Figures
Abstract
The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.
Citation: Vey G, Moreno-Hagelsieb G (2012) Metagenomic Annotation Networks: Construction and Applications. PLoS ONE 7(8): e41283. https://doi.org/10.1371/journal.pone.0041283
Editor: Olle Terenius, Swedish University of Agricultural Sciences, Sweden
Received: March 1, 2012; Accepted: June 19, 2012; Published: August 7, 2012
Copyright: © Vey, Moreno-Hagelsieb. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: GV was supported by an NSERC Strategic Grant; GM-H’s research was supported by a NSERC Discovery Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The ubiquity of next-generation sequencing projects has vastly accelerated the accumulation of metagenomic sequence data. As of last year the Sequence Read Archive [1] exceeded 100 Terabases of open-access reads produced by next-generation sequencing efforts [2]. A common goal in attempting to understand the functional capabilities of newly sequenced microbial communities involves the annotation of putative genes through the assignment of biological functions. Such functional annotation relies heavily on homology-based annotation transfer using tools such as BLAST, HMMs, and motif finding algorithms [3]. In turn, the success of these approaches is necessarily bounded by the diversity of the reference databases that are used to find candidate annotations. However, an important proportion of microorganisms do not grow under common laboratory culturing conditions [4], [5]. This limited spectrum of cultured microbial diversity, combined with biases in applied research interests, has yielded a skewed representation within sequence annotation databases [6]. Because metagenomes represent an attempt to gain access to the uncultured majority, homology-based annotation methods rooted in limited experimental knowledge about the functional roles of gene products are insufficient to adequately address the influx of unknown genes [7].
Given the difficulties in the annotation of individual metagenomic genes, the derivation and comparison of biological interaction networks represents a promising prospect for metagenomic data sources. Nevertheless, interaction networks can reveal vital information about functional organization and activity [8]. For example, studies of interaction networks in Escherichia coli [9] and Saccharomyces cerevisiae [10] have provided a systems perspective of these genomes by enumerating their respective functional modules. Recently there have been several attempts to capture metagenomic analogs of traditional interaction networks through the prediction of metabolic pathways and functional modules. MetaPath [11] uses prior knowledge of metabolic pathways in conjunction with metagenomic sequence data to predict the occurrence of metabolic pathways in metagenomic data sources. In contrast, Konietzny et al. [12] used a Bayesian approach to find co-occurrence patterns for functional descriptors contained in microbial genome annotations in order to infer functional modules.
In the present work we exploit metagenomic operon predictions to derive functional interactions that are translated and categorized according to their associated functional annotations as provided in the IMG/M metagenomics database [13] (Figure 1). The result is a collection of discrete networks of weighted annotation linkages that are subsequently examined for the occurrence of annotation modules that portray the functional and hierarchical organization of various microbial communities. A variety of network perspectives and annotation categories are compared and their potential applications are discussed.
The relative proportions (%) of various data source types that were used are shown categorized according to IMG/M microbiome taxa at the class level. Panel A shows the proportions (%) with respect to the total number of data sets while Panel B shows the proportions (%) with respect to the total number of genes.
Results
In order to demonstrate the utility of metagenomic annotation networks, networks employing a variety of perspectives and annotation categories were constructed and compared. The network construction protocol proposed in this work is confined to a process of network prediction followed by network translation (Figure 2; see also Methods). Subsequent analyses of the resulting networks were performed in order to demonstrate potential uses and applications but not as part of the network construction protocol itself. Therefore, examples provided here involve the use of Cytoscape 2.8.1 [23] for network analyses and the MINE plugin [24] for the identification of putative annotation modules. However, these tools were selected on the basis of potential familiarity for readers and it is certainly possible to use any other software, plugins, or algorithms as required for particular investigations.
Operonic genes are predicted on the basis of co-direction and intergenic proximity using scaffolds containing more than one gene (Panel A). Operons and their constituent genes can be filtered according to the presence or absence of a target annotation such that at least one member of an operon is required to possess a target descriptor (Panel B). Note that the filter step is optional and can applied to obtain target perspective networks while being omitted in the construction of source perspective networks. Each gene in a given operon is mined for its various types of functional annotations where any particular type has a domain of existing values (Panel C). For each operon, the obtained functional annotations are used to infer bidirectional functional interactions for annotations having the same type but different values (Panel D). Note that interactions are inferred directly for immediately adjacent gene pairs and also transitively for downstream members within the same operon.
Four polyketide target perspective networks were constructed with progressively increasing target stringency and each network was translated into each of the four annotation categories. The proportion of nodes and edges in each polyketide network was compared to its corresponding overall network. Panel A shows that coverage for nodes decreased for all annotation categories with increasing target stringency and Panel B shows that coverage for edges also decreased for all annotation categories with increasing target stringency.
Target Perspective Networks
Networks can be constructed from a target perspective by using a keyword or series of keywords joined by logical operators to filter and reduce a set of results on the basis of keyword occurrence, or target hits. The goal is to constrain the resulting functional interactions so that they reflect a target-centric view for a domain of interest, such as interactions relating to cellulases. In the following examples single keywords of general interest, namely “polyketide” and “cellulase,” were used to select specific operons from the complete set of available operons thereby reducing the overall network into specific target perspective networks. However, it is possible to construct a target perspective network from a smaller and more specific range of data sets, such as using only human gut microbiomes (see Source Perspective Networks).
Prior to evaluating any target perspectives networks, the effects of target stringency were investigated. Specifically, operons can be qualified as target hits if a fixed number or scalable proportion of their member genes have an annotation that contains the target. To determine the effects of target stringency versus network coverage, four polyketide target perspective networks were constructed and the stringency for qualification was progressively increased. Operons in the first network were required to have at least one target hit, operons in the second network were required to have at least two target hits, and so on, up to and including a stringency of requiring at least four target hits. Furthermore, each of the four networks was translated into each the four different annotation categories (see Methods) resulting in four sets of four target perspective networks. Figure 3 shows the proportion of nodes and edges recovered from the equivalent overall network (i.e. no filtering) with respect to increasing target stringency across each annotation category. The results illustrated that coverage for both nodes and edges decreased for all annotation categories with increasing target stringency. Therefore, the target perspective networks that follow used the least stringent requirement (i.e. at least one target hit) in an attempt to maximize the diversity and number of putative functional interactions available for subsequent analyses.
A MetaCyc cellulase network was constructed that consisted of 213 nodes and 779 edges (Table 1). A highly connected central hub was observed that had the annotation PWY-1001: cellulose biosynthesis (Figure 4, Panel A). Five modules were identified within the network (Tables S1 and S2). The top ranked module (Figure 4, Panel B) contained annotations relating to amino acid degradation and biosynthesis (Table 2). The precise annotation terms were analyzed for more general themes and a highly cohesive module emerged that described aliphatic amino acid metabolism, with particular emphasis on branched-chain amino acids (BCAAs) (Figure 5). BCAA metabolism is consistent with functional expectations for ruminal bacteria such as members of the genus Peptostreptococcus [14]. Likewise, data from ruminal environments would be expected to contribute interactions to a metagenomic cellulase network.
The target perspective networks for cellulase functional interactions are shown where large node diameter represents high node degree within each respective network. Panel A shows a network constructed using MetaCyc annotations with a highly connected central hub having the annotation PWY-1001: cellulose biosynthesis. The highlighted nodes represent the top ranking module which is enlarged in Panel B. Panel C shows a network constructed using COG annotations and features a highly connected central hub with the annotation COG1363: cellulase M and related proteins. The highlighted nodes represent the top ranking module which is enlarged in Panel D.
The annotative themes for the top ranked MetaCyc module are depicted where the numeric values indicate the number of annotations belonging to a thematic category. Specifically, amino acid categories are represented vertically and metabolic categories are represented horizontally. Note, the vertical themes are encapsulatory while the horizontal themes are mutually exclusive. A variety of functional perspectives can be simultaneously visualized by way of the interacting and overlapping thematic sets.
A COG cellulase network was constructed that consisted of 301 nodes and 763 edges (Table 1). A highly connected central hub was observed that had the annotation COG1363: cellulase M and related proteins (Figure 4, Panel C). A total of 33 modules were identified within the network (Tables S1 and S2). The top ranked module (Figure 4, Panel D) contained annotations relating to ABC-type transport, permease, ATPase, as well as various other terms (Table 2). Furthermore, the term “uncharacterized” was observed in conjunction with several instances of the previously listed annotations. The precise annotation terms were analyzed for more general themes resulting in a less cohesive module than the top ranked MetaCyc module. Nevertheless, these annotations are generally consistent with secretion and transfer activities such as multienzyme secretion in the cellulolytic bacterium Clostridium thermocellum [15] and glycoside hydrolase secretion in Thermobifida fusca, a soil bacterium involved in the degradation of plant cell walls [16].
Source Perspective Networks
In contrast to the target perspective, a source perspective network involves generating all possible functional interactions but from a particular range or collection of data sets. In this case, the goal is to constrain functional interactions so that they reflect a source-centric view for a domain of interest, such as human gut interactions. Moreover, it is possible to integrate target and source perspectives by constructing a target perspective network from a particular collection of source related data sets. This approach can be used to find functional interactions that are simultaneously target-centric and source-centric, such as cellulase interactions occurring in the human gut. In the present work a human gut microbiome [17] was used to produce two source perspective networks.
A KEGG gut network was constructed that consisted of 153 nodes and 192 edges (Table 1). Unlike the target perspective networks, no central hub was observed (Figure 6, Panel A). A total of 11 modules were identified within the network (Tables S1 and S2). The top ranked module (Figure 6, Panel B) scored lower than either of the top ranked cellulase modules and contained a diverse range of annotation terms (Table 2). The terms glycolysis and pyruvate occurred frequently in the pathway annotations of this module and are likely indicative of core metabolic activities across the gut community. Additional terms like isoprenoid biosynthesis and mevalonate pathway may be associated with cholesterol and possibly the statin pathway of the host liver. In fact, recent evidence suggests that the enteric microbiome can moderate response to statins [18]. Moreover, the term phosphatidylcholine biosynthesis may offer another potential link to the host liver as phosphatidylcholine from non-microbial sources has been reported to be associated with significant liver protection as part of the silybin-phosphatidylcholine complex [19].
The source perspective networks for human gut functional interactions are shown where large node diameter represents high node degree within each respective network. Panel A shows a network constructed using KEGG annotations where the highlighted nodes represent the top ranking module which is enlarged in Panel B. Panel C shows a network constructed using TIGRFAM annotations where the highlighted nodes represent the top ranking module which is enlarged in Panel D.
A TIGRFAM gut network was constructed that consisted of 543 nodes and 607 edges (Table 1). Like the KEGG network, no central hub was observed (Figure 6, Panel C). A total of 57 modules were identified within the network (Tables S1 and S2). The top ranked module (Figure 6, Panel D) scored slightly higher than the top ranked KEGG module but lower than either of the top ranked cellulase modules (Table 2). The annotation term ribosomal protein dominated this module and often occurred in conjunction with the term bacterial/organelle. The result was a highly cohesive module that involved bacterial ribosomal proteins. Like the glycolysis features of the aforementioned KEGG module, this is potentially indicative of core metabolic activities across the gut community.
Comparative Networks
Provided that two or more networks share the same perspective and a common annotative basis, it is possible to perform set theoretic operations that result in newly generated comparative networks. In the present work a second source perspective TIGRFAM network was generated using another human gut microbiome from the same study [17] that was used to produce the other source perspective networks. The TIGRFAM networks were compared to produce two new networks, an intersection network and a difference network.
A gut intersection network was constructed that consisted of 407 nodes and 278 edges (Table 1). This network contained a much lower ratio of edges to nodes than the non-comparative networks (Figure 7, Panel A). A total of 19 modules were identified within the network (Tables S1 and S2). The top ranked module (Figure 7, Panel B) scored the same as the top ranked TIGRFAM module (i.e. module derived using only one gut microbiome) and was composed of the same annotation terms (Table 2). In fact, the top ranked intersection module was a subset with all 13 nodes occurring in the superset of the 18 nodes that comprised the top ranked TIGRFAM module. Compared to the TIGRFAM module, the result was a reduced but highly cohesive module that similarly involved bacterial ribosomal proteins.
The comparative networks for human gut functional interactions are shown where large node diameter represents high node degree within each respective network. Specifically, two networks were constructed using TIGRFAM annotations and compared for mutual versus exclusive nodes. Panel A shows the intersection of the networks where the highlighted nodes represent the top ranking module which is enlarged in Panel B. Panel C shows the difference of the networks where the highlighted nodes represent the top ranking module which is enlarged in Panel D.
A gut difference network was constructed that consisted of 356 nodes and 329 edges (Table 1). This network contained a higher ratio of edges to nodes than the intersection network but was still slightly lower than the ratio in the TIGRFAM network (Figure 7, Panel C). A total of 20 modules were identified within the network (Tables S1 and S2). The top ranked module (Figure 7, Panel D) scored roughly the same as the top ranked TIGRFAM module and was composed of similar annotation terms (Table 2). While this module was also dominated by the theme of ribosomal proteins it was however more diverse and the ribosomal proteins terms frequently occurred in conjunction with the terms eukaryotic and/or archaeal, rather than bacterial.
Discussion
The modules derived in this work are of particular interest because they represent functional metamodules. This is because it is not possible to resolve whether the activities of a single module are accomplished by a single microbial species or if they represent composite functionality produced by the greater microbial community. Therefore, metamodules provide a systems perspective at the community level. In addition, these modules provide a direct characterization of functional capability and organization as opposed to an inferred characterization on the basis of taxonomic composition. This marks an important departure from previous taxonomy driven approaches because they are susceptible to effects of community functional plasticity [20], [21] that can cloud the taxonomy versus function relationship. However, this does not exclude the incorporation of concurrent taxonomic information that could bolster the interpretation of certain data sets. As a result, metamodules can provide crucial functional insight for a variety of applied pursuits.
Modules from target perspective networks have the potential to reveal novel metabolic relationships that can subsequently assist in the hunt for new biocatalyst candidates. This process can be regarded as a metagenomic analog to the ‘guilt by association’ principle [22] that has been previously used to infer contextual information at the genomic level. In the case of the presented cellulase networks, modules that contain annotations with keywords like unknown or uncharacterized can be used to highlight genes of particular interest since annotation values (e.g. COG1699) can be easily traced back to their source genes in the raw data. This provides an expedient method to recover a shortlist of promising genes from among a raw data set that may contain tens of millions of otherwise indistinguishable records. Mining candidate genes that can be subjected to more rigorous analyses can be applied to a broad collection of interests ranging from novel glycoside hydrolase detection for biomass degradation [23] to prebiotic molecule discovery for human health applications [24].
Modules from source perspective networks have the potential to reveal how particular microbial environments orchestrate functional interactions to achieve specific functional capacities and hierarchical organization. Although gene-centric analyses have been previously applied to metagenomic functional evaluation [25], they lack the ability to provide a systems perspective of functional organization. This is because gene content analyses cannot reveal the functional interactions that are essential in understanding how various microbial communities cooperatively achieve their specific functional capabilities. In the case of the presented human gut networks, it becomes possible to speculate not only on how the gut microbiome interacts among its constituents but also on how it exerts a collective effect on host metabolic activities. Currently this is a topic of tremendous interest and many research ventures could be served by analysis and interpretation of metamodules recovered from source perspective networks. For example, metamodules from various human microbiomes could be compared to functional modules from disease related functional linkage networks [26] in order to provide complementary analyses.
The motivation for comparative networks follows logically from the utility of source perspective networks since modules from comparative networks can expose commonalities and differences in functional configurations between different data sources. Such comparisons can be used to contrast vastly different microbial environments or to find mutual cores within closely related habitats, such as the human gut of various individuals. In addition, the approach taken in the current work differs from past studies involving comparative metagenomics because it is not affected by the previously discussed limitations of taxonomy based methods and it provides information beyond the previously mentioned gene-centric analyses. In the case of the presented comparative gut networks, it is possible to see that essential core modules could be developed for a variety of human microbiomes by deriving respective intersection networks from sets of multiple participants. The ability to directly contrast and compare metagenomic functional repertoires can offer tremendous utility to existing comparative research areas such as obese versus lean gut microbiomes [27] and control versus autistic gut microbiomes [28].
The implementation presented here was based on several simplifications and assumptions that could be addressed by future works. The use of transitive functional interactions favoured the formation of complete subgraphs (i.e. a component where each node has an edge to every other node). Although this was done to maximize functional information, it could also have contributed to an inflation of network edges that can bias module-finding algorithms. Other implementations should consider the prospect of constrained transitivity as a comparison. Similarly, the confidence thresholds for defining operons should be further tested in a metagenomic context and this could be performed in conjunction with limits for transitivity in order to characterize the interaction of these two essential factors. Further still, the operon reference data obtained from RegulonDB [29] represents knowledge derived from a classic model organism. Given that metagenomes offer access to the uncultured microbial majority, the applicability of such reference data remains to be established. In general, an improved understanding of the properties of metagenomic operons (e.g. size, composition, frequency, etc.) would benefit metagenomic annotation networks and related interests.
Methods
Metagenomic genes were parsed from downloaded raw data and used in a two-phase protocol consisting of network prediction followed by network translation. All operations were computationally implemented in Java and run on a Gateway NV59 laptop using an Intel Core i3-330 M processor.
Data Preparation
The raw data consisted of the complete set of public metagenomes available from the Integrated Microbial Genomes with Microbiome samples (IMG/M) metagenomics database [13] as of late August 2011. This included 224 data sets comprised of 40,189,394 total genes, distributed across 40,325,419 scaffolds. The simulated data sets (simLC, simMC, simHC) were removed, as well as any datasets that did not contain gene coordinate information (DRU, VLU, Yorkshire Pig Fecal Sample 266, Yorkshire Pig Fecal Sample 267), since these coordinates are required for the network prediction phase (see Network Prediction). The remaining 217 datasets included 39,660,386 total genes, from which 207,097 rRNA genes were excluded, leaving an aggregate working data set of 39,453,289 protein-coding genes.
We selected the IMG/M as data source for three reasons: (i) it offered a very large amount of data from a diverse range of environments (Figure 1); (ii) virtually all of the annotated genes (>99.5%) included information about their position and strand within the scaffolds in which they occurred; (iii) there was a high proportion of sufficiently assembled scaffolds such that multiple genes could occur within a single scaffold. This is in stark contrast to repositories that primarily offer data from short reads that frequently lack a single gene, let alone multiple genes. Overall, these factors are indicative of a current dichotomy in sequence databases: submitter-biased, such as MG-RAST [30], which cater to needs of authors that require a public depository of their data; versus query-biased, such as the IMG/M, which are focused on offering the expedient retrieval of data.
Network Prediction
Operons were predicted in scaffolds containing two or more adjacent genes in the same strand (Figure 2, Panel A) using a previously published method based on intergenic distances [D = gene2_start − (gene1_end +1)], where the likelihood for two genes to be in the same operon given the distance between them is assigned based on the ratio of known genes in operons to known genes in different transcription units found at such distance [31], [32], [33]. A minimum threshold of confidence was selected that is equivalent to a positive predictive value of 0.85 (meaning that 85% of the predictions are expected to consist of true positives), as evaluated against known operons of Escherichia coli K12 found in RegulonDB [29]. Next, functional interactions were defined in a pairwise manner for all members of a predicted operon. For example, an operon with the consecutive gene members a, b, and c would yield predicted functional interactions for the adjacent pairs ab and bc, plus an additional transitive functional interaction, namely ac. In the case of target perspective networks (see Results) operons were filtered according to the presence or absence of a target annotation by requiring a minimum number of member genes to contain a specific keyword descriptor (Figure 2, Panel B). The effects of target stringency (i.e. the size of the minimum number) were also tested (see Results).
Network Translation
Each gene in a given operon was mined for the following types of functional annotations: MetaCyc pathways [34], COGs [35], KEGG pathways [36], and TIGRFAMs [37]. A gene may have multiple annotation types and also have multiple values for a given type (Figure 2, Panel C). For each operon, the obtained functional annotations were used to infer functional interactions for annotations having the same type but different values. Translated interactions were inferred directly for immediately adjacent gene pairs but also transitively for downstream members within the same operon (Figure 2, Panel D). Note that the use of transitive translations is necessarily a reflection of the transitivity implemented in the network prediction phase.
The interactions were sorted by annotation type in order to derive a collection of discrete annotation networks for any given data source where each network had a particular annotative basis, such as MetaCyc or COG. This was possible because the translation of interacting genes into interacting annotations generated a unique set of nodes and edges with respect to each of the annotative bases. Moreover, specific annotation values (e.g. COG1363) were considered to be synonymous with their textual descriptors (e.g. cellulase M and related proteins) thereby providing a means for the conversion of nodes into a more verbose form. We note that it would have been possible to use the descriptors that were already available in the source data, rather than using the categorized annotations. The raw descriptors were not used in order to contrast the differences between specific annotative bases and also to avoid inflation caused by the redundant duplication of synonymous descriptors that varied only in terms of minor formatting features (i.e. lexicographical redundancy). Moreover, using specific categorized annotations produced connections between otherwise disjoint subgraphs thereby yielding a more connected network. However, future works may utilize the raw descriptors if the goal is to create a single global network of annotation linkages, regardless of annotation category.
Conclusion
Metagenomic annotation networks offer a novel taxonomy-free approach for understanding the functional capacity and hierarchical organization of integrated microbial communities. In particular, these networks can be analyzed for functional metamodules that subsequently provide a systems perspective at the microbial community level. Modules from target perspective networks can be used to infer interactions for a given gene or protein of interest. In turn, these interactions can be instrumental in revealing novel metabolic relationships that can subsequently assist in the hunt for new biocatalyst candidates. Modules from source perspective networks reveal how particular microbial environments orchestrate functional interactions to achieve specific functional capacities and hierarchical organization. This offers a mechanism of functional characterization that goes beyond gene-centric analyses. Modules from comparative networks can expose commonalities and differences between functional configurations from different data sources. These comparisons can be used to contrast vastly different microbial environments or to find mutual cores within closely related habitats, such as the human gut of various individuals. Comparing the functional repertoire of human microbiomes will be especially informative for future works of medical interest.
Supporting Information
Table S1.
Functional keys of genes in clusters analyzed in this work.
https://doi.org/10.1371/journal.pone.0041283.s001
(XLS)
Table S2.
Functional annotations for clusters analyzed in this work.
https://doi.org/10.1371/journal.pone.0041283.s002
(XLS)
Acknowledgments
The authors thank Trevor Charles for comments on the manuscript. GV thanks Mélanie Lafrance for additional comments and the IMG/M technical support staff for assistance in obtaining raw data.
Author Contributions
Conceived and designed the experiments: GMH GV. Performed the experiments: GV. Analyzed the data: GV GMH. Contributed reagents/materials/analysis tools: GV GMH. Wrote the paper: GV GMH.
References
- 1. Leinonen R, Sugawara H, Shumway M (2011) International Nucleotide Sequence Database C. The sequence read archive. Nucleic Acids Res 39: D19–21.
- 2. Kodama Y, Shumway M, Leinonen R (2012) International Nucleotide Sequence Database C. The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res 40: D54–56.
- 3. Wooley JC, Godzik A, Friedberg I (2010) A primer on metagenomics. PLoS computational biology 6: e1000667.
- 4. Ferrer M, Martinez-Abarca F, Golyshin PN (2005) Mining genomes and ‘metagenomes’ for novel catalysts. Curr Opin Biotechnol 16: 588–593.
- 5. Tringe SG, Rubin EM (2005) Metagenomics: DNA sequencing of environmental samples. Nat Rev Genet 6: 805–814.
- 6. Pignatelli M, Aparicio G, Blanquer I, Hernandez V, Moya A, et al. (2008) Metagenomics reveals our incomplete knowledge of global diversity. Bioinformatics 24: 2124–2125.
- 7. Janga SC, Diaz-Mejia JJ, Moreno-Hagelsieb G (2011) Network-based function prediction and interactomics: the case for metabolic enzymes. Metab Eng 13: 1–10.
- 8. Sun MG, Kim PM (2011) Evolution of biological interaction networks: from models to real data. Genome Biol 12: 235.
- 9. Peregrin-Alvarez JM, Xiong X, Su C, Parkinson J (2009) The Modular Organization of Protein Interactions in Escherichia coli. PLoS computational biology 5: e1000523.
- 10. Hsu JT, Peng CH, Hsieh WP, Lan CY, Tang CY (2011) A novel method to identify cooperative functional modules: study of module coordination in the Saccharomyces cerevisiae cell cycle. BMC Bioinformatics 12: 281.
- 11. Liu B, Pop M (2011) MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets. BMC Proc 5 Suppl 2S9.
- 12. Konietzny SG, Dietz L, McHardy AC (2011) Inferring functional modules of protein families with probabilistic topic models. BMC Bioinformatics 12: 141.
- 13. Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, et al. (2008) IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Res 36: D534–538.
- 14. Chen GJ, Russell JB (1989) Sodium-dependent transport of branched-chain amino acids by a monensin-sensitive ruminal peptostreptococcus. Appl Environ Microbiol 55: 2658–2663.
- 15. Nataf Y, Yaron S, Stahl F, Lamed R, Bayer EA, et al. (2009) Cellodextrin and laminaribiose ABC transporters in Clostridium thermocellum. J Bacteriol 191: 203–209.
- 16. Lykidis A, Mavromatis K, Ivanova N, Anderson I, Land M, et al. (2007) Genome sequence and analysis of the soil cellulolytic actinomycete Thermobifida fusca YX. J Bacteriol 189: 2477–2486.
- 17. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, et al. (2006) Metagenomic analysis of the human distal gut microbiome. Science 312: 1355–1359.
- 18. Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, et al. (2011) Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One 6: e25482.
- 19. Kidd P, Head K (2005) A review of the bioavailability and clinical efficacy of milk thistle phytosome: a silybin-phosphatidylcholine complex (Siliphos). Altern Med Rev 10: 193–203.
- 20. Dinsdale EA, Edwards RA, Hall D, Angly F, Breitbart M, et al. (2008) Functional metagenomic profiling of nine biomes. Nature 452: 629–632.
- 21. Manichanh C, Reeder J, Gibert P, Varela E, Llopis M, et al. (2010) Reshaping the gut microbiome with bacterial transplantation and antibiotic intake. Genome Res 20: 1411–1419.
- 22. Aravind L (2000) Guilt by association: contextual information in genome analysis. Genome Res 10: 1074–1077.
- 23. Li LL, Taghavi S, McCorkle SM, Zhang YB, Blewitt MG, et al. (2011) Bioprospecting metagenomics of decaying wood: mining for new glycoside hydrolases. Biotechnol Biofuels 4: 23.
- 24. Candela M, Maccaferri S, Turroni S, Carnevali P, Brigidi P (2010) Functional intestinal microbiome, new frontiers in prebiotic design. Int J Food Microbiol 140: 93–101.
- 25. Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, et al. (2005) Comparative metagenomics of microbial communities. Science 308: 554–557.
- 26. Rende D, Baysal N, Kirdar B (2011) A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders. Mol Biosyst 7: 2205–2219.
- 27. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, et al. (2009) A core gut microbiome in obese and lean twins. Nature 457: 480–484.
- 28. Finegold SM, Dowd SE, Gontcharova V, Liu C, Henley KE, et al. (2010) Pyrosequencing study of fecal microflora of autistic and control children. Anaerobe 16: 444–453.
- 29. Gama-Castro S, Salgado H, Peralta-Gil M, Santos-Zavaleta A, Muniz-Rascado L, et al. (2011) RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units). Nucleic acids research 39: D98–105.
- 30. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, et al. (2008) The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9: 386.
- 31. Salgado H, Moreno-Hagelsieb G, Smith TF, Collado-Vides J (2000) Operons in Escherichia coli: genomic analyses and predictions. Proc Natl Acad Sci U S A 97: 6652–6657.
- 32. Moreno-Hagelsieb G, Collado-Vides J (2002) A powerful non-homology method for the prediction of operons in prokaryotes. Bioinformatics 18 Suppl 1S329–336.
- 33. Vey G, Moreno-Hagelsieb G (2010) Beyond the bounds of orthology: functional inference from metagenomic context. Mol Biosyst 6: 1247–1254.
- 34. Caspi R, Altman T, Dreher K, Fulcher CA, Subhraveti P, et al. (2012) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 40: D742–753.
- 35. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, et al. (2003) The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4: 41.
- 36. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40: D109–114.
- 37. Selengut JD, Haft DH, Davidsen T, Ganapathy A, Gwinn-Giglio M, et al. (2007) TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic Acids Res 35: D260–264.