Katy Wolstencroft

A healthy lifestyle is the basis for adequate management in cardiovascular and other chronic conditions. The BENEFIT program aims to provide long-term support to patients with cardiovascular disease (CVD) in their own home with a healthy lifestyle.The FAIR Data approach in BENEFIT standardises and structures the data collected in the project, for better use and reuse. This metadata record describes the data available and provides a contact point for researchers who would like to apply to use the ...

This project collects and maintains minimum information metadata templates designed and used in the National NL-Bioimaging initiative. The templates are based on the REMBI standards and contribute to FAIR bioimaging data in the Netherlands, together with data management in the OMERO database.

FAIRDOM will establish a support and service network for European Systems Biology. We will serve projects in standardising, managing and disseminating data and models in a FAIR manner: Findable, Accessible, Interoperable and Reusable.

supplementary materials

Aim of HUMET Startup Project is to make an inventory of expertise, research goals, resources etc. of the HUMET Coaliytion of the Willing.

Data and experimental methods to support the work in the following paper:

Establishing Consensus Annotation for the Hallmarks of Cancer, 2020, Yi Chen, F.J.Verbeek and K.Wolstencroft, in submission

The main objectives of SysMO-DB are to: facilitate the web-based exchange of data between research groups within- and inter- consortia, and to provide an integrated platform for the dissemination of the results of the SysMO projects to the scientific community. We aim to devise a progressive and scalable solution to the data management needs of the SysMO initiative, that:

• facilitates and maximises the potential for data exchange between SysMO research groups;
• maximises the ‘shelf life’ and ...

The BENEFIT project is an interdisciplinary eHealth initiative to offer long-term lifestyle support for patients with cardiovascular disease (CVD). Data collected from BENEFIT patients is heterogeneous and encompasses demographics, observational lifestyle data, self-reported motivation for lifestyle change and physical measurements. Analysing these results can provide new insights into the effects of different intervention strategies used in the cardiac rehabilitation programme on maintaining a ...

User metadata is an essential part of experimental data. Scientists need to understand underlying conditions and experimental procedures in order to model or investigate relevant biological questions. Currently, only a small fraction of the High Content SCreening (HCS) investigations are deposited for reuse by the community, and an even smaller fraction of that data is standards-compliant. For reusing data, scientists need to be able to understand how data was generated, under which experimental ...

The hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. In this investigation we compare cancer hallmark mapping strategies from different studies, based on Gene Ontology and biological pathway annotation. By analysing the semantic ...

Investigating oscillations at the level of yeast populations and individual cells

The hallmarks mapping schemes under comparison were developed over the period of 7 years and therefore were developed using different versions of the Gene Ontology and associated annotation. Understanding which differences between mapping schemes were the result of topological or annotation changes to GO could therefore help to further refine consensus and make results and conclusions more comparable between studies.

Multiple studies have devised mapping schemes to associate cancer hallmarks with Gene Ontology terms and biological pathway. This study compares the similarities and differences between them, in order to establish consensus knowledge.

This study examines how different hallmark gene datasets intersect with prognostic cancer genes

Here you will find guidelines for creating MIAPE compliant proteomics data files as well as examples and links to online tools and resources

Here you will find guidelines for creating MAGE-TAB compliant transcriptomics data files as well as examples and links to online tools and resources.

Some generic examples of transcriptomics templates that conform to the MAGE-TAB specification. These templates were created and modified from templates produced by ArrayExpress and GEO. These templates are generic and non-specific for any particular array platform.

Some examples of proteomics templates for Mass Spectrometry data that conform to the MIAPE specification

This Excel template is the general (master) template for any type of metabolomics data. It can be used as it is, or extended and modified to create a more specific templates for particular technologies and assay types.

Some examples of transcriptomics templates for Affymetrix data that conform to the MAGE-TAB specification. These templates were taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using these templates will mean easier submission to GEO/ArrayExpress and greater consistency of data in SEEK.

A example of an RT-PCR Excel template. RT-PCR is Reverse Transcriptase PCR (NOT to be confused with Real Time PCR, which is normally referred to as qPCR)

This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using templates will mean easier submission to public databases on publication and greater consistency of data in SEEK.

This Excel template is an example taken from the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html#GAtemplates) which has been modified to conform to the SysMO JERM (Just Enough Results Model). Using templates helps with searching and comparing data as well as making it easier to submit data to public repositories for publications.

Some examples of transcriptomics templates for NimbleGen data that conform to the MAGE-TAB specification. These templates were taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using templates will mean easier submission to GEO/ArrayExpress upon publication and greater consistency of data in SEEK for easier searching and comparing.

Examples of proteomics templates for gele electrophoresis data that conform to the MIAPE-GE specification

Concentration of glycolytic intermediates over time

A Weighted Gene Co-Expression Network Analysis (WGCNA) of breast cancer prognostic genes (derived from transcriptome data from the TCGA Genomics Data Commons (GDC) data portal (https://portal.gdc.cancer.gov/)), and cancer hallmark genes.

A Jaccard Index of the overlap between prognostic and hallmark genes for 17 cancer types across different mapping schemes. The impact of selecting different mapping schemes was assessed by pairwise comparisons where there were 5 or more shared genes.

An example template for MAGE-TAB compliant transcriptomics data. This example only covers the Investigation Description Format (IDF) and the Sample and Data Relationship Format (SDRF) worksheets (i.e. the metadata from a microarray experiment). This template was generated automatically using the ArrayExpress submission help tool (http://www.ebi.ac.uk/cgi-bin/microarray/magetab.cgi)

An example of a JERM-compliant template for RT-PCR data

This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics.

An example of a blank data sheet for Mass spectrometry data. This is not SysMO specific, but it is an example of MIAPE compliant data.

The shreadsheet contains macros to help you annotate your data with terms from controlled vocabularies and ontologies

Excel sheet template : concentrations of intracellular metabolites

This Excel template is the general (master) template for any type of metabolomics data. It can be used as it is, or extended and modified to create a more specific templates for particular technologies and assay types.

Report of the Data Management Foundry Workshop, 19th-20th of March, 2012, Vienna, Austria

This is an Excel template for Mass Spec data that contains all the necessary MIAPE metadata fields. It was derived from examples on the PRIDE website (PRoteomics IDEntifications database http://www.ebi.ac.uk/pride/)

An example of a completed data sheet for Mass spectrometry data. This is not SysMO specific, but it is an example of a MIAPE compliant data format taken from the PRIDE website (http://www.ebi.ac.uk/pride).

This template is a JERM compliant spreadsheet for use with gel electrophoresis data. It is in the style of the JERM MASTER template with worksheets for metadata, organism_sample, instrument and data. To conform to MIAPE-GE all worksheets are mandatory. A detailed description of MIAPE-GE is available from http://www.psidev.info/miape/MIAPE_GE_1_4.pdf

This report gives an overview of the sessions and outcomes from the recent SEEK Users meeting in Berlin. Please see related 'Presentation' entries for related slides and supplementary material.

This Excel template is for use with affy transcriptomics data in Matrix table format. It was created from a template on the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/geo_affy.html) and modified to conform to the SysMO JERM for transcriptomics.

This Excel template is for use with affy Chip-chip data where the results of the primary analysis are reported in BAR and BED files. It was created from a template on the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/geo_affy.html) and modified to conform to the SysMO JERM for transcriptomics.

This Excel template is an example taken from the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html#GAtemplates) which has been modified to conform to the SysMO JERM (Just Enough Results Model). Using templates helps with searching and comparing data as well as making it easier to submit data to public repositories for publications.

This template is for recording gene expression data from the NimbleGen platform. This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using these templates will mean easier submission to GEO/ArrayExpress and greater consistency of data in SEEK.

This template is for recording genome data from the NimbleGen platform. This template was taken from the GEO website (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html) and modified to conform to the SysMO-JERM (Just enough Results Model) for transcriptomics. Using these templates will mean easier submission to GEO/ArrayExpress and greater consistency of data in SEEK.

This document outlines our plans for making it easier to publish assets from SEEK to enable people from outside the consortium to see them.

An overview of creating MAGE-TAB compliant spreadsheets for transcriptomics data in SysMO SEEK

MIAPE (Minimum Information About a Proteomics Experiment) is the recommended format for proteomics data in SysMO-SEEK. The document attached provides more information and links to tools and resources.

FAIRDOM introduction by Olga Krebs

Presentation by Carole Goble at the International Semantic Web Conference 2013 in Sydney Australia about the use of semantic web technology in SEEK and RightField.

Introduction to RightField and Demo were presented at the Virtual Liver Network PALs meeting on 31st of October in Hünfeld, Germany

Katy Wolstencroft's talk at the "3rd Eagle Genomics Symposium:Will Big Data and Bigger Cuts Cripple Bioinformatics?", presented on 21st March 2013.

http://www.eaglegenomics.com/2013/01/eagles-third-symposium-how-the-efficient-bioinformatician-reduces-rd-costs/

This document is a summary of the SysMO-DB PALs meeting on the 29-30 November 2012

Issues and considerations for data management as SysMO projects approach the end of their funding

This talk was presented at the IEEE e-Science conference, Chicago, USA (8-12 October 2012). http://www.ci.uchicago.edu/escience2012/ It describes the latest developments in RightField for both semantic data annotation and extraction to RDF.

SEEK at ICSB 2012 (presented by Olga Krebs)

An overview of the SEEK for the HCLS Systems Biology Task force meeting

This presentation gives an overview of the way we structure and share data in the SEEK using JERM templates, and how we use the ISA hierarchy of Investigations, Studies, and Assays to associate experiments and data sets together. These slides were presented at the SEEK Users meeting in Berlin 2012.