Integrating biological data – the Distributed Annotation System

Andrew M Jenkinson,Robert D Finn,Eugene Kulesha,Andreas Kähäri,Ewan Birney,Rafael C Jimenez,Mario Albrecht,Gabrielle A Reeves,José R Macías,Hagen Blankenburg,Andreas Prlić,Tim Jp Hubbard,Henning Hermjakob,Thomas Down,Philip Jones

doi:10.1186/1471-2105-9-s8-s3

Abstract

BackgroundThe Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics.ResultsHere we describe the various infrastructure components of DAS and present a new extended version of the DAS specification. Version 1.53E incorporates several recent developments, including its extension to serve new data types and an ontology for protein features.ConclusionOur extensions to the DAS protocol have facilitated the integration of new data types, and our improvements to the existing DAS infrastructure have addressed recent challenges. The steadily increasing numbers of available data sources demonstrates further adoption of the DAS protocol.

Highlights

The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources
Whilst drawing conclusions based on the results of multiple experiments is by no means a new concept in biology, omics data and in silico analyses make traditional ad hoc methods of publishing and sharing data impractical
In recent years the DAS protocol has been expanded beyond the core specification to cater for the data integration needs of additional areas of biological research

Summary

Introduction

The Distributed Annotation System (DAS) is a widely adopted protocol for dynamically integrating a wide range of biological data from geographically diverse sources. DAS continues to expand its applicability and evolve in response to new challenges facing integrative bioinformatics. The abundance of data in the post-genomics era is a major boon for life science researchers. Data from disparate sources arguably have the most value when considered in context with each other. With the trend for data expansion set to continue and the highly collaborative approaches of major projects such as ENCODE [1], integration is likely to become an increasingly important focus of bioinformatics. Efforts to integrate data sources may be broadly categorised by their motivation: 1. Aggregating and presenting data in an accessible format

Results

Discussion

Conclusion