laevigata x H. rubra hybrids Telomere length was compared in three known age classes – two, three and four years old using muscle tissue from the foot. H. rubra Telomere length was compared in a range of different sized abalone using muscle tissue from the foot. Shell size is also given for each abalone Saccostrea glomerata Three sheets are contained within the file. a) Samples came from Moreton Bay Queensland in 2007. Telomere length was compared in two tissues (gill and mantle) of oysters in three age groups (1, 3 and 4 years) b) Samples came from Moreton Bay Queensland in 2009. Telomere length was compared in three age classes using DNA from gill tissue only c) Samples came from Wallis Lake, New South Wales. Telomere length was estimated from whole body minus the shell from 1 year old oysters, gill tissue of 3 age classes (1.5 years, 3 and 4 years), mantle tissue of two age classes (3 and 4 years). Sagmariasus verreauxi (formerly Jasus verreauxi) Telomere length was estimated from abdomen tissue of puerulus, gill and muscle tissue of 3 year old, large and very large size classes of lobsters. Jasus edwardsii Telomere length was measured in two size classes of lobsters- adults of varying sizes using muscle tissue and puerulus using tissues from the abdomen minus the exoskeleton. Metapenaeus macleayi Telomere length was measured in three size classes of school prawns adults. Muscle tissue was used, minus the exoskeleton.

Phosphine fumigation of cool grain

The biosecurity problem addressed was the need to understand and evaluate phosphine fumigation of cool grain (i.e. 20°C or less) as a means of controlling resistant biotypes of insect pests of stored grain which are major EPPs threatening the grain industry. The benefits of cooling and phosphine fumigation are that cooling preserves grain quality and reduces insect population growth, and phosphine kills insects and has a residue free status in all major markets. The research objectives were to: - conduct laboratory experiments on phosphine efficacy against resistant insects in cool grain, and determine times to population extinction. - conduct laboratory experiments on phosphine sorption in cool grain and quantify. - complete fumigation trials in three states (Queensland, WA and NSW) on cool grain stored insealed farm silos. - make recommendations for industry on effective phosphine fumigation of cool grain. Phosphine is used by growers and other stakeholders in the grain industry to meet domesticand international demands for insect-free grain. The project aim was to generate new information on the performance of phosphine fumigation of cool grain relevant to resistant biotypes. Effective control of resistant biotypes using phosphine to fumigate cool grain will benefit growers and other sectors of the grain industry, needing to fumigate grain in the cooler months of the year, or grain that has been cooled using aeration.

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.