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Public data and open source tools for multi-assay genomic investigation of disease (2016)

Kannan, L., Ramos, M., Re, A., El-Hachem, N., Safikhani, Z., Gendoo, D. M. A., Davis, S., Gomez-Cabrero, D., Castelo, R., Hansen, K. D., Carey, V. J., Morgan, M., Culhane, A. C., Haibe-Kains, B., Waldron, L.

Oxford University Press

In: Briefings in Bioinformatics

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Publikationsdatum: 2016-07-16

Beschreibung: Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Print ISSN: 1467-5463

Digitale ISSN: 1477-4054

Thema: Biologie , Informatik

Publiziert von Oxford University Press

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Supervised, semi-supervised and unsupervised inference of gene regulatory networks (2014)

Maetschke, S. R., Madhamshettiwar, P. B., Davis, M. J., Ragan, M. A.

Oxford University Press

In: Briefings in Bioinformatics

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Publikationsdatum: 2014-03-18

Beschreibung: Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data. The results reveal low prediction accuracies for unsupervised techniques with the notable exception of the Z-SCORE method on knockout data. In all other cases, the supervised approach achieved the highest accuracies and even in a semi-supervised setting with small numbers of only positive samples, outperformed the unsupervised techniques.

Print ISSN: 1467-5463

Digitale ISSN: 1477-4054

Thema: Biologie , Informatik

Publiziert von Oxford University Press

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Extracting reaction networks from databases-opening Pandora's box (2014)

Fearnley, L. G., Davis, M. J., Ragan, M. A., Nielsen, L. K.

Oxford University Press

In: Briefings in Bioinformatics

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Publikationsdatum: 2014-11-20

Beschreibung: Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell–cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge.

Print ISSN: 1467-5463

Digitale ISSN: 1477-4054

Thema: Biologie , Informatik

Publiziert von Oxford University Press

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Supervised, semi-supervised and unsupervised inference of gene regulatory networks (2013)

Maetschke, S. R. ; Madhamshettiwar, P. B. ; Davis, M. J. ; [weitere]

Oxford University Press

In: Briefings in Bioinformatics. 2013; 15(2): 195-211. Published 2013 May 21. doi: 10.1093/bib/bbt034.

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Publikationsdatum: 2013-05-21

Print ISSN: 1467-5463

Digitale ISSN: 1477-4054

Thema: Biologie , Informatik

Publiziert von Oxford University Press

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PATRIC as a unique resource for studying antimicrobial resistance (2017)

Antonopoulos, Dionysios A ; Assaf, Rida ; Aziz, Ramy Karam ; [weitere]

Oxford University Press

In: Briefings in Bioinformatics. 2017; 20(4): 1094-1102. Published 2017 Jul 31. doi: 10.1093/bib/bbx083.

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Publikationsdatum: 2017-07-31

Beschreibung: The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other ‘omic’ data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Print ISSN: 1467-5463

Digitale ISSN: 1477-4054

Thema: Biologie , Informatik

Publiziert von Oxford University Press

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