Fig 1.
A flowchart depicting the overall workflow of Global Mapper module of Vikodak. This module enables users to computationally estimate the relative abundance of various metabolic pathways in a given sample/ environment. It helps in quantifying the contribution of individual microbes (in that sample) to the predicted functions at all three tiers of KEGG hierarchy. These functionalities are achieved using two distinct algorithmic workflows, viz. co-metabolism and independent contributions. For filtering out spuriously predicted functions, the module incorporates two mechanisms, namely, Pathway Exclusion Cut-off (PEC) and ‘Removal of eukaryotic functions’.
Fig 2.
Comparison of Vikodak with PICRUSt using datasets from distinct environments.
JSD based PCoA ordination of functional inferences obtained using (a) Vikodak (b) PICRUStfor 364 samples pertaining to three distinct environments viz. Human Gut, Nematode and Amazonian soil. In spite of adopting different methodologies for inferring functions from 16S datasets, both Vikodak and PICRUSt are able to obtain a distinct spatial clustering of datasets according to the sampling environments. S1 Table may be referred for details pertaining to the sourced metagenomes.
Fig 3.
Comparison of Vikodak with PICRUSt using datasets from physiologically distinct body sites in humans.
JSD based PCoA ordination of functional inferences obtained using (a) Vikodak (b) PICRUSt for 1291 samples pertaining to four distinct human body sites, viz. Gut (HMP and Prebiotics), Oral Cavity (Oropharynx, Sub-gingival and Sputum), Skin and Vagina. The pattern of clustering indicates notable differences in the performance of Vikodak and PICRUSt. Vikodak-derived functional profiles of physiologically similar environments are observed to exhibit a logical/ expected pattern of clustering as compared to PICRUSt. For instance, Vikodak-derived functional profiles of all gut samples are observed to cluster closely together despite belonging to different experiments (and geographies). A similar pattern of clustering is observed for samples belonging to the oral cavity. S1 Table may be referred for details pertaining to the sourced metagenomes.
Fig 4.
Validation of Pathway Exclusion Cut-off (PEC) value.
Graphical depiction of the quantum of eukaryotic functions predicted by Vikodak at different PEC values. Results indicate a consistent decrease in the proportion of predicted eukaryotic functions with increasing PEC value. While PD and PH represent datasets comprised of microbiome samples obtained from ‘subjects with Periodontitis’ and ‘Periodontally healthy controls’ (Griffen et al., 2012), PsC and PsL represent samples pertaining to Psoriasis Control and Psoriasis Lesional datasets (Alekseyenko et al., 2013).
Fig 5.
Ordination analysis of datasets pertaining to subjects with Periodontitis (PD, PS)and Periodontally healthy controls (PH) datasets using Vikodak derived functional profiles.
PCA-based ordination analysis carried out on Pathway Abundance Profiles (obtained using Global Mapper module) of all 91 samples of Periodontitis datasets (Griffen et al., 2012). Cluster points in red and peach pertain to PD and PS datasets. Cluster points in green represent PH datasets.
Fig 6.
Results of ISFA pertaining to PD (subjects with Periodontitis, deep pockets) and PH (Peridontically healthy controls) datasets.
Results of Inter Sample Feature Analysis (at various PEC values: 50–90) aimed at identification of significantly differentiating functions between PD and PH datasets (Griffen et al., 2012). Green color indicates that the corresponding feature was observed to be significantly differentiating under the given PEC value, while red color indicates the contrary. Identifying functions that are reported as significantly different at most PEC thresholds greatly increase the confidence in the set of functions identified as significantly different.
Table 1.
beta-lactam resistance contribution in datasets obtained from Periodontally healthy controls(PH) and subjects with Periodontitis (PD).
Individual contributions of resident microbes of PH (healthy samples) and PD (deep pocket samples) of Periodontitis study (Griffen et al., 2012) towards 'beta-lactam resistance' function. Generahighlighted in 'bold font' pertain to taxa that have been previously associated with periodontitis.
Fig 7.
Utility of ‘User-Data-Mapping-File’ in comparing PD (subjects with Periodontitis, deep pockets) and PH (Periodontally healthy controls) datasets using Local Mapper.
Graphical visualization of (qualitative) relative abundance of various enzymes pertaining to 'Synthesis and Degradation of Ketone Bodies' function in PH and PD datasets (Griffen et al., 2012). Higher the intensity of color, greater is the relative abundance of the corresponding enzyme. The visualization was created using the 'User-Data-Mapping-File (example: S2 File, section J)' generated using Local Mapper Module of Vikodak.
Fig 8.
Comparison of PD (subjects with Periodontitis, deep pockets) and PH (Periodontally healthy controls) datasets in terms of 'Synthesis and Degradation of Ketone Bodies' function using Local Mapper.
Graphical visualization of (quantitative) relative abundance of various enzymes pertaining to 'Synthesis and Degradation of Ketone Bodies' function in PH and PD datasets (Griffen et al., 2012). Highlighted EC number corresponds to ‘acetoacetate decarboxylase’ that catalyzes the degradation of acetoacetate (ketone body). The visualization was created using the 'Effective-Enzyme-Abundance-Profiles (example: S2 File, section J) generated using Local Mapper Module of Vikodak.
Table 2.
Relevance of Vikodak derived differentiating (functional) features between PD and PH datasets.
A (literature-mined) summary of the biological relevance of other features identified (by Vikodak) as significantly differentiating between datasets obtained from Periodontally healthy controls (PH) and subjects with Periodontitis (PD). These datasets were obtained from Griffen et al. (2012).
Fig 9.
Function based microbial interaction network in (A) subjects with Periodontitis, deep pocket samples [PD] and (B) Periodontally healthy controls [PH].
Microbial Interaction Networks generated using the abundance data pertaining to the functional contribution of various microbes in (A) PD datasets (B) PH datasets (Griffen et al., 2012). The networks may thus be interpreted as an index of functional interactions/dynamics between the resident microbes of PD/PH environment. The networks are plotted in 'Degree Sorted Circular Layout' using Cytoscape 3.0.2. Bigger nodes pertain to microbes with 'higher degree' of interaction, while smaller nodes indicate the microbes with 'less degree' of interaction. Color of the edges serve as an index of positive (black) and negative (red) interactions between the constituent microbes of the networks. Color of the nodes pertain to different phylum affiliations of the microbes (red: Proteobacteria; blue: Actinobacteria; green: Firmicutes; violet: Bacteroidetes; fluorescent green: Fusobacteria; peach: Other).
Fig 10.
A schematic depiction of the overall workflow and application(s) of Vikodak. Three distinct functional modules, viz. Global Mapper, Inter Sample Feature Analyzer (ISFA), and Local Mapper, each catering to specific end-user requirements are depicted. Given a microbial abundance data profile of an environmental sample (e.g. 16S sequencing data classified using RDP classifier), the Global Mapper module enables (a) an in silico estimation of the relative abundance of various metabolic pathways in that sample, (b) quantifying the contribution of individual microbes (in that sample) to the predicted functions (at all three tiers of KEGG hierarchy), and (c) identification of the core set of metabolic functions defining a particular environment. The ISFA module in Vikodak is an extension of the Global Mapper module and is designed for performing a rigorous (pair-wise) comparative statistical analysis of the (inferred/predicted) functional profiles generated from two or more environments. The Local Mapper module further enables end-users to probe, in greater detail, the enzyme abundance profile(s) of individual metabolic pathway(s) identified as (a) the 'core' in one or more environments, or (b) differentially abundant between two or more environments.