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Professor Mark Ragan Mark Ragan is Professor and Co head of the Division of Genomics of Development and Disease at the Institute for Molecular Bioscience, The University of Queensland in Brisbane, Australia.
He was pandora charms official founding Head of IMB's former Division of Genomics Computational Biology (2000 2014), founding Director of the Australian Research Council (ARC) Centre of Excellence in Bioinformatics (2003 2015), and co founder of QFAB Bioinformatics. Mark is a graduate of the University of Chicago (Biochemistry) and Dalhousie University (Biology). His 200+ peer reviewed research publications in biochemistry, molecular biology, evolutionary biology, genomics, algorithmics, bioinformatics and computational biology have attracted more than 11300 citations. Core technologies in his research group (integration of large bioscience data, scalable algorithms on trees and networks, bioinformatic workflows, high performance and data centric computing) are applied to problems of genome sequencing and de novo assembly, comparative evolutionary genomics, and inference of biomolecular networks particularly in the coral reef symbiont Symbiodinium, and in targeting therapies against DNA damage repair networks in familial breast cancer. Mark is also involved in national and international infrastructure initiatives in genomics, computing, data and bioinformatics services. Inference of biomolecular interaction networks in cancer Life is sustained by networks of biomolecular interactions within and among cells. pandora sister charm As a cell develops, its regulatory and transcriptional program unfolds. Successive states of this program the developmental trajectory can be defined by differential abundance necklace for pandora beads of individual gene products at different subcellular locations. cancer. We are using data from whole genome and exon sequencing, transcriptomics and epigenomics to infer and analyse signalling and transcriptional regulatory networks in normal and malignant cells. We are developing analytical modules that use machine learning, ontologies and advanced data integration to infer local network topology. We run these workflows on high performance computing infrastructure, and collaborate with specialists in systems modelling. Lateral genetic transfer and the evolution of pathogenicity In many bacteria, genetic material can be transmitted not only vertically from parent to offspring within a cellular lineage, but also laterally (horizontally) among unrelated lineages. Lateral genetic transfer (LGT) can occur not only under strongly selective conditions such as those found in hospitals or agricultural feedlots but also in natural environments, generating metabolic innovation and enabling the origin and spread of multidrug resistant superbugs. We are developing novel, highly scalable bioinformatic workflows that identify genomic regions of lateral origin, infer LGT relationships and delineate communities of genetic exchange. We collaborate with genomic scientists to sequence and annotate bacterial genomes, with computer scientists to develop rigorous algorithms and carry out exact computation at extremely large scale, and with experimentalists to validate our predictions and understand their biological consequences. We are investigating the role of LGT in the evolution of cellular networks of gene regulation, protein protein and protein small molecule interaction, and signalling in specific groups of pathogenic bacteria. We are also funded to examine the interplay among units of lateral transfer, structural features of genetic exchange communities, and systems properties of genetic flow across the microbial biosphere including phage and plasmid vectors. Our research in this project area involves the development and application of novel algorithmic approaches to sequence comparison, phylogenetic inference and the discovery of genomic regions of lateral origin. Among these novel approaches are so called "alignment free" methods based on the statistical comparison of distributions of short character strings (variously called k mers, n grams or words). Sequencing the genome of Symbiodinium In collaboration with partners in the Reef Future Genomics 2020 consortium led by the Great Barrier Reef Foundation, we have begun to assemble the genome of an algal symbiont of coral. We gratefully acknowledge the support of the GBRF and its partners: Bioplatforms Australia, Rio Tinto Coal, and a private foundation that wishes to remain anonymous. Dinoflagellate genomes can be up to 100 times the size of the human genome. The Symbiodinium genome is relatively small about the size of the human genome but like other dinoflagellate genomes has distinctive features that make sequence assembly and annotation a challenge. cheapest place to buy pandora charms In collaboration with biomedical researchers and clinicians we analyse and model the complex network of biomolecular interactions that underlie chronic human disease, particularly cancer. In collaboration with marine and microbial biologists and ecologists, agricultural researchers and resource managers we analyse and model the communities of genetic exchange in terrestrial (soil) and marine environments. We engage public and private sector organisations, and contribute to national and state policy in the application of genome scale data to biotechnology, chronic disease and coastal ecosystem management. Journal Article: The metastasis suppressor RARRES3 as an endogenous inhibitor of the immunoproteasome expression in breast cancer cells Anderson, Alison M., Kalimutho, Murugan, Harten, Sarah, Nanayakkara, Devathri M., Khanna, Kum Kum and Ragan, Mark A. (2017) The metastasis suppressor RARRES3 as an endogenous inhibitor of the immunoproteasome expression in breast cancer cells. Journal Article: Alignment free microbial phylogenomics under scenarios of sequence divergence, genome rearrangement and lateral genetic transfer Bernard, Guillaume, Chan, Cheong Xin and Ragan, Mark A. (2016) Alignment free microbial phylogenomics under scenarios of sequence divergence, genome rearrangement and lateral genetic transfer. Journal Article: Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks Taherian Fard, Atefeh, Srihari, Sriganesh, Mar, Jessica C. and Ragan, Mark A. (2016) Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks.
NPJ Systems Biology and Applications,Journal Article: Inferring phylogenies of evolving sequences without multiple sequence alignment Chan, Cheong Xin, Bernard, Guillaume, Poirion, Olivier, Hogan, James M. and Ragan, Mark A. (2014) Inferring phylogenies of evolving sequences without multiple sequence alignment.
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