Developing a new business model for enabling research - the case of the ACPFG in Australia

Publisher's postprint archived as permitted by publisher.The way in which companies, research centres and educational institutions are organised and structured may provide a competitive advantage for commercialisation, in particular if companies are dependent on the deployment of complementary assets and capabilities by third parties. This paper presents the case of the Australian Centre for Plant Functional Genomics (ACPFG), a private agricultural biotechnology (agbiotech) company specialising in early stage Research and Development (R&D) to produce superior adapted cereal varieties, tolerant to abiotic stress conditions such as drought, frost, salt, or mineral toxicity, all of which have a direct and negative impact on plant growth and crop productivity. The organisational structure of the company has been influenced and shaped by Government policy, shareholders expectations and trends in the agbiotech industrial organisation. It has proved attractive to potential alliance partners for collaborative R&D and commercialisation. We present the ACPFG as a new business model to fund basic research and facilitate technology transfer.Stephanie C. Agius, David Corkindale, Antonio G. Dottore, Michael Gilber

Predicting protein function by machine learning on amino acid sequences – a critical evaluation

Copyright @ 2007 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins. Results: Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier. Conclusion: We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function

TRAPID : an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes

Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system

Grid infrastructures for secure access to and use of bioinformatics data: experiences from the BRIDGES project

The BRIDGES project was funded by the UK Department of Trade and Industry (DTI) to address the needs of cardiovascular research scientists investigating the genetic causes of hypertension as part of the Wellcome Trust funded (£4.34M) cardiovascular functional genomics (CFG) project. Security was at the heart of the BRIDGES project and an advanced data and compute grid infrastructure incorporating latest grid authorisation technologies was developed and delivered to the scientists. We outline these grid infrastructures and describe the perceived security requirements at the project start including data classifications and how these evolved throughout the lifetime of the project. The uptake and adoption of the project results are also presented along with the challenges that must be overcome to support the secure exchange of life science data sets. We also present how we will use the BRIDGES experiences in future projects at the National e-Science Centre

Are there laws of genome evolution?

Research in quantitative evolutionary genomics and systems biology led to the discovery of several universal regularities connecting genomic and molecular phenomic variables. These universals include the log-normal distribution of the evolutionary rates of orthologous genes; the power law-like distributions of paralogous family size and node degree in various biological networks; the negative correlation between a gene's sequence evolution rate and expression level; and differential scaling of functional classes of genes with genome size. The universals of genome evolution can be accounted for by simple mathematical models similar to those used in statistical physics, such as the birth-death-innovation model. These models do not explicitly incorporate selection, therefore the observed universal regularities do not appear to be shaped by selection but rather are emergent properties of gene ensembles. Although a complete physical theory of evolutionary biology is inconceivable, the universals of genome evolution might qualify as 'laws of evolutionary genomics' in the same sense 'law' is understood in modern physics.Comment: 17 pages, 2 figure

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster

An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa

Open Access Journal; Published online: 06 Nov 2019Maize production in West and Central Africa (WCA) is constrained by a wide range of interacting stresses that keep productivity below potential yields. Among the many problems afflicting maize production in WCA, drought, foliar diseases, and parasitic weeds are the most critical. Several decades of efforts devoted to the genetic improvement of maize have resulted in remarkable genetic gain, leading to increased yields of maize on farmers’ fields. The revolution unfolding in the areas of genomics, bioinformatics, and phenomics is generating innovative tools, resources, and technologies for transforming crop breeding programs. It is envisaged that such tools will be integrated within maize breeding programs, thereby advancing these programs and addressing current and future challenges. Accordingly, the maize improvement program within International Institute of Tropical Agriculture (IITA) is undergoing a process of modernization through the introduction of innovative tools and new schemes that are expected to enhance genetic gains and impact on smallholder farmers in the region. Genomic tools enable genetic dissections of complex traits and promote an understanding of the physiological basis of key agronomic and nutritional quality traits. Marker-aided selection and genome-wide selection schemes are being implemented to accelerate genetic gain relating to yield, resilience, and nutritional quality. Therefore, strategies that effectively combine genotypic information with data from field phenotyping and laboratory-based analysis are currently being optimized. Molecular breeding, guided by methodically defined product profiles tailored to different agroecological zones and conditions of climate change, supported by state-of-the-art decision-making tools, is pivotal for the advancement of modern, genomics-aided maize improvement programs. Accelerated genetic gain, in turn, catalyzes a faster variety replacement rate. It is critical to forge and strengthen partnerships for enhancing the impacts of breeding products on farmers’ livelihood. IITA has well-established channels for delivering its research products/technologies to partner organizations for further testing, multiplication, and dissemination across various countries within the subregion. Capacity building of national agricultural research system (NARS) will facilitate the smooth transfer of technologies and best practices from IITA and its partners

Next-Generation Sequencing:Application in Liver Cancer-Past, Present and Future?

Hepatocellular Carcinoma (HCC) is the third most deadly malignancy worldwide characterized by phenotypic and molecular heterogeneity. In the past two decades, advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. More recently, improvements of sophisticated next-generation sequencing (NGS) technologies have enabled complete and cost-efficient analyses of cancer genomes at a single nucleotide resolution and advanced into valuable tools in translational medicine. Although the use of NGS in human liver cancer is still in its infancy, great promise rests in the systematic integration of different molecular analyses obtained by these methodologies, i.e., genomics, transcriptomics and epigenomics. This strategy is likely to be helpful in identifying relevant and recurrent pathophysiological hallmarks thereby elucidating our limited understanding of liver cancer. Beside tumor heterogeneity, progress in translational oncology is challenged by the amount of biological information and considerable “noise” in the data obtained from different NGS platforms. Nevertheless, the following review aims to provide an overview of the current status of next-generation approaches in liver cancer, and outline the prospects of these technologies in diagnosis, patient classification, and prediction of outcome. Further, the potential of NGS to identify novel applications for concept clinical trials and to accelerate the development of new cancer therapies will be summarized