Marine Biotechnology: A New Vision and Strategy for Europe

Marine Board-ESF The Marine Board provides a pan-European platform for its member organisations to develop common priorities, to advance marine research, and to bridge the gap between science and policy in order to meet future marine science challenges and opportunities. The Marine Board was established in 1995 to facilitate enhanced cooperation between European marine science organisations (both research institutes and research funding agencies) towards the development of a common vision on the research priorities and strategies for marine science in Europe. In 2010, the Marine Board represents 30 Member Organisations from 19 countries. The Marine Board provides the essential components for transferring knowledge for leadership in marine research in Europe. Adopting a strategic role, the Marine Board serves its Member Organisations by providing a forum within which marine research policy advice to national agencies and to the European Commission is developed, with the objective of promoting the establishment of the European Marine Research Area

SNP discovery using next generation transcriptomic sequencing in Atlantic herring (Clupea harengus)

The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring (Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild

Stage 1 Second Year Review of Value Added Wheat CRC Ltd

Established and supported under the Australian Government’s Cooperative Research Centre Progra

Construction and validation of a Bovine Innate Immune Microarray

BACKGROUND: Microarray transcript profiling has the potential to illuminate the molecular processes that are involved in the responses of cattle to disease challenges. This knowledge may allow the development of strategies that exploit these genes to enhance resistance to disease in an individual or animal population. RESULTS: The Bovine Innate Immune Microarray developed in this study consists of 1480 characterised genes identified by literature searches, 31 positive and negative control elements and 5376 cDNAs derived from subtracted and normalised libraries. The cDNA libraries were produced from 'challenged' bovine epithelial and leukocyte cells. The microarray was found to have a limit of detection of 1 pg/μg of total RNA and a mean slide-to-slide correlation co-efficient of 0.88. The profiles of differentially expressed genes from Concanavalin A (ConA) stimulated bovine peripheral blood lymphocytes were determined. Three distinct profiles highlighted 19 genes that were rapidly up-regulated within 30 minutes and returned to basal levels by 24 h; 76 genes that were up-regulated between 2–8 hours and sustained high levels of expression until 24 h and 10 genes that were down-regulated. Quantitative real-time RT-PCR on selected genes was used to confirm the results from the microarray analysis. The results indicate that there is a dynamic process involving gene activation and regulatory mechanisms re-establishing homeostasis in the ConA activated lymphocytes. The Bovine Innate Immune Microarray was also used to determine the cross-species hybridisation capabilities of an ovine PBL sample. CONCLUSION: The Bovine Innate Immune Microarray has been developed which contains a set of well-characterised genes and anonymous cDNAs from a number of different bovine cell types. The microarray can be used to determine the gene expression profiles underlying innate immune responses in cattle and sheep

The translational challenge in chagas disease drug development

Chagas disease is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi. There is an urgent need for safe, effective, and accessible new treatments since the currently approved drugs have serious limitations. Drug development for Chagas disease has historically been hampered by the complexity of the disease, critical knowledge gaps, and lack of coordinated R&D efforts. This review covers some of the translational challenges associated with the progression of new chemical entities from preclinical to clinical phases of development, and discusses how recent technological advances might allow the research community to answer key questions relevant to the disease and to overcome hurdles in R&D for Chagas disease.Fil: Kratz, Jadel M.. No especifíca;Fil: Gonçalves, Karolina R.. Universidade de Sao Paulo; BrasilFil: Romera, Lavínia M. D.. Universidade de Sao Paulo; BrasilFil: Borsoi Moraes, Carolina. Universidade Federal de Sao Paulo; BrasilFil: Bittencourt Cunha, Paula. Universidade de Sao Paulo; Brasil. Universidade Federal de Sao Paulo; BrasilFil: Schenkman, Sergio. Universidade Federal de Sao Paulo; BrasilFil: Chatelain, Eric. No especifíca;Fil: Sosa-Estani, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; Argentin

Genome wide SNP discovery in flax through next generation sequencing of reduced representation libraries

BACKGROUND: Flax (Linum usitatissimum L.) is a significant fibre and oilseed crop. Current flax molecular markers, including isozymes, RAPDs, AFLPs and SSRs are of limited use in the construction of high density linkage maps and for association mapping applications due to factors such as low reproducibility, intense labour requirements and/or limited numbers. We report here on the use of a reduced representation library strategy combined with next generation Illumina sequencing for rapid and large scale discovery of SNPs in eight flax genotypes. SNP discovery was performed through in silico analysis of the sequencing data against the whole genome shotgun sequence assembly of flax genotype CDC Bethune. Genotyping-by-sequencing of an F(6)-derived recombinant inbred line population provided validation of the SNPs. RESULTS: Reduced representation libraries of eight flax genotypes were sequenced on the Illumina sequencing platform resulting in sequence coverage ranging from 4.33 to 15.64X (genome equivalents). Depending on the relatedness of the genotypes and the number and length of the reads, between 78% and 93% of the reads mapped onto the CDC Bethune whole genome shotgun sequence assembly. A total of 55,465 SNPs were discovered with the largest number of SNPs belonging to the genotypes with the highest mapping coverage percentage. Approximately 84% of the SNPs discovered were identified in a single genotype, 13% were shared between any two genotypes and the remaining 3% in three or more. Nearly a quarter of the SNPs were found in genic regions. A total of 4,706 out of 4,863 SNPs discovered in Macbeth were validated using genotyping-by-sequencing of 96 F(6) individuals from a recombinant inbred line population derived from a cross between CDC Bethune and Macbeth, corresponding to a validation rate of 96.8%. CONCLUSIONS: Next generation sequencing of reduced representation libraries was successfully implemented for genome-wide SNP discovery from flax. The genotyping-by-sequencing approach proved to be efficient for validation. The SNP resources generated in this work will assist in generating high density maps of flax and facilitate QTL discovery, marker-assisted selection, phylogenetic analyses, association mapping and anchoring of the whole genome shotgun sequence

A Generic Library of Problem Solving Methods for Scheduling Applications

In this thesis we propose a generic library of scheduling problem-solving methods. As a first approximation, scheduling can be defined as an assignment of jobs and activities to resources and time ranges in accordance with a number of constraints and requirements. In some cases optimisation criteria may also be included in the problem specification. Although, several attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision, which is needed both to obtain a formal account of scheduling problem solving and to provide effective support for development of scheduling applications by reuse. Our library subscribes to the Task-Method-Domain-Application (TMDA) knowledge modelling framework, which provides a structured organisation for the different components of the library. In line with the organisation proposed by TMDA, we first developed a generic scheduling task ontology, which formalises the space of scheduling problems independently of any particular application domain, or problem solving method. Then we constructed a task-specific, but domain independent model of scheduling problem-solving, which generalises from the variety of approaches to scheduling problem-solving, which can be found in literature. The generic nature of this model was demonstrated by constructing seven methods for scheduling, as alternative specialisation of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for the analysis and development of scheduling applications

The use of EST expression matrices for the quality control of gene expression data and the development of improved algorithms for gene expression profiling in cancer

There are currently a few bioinformatics tools, such as dbEST, DDD and GEPIS to name a few, which have been widely used to retrieve and analyse EST data for gene expression levels. The Cancer Genome Anatomy Project (CGAP, run by NCBI) cDNA xProfiler and eDNA DGED tools can be used to examine EST to compare gene expression levels between cancer and normal tissue. However, neither COAP nor other similar tools provide an easy way to compare expression in normal and cancerous tissue with e.g. expression levels in related or proximal tissues at the same time while also presenting that data for study separately. Furthermore, the expression data are often assumed to be correct and no quality control tools are made available at eGAP, dbEST and GEPIS. In this study the CGAP tools were recreated with the aim of enabling a wider range of tissues to be searched and compared in a single search. The CGAP tools were found to contain many errors in their library and gene parsing algorithms, for which solutions were implemented in the recreated algorithms. A method was also devised for the tissue origin of EST libraries to be verified and for the uncharacterised libraries to be annotated with a likely tissue of origin using EST data alone. An initial list of tissue-specific genes was optimised to create gene expression matrices which could be used to determine the tissue origin of a library. The matrices were demonstrated to show potential for cancer staging and for the indication of the degree of normalisation of a library in addition to tissue typing, making tissue-specific expression a suitable quality control method for expression data. Together the improved expression profiling algorithm and the expression matrices provide new tools to assess the quality of EST data and their suitability for expression profiling.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A new computational framework for the classification and function prediction of long non-coding RNAs

Long non-coding RNAs (lncRNAs) are known to play a significant role in several biological processes. These RNAs possess sequence length greater than 200 base pairs (bp), and so are often misclassified as protein-coding genes. Most Coding Potential Computation (CPC) tools fail to accurately identify, classify and predict the biological functions of lncRNAs in plant genomes, due to previous research being limited to mammalian genomes. In this thesis, an investigation and extraction of various sequence and codon-bias features for identification of lncRNA sequences has been carried out, to develop a new CPC Framework. For identification of essential features, the framework implements regularisation-based selection. A novel classification algorithm is implemented, which removes the dependency on experimental datasets and provides a coordinate-based solution for sub-classification of lncRNAs. For imputing the lncRNA functions, lncRNA-protein interactions have been first determined through co-expression of genes which were re-analysed by a sequence similaritybased approach for identification of novel interactions and prediction of lncRNA functions in the genome. This integrates a D3-based application for visualisation of lncRNA sequences and their associated functions in the genome. Standard evaluation metrics such as accuracy, sensitivity, and specificity have been used for benchmarking the performance of the framework against leading CPC tools. Case study analyses were conducted with plant RNA-seq datasets for evaluating the effectiveness of the framework using a cross-validation approach. The tests show the framework can provide significant improvements on existing CPC models for plant genomes: 20-40% greater accuracy. Function prediction analysis demonstrates results are consistent with the experimentally-published findings