10,751 research outputs found

    Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

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    Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis

    mockrobiota: a Public Resource for Microbiome Bioinformatics Benchmarking.

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    Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/. The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community

    Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

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    Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

    Integrative biological simulation praxis: Considerations from physics, philosophy, and data/model curation practices

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    Integrative biological simulations have a varied and controversial history in the biological sciences. From computational models of organelles, cells, and simple organisms, to physiological models of tissues, organ systems, and ecosystems, a diverse array of biological systems have been the target of large-scale computational modeling efforts. Nonetheless, these research agendas have yet to prove decisively their value among the broader community of theoretical and experimental biologists. In this commentary, we examine a range of philosophical and practical issues relevant to understanding the potential of integrative simulations. We discuss the role of theory and modeling in different areas of physics and suggest that certain sub-disciplines of physics provide useful cultural analogies for imagining the future role of simulations in biological research. We examine philosophical issues related to modeling which consistently arise in discussions about integrative simulations and suggest a pragmatic viewpoint that balances a belief in philosophy with the recognition of the relative infancy of our state of philosophical understanding. Finally, we discuss community workflow and publication practices to allow research to be readily discoverable and amenable to incorporation into simulations. We argue that there are aligned incentives in widespread adoption of practices which will both advance the needs of integrative simulation efforts as well as other contemporary trends in the biological sciences, ranging from open science and data sharing to improving reproducibility.Comment: 10 page

    Eliciting the Functional Taxonomy from protein annotations and taxa

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    The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional information for a vast amount of proteins. With these data available, we have developed FunTaxIS, a tool that is the first attempt to infer functional taxonomy (i.e. how functions are distributed over taxa) combining functional and taxonomic information. FunTaxIS is able to define a taxon specific functional space by exploiting annotation frequencies in order to establish if a function can or cannot be used to annotate a certain species. The tool generates constraints between GO terms and taxa and then propagates these relations over the taxonomic tree and the GO graph. Since these constraints nearly cover the whole taxonomy, it is possible to obtain the mapping of a function over the taxonomy. FunTaxIS can be used to make functional comparative analyses among taxa, to detect improper associations between taxa and functions, and to discover how functional knowledge is either distributed or missing. A benchmark test set based on six different model species has been devised to get useful insights on the generated taxonomic rules

    Repository of NSF Funded Publications and Data Sets: "Back of Envelope" 15 year Cost Estimate

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    In this back of envelope study we calculate the 15 year fixed and variable costs of setting up and running a data repository (or database) to store and serve the publications and datasets derived from research funded by the National Science Foundation (NSF). Costs are computed on a yearly basis using a fixed estimate of the number of papers that are published each year that list NSF as their funding agency. We assume each paper has one dataset and estimate the size of that dataset based on experience. By our estimates, the number of papers generated each year is 64,340. The average dataset size over all seven directorates of NSF is 32 gigabytes (GB). A total amount of data added to the repository is two petabytes (PB) per year, or 30 PB over 15 years. The architecture of the data/paper repository is based on a hierarchical storage model that uses a combination of fast disk for rapid access and tape for high reliability and cost efficient long-term storage. Data are ingested through workflows that are used in university institutional repositories, which add metadata and ensure data integrity. Average fixed costs is approximately .0.90/GBover15−yearspan.Variablecostsareestimatedataslidingscaleof.0.90/GB over 15-year span. Variable costs are estimated at a sliding scale of 150 - 100pernewdatasetforup−frontcuration,or100 per new dataset for up-front curation, or 4.87 – 3.22perGB.Variablecostsreflecta3Thetotalprojectedcostofthedataandpaperrepositoryisestimatedat3.22 per GB. Variable costs reflect a 3% annual decrease in curation costs as efficiency and automated metadata and provenance capture are anticipated to help reduce what are now largely manual curation efforts. The total projected cost of the data and paper repository is estimated at 167,000,000 over 15 years of operation, curating close to one million of datasets and one million papers. After 15 years and 30 PB of data accumulated and curated, we estimate the cost per gigabyte at 5.56.This5.56. This 167 million cost is a direct cost in that it does not include federally allowable indirect costs return (ICR). After 15 years, it is reasonable to assume that some datasets will be compressed and rarely accessed. Others may be deemed no longer valuable, e.g., because they are replaced by more accurate results. Therefore, at some point the data growth in the repository will need to be adjusted by use of strategic preservation

    Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

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    A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval
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