Browsing by Subject "Genome analysis"

Now showing 1 - 6 of 6
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    Open Access
    Comparison of a real-time multiplex PCR and sequetyping assay for pneumococcal serotyping
    (Public Library of Science, 2015) Dube, Felix S; van Mens, Suzan P; Robberts, Lourens; Wolter, Nicole; Nicol, Paul; Mafofo, Joseph; Africa, Samantha; Zar, Heather J; Nicol, Mark P
    BACKGROUND: Pneumococcal serotype identification is essential to monitor pneumococcal vaccine effectiveness and serotype replacement. Serotyping by conventional serological methods are costly, labour-intensive, and require significant technical expertise. We compared two different molecular methods to serotype pneumococci isolated from the nasopharynx of South African infants participating in a birth cohort study, the Drakenstein Child Health Study, in an area with high 13-valent pneumococcal conjugate vaccine (PCV13) coverage. METHODS: A real-time multiplex PCR (rmPCR) assay detecting 21 different serotypes/-groups and a sequetyping assay, based on the sequence of the wzh gene within the pneumococcal capsular locus, were compared. Forty pneumococcal control isolates, with serotypes determined by the Quellung reaction, were tested. In addition, 135 pneumococcal isolates obtained from the nasopharynx of healthy children were tested by both serotyping assays and confirmed by Quellung testing. Discordant results were further investigated by whole genome sequencing of four isolates. RESULTS: Of the 40 control isolates tested, 25 had a serotype covered by the rmPCR assay. These were all correctly serotyped/-grouped. Sequetyping PCR failed in 7/40 (18%) isolates. For the remaining isolates, sequetyping assigned the correct serotype/-group to 29/33 (88%) control isolates. Of the 132/135 (98%) nasopharyngeal pneumococcal isolates that could be typed, 69/132 (52%) and 112/132 (85%) were assigned the correct serotype/-group by rmPCR and sequetyping respectively. The serotypes of 63/132 (48%) isolates were not included in the rmPCR panel. All except three isolates (serotype 25A and 38) were theoretically amplified and differentiated into the correct serotype/-group with some strains giving ambigous results (serotype 13/20, 17F/33C, and 11A/D/1818F). Of the pneumococcal serotypes detected in this study, 69/91 (76%) were not included in the current PCV13. The most frequently identified serotypes were 11A, 13, 15B/15C, 16F and 10A. CONCLUSION: The rmPCR assay performed well for the 21 serotypes/-groups included in the assay. However, in our study setting, a large proportion of serotypes were not detected by rmPCR. The sequetyping assay performed well, but did misassign specific serotypes. It may be useful for regions where vaccine serotypes are less common, however confirmatory testing is advisable.
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    The development of computational biology in South Africa: successes achieved and lessons learnt
    (Public Library of Science, 2016) Mulder, Nicola J; Christoffels, Alan; De Oliveira, Tulio; Gamieldien, Junaid; Hazelhurst, Scott; Joubert, Fourie; Kumuthini, Judit; Pillay, Ché S; Snoep, Jacky L; Bishop, Özlem Tastan; Tiffin, Nicki
    Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt.
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    Generation of genic diversity among Streptococcus pneumoniae strains via horizontal gene transfer during a chronic polyclonal pediatric infection
    (Public Library of Science, 2010) Hiller, N Luisa; Ahmed, Azad; Powell, Evan; Martin, Darren P; Eutsey, Rory; Earl, Josh; Janto, Benjamin; Boissy, Robert J; Hogg, Justin; Barbadora, Karen
    Although there is tremendous interest in understanding the evolutionary roles of horizontal gene transfer (HGT) processes that occur during chronic polyclonal infections, to date there have been few studies that directly address this topic. We have characterized multiple HGT events that most likely occurred during polyclonal infection among nasopharyngeal strains of Streptococcus pneumoniae recovered from a child suffering from chronic upper respiratory and middle-ear infections. Whole genome sequencing and comparative genomics were performed on six isolates collected during symptomatic episodes over a period of seven months. From these comparisons we determined that five of the isolates were genetically highly similar and likely represented a dominant lineage. We analyzed all genic and allelic differences among all six isolates and found that all differences tended to occur within contiguous genomic blocks, suggestive of strain evolution by homologous recombination. From these analyses we identified three strains (two of which were recovered on two different occasions) that appear to have been derived sequentially, one from the next, each by multiple recombination events. We also identified a fourth strain that contains many of the genomic segments that differentiate the three highly related strains from one another, and have hypothesized that this fourth strain may have served as a donor multiple times in the evolution of the dominant strain line. The variations among the parent, daughter, and grand-daughter recombinant strains collectively cover greater than seven percent of the genome and are grouped into 23 chromosomal clusters. While capturing in vivo HGT, these data support the distributed genome hypothesis and suggest that a single competence event in pneumococci can result in the replacement of DNA at multiple non-adjacent loci.
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    myKaryoView: a light-weight client for visualization of genomic data
    (Public Library of Science, 2011) Jimenez, Rafael C; Salazar, Gustavo A; Gel, Bernat; Dopazo, Joaquin; Mulder, Nicola; Corpas, Manuel
    The Distributed Annotation System (DAS) is a protocol for easy sharing and integration of biological annotations. In order to visualize feature annotations in a genomic context a client is required. Here we present myKaryoView, a simple light-weight DAS tool for visualization of genomic annotation. myKaryoView has been specifically configured to help analyse data derived from personal genomics, although it can also be used as a generic genome browser visualization. Several well-known data sources are provided to facilitate comparison of known genes and normal variation regions. The navigation experience is enhanced by simultaneous rendering of different levels of detail across chromosomes. A simple interface is provided to allow searches for any SNP, gene or chromosomal region. User-defined DAS data sources may also be added when querying the system. We demonstrate myKaryoView capabilities for adding user-defined sources with a set of genetic profiles of family-related individuals downloaded directly from 23andMe. myKaryoView is a web tool for visualization of genomic data specifically designed for direct-to-consumer genomic data that uses publicly available data distributed throughout the Internet. It does not require data to be held locally and it is capable of rendering any feature as long as it conforms to DAS specifications. Configuration and addition of sources to myKaryoView can be done through the interface. Here we show a proof of principle of myKaryoView's ability to display personal genomics data with 23andMe genome data sources. The tool is available at: http://mykaryoview.com .
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    A novel glucagon-related peptide (GCRP) and its receptor GCRPR account for coevolution of their family members in vertebrates
    (Public Library of Science, 2013) Park, Cho Rong; Moon, Mi Jin; Park, Sumi; Kim, Dong-Kyu; Cho, Eun Bee; Millar, Robert Peter; Hwang, Jong-Ik; Seong, Jae Young
    The glucagon (GCG) peptide family consists of GCG, glucagon-like peptide 1 (GLP1), and GLP2, which are derived from a common GCG precursor, and the glucose-dependent insulinotropic polypeptide (GIP). These peptides interact with cognate receptors, GCGR, GLP1R, GLP2R, and GIPR, which belong to the secretin-like G protein-coupled receptor (GPCR) family. We used bioinformatics to identify genes encoding a novel GCG-related peptide (GCRP) and its cognate receptor, GCRPR. The GCRP and GCRPR genes were found in representative tetrapod taxa such as anole lizard, chicken, and Xenopus , and in teleosts including medaka, fugu, tetraodon, and stickleback. However, they were not present in mammals and zebrafish. Phylogenetic and genome synteny analyses showed that GCRP emerged through two rounds of whole genome duplication (2R) during early vertebrate evolution. GCRPR appears to have arisen by local tandem gene duplications from a common ancestor of GCRPR , GCGR , and GLP2R after 2R. Biochemical ligand-receptor interaction analyses revealed that GCRP had the highest affinity for GCRPR in comparison to other GCGR family members. Stimulation of chicken, Xenopus , and medaka GCRPRs activated Gα s -mediated signaling. In contrast to chicken and Xenopus GCRPRs, medaka GCRPR also induced Gα q/11 -mediated signaling. Chimeric peptides and receptors showed that the K 16 M 17 K 18 and G 16 Q 17 A 18 motifs in GCRP and GLP1, respectively, may at least in part contribute to specific recognition of their cognate receptors through interaction with the receptor core domain. In conclusion, we present novel data demonstrating that GCRP and GCRPR evolved through gene/genome duplications followed by specific modifications that conferred selective recognition to this ligand-receptor pair.
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    A quick guide for building a successful bioinformatics community
    (Public Library of Science, 2015) Budd, Aidan; Corpas, Manuel; Brazas, Michelle D; Fuller, Jonathan C; Goecks, Jeremy; Mulder, Nicola J; Michaut, Magali; Ouellette, B F Francis; Pawlik, Aleksandra; Blomberg, Niklas
    "Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding activities; (iv) interactions and engagement with professionals from other fields; and (v) other activities beneficial to individual participants, communities, and the scientific field as a whole. It is thus beneficial at many different levels to understand the general features of successful, high-impact bioinformatics communities; how individual participants can contribute to the success of these communities; and the role of education and training within these communities. We present here a quick guide to building and maintaining a successful, high-impact bioinformatics community, along with an overview of the general benefits of participating in such communities. This article grew out of contributions made by organizers, presenters, panelists, and other participants of the ISMB/ECCB 2013 workshop "The 'How To Guide' for Establishing a Successful Bioinformatics Network" at the 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and the 12th European Conference on Computational Biology (ECCB).