INDIGO - INtegrated data warehouse of microbial genomes with examples from the red sea extremophiles

Abstract

Background: The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.

Results: We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments.

Conclusions: We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.

Figure 1. Workflow of annotation process and data warehousing.

Here, the section marked (A) shows steps in the annotation process. Section (B) shows a PERL based conversion of annotations into an XML schema - validated using the class attributes and data types defined in the genomic model, and finally, section (C) shows the process of data warehouse development steps.

Figure 2. Annotation comparison for E. coli O104 (TY2482) among AAMG pipeline, BG7 and reference annotation set from Broad Institute.

Regarding the CDS annotation AAMG ranks second (with only 2 CDS region less annotated than BG7), while in annotation of orphan (hypothetical) CDS products (the less the better) and in annotation of functional (non-hypothetical) CDS products (the more the better) AAMG performs the best.

Figure 3. A) Keyword and B) Query builder search interface to INDIGO.

The keyword search interface shows an example of the search for “benzoate degradation”. Results are categorized on the left side of the resulting page, showing the number of hits found for genes, domains, pathways, etc. These results are further categorized into hits per genome for different organisms. Clicking on any of these categories shows filtered results. The query builder interface has an option to include or constrains an annotation class attribute, e.g. pathway name is constrained for “benzoate degradation”, while the organism attribute ‘short name’ is constrained to “SSPSH”. The annotation feature class attributes to be included in the result list here are gene db identifier, symbol, organism’s short name and pathway name. User can select any of the available annotation class attributes making it possible to integrate annotation from several different sources. Results of constrained query builder search are shown as a list. There are summary and filter options on the list page that allow a user to further analyze these results.

Figure 4. Region search interface.

This figure shows features (genes) for a region using coordinates (Contig3:198625-229704) from organism Haloplasma contractile (HLPCO). This region shows the cell Division and Cell Wall (DCW) biosynthesis gene cluster. An integrated genome browser view available via Region search results page, shows here the arrangement of genes in this region of the contig from HLPCO . The table below this section shows genome region, data export options, basic details of the feature (genes), type of features and their location on the genome. The create list by feature link saves this gene list in the data warehouse for further analysis. This list stays permanently if the user is logged in.

Figure 5. A) Gene Ontology, B) Protein Domain and C) Pathway enrichment analysis.

The figure shows a snapshot obtained in case when a term “cell cycle” was searched through the keyword search option and resulting genes were saved in a list that shows enrichment of GO, protein domain and pathways in comparison to the rest of the data in INDIGO. The number of hits shown for reach category can be saved as lists for further analysis.

Figure 6. Benzoate degradation in Salinisphaera shabanensis.

The genes from Salinisphaera shabanesis associated with Benzoate degradation pathway by INDIGO are shown in Red. INDIGO developed a functionality, available for all pathways present in INDIGO, that generates a specific URL to automatically display KEGG Orthologs from INDIGO on to pathway diagrams at KEGG webserver.