In eukaryotes, the cell is divided into several compartments enclosed by unitary membranes. Such compartmentalization is critical for cells to restrict different pathways to be carried out in different subcellular regions. The summary and classification of subcellular localizations of metabolic pathways are the first steps towards understanding their roles in spatial differentiation and the specialization of metabolic pathways in different organisms.Integrating the subcellular localization of enzymes and their pathways from UniProt Knowledgebase and KEGG pathway databases, we present the first database for subcellular localization of 43014 pathways from 80676 UniProt entries and their pathway annotations from UniProt and KEGG pathway databases. To extract pathway localization across organisms, we defined 889 superpathways as clusters of basic pathways with the same pathway annotations from different organisms. Over eighty-eight percent of superpathways in the Swiss-Prot dataset occur in cytoplasm and mitochondria. And over seventy percent of UniProt superpathways have multiple localization annotations. We summarized four common reasons for the multiple localization of superpathways. Based on this database, we also discovered 88 potential transport systems between different steps of multiply localized pathways and 45 duplicated genes from 17 pathways, occurring in parallel in several locations in humans.PathLocdb is a free web-accessible database that enables biochemical researchers to quickly access summarized subcellular localization of pathways from UniProt and KEGG pathway databases. As the first effort to systematically integrate pathway localization, this database is very useful in discovering the variation of localization of pathways between organisms and also cross-talk between different organelles within a pathway. The Pathlocdb database is available at http://pathloc.cbi.pku.edu.cn. In eukaryotes, cells are subdivided into membrane-bound subcellular organelles . SubcellHowever, the subcellular localization information of pathways and their related enzymes has not been systematically integrated. The popular pathway databases such as KEGG, BioCyc, MPW and aMAZE are constructed for specific research tasks such as the graphic representation or reconstruction of metabolic pathways -10. ThusSummary and classification of subcellular localization of metabolic pathways are the first steps towards understanding their roles in spatial differentiation and functional specialization. Here, we present the first effort to systematically collect pathway localization information from the UniProt  and KEGGIntegration of UniProt and KEGG Ligand databases provides many clues for studying pathways and pathway evolution. Here we present four types of applications for further studies.Our strategy to summarize the pathway localization was mainly based on the pathway and subcellular localization annotations from their participating enzymes. Thus, only enzymes with both subcellular localization and pathway annotations satisfied our criterion. The enzyme localization data are from UniProtKB/Swiss-Prot (version 56.8) in UniProt  databases. The pathway annotations were fro UniProtKB/Swiss-Prot (version 56.8) and KEGG ligand databases (version 44.0). In total, the Pathlocdb database consists of subcellular localization information of 43014 pathways summarized from 80676 UniProt entries and their pathway annotations from UniProt and KEGG pathway databases. To extract pathway localization across organisms, we defined a superpathway as a cluster of basic pathways with the same name from different organisms. A total of 889 superpathways across 33953 organisms were extracted from 43014 pathways. Due to differences in data quality and in pathway annotations, three datasets were collected. The high quality dataset contains 3448 pathways with localization information as well as 337 annotated superpathways across 795 organisms summarized from 6630 UniProtKB/Swiss-Prot entries. The KEGG pathway localization dataset includes 2535 pathways and 215 superpathways with localization information of the participating enzymes summarized from 12281 UniProtKB/Swiss-Prot entries of 42 organisms. A comprehensive dataset including 37029 pathways with localization information was collected from 70566 UniProt entries involving 33953 organisms.A quick comparison of localization distribution of superpathways in UniProtKB/Swiss-Prot and KEGG datasets reveals different pathway notations. The top ten subcellular locations where superpathways localize in UniProtKB/Swiss-Prot dataset cover more than 99% of annotated superpathways Fig. . AlthougAs the pathways in UniProtKB/Swiss-Prot do not combine biological processes of different organisms, it is useful to survey pathway localization differences in different organisms. Many fundamental biological processes with conserved localization are functionally related to diseases. For instance, the pathway “polyol metabolism; myo-inositol degradation into D-glucuronate; D-glucuronate from myo-inositol” in our database is involved in the phosphatidylinositol second messenger system (PI-cycle). The change of its conserved location in the cytoplasm to other regions could cause many neurological diseases . The simCullin-1 in our database belonging to “protein modification; protein neddylation” in Arabidopsis thaliana mainly localizes to the nucleus during interphase and preprophase [Using our database, 63 organism specific superpathways are found Fig. , 35 of tprophase . This isprophase . In totaPathways with multiple locations are a prevalent phenomenon. Among the annotated 337 UniProtKB/Swiss-Prot superpathways, 235 of them occur in multiple locations. Here we propose four common possible reasons giving rise to these multiple localization annotation of 337 UniProtKB/Swiss-Prot superpathways at the subcellular level. Firstly, different organisms have different pathways, resulting in multiple annotations of subcellular localization of superpathways. The most common examples for this category are the pathways which generate ATP from mitochondria and chloroplasts in different organism groups. Secondly, many pathways are composed of a series of steps spanning several subcellular locations. Like a production line, many intermetabolites are produced from one organelle to another, stepwise in pathways. Thirdly, some pathways occur in parallel in several subcellular locations in a single organism. The obvious example is fatty acid β-oxidation occurring in the mitochondria as well as the peroxisome . The othRegardless of the complex reasons leading to multiple annotation of pathway localization, each possible reason is useful for researchers to further explore its potential significance. The transport system or cross-talk between organelles are made up of common processes for translocation of intermediates ,19. Focu17-beta-HSD 1 and 17-beta-HSD 12 as examples, their protein products are localized to the cytoplasm and the endoplasmic reticulum respectively. Both proteins are involved in the “Steroid biosynthesis; estrogen biosynthesis” pathway. Similar phenomena also exist in the “Protein modification; protein sumoylation” and “Protein modification; protein ubiquitination” pathways in human. Occurrence of such a high proportion of enzyme duplication during pathway evolution also deserves further study.Using our database, 28 pathways occurring in parallel in several locations in human are found. On combini ng these pathways with the gene duplication dataset in human ,21, 45 gTo our knowledge, the present study represents the first of its kind, presenting a genome-wide collection of subcellular localization of metabolic pathways in cells across 33953 organisms. With high quality data from the UniProtKB/Swiss-Prot and KEGG databases, we have compared the pathway localization annotations of the two databases and found that the contributions from KEGG are larger than those from UniProtKB/Swiss-Prot.A characteristic feature of this study is the summary of common reasons for the multiple annotations of pathway localization. Recently high throughput proteomics data suggest that proteins with multiple locations constitute 39 percent of the total 1404 proteins in mouse liver . Our datBased on the multiple localization pathways from different organisms, we discovered possibly for the first time, all the conserved pathway localizations across organisms and organism-specific pathway localizations. This systematic comparison of pathway localization between organisms reveals that numerous pathways occur at different locations, providing clues for the differentiation and specialization of the pathway localizations between organisms.Focusing on the 28 pathways occurring in parallel in multiple locations in human, we identified 17 pathways containing gene duplication events to gain new locations. According to one of the most popular pathway evolution models, the “patchwork model”, enzymes with broad substrate specificities are more likely to be recruited to execute a new metabolic function . AlthougIn conclusion, these analyses demonstrate that our database is valuable in the discovery of potential cross-talk between different steps and the variation of localization of pathways between organisms.http://pathloc.cbi.pku.edu.cn.PathLocdb was constructed as a free web-accessible database and analysis server to enable biochemical researchers to quickly access to summarized subcellular localization of metabolic pathways of UniProt and KEGG pathway database. As the first effort to systematically collect pathway localization, this database is very useful in discovering the variation of localization of pathways between organisms and also cross-talk between different organelles within a pathway. The PathLocdb database can be accessed at An automatic computational pipeline was implemented to extract subcellular localization and pathway annotations from UniProtKB/Swiss-Prot and KEGG ligand databases using Perl scripts and the Swissknife module. EukaryoTo compare the subcellular localization annotations of pathways from different organisms, we defined a superpathway as a cluster of pathways having the same annotation from different organisms. The reference pathways in KEGG are combined multiple pathways from different organisms. Thus, tAs an automatic pipeline for data collection, our database will be easy to update regularly when new versions of Swiss-Prot and KEGG Ligand are available. Besides, more data from other pathway databases and literatures will be compiled in the future.All data and information in PathLocdb were stored in a MySQL relational database on a Linux server. The database provides a web interface that allows researchers to hierarchically browse the subcellular localizations of 887 superpathways using different features Fig. . Web-basMZ conceived of the work, carried out all analyses and helped write the manuscript. HQ helped write the manuscript.The summarized 56 superpathways with completely different locations across organisms are shown in Additional file 1.Click here for fileThe 184 superpathways with partially different localization patterns between organisms are shown in Additional file 2.Click here for fileThe 88 pathways with multiple locations summarized from the multiple locations of their steps are shown in Additional file 3. They provide information on potential existence of transport system between these pathways with different localizations and different steps.Click here for fileThe 45 genes from 17 of the 28 pathways which are detected to be duplicated during evolution are shown in Additional file 4.Click here for file


Gene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families.graphite, a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The graphite package is available as part of the Bioconductor software suite.Here we present graphite is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements. A great deal of effort has been recently directed towards the study of gene sets (hereafter GSA) in the context of microarray data analysis. The aim is to identify groups of functionally related genes with possibly moderate, but coordinated, expression changes. Several GSA tests, both univariate and multivariate, have been recently developed. See  for a coSPIA) attempting to capture several aspects of the data: the fold change of differentially expressed genes (DEGs), the pathway enrichment and the topology of signaling pathways. In particular, SPIA enhances the impact of a pathway if the DEGs tend to lie near its entry points. Massa et al. [1] "acetylation and deacetylation of rela in nucleus"[2] "actions of nitric oxide in the heart"[3] "activation of camp-dependent protein kinase pka"> biocarta[["ras signaling pathway"]]"ras signaling pathway" pathway from BioCartaNumber of nodes = 18Number of edges = 22Type of identifiers = nativeRetrieved on = 2011-05-12or its position in the list of pathways:> p <- biocarta [[175]]> p@title[1] "ras signaling pathway"In the network, nodes represent genes and edges functional relationships among them. Nodes can have heterogeneous IDs  and edges can be characterized by multiple functional relationships.pathwayGraph builds a graphNEL object from a pathway p:The function > g <- pathwayGraph(p)> gA graphNEL graph with directed edgesNumber of Nodes = 18Number of Edges = 23> edgeData(g) [1]$ 'EntrezGene:10928 | EntrezGene:5879'$ 'EntrezGene: 10928 | EntrezGene:5879 '$weight[1] 1$ 'EntrezGene:10928 | EntrezGene:5879 '$edgeType[1] "catalysisOut (ACTIVATION)"converterIdentifiers allows the user to map such variety of IDs to a single type (Entrez-Gene or Gene Symbol). For the ID conversion graphite uses the data provided by the Bioconductor package org.Hs.eg.db. This mapping process, however, may lead to the loss of some nodes  and has an impact on the topology of the network (one ID may correspond to multiple IDs in another annotation or vice versa).The function > pEntrez <- convertIdentifiers > pEntrez"ras signaling pathway" pathway from BioCartaNumber of nodes = 20Number of edges = 20Type of identifiers = Entrez GeneRetrieved on = 2011-05-12> nodes(pEntrez)[1:10][1] "10928" "1147" "3265" "387" "4303" "5295" "572" "5879" "5894" "5898"graphite uses the Rcytoscape package to export the network to Cytoscape [Several pathways have a huge number of nodes and edges, thus there is the need of an efficient system of visualization. To this end ytoscape . Cytosca> cytoscapePlot)The network will be automatically loaded into Cytoscape. See Figure In order to verify our signal propagation strategy we perform a simulation study. Using the insulin signaling pathway of the KEGG database we select as differentially expressed 22 genes lying on the signal paths highlighted in Figure FC μ~ U ; 2) we randomly generate log fold change values  of the differentially expressed genes as i δ~ N  ; 3) we run the SPIA[Our simulation is based on the following steps: 1) we randomly generate  the SPIA algorithAs shown in Figure The dataset, recently published by , characthttp://www.bioconductor.org/help/publications/2003/Chiaretti/chiaretti2/) Expression values, appropriately normalized according to rma and quantile normalization, derived from Affymetrix single channel technology, consist of 37 observations from one experimental condition  and 41 observations from another experimental condition . Probes platform have been annotate using EntrezGene custom CDF version 14 [Data is available at the Bioconductor site  on the entire list of genes belonging to a pathway. Here, differentially expressed genes required for SPIA package have been identified using RankProd test [F D R <0.01), while the test on the mean has been chosen for topologyGSA package.We report the results obtained by SPIA and topopologyGSA on the grod test  , the results testify the feasibility of performing analyses using Our results highlight that the hierarchical pathway structure and the reduced dimension of the pathways characterizing respectively the Reactome and Biocarta databases jointly with the specialized cancer pathways of the NCI databases allow the user to have deeper insight into the data.graphite networks identified as significantly altered in the previous analysis.To highlight the usefulness of topological analysis in the context of transcriptomic data interpretation, we report two topologyGSA. Figure graphite network from KEGG database with differentially expressed genes mapped with different colors according to fold change sign. It is interesting to note the presence of several OR groups , single members of which resulted to be differentially expressed. Two clear deregulated paths starting from BCR and ABL1 genes towards apoptosis and NFKB pathways highlight the power of topological analysis to deeper investigate signal cascades within large pathways.Chronic myeloid leukemia pathway includes both genes, BCR and ABL1, and was identified as differentially expressed between BCR/ABL positive and negative patients by KEGGgraph[NCIgraph that imports BioPax models from Cytoscape [Currently there are two Bioconductor packages that try to convert pathway to the SIF model. KEGGgraph that parytoscape  without graphite, on the other hand, is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis graphite acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.It is evident that gene set analysis is moving towards considering pathway topology as a crucial feature. A correct conversion of the pathway topology to a gene network becomes therefore important. Available packages are not able to correctly reconstruct the signal transduction in most cases. graphite• Project name: http://www.bioconductor.org/packages/devel/bioc/html/graphite.html • Operating system(s): Platform independent• Project home page: • Programming language: R• Other requirements: Bioconductor• License: GNU AGPL• Any restrictions to use by non-academics: noneGS, EC and CR jointly define the concept proposed. GS, EC developed the methods proposed and performed the analysis on gene expression data. CR supervised the work and wrote the paper. DC Participated in the critical definition of the concept proposed and participated in drafting and commenting critically the manuscript. All Authors read and approved the final manuscript.Example of KGML and owl. Additional file 1: bmc-supp.pdf, 116 K. http://www.biomedcentral.com/imedia/1501537976613594/supp1.pdfClick here for file


The ultimate goal of cancer systems biology nowadays is thus, unraveling the mechanisms of action, regulation and control in the complex tangle of biochemical and biophysical interactions behind cancer biology. An outstanding example of this trend is given in the paper by Kessler and coworkers . On thiscum grano salis. The relationship between signaling networks, transcriptional networks and biochemical/metabolic pathways (that in the living cells correspond to one unified machinery notwithstanding the fragmented view that we used to have on them)  , but in compensation performs qualitatively better in that it provides (by means of explicit pathway reconstruction/recognition) tools that may be used to develop further biological insight in the phenomena. One may argue, however, that one of the reasons for current quantitative deficiencies in pathway-oriented approaches to outperform gene-centered ones, is the fact that annotation databases for individual molecules are currently much better curated than pathway databases. Other factors may be the methods for pathway reconstruction (often probabilistic in nature) and the presence of crosstalk/crosslink phenomena.For instance, Kessler et al.'s classifier does not perform choose to focus on a core network centered in kinase activity and serine/threonine kinase receptors supplemented with well-known signaling processes in cancer that do not rely on kinase dynamics, such as the Notch, Hedgehog and Wnt pathways. By doing this, they introduce modeling hypotheses that induce a structure in the network modules that they will analyze later; namely a LIGAND/RECEPTOR/ADAPTOR generalistic mechanism, sometimes supplemented with CoActivators and CoInhibitors. The rationale behind this was that …separating indirectly activating and inhibiting factors from pathway core modules allows assessment of the functional importance of a module through different levels of the network hierarchy… thus permitting a better optimized analysis of such modules. Then they use the module-based network activity as a proxy to mimick the activity of the signaling pathways, yet in my opinion the authors could have gone even one step further in discussing the functional roles of the pathways that their method identified. For instance, by discussing the potential interactions between the AKT and RAS pathways, or the verstaile role of PDGFR as concertator in a number of biological processes and how did these phenomena may affect their classification strategies.The manuscript by Kessler et al, is also a good example of the combination of data-driven approaches  working in addition to (or if you wish on top of) hypothesis-driven research. The authors decided to study whole genome expression profiles, yet since they were interested in the role of signaling dynamics, they In silico version of Functional Genomics; I'd like to call this approach Functional Systems Biology. The manuscript by Kessler et al. provides a nice example of the state-of-the-art of such a nascent discipline.All in all, the authors are taking definite steps advancing into a more physiological, mechanistic approach to computational systems biology  that I like to see as the


This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype  in the posterior fornix. An implementation of our methodology is available at  The human body is inhabited by trillions of bacteria and other microbes, which have recently been studied in many different habitats  by the Human Microbiome Project (HMP). These microbial communities were assayed using high-throughput DNA sequencing, but it can be challenging to determine their biological functions based solely on the resulting short sequences. To reconstruct the metabolic activities of such communities, we have developed HUMAnN, a method to accurately infer community function directly from short DNA reads. The method's accuracy was validated using a collection of synthetic microbial communities. Applying HUMAnN to data from the HMP, we showed that, unlike individual microbial species, many metabolic processes were present among all body habitats. However, the frequencies of these processes varied dramatically, and some were highly enriched within individual habitats to provide niche specialization . Other community functions were linked specifically to properties of the human host, such as biochemical processes only present in vaginal habitats with particularly high or low pH. Studying additional environmental or disease-associated communities using HUMAnN will further improve our understanding of how the microbial organisms in a community are linked to the biological processes they carry out. Human-associated microbial communities interact directly with their hosts by means of metabolic products and immune modulation, and environmental communities are further responsible for a wide range of biochemical activities The functions of human-associated microbes are of particular interest, and the Human Microbiome Project HMP  has thusMost analytical methods for microbial community assays focus on organismal membership and population structure, i.e. “who's there” in a community http://huttenhower.sph.harvard.edu/humann, and the methodology can be equivalently applied to metatranscriptomic or metaproteomic data using any gene or pathway catalog of interest for future studies.In order to determine the distribution of microbial function within the human microbiome, we thus first validated HUMAnN's ability to quantify metabolic pathway abundances in four synthetic metagenomes containing up to 100 organisms. These were recovered with correlations over 0.9, consistently outperforming best-BLAST-hit approaches. We proceeded to scale our analysis to perform metabolic reconstruction on 649 human microbiome samples drawn from the buccal mucosa, supragingival plaque, and tongue dorsum , anterior nares , retroauricular crease (skin), and stool (gut) communities from 102 individuals. We identified 196 metabolic pathways and 168 small modules that were differentially abundant among body sites, and we highlight here associations with environmental pH and enrichment for glycosaminoglycan degradation as examples from the vaginal and gut communities, respectively. Metabolic module abundances were substantially more variable among body sites and among individuals than was module coverage, indicating a connection between selective pressures in each microbial niche and the pathways carried by members of the community. Finally, as HUMAnN simultaneously reconstructs large pathways, specific metabolic modules, and individual enzymatic gene families, we discuss an example of glutamate metabolism in the gut community as it interacts with specific carbohydrate active enzymes CAZys . An implHere, we describe the methodology employed in this study in two parts: first, the computational pipeline for metagenomic metabolic reconstruction implemented in HUMAnN, and second its application to the 741 microbial community samples of the Human Microbiome Project. HUMAnN inputs metagenomic DNA sequences and infers community-wide gene and pathway abundances through a process of seven steps :Short reads are sequenced from a community sample, quality and length filtered, and screened for residual host (human) DNA. This process is carried out by the user externally to HUMAnN.Reads are searched against a characterized protein sequence database. HUMAnN can operate using results from several standard or accelerated translated BLAST implementations and from different orthologous protein family catalogs; for the HMP we employed MBLASTX and the KEGG Orthology (see below).For each metagenomic sample, HUMAnN recovers the abundances of individual orthologous gene families by counting its reads' BLAST hits in a weighted manner, normalized by each gene family's average sequence length.Genes are assigned to pathways using MinPath Pathways unlikely to be present based on the BLAST hits' approximate organismal profiles are removed in a taxonomic limitation step, which also allows normalization for genes' average copy number.A biological smoothing or gap filling step is performed, preventing small numbers of apparently absent genes in an otherwise abundant pathway from diminishing its presence due to noise.Finally, HUMAnN assigns each pathway a coverage (presence/absence) score in each sample based on the detection of all of its constituent genes, as well as an abundance score indicating its relative abundance in the sample's metagenome.HUMAnN has additionally adapted ecological diversity metrics in order to provide functional diversity and richness profiles for each sample, and we validated its gene- and pathway-level accuracy using four synthetic communities of varying complexity.To assess microbial community function and metabolism in the human microbiome, we applied this process to the metagenomic data generated by the HMP The filtering criteria applied to HMP short reads are representative of HUMAnN's sequence preprocessing requirements. As fully described elsewhere by the HMP Sequences passing preprocessing criteria are then searched against a characterized protein sequence database. For the HMP, we employed MBLASTX , an accelerated translated BLAST implementation, with default parameters against a functional sequence database including the KEGG Orthology v54 i consists of a set of one or more specific gene sequences iG = {ig,1, ig,2, …} from individual organisms annotated in KEGG v54. Orthologous family abundances iw were calculated independently within each metagenome for KO i and read j as:g| is the nucleotide length of gene sequence g in KO i, |iG| is the number of such sequences, and i,jp is the p-value of the MBLASTX hit of read j to sequence g (or 1 if no such hit occurred), calculated from the E-value as p = 1-eE−. That is, the relative abundance of KO i in a metagenome is the number of reads j that map to a gene sequence in the family, weighted by the inverse p-value of each mapping and normalized by the average length of all gene sequences in the orthologous family. Comparisons among alternative weighting schemes  suggested that the specific method by which multiple BLAST hits were combined had little effect on outcome  as defined by KEGG i with a vector of relative abundances w =  in each metagenome. For the HMP, KOs were then assigned to zero or more pathways and modules (both as defined in KEGG) using MinPath v1.2 pw = , where i,pw = jw for all pathways p, and an analogous mw for modules.For each sample, the process above assigns each KO family a relative abundance; KOs are then consolidated into one or more pathways (or modules) using MinPath For evaluation purposes, best-BLAST-hit pathway and module assignments in Supplemental o in KEGG were calculated as a sum over all weighted, normalized BLAST hits to sequences from that organism:ow values over all organisms' genomes in which it was annotated. Finally, any  pair with two or more IDs and corresponding to a pathway/module with observed relative abundance below the average expected abundance for that ID was removed. That is, for o,pδ = 1 if pathway p was annotated to organism o in KEGG and 0 otherwise, a pathway's expected abundance was:i,pw1 such that i,pw2>i,pw1>0 and We found an additional module/pathway filter step to be useful in removing false positive pathways selected by MinPath. Specifically, by retaining a very approximate organismal abundance profile of gene families hit during the initial BLAST process, HUMAnN is able to remove pathways in gross disagreement with observed taxa in an unsupervised manner. This is performed leniently in order to be minimally disruptive in e.g. microbial communities rich in uncharacterized organisms, and often results in depletion of false positive metazoan pathways. Specifically, taxonomic limitation is performed by removing only  tuples for which the same KO was assigned to multiple pathways or modules. For each sample, approximate abundances for each organism When performing this step, HUMAnN can additionally use the data on approximate taxonomic composition to divide each gene's abundance by its expected copy number in the detected organisms, providing a degree of additional normalization (as gene family copy number should not influence the abundance of pathways in which they're carried). This contrast is reflected in Supplemental p such that there existed some i,pw>0, let i,pw over all i∈p, and each i,pw for i∈p was set to max and abundance values for KEGG modules and pathways. These two types of entities are quantified somewhat differently by HUMAnN, but with equivalent semantics. Pathways are defined as unordered sets of orthologous gene families; modules are defined by KEGG as combinations of required, optional, or complementary genes in notation resembling conjunctive normal form. In both cases, coverage is calculated to indicate the likelihood that all genes needed to operate the pathway or module are present; abundance is calculated as the average copy number of the pathway or module's operational subset. The definition of “operation” changes since pathways, as unordered sets, are assumed to include redundant genes (which are not explicitly indicated), whereas alternative means of accomplishing a specific metabolic module can be explicitly taken into account.pw, coverage for each pathway p in a sample was calculated as the fraction of KOs in the pathway that were confidently present, specifically with abundance greater than the overall sample median. That is:p/2] the most abundant half of i,pw.Thus each pathway's coverage and abundance were calculated fairly simply. Given the vector mw, coverage for each module m in a sample was calculated as the harmonic mean of the Χ2 CDF with i,mw for each required i∈m, maximizing over optional genes i and alternative submodules. That is, the probability of each gene family i being present in pathway p by chance was assigned based on the sample-wide median abundance i, mw, replacing pathways' arithmetic means since alternative enzymes are explicitly known and taken into account. These choices of parameterization both for pathways and for modules were again validated using multiple synthetic communities  the pathways/modules annotated to at least one organism in the community and B) multiplying these by the organisms' abundances, respectively. Inferred pathway and module coverages and abundances were also calculated by applying HUMAnN to these synthetic reads as described above for the HMP samples. All software implementing these processes and the specific error model, synthetic communities, and data used for HUMAnN in the HMP are available at http://hmpdacc.org/HMMRC. Taxonomic abundances derived from shotgun data are provided at http://hmpdacc.org/HMSCP, and input Illumina reads at http://hmpdacc.org/HMIWGS. The open source HUMAnN software can be obtained at http://huttenhower.sph.harvard.edu/humann.All metabolic reconstructions generated by this study are publicly available at We first validated HUMAnN's ability to accurately quantify microbial community function using a collection of four synthetic metagenomes. We proceeded to reconstruct metabolic pathways and modules for 741 human microbiome samples comprising a total of 3.5 Tbp of sequence from 18 body habitats from 102 subjects assayed metagenomically by the HMP. 686 of these samples passed quality control see , and aftTo assess the accuracy of HUMAnN's metabolic reconstructions, we constructed four synthetic metagenomic datasets with known functional profiles. Patterned on the study of Mavromatis et al In all four communities, HUMAnN recovered metabolic module abundances with a correlation above 0.88 and a partial AUC at 10% false positives (pAUC10) above 0.73 . Perform−4), typically short proteins <150AAs such as K13771  or K10533 . Likewise only 7 false positive proteins were detected based on closely related orthologs or strongly conserved domains (0.027% at >10−4), including K08721/OprJ  and K11187/peroxiredoxin 5 . False positive modules  were near-uniformly small gene sets overlapping with modules truly present . Conversely, false negatives  were most often small pathways present only in very low-abundance organisms, e.g. bicarbonate transport  or mannopine transport . We specifically tuned HUMAnN to prefer false negatives to false positives based on these communities  and 93% of low-complexity (even) modules were correctly called present or absent, and their inferred abundances were consistently well-correlated with the gold standard . These uWe employed this optimized system to study microbial metabolism at seven body sites spanning the human microbiomes of 102 subjects. HUMAnN was applied to these data as described above, yielding the relative abundances and coverages both of functional modules and of full pathways, as well as the abundances of individual orthologous gene families. We first focused on analysis of small metabolic modules (ave. 11.2 sd. 9.2 genes), and 232 such modules were detected in at least one of the 649 samples; larger, more broadly defined pathways were also reconstructed from the HMP data and are described below. Both modules and pathways were reconstructed by HUMAnN as coverages  and as relative abundances for each sample . Although this stringent core is itself moderately small, most other modules were consistently present or absent across body sites, with only 24 showing strongly differential coverage among habitats  degradation in the gut were among those most differentially abundant among habitats. These included chondroitin sulfate degradation, dermatan sulfate degradation, and keratan sulfate degradation, as well as the related uronic acid metabolism. All four of these modules are involved in animal proteoglycan degradation for microbial carbohydrate utilization −4% sd. 0.003% in the gut, <10−5% elsewhere). This is due exclusively to the absence of the module's input enzyme, heparanase (K07964-5), a typically eukaryotic gene family implicated in tumor metastasis −5% sd. 3·10−7%). Given this ubiquity, it should be noted that GAGs possess wide-ranging activities including anti-cancer HUMAnN's reconstruction further allowed examination of the individual orthologous gene families participating in these interrelated pathways. Chondroitin and dermatan sulfate degradation share the greatest overlap with 3 KO families, including beta-glucuronidase (K01195). Beta-glucuronidase is prevalent in the gut, averaging 0.03% sd. 0.01% in our data, in contrast to all other nonzero KOs (ave. 0.007% sd. 0.03%). It is one of the primary enzymes involved in metabolism both of food matter and of pharmaceuticals Bacillus cereus, B. thuringiensis, and the archaeon Archaeoglobus fulgidus, none of which were detected in any vaginal samples We next examined modules differentially abundant in the 53 HMP vaginal posterior fornix samples , which was also the case in the HMP posterior fornix data Lactobacilli are known to specialize in exactly these areas of nutrient uptake and carbohydrate metabolism L. crispatus and lacking in L. gasseriGiven their lower diversity, the functional profiles of these vaginal microbiomes proved to be particularly informative when associated with host phenotype and microbial membership. Taxonomic profiles detailing the abundances of microorganisms in these communities were available from the HMP's 16S rRNA gene surveys −4) than any metabolic association with BMI in the stool community , with the metagenomic abundance of lysine biosynthesis consistently increasing and that of sulfate transport decreasing at greater BMI. Additional associations beyond this false discovery rate are included in Supplemental A surprising feature of these data, however, was the observation of few other robust correlations between microbial metabolic abundances and host phenotype outside of the vaginal community, in contrast to recent studies of the gut microbiota The analyses described up to this point, and the primary outputs of HUMAnN for metagenomic samples, focus on the coverages and abundances of small functional modules and individual gene families. Such modules are typically defined to carry out a specific metabolic step, such as production of a single amino acid from its immediate precursor. They contain an average of only ∼10 genes and are structured to include both “and” relationships  and “or” relationships . However, KEGG and other functional catalogs also define large pathways with up to several hundred genes that lack the complex combinations of relationships used to define small modules. HUMAnN can additionally recover coverages and abundances for any such pathways represented by unstructured sets of gene family identifiers. To this end, we analyzed 297 KEGG pathways present in the seven HMP body sites, containing 52 sd. 48 genes on average. While HUMAnN successfully recovered coverage and abundance information for these larger pathways in the HMP data  were at most 50% covered among all 649 samples; that is, although portions of the pathways were detected, at least half of the associated gene families were missing in every assayed microbial community. Of the remaining 107 pathways, 96 (90%) had a coefficient of variation below one, indicating variation in coverage lower than their mean across all samples. In other words, only small portions of most KEGG pathways are present in the human microbiome. Those pathways that were consistently present tended to be so broadly defined  that they provided too coarse a view with which to observe metabolic variation and niche specialization among body sites. Nevertheless, applying the same significance criteria as described above, 196 pathways (66%) were differentially abundant in at least one body site using LEfSe. These are detailed in Supplemental In contrast to this variation, however, we were again able to recover a subset of “core” pathways with moderate coverage and low variability across all sites of the human microbiome. The pathways with the lowest coefficient of variation across all samples in our data represented surprisingly diverse biochemistry, including terpenoid biosynthesis (ko00900), RNA degradation (ko03018), pyruvate metabolism (ko00620), and one-carbon metabolism (ko00670). These pathways were all present at an average of at least 0.5% relative abundance over all samples, with average coverages from 41% (of 75 genes in ko03018) to 77% (of 73 genes in ko00620). Each of these pathways followed a very characteristic pattern: their overall pathway abundance remained near-constant across body site niches, while the modules implementing each pathway varied significantly. Terpenoid biosynthesis, for example, can be performed either by the mevalonate (module M00095) or by the non-mevalonate (M00096) module; both were present in the oropharynx and skin, only the former was present in the posterior fornix, and only the latter in the gut . 101 total large pathways varied little in site-specific abundance among individuals, but smaller modules within them (such as proline biosynthesis) showed greater inter-subject variability. Finally, these module-specific changes in abundance almost always  correlated with one or more individual CAZy gene families detected within the same metagenome independently of the pathway's constituent genes. Together, these data suggest that while the basics of microbial metabolism remain stable among human microbiome body sites and individuals, the modules and enzymes operating as specific metabolic producers and consumers within pathways vary among environments and subjects to adapt to changing nutrient and metabolite availabilities Culture-independent metagenomic sequencing of microbial communities provides a wealth of data regarding their potential biological functions, particularly as studied for the human body by the Human Microbiome Project. Here, we have described the development of the HUMAnN methodology for high-throughput metagenomic functional reconstruction and its application to 649 communities from 7 body habitats sequenced as part of the HMP. Validation of HUMAnN's accuracy using four additional synthetic communities of increasing complexity demonstrated its ability to quantify both pathway presence and relative abundance, with correlations to true abundances >0.9. When analyzing the human microbiome, relatively few modules were specifically present or absent in any one body habitat, but over two thirds varied in abundance by habitat and 24 were core to all hosts and habitats. Less variation was evident among hosts, although we demonstrated one example in which nutrient transport and mechanisms of central carbon metabolism were strongly associated with vaginal pH. HUMAnN's functional reconstructions include the abundances of large, general pathways, smaller and more specific metabolic modules, and individual orthologous gene families; each data type proved to show distinct patterns of variation among body sites and to provide a different perspective on underlying microbial community function.Characterization of microbial communities by large-scale shotgun metagenomic sequencing is a relatively recent advance, and computational methods for assessing these data in terms of biological function are under active development. Several previous studies have analyzed individual reads or assembled contigs using direct annotation by BLAST to orthologous gene families It should be noted that when analyzing metagenomic data as described here, HUMAnN reconstructs a profile of a microbial community's metabolic potential, not its metabolic activity per se. The abundances of gene families and pathways inferred by the system describe only the enzymes encoded by one or more microbial genomes, and their relationship to realized transcriptional or protein activity may not be straightforward in the absence of additional metatranscriptomic, metaproteomic, or metametabolomic data The results produced by HUMAnN and analyzed above for the human microbiome include the application of several community diversity measures to microbial function. Such measures are typically applied instead to organismal abundances, where α-diversity summarizes complexity and types of different organisms within a community and β-diversity the similarities (or differences) between multiple communities' structures A key consideration during our development of the HUMAnN pipeline was versatility; the software implementation can easily be extended to assess any functional catalog, characterized sequences, or metagenomic sequences (e.g. 454 reads). In other analyses by the HMP, additional protein databases including MetaCyc In closing, we would like to emphasize that HUMAnN's current approach to microbial community functional reconstruction is explicitly independent of the organismal membership of these communities. It was designed to complement taxonomic classifications of community structure, and integration of community function with membership is an area of further ongoing work Figure S1Evaluation of parameter settings and processing modules in the HUMAnN pipeline. Four synthetic metagenomes  were used as a gold standard to evaluate variants of the HUMAnN pipeline for both metabolic modules (Mod.) and full KEGG pathways (Path). The accuracies of relative abundance estimates (left) were evaluated using Pearson correlation with the gold standards Click here for additional data file.Figure S2Performance of HUMAnN abundance and coverage inference as compared to a best-BLAST-hit (BBH) approach. The HUMAnN pipeline for KEGG metabolic modules and pathways was compared to a best-BLAST-hit approach to module reconstruction. Evaluations of abundance  and coverage  are summarized in the first row. Subsequent rows show full scatterplots with regressions  and ROC curves  for each individual method over the entire gold synthetic community gold standards. These include A) high-complexity (100 organisms) staggered abundances B) low-complexity (20 organisms) staggered, C) high-complexity even abundances, and D) low-complexity even.(PNG)Click here for additional data file.Figure S3Accuracy of HUMAnN relative abundance predictions for individual KOs. Predicted versus actual abundances sunflower plotted for each of the 13,980 KEGG Orthology gene families (KOs) evaluating four E-value weighting schemes  and best-BLAST-hit alone in the A) the staggered high-complexity synthetic community, B) the staggered low-complexity, C) even high-complexity, and D) even low-complexity. Pearson correlation coefficients and regressions of arcsine square root transformed values are also reported. The performance of HUMAnN on the overall population of individual genes did not differ significantly from that of a best-BLAST-hit approach, although the preservation of additional information from multiple hits for later use (using any weighting scheme) does improve module and pathway abundance recovery Click here for additional data file.Figure S4Evaluation of individual HUMAnN processing modules on 10 synthetic metagenomes. An additional 10 synthetic metagenomes were generated with high-complexity (100 organisms) and random lognormally distributed abundances. These were searched against KEGG protein sequences using USEARCH, allowing multiple hits with maximum e-value 1. All combinations of select HUMAnN modules were then assessed, including best-BLAST-hit versus multiple hits weighted by p-value and the presence or absence of taxonomic limitation with or without copy number normalization. HUMAnN default settings are highlighted in gray. Processing steps recapitulated their behavior as observed in Supplemental (PDF)Click here for additional data file.Figure S5Co-clustering of metabolic pathways and microbes from community-specific genera in the vaginal microbiome. Hierarchical clustering (average linkage using uncentered Pearson correlation) was applied jointly to the eight signature organisms from the five lineages known to characterize different microbiome types (PNG)Click here for additional data file.Table S1Significant associations between microbial function and host phenotype in seven habitats of the human microbiome. Each row reports a significant body-site-specific association between microbial functional module abundance and host metadata, including biometrics  as well as sample processing (e.g. sequence depth). P-values were calculated using Spearman's r for continuous metadata and the Kruskal-Wallis nonparametric ANOVA for categorical features as reported in the (XLSX)Click here for additional data file.Table S2Community composition of four synthetic metagenomes used to evaluate performance of metabolic reconstruction. Microbes for the low-complexity (20 organism) community weree hand-chosen from clades representative of the human microbiome. Their relative abundances in the staggered community were likewise chosen to be roughly representative of physiological occurrence rates. Organisms in the high-complexity (100 organism) synthetic community were randomly selected from KEGG high-quality manually curated finished genomes. Their abundances in the staggered community were randomly generated from a lognormal distribution. These synthetic community designs for computational performance evaluation were inspired by those of (XLSX)Click here for additional data file.Table S3Gold standard abundances of KEGG gene families, pathways, and modules for four synthetic metagenomes. A ground truth of genes, small functional modules, and large pathways in each of four synthetic metagenomes was calculated from the organismal compositions in Supplemental (XLSX)Click here for additional data file.Table S4HUMAnN coverage estimates for functional modules in the human microbiome. Inferred module presence/absence confidence values for each of 227 KEGG modules in 649 samples spanning seven body sites across the human microbiome.(XLSX)Click here for additional data file.Table S5HUMAnN relative abundance estimates for functional modules in the human microbiome. Inferred module abundance values for each of 250 KEGG modules in 649 samples spanning seven body sites across the human microbiome.(XLSX)Click here for additional data file.Table S6Core and differentially covered modules in the human microbiome. Metabolic modules determined to be core and variable across body sites based on coverage estimates from the HUMAnN pipeline. Core modules were defined with coverage ≥0.9 (or at a lower stringency ≥0.3 threshold) in ≥90% of the samples from each body site. Variable modules were defined to be present (as above) in at least one body site and absent (≤0.1 in ≥90% of samples) in at least one body site. 24 samples. Using these definitions, 16 modules were core across all body sites at coverage 0.9 and 24 at 0.3, and 24 modules were variably covered among body sites.(XLSX)Click here for additional data file.Table S7Modules and pathways differentially abundant in at least one body site as determined by LEfSe. We applied the LEfSe (XLSX)Click here for additional data file.Table S8HUMAnN coverage estimates for metabolic pathways in the human microbiome. Inferred pathway presence/absence confidence values for each of 281 KEGG pathways in 649 samples spanning seven body sites across the human microbiome.(XLSX)Click here for additional data file.Table S9HUMAnN relative abundance estimates for metabolic pathways in the human microbiome. Inferred pathway abundance values for each of 297 KEGG pathways in 649 samples spanning seven body sites across the human microbiome.(XLSX)Click here for additional data file.Table S10Significant associations between KEGG modules and individual carbohydrate active enzyme (CAZy) gene families. Statistically significant Spearman correlations between module abundances (from HUMAnN) and CAZy abundances (independently generated from the same metagenomic data by (XLSX)Click here for additional data file.


A MySQL server is available for demonstration purposes at hydrax.icgeb.trieste.it:3307.The Java BioWareHouse (JBioWH) project is an open-source platform-independent programming framework that allows a user to build his/her own integrated database from the most popular data sources. JBioWH can be used for intensive querying of multiple data sources and the creation of streamlined task-specific data sets on local PCs. JBioWH is based on a MySQL relational database scheme and includes JAVA API parser functions for retrieving data from 20 public databases . It also includes a client desktop application for (non-programmer) users to query data. In addition, JBioWH can be tailored for use in specific circumstances, including the handling of massive queries for high-throughput analyses or CPU intensive calculations. The framework is provided with complete documentation and application examples and it can be downloaded from the Project Web site at Database URL:http://code.google.com/p/jbiowh Integrating heterogeneous bioinformatics data is perhaps one of the most challenging tasks in biomedical research today. The fast accumulation of data is only one side of the problem. In addition, the number of data types used by different user groups in life sciences is also rapidly increasing [for recent reviews see ]. IntegrConceptual integration of data is a common issue in all database integration efforts. For instance, the contents and meaning of the term ‘protein’ are different in Uniprot  and in DLarge central resources such as those of the NCBI, the EBI and KEGG make great efforts to integrate various databases and data sets to provide a fundamental and high-level service for individual biologists around the world. The integrated databases available on the Internet [e.g. Ensemble , BioMartOne interesting direction that is likely to further increase the interest in local data integration is the fast development of specialized bioinformatics computing environments. Examples of these include Bioperl , BiopythAnother well-known example is BioWarehouse, an open-source toolkit for building bioinformatics database warehouses in MySQL and Oracle . BioWareDespite the development of these data integration approaches, a number of specific challenges still remain. The format of data continually changes owing to new standards or new methods of data collection. Moreover, new cross-references are being added to databases, meaning that biological data integration projects must make special efforts to update integrational schemas to include cross-references between data sets as well as to add new databases. Open-source community-based projects hold a promise in this respect, which is why we decided to develop a data integration framework for the Java environment. Java BioWareHouse (JBioWH) is an open-source framework aiming to reach users who need to query multiple public data sets in a flexible way using their PC or a local workstation. JBioWH is based on the free and platform-independent Java language to create a MySQL-based data warehouse that is built around a modular relational database scheme. The aim of JBioWH is to allow users to construct application-specific databases; in this article, we present a demo example of integrating 20 public databases that contain 13 biological data types. The data are linked by cross-references, meaning that complex SQL queries can be executed. The project is continually updated by introducing new biological databases, extra functionalities to the Java API and new features to the Desktop Client.The framework consists of three main parts: (i) the data sources, (ii) the relational schema and (iii) the Java API. The structure of the JBioWH can be seen on JBioWH contains data retrieved from 20 public databases. For the integrated database, we designed a relational database scheme in SQL. The relational scheme contains 363 tables including auxiliary tables for cross-references and for speeding up the ‘join’ operations. A Java API has been designed for maintaining the relational database and querying the data. This package contains the main Java classes used for database maintenance. The imported biological data are grouped into 10 modules according to their biological type. The modules use the Java Persistence API (JPA) to manage the relational database in Java. The core classes use the Eclipse Link Library (version 2.3.2) to map object-oriented models onto MySQL tables in the back-end. The modules implement the abstract SearchFactory class to execute queries without using SQL. Each module implements loader and parser functions to connect to the source databases, retrieve data in flat file format, parse and put the data into the relational database. Modules can be loaded or unloaded independently from each other; hence, unnecessary data can be easily omitted to save storage space and to speed up query execution. The biological databases are often large and their syntax is often poorly defined. This problem frequently causes loader failures in the available integration frameworks, or worse, it can corrupt the biological data themselves. The loader functions of JBioWH were designed to preserve the biological data so that the loader process stops in case of errors in the database format or structure. The Java classes were designed according to the standard Java design patterns. http://code.google.com/p/jbiowh/wiki/JBioWHGraph).JBioWH also includes a global class called ‘JBioWHGraph’, which can be used to create complex graphs from queries. The graph created by this class is a set of vertices that are the biological objects  and a set of edges that are the relationship between them. The graph is created using a recursive algorithm following the JPA relationship between the entities. This approach permits the creation of directed or undirected graphs, including weighted edges, depending on the relationship between the vertices. In addition, the class provides a set of methods to retrieve some of the most common properties of the graph such as node connectivities, size and diameter of a graph, and it also allows operations like shortest path identification (for a complete description see The ‘JBioWHGraph’ class can be extended to create different kinds of graphs (see ‘Examples’ section below). This extension also offers a tool to create graphs using the biological objects as edges denoting a relationship between other objects that the protein is linked to. Currently three classes are available, the ‘TaxonomyGraph’ class, the ‘ProteinProteinGraph’ class and the ‘DrugPathwayGraph’ class. ‘TaxonomyGraph’ creates a directed graph with the hierarchical structure starting from a Taxonomy parent. This particular graph is useful in queries to be executed over an entire taxon such as a genus or family. ‘ProteinProteinGraph’ creates an undirected and weighted graph of protein–protein interactions. This class uses the Protein–Protein interaction data sets (such as MIF25) to create the edges between the proteins using the ‘MIFEntryInteraction’ class. The edges are weighted depending on how many times each interaction is reported by different data sets. The ‘DrugPathwayGraph’ class creates an undirected and weighted graph of drug pathways. This graph uses the proteins as edges creating the relationship between drugs and pathways. The edges are weighted depending on how many times each relationship (drug–pathway) is created by different proteins.http://code.google.com/p/jbiowh).The JBioWH Desktop Client application has been developed for users who are not familiar with SQL script or the Java programming language. The client provides a graphical interface to access, manipulate and execute complex queries by simple mouse clicking from the integrated database  and MySQL Server 5.5 or later . The Desktop Client can only be used with the Java virtual machine and the MySQL Java connector. The tables and the relational scheme have been implemented in a MySQL relational DBMS (version 5.5). The tables use the MYISAM engine and include the index creation syntax.http://code.googlecode.com/p/jbiowh contains the complete documentation of the JBioWH including the (i) relational scheme diagrams for each module ; (ii) a JavaDoc for the detailed description of Data Parsers and Desktop Client classes/methods; and (iii) a complete user manual for the JBioWH Desktop Client along with examples. A Google group called jbiowh-discuss was created for posting questions and ideas (https://groups.google.com/d/forum/jbiowh-discuss). A MySQL database server, designed for demonstration purposes, is available at hydrax.icgeb.trieste.it:3307.The system has been tested on Windows 7, Linux and in mixed environments where the MySQL DBMS was running on a Linux server and the Desktop Client was running on Windows. The project’s Web site at JBioWH is under continuous development and includes the addition of further data sources, new functionalities, fixing bugs and adapting the format to changes in the data sources. Releases and updates will be provided via the project’s Web site. The JBioWH project provides a demo database hosted at one of our servers. This database is updated once in a month. If it is loaded with all the data, JBioWH requires ∼300 GB storage space (10 January 2013). On the other hand, JBioWH is modular, so only a small part of the data may be necessary for any given project. We also note that JBioWH does not contain the data themselves, and the end-user needs to get all the licenses and permissions to use the individual databases.http://code.google.com/p/jbiowh/wiki/Ex1SearchTab).Examples of simple data retrieval  are shown in More complex questions can be solved using the JBioWH API. These kinds of questions can not be solved directly using SQL language (neither the Desktop Client).Streptococcus pneumoniae R6, which encodes the protein Endo-alpha N-acetylgalactosaminidase (Q8DR60). As the answer, JBioWH will find gene ‘spr0329’ (GeneId: 934791). This gene encodes protein ‘Q8DR59’, a penicillin-binding protein that is the target of Oxacillin, Hetacillin, Nafcillin, Ampicillin, Cefalotin, Azidocillin, Cefotaxime, Cefoxitin and Cephalexin. To answer this question one needs to retrieve gene and chromosomal position information from the Gene and Genome databases, respectively, to identify the corresponding proteins in the UniProt database. Subsequently, JBioWH retrieves antibiotic information for these proteins from DrugBank. The total execution time for this operation is 10 s. The complete description and the source code are available at https://code.google.com/p/jbiowh/wiki/Example7.The JBioWH API can be used to answer queries that cannot be easily handled by the SQL language. Recursive queries such as finding nearest neighbors in terms of metabolism, taxonomy or chromosomal locations are typical examples of this kind of a problem. For instance, we want to find out if there are antibiotics that target a certain chromosomal region. As an example we take the ±10 genes the chromosomal neighborhood of the gene ‘spr0328’ of Streptococcus and Burkholderia. To answer this question we have to retrieve the taxonomic groups first. For this purpose JBioWH uses graph structures that can be created by extending the ‘BioWHGraph’ class in Java. For instance, the ‘TaxonomyGraph’ class represents the hierarchical structure of a Taxonomy family. https://code.google.com/p/jbiowh/wiki/Example5.We can extend the scope of this question for an entire taxonomic subgroup. The question now is whether the orthologs of gene ‘spr0328’ in a certain taxonomic group have chromosomal neighbors that encode for antibiotic targets. We will use the ±10 genes neighborhoods in two genera, spr0328 from all Streptococci, located within a 10-gene neighborhood spr0328 and then locate the antibiotic target genes. For the location of the orthologs JBioWH uses UniRef clusters as an approximation [the COG  that harbor at least two genes encoding antibiotic targets. Again, we will use the genera Finally we show examples related to drugs that act on similar targets. In the database, the drugs are linked to proteins, and proteins are members of a network of metabolic pathways. In this system, two drugs can be (i) target neighbors, if they act on the same protein (ii) pathway neighbors, if they act on proteins that belong to the same metabolic pathway or (iii) distant neighbors, if they act on different pathways, and in the latter case it is important to know, in addition, how far apart in the metabolic network the two drugs are because distant relationships can be biologically meaningless. Questions related to (i) and (ii) can be answered by SQL queries but they need multiple joints, which makes the search time-consuming, especially in the case of (ii). Questions of type (iii), however, involve a prohibitively large number of multiple SQL join operations. JBioWH can handle these complex queries because of the graph structures implemented using the ‘DrugPathwayGraph’ class.S. pneumoniae R6’ (TaxId: 171101). Other links of Hetacillin are not reported even though its metabolite Ampicillin is well-known to act on various organisms. If we use a simple SQL query, only the links to ‘S. pneumoniae R6’ will be found. However, the ‘DrugPathwayGraph’ class can be used to find all target neighbors, pathway neighbors and also distant neighbors of the ‘Hetacillin’. The answer provided by JBioWH is that (i) the drug has 19 target neighbors that would act on the same protein target, (ii) has 38 pathway neighbors and (iii) the drug has 35 nearest distant neighbors that are identified as antibiotics. The execution time was 300–400 s.‘Hetacillin’ (DrugBank id: DB00739) is a beta-lactam that does not have intrinsic antibacterial activity, but is converted in the human body to Ampicillin, which is active against a variety of organisms. In DrugBank, Hetacillin is reported to act only on ‘Penicillin-binding protein 1A’ (UniProt Id: PBPA_STRR6) and ‘Penicillin-binding protein 2B’ (UniProt Id: PBP2_STRR6) both of which are parts of the ‘Peptidoglycan biosynthesis’ (KEGG: spr00550) of ‘S. pneumoniae R6’ (TaxId: 171101), ‘Escherichia coli str. K-12 substr. MG1655’ (Taxid: 511145) and ‘Clostridium perfringens str. 13’ (Taxid: 195102). This answer can be obtained using the ‘DrugPathwayGraph’ class as described in https://code.google.com/p/jbiowh/wiki/Example10.In addition, the drug–pathway graph can be useful for identifying antibiotic drugs that target the same pathway in other organisms. For instance, ‘Ceftazidime’ (DrugBank Id: DB00438) and ‘Cyclacillin’ (DrugBank Id: DB01000) are antibiotic drugs that target the same pathway as Hettacillin, but in three different organisms, ‘http://code.google.com/p/jbiowh/wiki/Examples.Further examples can be seen on the project’s Web site We point out that graph-based queries cannot be easily answered by SQL-based systems such as relational databases that do not have graph extensions, and these queries are practically impossible to answer using the traditional central resources or federated databases such as Biomart , 21. AltThe JBioWH framework provides an open-source Java API for integrating biological data from various public databases in a data warehouse manner. The aim of JBioWH is to allow users to construct application-specific databases; in this article, we present a demo example of integrating 18 data sources. The integrated database is hosted on a local computer and so it can be used for data-intensive calculations. This feature is especially important for queries that are not, or not easily, accommodated on central data resources. We note also that this feature could be important in environments with slow, or limited, Internet access. The relational schema of JBioWH is defined in a MySQL DBMS, and contains Java classes and parsers that load the modules of the JBioWH with data from public databases. Finally, JBioWH includes an API interface for programmatic access and a Desktop Client that lets users easily manipulate and query data via a graphical interface.Future work will aim to integrate further biological data sources and the implementation of other DBMS such as PostgreSQL and Oracle. We continue developing the Desktop Client by extending it with new functionalities. Future releases will appear at the project’s Web site. Finally, we encourage users to send us their recommendations and requests via the jbiowh-discuss Google Group.


Model organisms are widely used for understanding basic biology, and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models, and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community. DrosophilaModel organism research in biological sciences is widespread and essential. From the classic cell cycle studies in yeastSuch cross-organism comparative analysis is powerful for a number of reasons. Making analogous observations in different species can strengthen evidence for a hypothesis, systematically remove gaps in knowledge, and also lead to novel findings, such as a detailed understanding of evolutionary principles and the differences between organismsAlthough cross-species studies are extremely powerfulInfrastructure already exists for compiling, curating and standardizing available data for each of the model organisms in the form of freely available model organism databases (MODs). They are valuable resources to their research communities worldwide because they provide detailed levels of annotation with a particular focus on reliability and manual curation. However, although the infrastructure is highly developed within each MOD for a single organism, the fact that conventions tend to vary between the databases provides barriers to comparative analysis. Much work was therefore needed to provide data in a consistent format in combination with a unified interface and tools to facilitate consistent comparative analysis. By working together, the MODs, as existing information hubs, were ideally positioned to establish such an infrastructure with the aim of enabling easier cross-organism analysis.C. elegans and D. melanogaster genomes (modENCODEFive of the major Model Organism Databases (MODs) have adopted the InterMine data integration platform to provide flexible searching and data mining interfaces to their user communities, the first time these widely used resources have converged on a common software platform . InterMimodENCODE drug dismodENCODE, fruit fmodENCODE and mitomodENCODE.Here we introduce and describe the progress of the InterMOD project (intermod.intermine.org), an international consortium composed of the above five model organism databases and the InterMine group. The consortium aims to make it easier to carry out cross-MOD comparative analysis and to relate MOD data to medical research. Specific goals are establishing common infrastructure, standardising terminology, implementing standardised links between the different model organisms, and creating common analysis tools.Establishing the infrastructure for cross-organism research has required the development of web services, creation of independent embeddable web components and unification of the data model used by each of the InterMine instances. The web services allow communication between individual InterMine databases in a common language, embeddable web components promote tool and data sharing, while the unified data model ensures consistency in the data represented in each of the MOD InterMine instances and provides the basis for re-usable tools and services.InterMine is underpinned by a set of RESTful web services that give programmatic access to all core functionality. The existence of web services enables automation of comparative analysis across all existing InterMine instances as well as the development of new tools. In particular this means that tools and reports can be created that draw on data from more than one model organism InterMine database and researchers can write their own analysis pipelines utilizing any or all of the consortium databases.The web services are platform independent, meaning that users can choose the most suitable programming platform for their needs. While any language capable of making standard HTTP requests can be used, library support is provided in five commonly used programming languages – Python, Perl, Java, JavaScript and Ruby.http://intermine.org/embedding), develop tools based on InterMine's reusable, interactive components using the examples given, with the choice of technologies supported  being at the cutting edge of what is currently available. Thus, any of the MODs can create new tools that can easily be shared with all MOD InterMine instances; recently, SGD developed the YeastGenome iPhone appKey components of the InterMine web interface  have been re-designed as organism agnostic, reusable, interactive components that can be embedded in any web site. This approach has two advantages: 1) As new tools easily can be incorporated into the existing InterMine framework, it allows developers to make their own tools and share them. In practice this means developers can now, following the documentation .genes and proteins, related types such as publications as well as data types that describe data including data sources and evidence codes . The InterMOD Interoperability Working Group was formed with representatives of each MOD to review such comparisons and decide on which data types and fields should be part of the core model and therefore consistent between the different InterMOD InterMine databases. In case of a conflict or duplication, a standard is agreed and each InterMine instance updates their model. As a simple example, some InterMine instances had a Gene “description” field and others had a Gene “summary” field, containing essentially the same information.We expect such data model refinements to remain an ongoing process as further tools are constructed, with the ultimate aim being to capture the major data fields relevant to cross-organism interoperation. Current efforts are focused on homology, phenotype and strain/allele data. The core model can be viewed online at github.com/intermine/intermine/blob/dev/bio/core/core.xml.Such synchronization of the core model underpins the ability to construct cross-database queries, as the same query (e.g. requesting homologs of specified mouse genes) can be sent to any InterMine instance without modification if the data models are all the same. This also helps the MOD sites to collate related information from multiple InterMine instances and, for example, present them to their users within a single web page. In addition such uniformity helps end users: having learned how to use one consortium InterMine database, the others provide the same core data and behave similarly for both web interface and web services.It has required substantial time and effort to establish the core model and the working practices necessary to coordinate further extensions to it. Together with the establishment of the underlying framework of web services and embeddable web components, this unified data model has paved the way for the development of cross-species analysis tools and reports. Links between model organisms can be made through a number of different data types and in particular through orthology and ontologies as described below in more detail.Orthology is a widely used basis for cross-organism analysis utilizing the shared ancestry of genes, and was thus the first linking point established between the different InterMOD data warehouses. Using orthology relies on mappings that can be difficult to define unambiguously and are the outputs of multiple significant research efforts including PantherAt the simplest level orthology mappings enable linking between the different InterMine instances. For example, a researcher with a list of mouse genes in MouseMine can jump to the equivalent (orthologous) rat gene list in RatMine. Such mappings also enable more detailed comparisons, such as looking for orthologous functional information. For example, a tool was developed to survey pathway annotations across organisms . For a gDrosophila researchers connect basic research on a particular gene to its potential medical relevance. It is envisaged that future tools may link directly through ontology terms. However, currently, many ontologies are specific for a particular species and cannot directly be used for interoperation between species. Specialists in ontologies are developing bridging ontologies that map anatomical features in one organism to the corresponding ones in another and the same is being done for phenotypes2021Biological ontologies have been developed to provide a powerful language for describing among others, biological processes, functions, locations and sequence features. They lay the groundwork for cross-species database interoperability by allowing annotation and analysis using a set of common terms14The InterMOD consortium has developed a framework and working practices to allow collaboration between five of the major model organism databases. Public InterMine instances are available for budding yeast, mouse, rat, and zebrafish, with the nematode worm one currently close to public beta release. A core data model essential to enable interoperation between the different databases has been established, together with a working group to review and expand the core model as needed for future developments. Orthology has been used to create a number of tools, including an ortholog exporter, a tool for obtaining disease data, and a pathway comparison tool. Work is continuing on consolidating models for gene expression, protein domains and ontologies, which will facilitate development of further tools. Improvements were also made to the InterMine infrastructure, such as expansion of web services and adding the capability for embedding analysis tools in external websites.Having established the core model, databases, infrastructure, demonstration tools and working practices there is still much work for the consortium to do. Refinements to existing functionality include providing users with more choice in the way orthology is used for interoperation; this may include both a choice of methods, but also choice within a particular mapping. For instance, in some cases users are interested in the single best choice of ortholog and in other situations all likely orthologs are of interestOther kinds of mappings, such as synteny, are also of interest. Providing regions of shared synteny will allow researchers to compare features such as regulatory regions and transcription factor binding profiles across species. Similarly, refinement of the core model will facilitate development of tools for cross-species gene expression and protein interaction network analysis. Potentially, a large range of cross-species information can be displayed on interactive report pages . HumanMine should provide a common interoperation stage for the other InterMOD databases, so enhancing translational research by enabling a uniform way for model organism researchers to relate their findings to human data, as well as making it easier for medical researchers to access relevant model organism data.Apart from interoperation, there is a clear advantage in unifying model organism data using a common platform in that it reduces the time and effort required to learn many different user interfaces. For example, a researcher working in yeast biology can repeat analyses on zebrafish or fly data without further training, benefit from similar tools and retrieve data in a consistent format.In conclusion, the InterMOD consortium has built a number of organism-specific InterMine data warehouses using a shared data framework, and establishing cross-species interoperation between them. The consortium has drawn on previous work such as the Gene and Sequence Ontologies, and anticipates the maturation of further ontologies such as phenotype and anatomical ontologies. The collaboration between the InterMine team and model organism databases within this consortium promises a platform for efficient cross-species comparative analysis. It also provides the potential for further synergies in quality checking and building common tools for the benefit of the broader biomedical research community. Importantly, the links that will be established to a core set of human data will provide greater opportunities for medical research to benefit from model organism findings, as well as providing the framework of cross-species validation valuable for translating model organism research into medical applications.http://www.gnu.org/licenses/lgpl.html), and the code and documentation can be obtained from http://www.intermine.org, along with links to the different existing InterMine databases. The technical details of the InterMine framework, its associated software dependencies and comparison to other systems have been previously describedAll data warehouses for the different model organisms were set up using the open-source InterMine platform (version 1.1). InterMine is available under an LGPL licence  are the primary identifiers, while other identifiers  are saved as cross references. NCBI identifiers are used in the case of human genes. For historical reasons, some MODs have more than one identifier for each gene. In these cases the second MOD identifier is stored in the “secondary identifier” column. In earlier databases, some InterMine instances had specific fields to hold cross-references, e.g. “RefSeq identifier” or “GenBank identifier”. However, given the sheer number of external databases in existence, and the fact that many hold multiple identifiers for the same gene, it was considered too unwieldy to create a specific field for each external database. Therefore, to ensure consistency, all external identifiers are now stored as a set of “cross-references”.Another example where significant model-reconciliation needed to be addressed was with “sequence features”- i.e. how the InterMine databases provide information about what a particular sequence feature is. In addition to different field names for feature types, specifically, “feature type”, “gene type” and “type”, there were also differences in the contents of the fields, with some using their own terms while others used terms defined by the Sequence Ontology. During the data-loading phase every sequence feature in InterMine automatically is assigned a term from the Sequence Ontology based on the feature type and this is stored as a Sequence Ontology term referenced from Gene. For example, every gene in the database has a reference to the Sequence Ontology term “gene” (SO:0000704). To synchronize both the data model and the terminology, it was agreed to remove all type-related fields from the data model of each InterMine instance and use this reference to the SO term instead.Each InterMine instance uses a default mapping for orthologous genes, which are then used by the analysis tools, for example to compare pathways. If no local ortholog mappings are available, the genes are sent to the other linked InterMine databases, and the orthologs are mapped using the local mappings available in those InterMine instances. InterMine web services are used exchange searches and results between the different InterMine databases.The choice of mappings varies between different InterMine instances, with the choice of default mapping determined by the coverage offered by the different mappings for the model organism in question. The homolog data parsers available from the InterMine library include TreeFamThe pathway data is imported from sources including KEGGModel organism-specific InterMine instance development and maintenance: H.M., S.N. and J.R. for MouseMine; A.V., P.J., S.T. and E.W. for RatMine; J.D.W., T.H., Q.T. and L.S. for WormMine; K.K., R.B., G.B., B.H. and J.M.C. for YeastMine; S.A.T.M., C.P. and M.W. for ZFINMine; J.S., R.L., J.A., R.N.S. and G.M. for FlyMine; J.S. coordinated the project and provided developer support. G.M. conceived and managed the project. J.A., J.S., R. L., J.M.C., M.W. and G.M. drafted the manuscript, with J.A. as lead drafter. All authors read and approved the final manuscript.