Fig 1.
Workflow options in the COGNIZER workflow.
A schematic representation of the four workflow options in the COGNIZER framework.
Fig 2.
Creation of the 'customized' COG database.
A schematic diagram illustrating the steps involved in the creation of the 'customized' COG database.
Fig 3.
Procedure adopted for obtaining cross-mapping information.
Procedure adopted for obtaining cross-mapping information amongst sequences in the COG and the other protein functional databases (KEGG, Pfam, GO and SEED).
Fig 4.
Workflows adopted in options 3 and 4 of the COGNIZER framework.
A flow-diagram depicting the steps adopted in workflow options 3 and 4 of the COGNIZER framework.
Table 1.
Evaluation of COGNIZER's annotation results in terms of positive predictive value (PPV) and negative predictive value (NPV).
Table 2.
Comparison of computing time required by different options in the COGNIZER framework.
Fig 5.
Correlation between prediction results obtained using option 1 and those obtained using the other three options in the COGNIZER framework.
A heat map of the correlation coefficients between the annotations obtained using option 1 and the other three options of COGNIZER framework. Pearson correlation coefficients were obtained with a p-value confidence of <0.00001. In option 1, the BLASTx method is employed (in the homology-search phase) for querying reads constituting metagenomic datasets. The search is performed against all sequences in the COG database. In the subsequent 'mapping' phase, for each query, functional annotations are inferred using COGNIZER's cross-mapping database. In option 2 the RAPSearch algorithm is used instead of BLASTx (in the homology-search phase). Option 3 and 4 are analogous to options 1 and 2 respectively, except that a reduced/customised COG database is used during the homology-search phase.