Updates to BioSamples database at European Bioinformatics Institute

Faulconbridge, Adam; Burdett, Tony; Brandizi, Marco; Gostev, Mikhail; Pereira, Rui Pedro; Vasant, Drashtti; Sarkans, Ugis; Brāzma, Alvis; Parkinson, Helen

doi:10.1093/nar/gkt1081

Cited by 30 publications

(23 citation statements)

References 14 publications

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“…EMBL-EBI's BioSamples (7) database is used to register sample metadata for all tissue donors, tissue samples, and iPSC lines generated by the project. The samples are registered immediately upon creation and before any assays are performed.…”

Section: Introductionmentioning

confidence: 99%

The human-induced pluripotent stem cell initiative—data resources for cellular genetics

et al. 2016

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The Human Induced Pluripotent Stem Cell Initiative (HipSci) isf establishing a large catalogue of human iPSC lines, arguably the most well characterized collection to date. The HipSci portal enables researchers to choose the right cell line for their experiment, and makes HipSci's rich catalogue of assay data easy to discover and reuse. Each cell line has genomic, transcriptomic, proteomic and cellular phenotyping data. Data are deposited in the appropriate EMBL-EBI archives, including the European Nucleotide Archive (ENA), European Genome-phenome Archive (EGA), ArrayExpress and PRoteomics IDEntifications (PRIDE) databases. The project will make 500 cell lines from healthy individuals, and from 150 patients with rare genetic diseases; these will be available through the European Collection of Authenticated Cell Cultures (ECACC). As of August 2016, 238 cell lines are available for purchase. Project data is presented through the HipSci data portal (http://www.hipsci.org/lines) and is downloadable from the associated FTP site (ftp://ftp.hipsci.ebi.ac.uk/vol1/ftp). The data portal presents a summary matrix of the HipSci cell lines, showing available data types. Each line has its own page containing descriptive metadata, quality information, and links to archived assay data. Analysis results are also available in a Track Hub, allowing visualization in the context of public genomic annotations (http://www.hipsci.org/data/trackhubs).

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Section: Introductionmentioning

confidence: 99%

The human-induced pluripotent stem cell initiative—data resources for cellular genetics

et al. 2016

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“…13 Recent years, RDF is gathering more attention in the bioinformatics community to describe various types of data, and some RDF-related tools (including SPARQL endpoints) are becoming available. [14][15][16] To date, these are still developing technologies, and many studies attempt to improve the calculation e±ciency and the usability. [17][18][19] Our SPARQL endpoint also leaves room to improve, although the continuing development of the ontology data and the related technologies would enable more complex queries with better usability in the foreseeable future.…”

Section: Resultsmentioning

confidence: 99%

PIERO ontology for analysis of biochemical transformations: Effective implementation of reaction information in the IUBMB enzyme list

Kotera

Nishimura

Nakagawa

et al. 2014

J. Bioinform. Comput. Biol.

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Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been veri¯ed, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been veri¯ed. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identi¯ers of the § § § Corresponding author. ¶ ¶ ¶ First and second author contributed equally to the paper.Journal of Bioinformatics and Computational Biology Vol. 12, No. 6 (2014) enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

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“…MS proteomics and RNAseq data obtained in the same study). There are, however, ongoing efforts to link different studies performed on the same sample 129. Over time, the existence of personalized sequence databases (from DNA exome sequencing), or the existence of public data containing both gene and protein expression data for a given sample will become commonplace, opening up yet more opportunities for data analysts.…”

Section: Discussionmentioning

confidence: 99%

Exploring the potential of public proteomics data

et al. 2015

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In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS‐based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re‐)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

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Updates to BioSamples database at European Bioinformatics Institute

Cited by 30 publications

References 14 publications

The human-induced pluripotent stem cell initiative—data resources for cellular genetics

The human-induced pluripotent stem cell initiative—data resources for cellular genetics

PIERO ontology for analysis of biochemical transformations: Effective implementation of reaction information in the IUBMB enzyme list

Exploring the potential of public proteomics data

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