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DASher: a stand-alone protein sequence client for DAS, the Distributed Annotation System
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. (Sonnhammer)
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. (Sonnhammer)
2009 (English)In: Bioinformatics, ISSN 1367-4803, Vol. 25, no 10, 1333-1334 p.Article in journal (Refereed) Published
Abstract [en]

The rise in biological sequence data has led to a proliferation of separate, specialized databases. While there is great value in having many independent annotations, it is critical that there be a way to integrate them in one combined view. The Distributed Annotation System (DAS) was developed for that very purpose. There are currently no DAS clients that are open source, specialized for aggregating and comparing protein sequence annotation, and that can run as a self-contained application outside of a web browser. The speed, flexibility and extensibility that come with a stand-alone application motivated us to create DASher, an open-source Java DAS client. Given a UniProt sequence identifier, DASher automatically queries DAS-supporting servers worldwide for any information on that sequence and then displays the annotations in an interactive viewer for easy comparison. DASher is a fast, Java-based DAS client optimized for viewing protein sequence annotation and compliant with the latest DAS protocol specification 1.53E. AVAILABILITY: DASher is available for direct use and download at including examples and source code under the GPLv3 licence. Java version 6 or higher is required.

Place, publisher, year, edition, pages
2009. Vol. 25, no 10, 1333-1334 p.
URN: urn:nbn:se:su:diva-34289DOI: 10.1093/bioinformatics/btp153ISI: 000265950600020PubMedID: 19297349OAI: diva2:284479
Available from: 2010-01-18 Created: 2010-01-07 Last updated: 2012-04-10Bibliographically approved
In thesis
1. Biological data exchange and the discovery of new protein families in metagenomic samples
Open this publication in new window or tab >>Biological data exchange and the discovery of new protein families in metagenomic samples
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The rise in sequence data has brought both challenges to the way we exchange biological information and opportunities to discover new protein families, primarily through the investigation of uncultured metagenomic samples.The Distributed Annotation System, or DAS, provided a means for exchanging protein sequence data, but there were no open source, stand-alone DAS clients optimized for integrating and viewing these data. To address this need, we developed DASher. Complementary to visualizing DAS data with DASher, we also created and made available ten servers to offer real-time protein feature predictions via DAS. While DAS works well for genomic data, there was no such framework for exchanging orthology data in a consistent way. Consequently, we developed the first standards for orthology data exchange, SeqXML and OrthoXML. 64 reference proteomes are now available in SeqXML, and 14 orthology providers have agreed to offer their predictions in OrthoXML. Besides creating a uniform representation of common data types, these standards enable direct comparison and assessment of competing methods for the first time.A substantial percentage of newly sequenced genes are ORFans, which have no match to previously known sequences. Metagenomics samples uncover sequences from uncultivable and therefore previously unseen species, and ORFans constitute much of the metagenomics data that are completely uncharacterized. ORFans are by definition impervious to standard similarity-based methods, and the few existing metagenomics gene-finding methods performed poorly on short, error-prone next-generation sequence data. Therefore, we designed a new approach to predict protein-coding gene families from metagenomic data and applied it to 17 virally-enriched metagenomes derived from human patients. Of the 456 putative ORFan families we found in the nearly 1 billion nucleotides sequenced from these libraries, we identified 32 putative novel protein families with strong support.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2012. 124 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
urn:nbn:se:su:diva-75108 (URN)978-91-74474-52-7 (ISBN)
Public defence
2012-05-11, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:30 (English)

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Manuscript.

Available from: 2012-04-19 Created: 2012-04-05 Last updated: 2012-04-11Bibliographically approved

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