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Event Date: 27 March 2014 Room B01 Clore Management Centre Birkbeck, University of London Malet Street London WC1E 7HX The Department of Biological Sciences at Birkbeck presents: The 2014 Bernal Lecture Professor Dame Janet Thornton…

This article is from Viruses , volume 2 . Abstract The drastic increase in the number of coronaviruses discovered and coronavirus genomes being sequenced have given us an unprecedented opportunity to perform genomics and bioinformatics analysis on this family of viruses. Coronaviruses possess the largest genomes (26.4 to 31.7 kb) among all known RNA viruses, with G + C contents varying from 32% to 43%. Variable numbers of small ORFs are present between the various conserved genes (ORF1ab, spike, envelope, membrane and nucleocapsid) and downstream to nucleocapsid gene in different coronavirus lineages. Phylogenetically, three genera, Alphacoronavirus, Betacoronavirus and Gammacoronavirus, with Betacoronavirus consisting of subgroups A, B, C and D, exist. A fourth genus, Deltacoronavirus, which includes bulbul coronavirus HKU11, thrush coronavirus HKU12 and munia coronavirus HKU13, is emerging. Molecular clock analysis using various gene loci revealed that the time of most recent common ancestor of human/civet SARS related coronavirus to be 1999–2002, with estimated substitution rate of 4×10−4 to 2×10−2 substitutions per site per year. Recombination in coronaviruses was most notable between different strains of murine hepatitis virus (MHV), between different strains of infectious bronchitis virus, between MHV and bovine coronavirus, between feline coronavirus (FCoV) type I and canine coronavirus generating FCoV type II, and between the three genotypes of human coronavirus HKU1 (HCoV-HKU1). Codon usage bias in coronaviruses were observed, with HCoV-HKU1 showing the most extreme bias, and cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape such codon usage bias in coronaviruses.

This article is from Viruses , volume 2 . Abstract The drastic increase in the number of coronaviruses discovered and coronavirus genomes being sequenced have given us an unprecedented opportunity to perform genomics and bioinformatics analysis on this family of viruses. Coronaviruses possess the largest genomes (26.4 to 31.7 kb) among all known RNA viruses, with G + C contents varying from 32% to 43%. Variable numbers of small ORFs are present between the various conserved genes (ORF1ab, spike, envelope, membrane and nucleocapsid) and downstream to nucleocapsid gene in different coronavirus lineages. Phylogenetically, three genera, Alphacoronavirus, Betacoronavirus and Gammacoronavirus, with Betacoronavirus consisting of subgroups A, B, C and D, exist. A fourth genus, Deltacoronavirus, which includes bulbul coronavirus HKU11, thrush coronavirus HKU12 and munia coronavirus HKU13, is emerging. Molecular clock analysis using various gene loci revealed that the time of most recent common ancestor of human/civet SARS related coronavirus to be 1999–2002, with estimated substitution rate of 4×10−4 to 2×10−2 substitutions per site per year. Recombination in coronaviruses was most notable between different strains of murine hepatitis virus (MHV), between different strains of infectious bronchitis virus, between MHV and bovine coronavirus, between feline coronavirus (FCoV) type I and canine coronavirus generating FCoV type II, and between the three genotypes of human coronavirus HKU1 (HCoV-HKU1). Codon usage bias in coronaviruses were observed, with HCoV-HKU1 showing the most extreme bias, and cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape such codon usage bias in coronaviruses.

This article is from Viruses , volume 2 . Abstract The drastic increase in the number of coronaviruses discovered and coronavirus genomes being sequenced have given us an unprecedented opportunity to perform genomics and bioinformatics analysis on this family of viruses. Coronaviruses possess the largest genomes (26.4 to 31.7 kb) among all known RNA viruses, with G + C contents varying from 32% to 43%. Variable numbers of small ORFs are present between the various conserved genes (ORF1ab, spike, envelope, membrane and nucleocapsid) and downstream to nucleocapsid gene in different coronavirus lineages. Phylogenetically, three genera, Alphacoronavirus, Betacoronavirus and Gammacoronavirus, with Betacoronavirus consisting of subgroups A, B, C and D, exist. A fourth genus, Deltacoronavirus, which includes bulbul coronavirus HKU11, thrush coronavirus HKU12 and munia coronavirus HKU13, is emerging. Molecular clock analysis using various gene loci revealed that the time of most recent common ancestor of human/civet SARS related coronavirus to be 1999–2002, with estimated substitution rate of 4×10−4 to 2×10−2 substitutions per site per year. Recombination in coronaviruses was most notable between different strains of murine hepatitis virus (MHV), between different strains of infectious bronchitis virus, between MHV and bovine coronavirus, between feline coronavirus (FCoV) type I and canine coronavirus generating FCoV type II, and between the three genotypes of human coronavirus HKU1 (HCoV-HKU1). Codon usage bias in coronaviruses were observed, with HCoV-HKU1 showing the most extreme bias, and cytosine deamination and selection of CpG suppressed clones are the two major independent biological forces that shape such codon usage bias in coronaviruses.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract Background: A multi-million dollar research initiative involving the National Institutes of Health (NIH), Wellcome Trust and African scientists has been launched. The initiative, referred to as H3Africa, is an acronym that stands for Human Heredity and Health in Africa. Here, we outline what this initiative is set to achieve and the latest commitments of the key players as at October 2013. Findings: The initiative has so far been awarded over $74 million in research grants. During the first set of awards announced in 2012, the NIH granted $5 million a year for a period of five years, while the Wellcome Trust doled out at least $12 million over the period to the research consortium. This was in addition to Wellcome Trust’s provision of administrative support, scientific consultation and advanced training, all in collaboration with the African Society for Human Genetics. In addition, during the second set of awards announced in October 2013, the NIH awarded to the laudable initiative 10 new grants of up to $17 million over the next four years. Conclusions: H3Africa is poised to transform the face of research in genomics, bioinformatics and health in Africa. The capacity of African scientists will be enhanced through training and the better research facilities that will be acquired. Research collaborations between Africa and the West will grow and all stakeholders, including the funding partners, African scientists, scientists across the globe, physicians and patients will be the eventual winners.

Ph.D. student Shatavia Morrison discusses her DNA research in the Department of Bioinformatics and Genomics at UNC Charlotte.

Ph.D. student Shatavia Morrison discusses her DNA research in the Department of Bioinformatics and Genomics at UNC Charlotte.

The website of the Bioinformatics, Genomics, and Proteomics program at Williams College.

This article is from International Journal of Molecular Sciences , volume 14 . Abstract Abiotic and biotic stresses lead to massive reprogramming of different life processes and are the major limiting factors hampering crop productivity. Omics-based research platforms allow for a holistic and comprehensive survey on crop stress responses and hence may bring forth better crop improvement strategies. Since high-throughput approaches generate considerable amounts of data, bioinformatics tools will play an essential role in storing, retrieving, sharing, processing, and analyzing them. Genomic and functional genomic studies in crops still lag far behind similar studies in humans and other animals. In this review, we summarize some useful genomics and bioinformatics resources available to crop scientists. In addition, we also discuss the major challenges and advancements in the “-omics” studies, with an emphasis on their possible impacts on crop stress research and crop improvement.

Bioinformatics, that is the interdisciplinary field that blends computer science and biostatistics with biological and biomedical sciences, is expected to gain a central role in next feature. Indeed, it has now affected several fields of biology, providing crucial hints for the understanding of biological systems and also allowing a more accurate design of wet lab experiments. In this work, the analysis of sequence data has be used in different fields, such as evolution (e.g. the assembly and evolution of metabolism), infections control (e.g. the horizontal flow of antibiotic resistance), ecology (bacterial bioremediation).

Git bundles for all non-empty code and wiki repositories under https://github.com/USDA-ARS-GBRU as of 2025-03-04 03:39 UTC. Retrieved with Git 2.30.2 using git clone --mirror $REPOURL $TMPCLONEDIR and GIT_DIR=$TMPCLONEDIR git bundle create $BUNDLENAME --all

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

To systematically review educational resources and identify gaps in training related to the application of genomics and bioinformatics for environmental surveillance of pathogens in Africa.

bioinformatics backgrounds

Dr. Dennis Livesay discusses the expertise of UNC Charlotte's Bioinformatics and Genomics Department.

Dr. Dennis Livesay discusses the expertise of UNC Charlotte's Bioinformatics and Genomics Department.

This article is from PLoS Computational Biology , volume 10 . Abstract None