doi: 10.17113/ftb.55.02.17.4749 

MEGGASENSE – The Metagenome/Genome Annotated Sequence Natural Language Search Engine: A Platform for the Construction of Sequence Data Warehouses

Ranko Gacesa^1,2, Jurica Zucko^1,3, Solveig K. Petursdottir⁴, Elisabet Eik Gudmundsdottir⁴, Olafur H. Fridjonsson⁴, Janko Diminic^1,3, Paul F. Long^2,5, John Cullum⁶, Daslav Hranueli^1,3, Gudmundur O. Hreggvidsson^4,7 and Antonio Starcevic^1,3*

¹SemGen Ltd., Lanište 5/D, HR-10 000 Zagreb, Croatia
²Institute of Pharmaceutical Science, King’s College London, Franklin-Wilkins Building, Stamford Street, London SE1 9NH, UK
³Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, HR-10 000 Zagreb, Croatia
⁴Matis Ltd., Vínlandslei? 12, IS-113 Reykjavík, Iceland
⁵Faculty of Life Sciences and Medicine, King’s College London, Franklin-Wilkins Building, Stamford Street, London SE1 9NH, UK
⁶Department of Genetics, University of Kaiserslautern, Postfach 3049, DE-67653 Kaiserslautern, Germany
⁷Faculty of Life and Environmental Sciences, University of Iceland, Sturlugötu 7, IS-101 Reykjavík, Iceland

Article history:
Received April 22, 2016
Accepted January 17, 2017

Key words:
bioprospecting, carbohydrate-modifying enzymes, DNA assembly

Summary:
The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya. The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel ‘functional’ assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.

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