Rosette chart is a feature that shows multiple different normalized metrics of it’s dataset, including Reanalyses, Citations, Views, Downloads and Connections. With this feature, we can see how much attention people are giving to this dataset by just a quick look. The chart can be found at the search’s result: Or in the dataset’s details page: The colour of each of the leaves in the flower is representing a different type of the dataset metrics:

Most data in the Datatsets Discovery Index can be accessed programmatically using a RESTful API. The API implementation is based on the Spring Rest Framework. Web-browsable API The OmicsDI API is web browsable, which means that: The query results returned by the API are available in JSONformat and also XML. This ensures that they can be viewed by human and accessed programmatically by computer. The main RESTful API page provides a simple web-based user interface, which allows developers to familiarize themselves with the API and get a better sense of the OmicsDI data before writing a single line of code.

One of the first request OmicsDI team (January/2017) received after the official release of the resource was the possibility to login into the system and associated to the user the related public datasets. The original request was informally made by Professor Rob Beynon of Liverpool University (@astacus) and replied by Laurent Gatto @lgatt0 . try @OmicsDI. That should do it, I believe — Laurent Gⓐtt⓪ (@lgatt0) January 3, 2017

The search results can be filtered or refined using different filters or terms (Figure 1). The OmicsDI web application supports at the moment nine different refinements: Omics Type, repository/database, Organisms, Tissue, diseases, Modifications (proteomics), Instruments and platforms, Publication data, Technology type. Figure 1: Filtering results of Search in the Browse Page Filter Box Figure 2: Tissue Filter Box Each Filter Box shows the number of datasets within each category (e.

The main goal of the Omics Discovery Index is to provide a platform for searching and linking omics public data. OmicsDI has implemented a unique and novel Search Engine for omics datasets including public and protected data. The OmicsDI Search Box Figure 1: OmicsDI Search Box The OmicsDI Search Box is the main component to searching in OmicsDI. The user can type a set of keywords that will enable the system to find the datasets containing those keywords.

The OmicsDI Home Page provides different blocks for navigating through the datasets, some of them are: 2D WordCloud; the species/organism/diseases Bubble Chart, repository/omics Bar Chart, Latest Datasets List, Most Accessed Datasets List, Datasets per year List. All the charts allow the user to search the data using the specific attribute. These boxes also act as a statistic component of the resource: for example, the pie chart shows how many datasets for each repository and omics the resource contains.

Omics Discovery Index is an integrated and open source platform facilitating the access and dissemination of omics datasets. It provides a unique infrastructure to integrate datasets coming from multiple omics studies, including at present proteomics, genomics, transcriptomics and metabolomics. OmicsDI stores metadata coming from the public datasets from every resource using an efficient indexing system, which is able to integrate different biological entities including genes, proteins and metabolites with the relevant life science literature.

OmicsDI has been built with the collaboration of multiple consortia and individual databases. This collaboration has enabled the standardization of the metadata across multiple resources and omics type. Each consortium group a set of databases around the same topic (e.g. proteomics) and has previously agree in a common metadata including Ontology Terms, Study Design, etc. At the same time, OmicsDI has collaborated with other individual archives and databases such as ArrayExpress or EGA.