In this paper we propose a new methodology for the analysis of metabolic networks. We use the notion of strongly connected components of a graph, called in this context metabolic building blocks. Every strongly connected component is contracted to a single node in such a way that the resulting graph is a directed acyclic graph, called a metabolic DAG, with a considerably reduced number of nodes. The property of being a directed acyclic graph brings out a background graph topology that reveals the connectivity of the metabolic network, as well as bridges, isolated nodes and cut nodes. Altogether, it becomes a key information for the discovery of functional metabolic relations. Our methodology has been applied to the glycolysis and the purine metabolic pathways for all organisms in the KEGG database, although it is general enough to work on any database. As expected, using the metabolic DAGs formalism, a considerable reduction on the size of the metabolic networks has been obtained, specially in the case of the purine pathway due to its relative larger size. As a proof of concept, from the information captured by a metabolic DAG and its corresponding metabolic building blocks, we obtain the core of the glycolysis pathway and the core of the purine metabolism pathway and detect some essential metabolic building blocks that reveal the key reactions in both pathways. Finally, the application of our methodology to the glycolysis pathway and the purine metabolism pathway reproduce the tree of life for the whole set of the organisms represented in the KEGG database which supports the utility of this research. Metabolism is the chemical system that generates the essential components for life. All living organisms possess an intricate network of metabolic routes for the biosynthesis of amino acids, nucleic acids, lipids and carbohydrates and for the catabolism of different compounds driving cellular processes. Traditionally, metabolism has been divided into metabolic pathways: subsystems of metabolism dealing with specific functions. However, it has become increasingly clear that metabolism operates as a highly integrated network .patchwork evolution model [panspermia theory [From this general perspective, co-analysis of phylogeny and metabolic networks can provide valuable insight in explaining the appearance and development of complex networks of interacting proteins and chemical molecules , 3, or yon model , while oa theory . TherefoOver the last ten years metabolic pathways have been the subject of a great deal of research, conducted primarily through two kinds of studies, focusing either on the analysis of single pathways –10, or oSince we are interested in a topological analysis of metabolic networks, we focus on a network based approach instead of other approaches like kinetic modeling, hybrid modeling or constraint based modeling. See  for a gometabolic building blocks. When every metabolic building block in the initial directed graph is contracted to a single vertex, the resulting graph is a directed acyclic graph, called a metabolic DAG, whose number of nodes is considerably reduced. Moreover, the property of being a directed acyclic graph brings out a background graph topology that reveals the connectivity of the network as well as bridges, isolated nodes and cut nodes, which become key information for the discovery of functional metabolic relations. The results of the preliminary tests we have carried on, reported in this paper, shows that our methodology fulfills the requirements of network size reduction while preserving information of interest.Hence, we propose a methodology for the analysis of metabolic networks that aims at providing a good balance between the information of interest that must be kept and a considerably reduction of the size of the network to facilitate its analysis and visualization. In this paper we introduce a new approach to metabolic networks modeling based on classical notions of graph theory, which applied to metabolic networks have proved to be successful. Namely, we used the notion of strongly connected components, which in this context we call This section describes the methodology proposed for the analysis of metabolic networks. We represent metabolic pathways and metabolic networks as directed graphs, called reaction graphs. The analysis of every network derives from the study of topological properties of the associated reaction graph. We introduce a suitable graph reduction, called metabolic DAG, which captures the network connectivity and reveals a set of key reactions as well as a modularization of the network. Furthermore, the metabolic networks analysis and comparison is easily obtained from the reaction graph reduction.Ri = , Ii being its substrate, Ei the enzyme that catalyzes the reaction and Oi its product.A metabolic network is the complete set of metabolic and physical processes that determine the physiological and biochemical properties of a cell. As such, these networks comprise the chemical reactions of metabolism, the metabolic pathways, as well as the regulatory interactions that guide these reactions. More precisely, a chemical reaction is a process that leads to the transformation of one set of chemical substances or metabolites called substrate, to another called product. Chemical reactions are catalyzed by enzymes that accelerate their rate. Thus, in this paper we denote a chemical reaction by directed graph is an ordered pair G =  where V is a set of nodes and E ⊆ V × V is a set of arcs. There is an arc from a node u ∈ V to a node v ∈ V if, and only if,  ∈ E. In this work, we model a metabolic network as a directed graph GR =  whose set of nodes is the set R of chemical reactions present in the metabolic network, and its set of arcs E is defined as follows: there is an arc from Ri =  to Rj =  if, and only if, there exists one metabolite c such that c ∈ Oi ∩ Ij. That is, there is an arc from Ri to Rj if, and only if, at least one metabolite in the product of Ri is in the substrate of Rj. Notice that, metabolites are not represented in our modeling, but we take them into account to define the arcs. Most of the reactions in metabolic networks are reversible. A reversible reaction can occur in two directions, from the reactants to the products (forward reaction) or vice versa (backward reaction). The direction depends on the kind of reaction, on the concentration of the metabolites, and on conditions such as temperature and pressure. In this work, we model reversible reactions by two different nodes, one for the forward reaction and the other for the backward reaction. The directed graph GR, modeling a metabolic network, is called a reaction graph.A Homo sapiens, available at the KEGG database [As we have already explained in the introduction section, reaction graphs may be huge. Indeed, with only one metabolic pathway we can get more than a hundred of nodes as it is the case of the purine metabolism pathway in database , whose rAs stated above, metabolic networks use to involve thousands of reactions. Therefore, the corresponding reaction graphs have thousands of nodes which difficult their analysis and study. In order to overcome this problem, we introduce metabolic DAGs, which consist on a condensation of the reaction graph that preserves the relations between reactions.directed acyclic graph, a DAG, is a directed graph with no directed cycles. In a directed graph, G, two nodes u,v are said to be biconnected if there is a path in each direction between them. A strongly connected component of a directed graph G is a subgraph such that every pair of nodes in it are biconnected, and it is maximal under inclusion with this property [G. If each strongly connected component is contracted to a single vertex, the resulting quotient graph is a DAG, the condensation of G. Notice that there is an arc from a node si to a node sj in the condensation of a directed graph G if, and only if, there is an arc in G from a node u ∈ si to a node v ∈ sj.A property . Since bGR, we can consider its collection of strongly connected components and compute its condensation, which will be a DAG. We call metabolic building blocks (MBBs for short) the strongly connected components in the reaction graph GR, and we call metabolic DAG (m-DAG for short) the condensation of GR, which is a DAG whose nodes are the MBBs of GR.Thus, for every reaction graph GR and its condensation, all the nodes in a MBB are the reactions that are biconnected among them in the reaction graph, that is, such that there is a path back and forward between them. In this sense, MBBs can be thought as robust subgraphs in the reaction graph. Moreover, the reactions that are not biconnected to any other reaction, which become a MBB by themselves in the m-DAG, may be necessary for the connectivity of the network. In this sense, the condensation of GR provides a modularity of the reaction graph that keeps the information of robustness and connectivity of the metabolic network.Intuitively, if we consider a reaction graph Homo sapiens  and in Thus, in R00703 and its reverse. As far as the arcs go, if we consider for instance the arc in the reaction graph from R00200 to R00703, then, there is also an arc in the m-DAG between the MBB 31 consisting only in R00200 and the MBB containing R00703, which is MBB 0.6.0.0.2.0.1.0. In R00200 appears to be crucial to preserve the connectivity of the network, since it connects MBB 32.0.0.0 with MBB 0.6.0.0.0.0.0 which have 21 and 6 reactions respectively. Actually, the detailed study of the glycolysis pathway from the KEGG database that we carried on, reported in the next section, shows that the essential reaction R00200 is present in any organism performing the glycolysis pathway, which also entails that the enzyme catalyzing this reaction is a key enzyme.In this case, the m-DAG that we obtain has seven MBBs, three of them with only one reaction and four of them with more than one reaction. The biggest one has 23 reactions two of them catalyzed by more than one enzyme, while the smallest one has only 2 reactions, Homo sapiens.Finally, since some readers are probably used to the glycolysis pathway description depicted in the KEGG database, we also show in In order to implement the methodology proposed in this paper, we consider Tarjan’s strongly connected components algorithm , which iO(|R| + |E|) where |R| is the number of nodes in the graph, in this case the number of reactions, and |E| is the number of arcs. The input in Tarjan’s algorithm is a directed graph, the reaction graph, while its output is a set of strongly connected components, the MBBs.Therefore, as the Tarjan’s algorithm is a variation of the depth first search algorithm, the overall complexity of this algorithm is MBBi with those other, MBBj, such that there exists at least one node in MBBi with one or more arcs to nodes belonging to MBBj. The overall algorithm to do this process is shown in Algorithm 1.Once the MBBs have been found, the m-DAG is built by connecting each strongly connected component Algorithm 1. Algorithm to build the m-DAG from the Strongly Connected Components.for allMBBi ∈ StronglyConnectedComponents doneighbours(MBBi) = ∅ for alln ∈ MBBido if ∃ m ∈ MBBj|n → m ∧ MBBj ≠ MBBithen  neighbours(MBBi) = neighbours(MBBi) ∪ {MBBj}   end if  end for for allMBBk ∈ neighbours(MBBi) do MBBi and MBBk  add an arc between end for end forThe result of this algorithm is a m-DAG whose nodes are the MBBs and whose arcs are their relations. In order to differentiate the MBBs, each one has been labelled with a number. This number corresponds with the one assigned by Tarjan’s algorithm. Thus, depending on the way the input graph is introduced to the algorithm, the numbers assigned may change.Moreover, since many studies involves more than one species, in order to compare their m-DAGs, it is useful to unify their MBBs labels, because to compare two m-DAGs we have to compare their MBBs. If those MBBs have an identification describing their characteristics it is easy to differentiate them and also to see their similarities. In order to obtain those identifiers, a list of all different strongly connected components is built, each one has its list of reactions and also its unique identifier. Two MBBs are the same if their reaction lists are equal, and they are different if one of them contains some reaction the other doesn’t have. It is also possible that one reaction list is a subset of the other. Thus, the unique identifier of each MBB must reflect these three cases: if one MBB is a subset of another, its identifier has a prefix corresponding with the largest one, if two MBBs are different they have a completely different identifier, otherwise they have the same identifier.reference pathway which is constructed, in the same way as in KEGG database, as the union of all reactions in every input reaction graph. The m-DAG obtained from the reference pathway is called a reference m-DAG. The set of reactions of every MBB appearing in a m-DAG of an organism is a subset of the set of reactions of a MBB from the reference m-DAG. The identifiers of the MBBs from the reference m-DAG will be the base of the identifiers of the MBBs of every m-DAG.To obtain this list of MBBs we start building the m-DAG of a Prior to describe the algorithm we have devised to build the identifiers, we have to introduce some notation:MBB) represents the set of the reactions belonging to MBB.react(MBB) is the identifier of every MBB. Each MBB have an unique identifier in order to be able to differentiate it among the others.id represents the set of the MBBi, such that react(MBBi) ⊆ react(MBB).subsets = react(MBBi) then MBBj) ← id(MBBi)  id(else if ∃ MBBi ∈ ref-DAG | react(MBBi) = react(MBBj) then  MBBj) ← next_identifier(subsets(MBBi))   id(MBBi) ← subsets(MBBi) ∪ {MBBj}   subsets(else  if ∃ MBBi ∈ ref-DAG | react(MBBj) ⊂ react(MBBi) then   MBBs → smallest MBB | MBBs ∈ subsets (MBBi) ∧ MBBj ⊂ MBBs    MBBs) ← subsets(MBBs) ∪ {MBBj}    subsets(MBBj) ← next_identifier(subsets(MBBs))    id(else   MBBj) ← new identifier    id ⊂ react(MBBj) then id(MBBj) is a prefix of the identifier of MBBi. If there exists MBBj in the list such that react(MBBj) ⊂ react(MBBi) then MBBj is relabeled using id(MBBi) as a prefix and all the MBBs in subsets(MBBj) are relabeled accordingly. Otherwise MBBi gets an new identifier not existing in the list.Now, we proceed to relabel the nodes in the m-DAG for each species considered. For every m-DAG we compare its MBBs with those of the list. Let Homo sapiens. The MBB with identifier 32.0.0.0 has a prefix, 32, which indicates that the reactions of this MBB are all in the MBB with the identifier 32 in the reference m-DAG. The same occurs with the MBB with identifiers 0.1.0, 0.6.0.0.2.0.1.0, 0.2.1.0.0.0 and 0.6.0.0.0.0.0, whose reactions belong to MBB 0 in the reference m-DAG. Furthermore, with this MBB labeling, we can easily see that there is more similarity between MBB 0.6.0.0.2.0.1.0 and MBB 0.6.0.0.0.0.0 than for instance to MBB 0.1.0.In this way all the m-DAGs and also all the MBBs are comparable. For instance, as can be seen in The presented algorithm is fast enough to be executed in a desktop or laptop computer. For example, in a 2.6 GHz Intel i7-6700HQ the average time needed to build a m-DAG for the purine metabolism is 0.63 seconds for each organism in the KEGG database. In the case of the glycolysis metabolism the average time needed to build a m-DAG is 0.03 seconds for each organism.http://bioinfo.uib.es/metabolomics/supplementary_material/ some supplementary material can be found as well as a web-based tool to give access to the researchers to our generated data.In In order to test the convenience of using m-DAGs to model and analyze metabolic networks, we considered the information available in the KEGG database corresponding to the glycolysis and the purine metabolic pathways. We decided to consider the glycolysis pathway since it is an ancestral set of chemical reactions present in almost every organism. On its turns, the purine pathway is also present in every organism and it is big enough to test the advantages of our methodology. Thus, with the information available on April 2016 in the KEGG database with 4158 different organisms, we ended up with 1653 different glycolysis pathways from 4140 organisms. These 1653 pathways were distributed in the six kingdoms defined in the KEGG database as suggested also by , 25. TheWe computed the 1653 m-DAGs from these 1653 different pathways and we analyzed them. From these 1653 m-DAGs, we obtained 299 MBBs. Tables in key MBBs for the glycolysis pathway of any organism, because they fix the connectivity in every glycolysis pathway.To study the biological information captured by the MBBs defined in this paper, we took advantage of the fact that the glycolysis pathway of any organism in the KEGG database is included in the glycolysis reference pathway. Thus, the corresponding reaction graphs also preserve this inclusion and, for every organism, its MBBs are also included in one MBB from the reference m-DAG see . Therefo(i) the MBB with a direct arc to reaction R00200, and, (ii) the MBB with a direct arc from reaction R00200. Hence, R00200 appears to be a crucial essential reaction that has been conserved among all organisms with glycolysis and has been preserved in every evolution step, since there is only one way to produce pyruvate from phosphoenol-pyruvate, and it is through reaction R00200 catalyzed by the enzyme 2.7.1.14 corresponding to a pyruvate kinase. As expected, few exceptions have been found, and only 18 organisms out of 4158 do not follow this rule. These 18 organisms are Nanoarchaeota, Alphaproteobacteria, Betaproteobacteria and Gammaproteobacteria  can be considered as the core of the glycolysis pathway.In the present study we have found two essential processes: eria See . For insHomo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae, Plasmodium falciparum, Escherichia coli and Thermoplasma volcanium belonging to the kingdoms, Animalia, Plantae, Fungi, Protista, Bacteria/Eubacteria, and Archaea/Archaeabacteria, respectively. We observe there that all the organisms has the essential reaction R00200, which appears to be crucial in the glycolysis pathway. We can also see how the MBBs are distributed in the two processes mentioned above. In green we show the MBBs with a direct arc to reaction R00200 and in blue the MBBs with a direct arc from reaction R00200.In order to reinforce the concept up to now investigated, in Pyrodictium delaneyi, which has a pathway alternative from glycolysis due to the fact that this organism habits in deep-sea hydrothermal vent sulfide chimneys [In the same way, we also developed a similar analysis considering again the six kingdoms. In this case, the purpose was to find out, with the information provided by the m-DAGs, what we called the kingdom reference m-DAG, which is the reference m-DAG for each kingdom. (See chimneys .(i) the MBB with a direct arc to reaction R00200, (ii) the MBB with a direct arc from reaction R00200 and (iii) the MBB that have only one reaction in the reference m-DAG. So, we considered the maximal MBBs in each class and thus we obtained the six kingdom reference m-DAGs shown in R00200. As far as the other MBBs in the third class goes, the Animalia have only two MBBs in the third class, as well as the Plantae and the Fungi. The Bacteria have the same MBBs in the third class as in the reference m-DAG and the Protista have three MBBs in the third class. Notice that from the results obtained in this test, we can discriminate whether a specific reaction in the glycolysis pathway belongs to a kingdom. For instance, if enzyme 3.2.1.86 is present, we can guarantee that it belongs to an organism in the Bacteria kingdom. While instead, if enzyme 4.1.1.49 is present, we can guarantee that it does not belong to Animalia nor Archaea. In Recall that, every MBB belongs to one of the following classes: Homo sapiens and the reference m-DAG, we can easily see that there is a sequence of six essential reactions present in both DAGs. This sequence of essential reactions produces the 5-Aminoimidazole-4-carboxamide ribonucleotide (AICAR) from the Ribose 5-phosphate. The fact that this sequence is present in the reference m-DAG entails that every organism with the purine metabolism pathway has exactly the same sequence. As we can also observe in the reference m-DAG, there are two MBBs with only one reaction, MBB 170 and MBB 179 connecting the sequence of essential reactions with MBB 0, a MBB with 139 reactions conducting to the Purines.We computed the m-DAG from each different pathway in the KEGG database and we analyzed them. Recall that with the information available on April 2016, we ended up with 2407 different pathways from 4158 organisms. From these 2407 m-DAGs we obtained 1386 MBBs. Again, to study the biological information captured by the MBBs defined in this paper, we took advantage of the fact that the purine metabolism pathway of any organism in the KEGG database is included in the purine metabolism reference pathway. Thus, the corresponding reaction graphs also preserve this inclusion and, for every organism, its MBBs are also included in one MBB from the reference m-DAG. Therefore, the information provided by the MBBs in the reference m-DAG is very valuable. Indeed, we computed the reference m-DAG, which is shown in (i) the sequence of essential MBBs that produces the (AICAR) from the Ribose 5-phosphate and, (ii) MBB 0 with all reactions conducting to the Purines, that is, either Adenine or Guanine. These two processes could be considered the core of the purine metabolism pathway for all the organisms in KEGG.Therefore, in this study we have found the following essential processes: Analogous to the glycolysis pathway, we also developed a similar analysis considering again the six kingdoms. We computed the reference m-DAG for each one called again the kingdom reference m-DAG shown in  Figs andR04560. It is a crucial essential reaction in Animalia, Plantae, Protista and Fungi that connects the two processes described as the core of the purine metabolism pathway. On the other hand, MBB 179, corresponding to R06975 is only present in one member of the Bacteriae (genus Peribacter) and in more than hundred members of the Archaea. Thus, we can conclude that in Procaryotes two different ways to connect the two essential processes have been found, in contrast to the only one way found in Eukaryotes. Interestingly, MBB 201, MBB 197, MBB 198, and MBB 203 are only present in Procaryotes.Notice the importance of MBB 170, corresponding to does metabolomics meet genomics?The second test performed to study the convenience of using m-DAGs to model metabolic networks, was to extend the MBBs information to obtain an overall clustering of all organisms with glycolysis and provide some contribution to answer the question, Ri =  and Rj =  be two reactions, their similarity score SimReact is given by the following formula [SimEnz is the enzyme similarity between Ei and Ej, SimComp is the compound similarity between the substrates Ii, Ij and SimComp is the compound similarity between the products Oi, Oj. The parameters we, wi and wo are fixed to we = 0.4 and wi = wo = 0.3 since, as stated in [To obtain a clustering of all organisms with glycolysis, we first defined a similarity measure between two m-DAGs based on the similarity of their MBBs. To define a similarity score of two MBBs, we first defined a reactions similarity score as it was done in . Let Ri  formula :SimReactated in , they prFor the enzyme and compound similarities in EC number (Enzyme Commission number) associated to each enzyme, which represents its catalytic activity. The EC number is a 4-level hierarchical scheme, d1.d2.d3.d4, developed by the International Union of Biochemistry and Molecular Biology (IUBMB) [For enzymes, we used the EC hierarchical similarity measure that is based on the comparison of the unique  (IUBMB) . Enzymese = d1.d2.d3.d4 and S depends on the length of the common prefix of their EC numbers:Given two enzymes arginase (e = 3.5.3.1) and creatinase (e′ = 3.5.3.3) is 0.75.For instance, the similarity between For compounds, we used a similarity based on the similarity measure computed by the SIMCOMP (SIMilar COMPound)  tool. GiX and Y of compounds, the score SimComp was computed by:Since a reaction may have more than one input (output) compound, we needed a way to combine the similarity between pairs of compounds computed by SIMCOMP. Given two sets X and Y are nodes and the weight of each arc  ∈ X × Y is the similarity value of x and y computed by SIMCOMP;– defining a complete bipartite graph in which the compounds in X and Y;– applying the maximum weighted bipartite matching algorithm to the resulting graph to obtain the best match between X|, |Y|}.– summing the scores of the best match and dividing it by max{|MBB1 and MBB2 its similarity score, Smbb, was computed by:Next, given two MBBs, MBB1 and MBB2 are nodes and the weight of each arc ∈MBB1 × MBB2 is SimReact;defining a complete bipartite graph in which the reactions in MBB1 and MBB2;applying the maximum weighted bipartite matching algorithm to the resulting graph to obtain the best match between MBB1|, |MBB2|}.summing the scores of the best match and dividing it by max{|Sim was computed by:Finally, the similarity measure between two m-DAG, mD1 and mD2 are nodes and the weight of each arc  ∈ mD1 × mD2 is Smbb;defining a complete bipartite graph in which the MBBs in mD1 and mD2;applying the maximum weighted bipartite matching algorithm to the resulting graph to obtain the best match between mD1|, |mD2|}.summing the scores of the best match and dividing it by max{|(i) the MBB with a direct arc to reaction R00200, (ii) the MBB with a direct arc from reaction R00200 and (iii) the MBB with only one reaction in the reference m-DAG. Then, to obtain the complete bipartite graph in the previous computation, we only considered the similarity measure of two MBBs in the same class and the weight of an arc between two MBBs in different classes was set to zero.In this test, since we had already analyzed the glycolysis pathways from the KEGG database, we considered the information captured by the reference m-DAG and we again split the MBBs in three classes: Once the similarity measure between every pair of glycolysis m-DAG was computed, we converted the similarity score into the following distance measure:Then, we performed the hierarchical clustering based on the distance measure above. To obtain the hierarchical clustering of the 1653 different m-DAGs we used the UPGMA method. In Again, the second test applied to the purine metabolism pathway to study the convenience of using metabolic DAGs to model metabolic networks, was to extend the MBBs information to obtain an overall clustering of all organisms, as it was done in the glycolysis test See . In thiscore of the glycolysis pathway and detect some essential MBBs that reveal the key reactions in the pathway as well as the core of the purine metabolism pathway. Finally, the application of this methodology to both pathways reproduces the tree of life, supporting the utility of this research.In this paper we introduce a new approach of metabolic networks modeling based on classical notions of graph theory, which applied to metabolic networks has been successful. Namely, we recover the notion of strongly connected components, that we call MBBs, to obtain a modularization of the network, called a m-DAG. One of the advantages of our methodology is to be general enough to perform for any database, although we run our tests using the data information in the KEGG database. We applied this methodology to two metabolic pathways to reinforce its utility. We considered the glycolysis (an energetic pathway) and the purine metabolism  present in the living beings. The glycolysis is a simple pathway which allows us to visualize the relation between the pathway and the m-DAGs, while the purine metabolism is big enough to test the convenience of the reduction defined in this work. Using the m-DAG formalism, not only a considerable reduction on the size of the metabolic network has been obtained, which facilitates the network comprehension, but also a new concept, the MBBs has been introduced. From the information captured by the m-DAG and its corresponding MBBs, we find the S1 TableA file containing the reactions and the amount of species in each kingdom that each MBB has in the glycolysis pathway.(CSV)Click here for additional data file.S2 TableSame data as in the (CSV)Click here for additional data file.S3 TableA file with the distances between every pair of MBBs in the glycolysis pathway based on the following distance measure: (CSV)Click here for additional data file.S4 TableA file with the distances between every pair of MBBs in the purine metabolism pathway based on the following distance measure: (ZIP)Click here for additional data file.S5 TableA file with the following information: for every glycolysis m-DAG, the reactions it has and its MBBs.(CSV)Click here for additional data file.S6 TableA file with the following information: for every purine metabolism m-DAG, the reactions it has and its MBBs.(ZIP)Click here for additional data file.S7 TableA file with the distances between every pair of m-DAGs, based on the similarity measure described in this paper.(ZIP)Click here for additional data file.S8 TableA file with the distances between every pair of m-DAGs, based on the similarity measure described in this paper.(ZIP)Click here for additional data file.S9 TableA file with the following information: for every m-DAG its amount of species in each kingdom.(CSV)Click here for additional data file.S10 TableA file with the following information: for every m-DAG its amount of species in each kingdom.(CSV)Click here for additional data file.S11 TableA worksheet combining the m-DAG related files for an easy and human readable access.(ZIP)Click here for additional data file.S12 TableA worksheet combining the MBB related files for an easy and human readable access.(ZIP)Click here for additional data file.S13 TableA worksheet combining the m-DAG related files for an easy and human readable access.(XLSX)Click here for additional data file.S14 TableA worksheet combining the MBB related files for an easy and human readable access.(XLSX)Click here for additional data file.S1 FigA dendrogram obtained with the hierarchical clustering of the MBBs using the distances in (PDF)Click here for additional data file.S2 FigA dendrogram obtained with the hierarchical clustering of the MBBs using the distances in (PDF)Click here for additional data file.S3 FigA file with the Animalia kingdom reference m-DAG for the glycolysis pathway.(PDF)Click here for additional data file.S4 FigA file with the Plantae kingdom reference m-DAG for the glycolysis pathway.(PDF)Click here for additional data file.S5 FigA file with the Fungi kingdom reference m-DAG for the glycolysis pathway.(PDF)Click here for additional data file.S6 FigA file with the Protista kingdom reference m-DAG for the glycolysis pathway.(PDF)Click here for additional data file.S7 FigA file with the Bacteria kingdom reference m-DAG for the glycolysis pathway.(PDF)Click here for additional data file.S8 FigA file with the Archaea kingdom reference m-DAG for the glycolysis pathway.(PDF)Click here for additional data file.S9 FigA file with the Animalia kingdom reference m-DAG for the purine metabolism pathway.(PDF)Click here for additional data file.S10 FigA file with the Plantae kingdom reference m-DAG for the purine metabolism pathway.(PDF)Click here for additional data file.S11 FigA file with the Fungi kingdom reference m-DAG for the purine metabolism pathway.(PDF)Click here for additional data file.S12 FigA file with the Protista kingdom reference m-DAG for the purine metabolism pathway.(PDF)Click here for additional data file.S13 FigA file with the Bacteria kingdom reference m-DAG for the purine metabolism pathway.(PDF)Click here for additional data file.S14 FigA file with the Archaea kingdom reference m-DAG for the purine metabolism pathway.(PDF)Click here for additional data file.S15 FigA dendrogram obtained with the hierarchical clustering of the m-DAGs for the glycolysis pathway using the distance defined in this paper.(PDF)Click here for additional data file.S16 FigA dendrogram obtained with the hierarchical clustering of the m-DAGs for the purine metabolism pathway using the distance defined in this paper.(PDF)Click here for additional data file.


We also generated metabolic network reconstructions for each metagenome using the Pathway Tools software, empowering researchers and clinicians interested in visualizing and interpreting metabolic pathways encoded by the human gut microbiome. For the first time, GutCyc provides consistent annotations and metabolic pathway predictions, making possible comparative community analyses between health and disease states in inflammatory bowel disease, Crohn’s disease, and type 2 diabetes. GutCyc data products are searchable online, or may be downloaded and explored locally using MetaPathways and Pathway Tools.Advances in high-throughput sequencing are reshaping how we perceive microbial communities inhabiting the human body, with implications for therapeutic interventions. Several large-scale datasets derived from hundreds of human microbiome samples sourced from multiple studies are now publicly available. However, idiosyncratic data processing methods between studies introduce systematic differences that confound comparative analyses. To overcome these challenges, we developed G Changes in microbiome structure and function have been implicated in numerous disease states including inflammatory bowel disease, cancer, and even cardiovascular disease3. Increasingly, researchers are using high-throughput sequencing approaches to study the genes and genomes of microbiomes and characterize diversity and metabolic potential in relation to health and disease states4, opening new opportunities for prevention and therapeutic intervention at the interface of microbial ecology, bioinformatics and medicine. The most densely colonized human habitat is the distal gut, inhabited by thousands of diverse microorganisms, as differentiated at the strain level. Despite providing essential ecosystem services, including nutritional provisioning, detoxification and immunological conditioning, the metabolic network driving matter and energy transformations by the distal gut microbiome remains largely unknown. Several large-scale metagenomic datasets (derived from hundreds of microbiome samples) from the Human Microbiome Project (HMP)5, Beijing Genomics Institute (BGI)6, and Metagenomes of the Human Intestinal Tract project (MetaHIT)7 are now available on-line, creating an opportunity for large-scale metabolic network comparisons.The myriad collections of microorganisms found on and in the human body are known as the human microbiome8, Metagenome Rapid Annotation using Subsystem Technology (MG-RAST)9, SmashCommunity10 and HUMAnN11. Differing pipelines used to process sequence information between studies introduces biases based on idiosyncratic formatting, and alternative annotations or algorithmic methods. Specifically, support for metabolic pathway annotation varies significantly among pipelines due to differences in reference database selection with resulting impact on metabolic network comparisons. The most common metabolism reference database currently in use is Kyoto Encyclopedia of Genes and Genomes (KEGG)12. Although extant pipelines often provide links to KEGG module and pathways maps12 (using KEGG ontology (KO) or pathway identifiers) that can be visualized with coverage or gene count information using programs like KEGG Atlas13, they do so using often incompatible formats. Such mapping is limited because there is no simple way to query, manipulate, or visualize the underlying implicit metabolic model directly. Moreover, prediction using KEGG results in amalgamated pathways with limited taxonomic resolution, impeding enrichment and association studies11.While the studies cited above provide the sequencing data, they do not provide the software environment used for generating their annotations. In contrast to these proprietary pipelines, over the past few years a number of metagenomic annotation pipelines available to third parties have emerged including IMG/M14 called MetaPathways, that guides construction of environmental Pathway Genome Databases (ePGDBs) from environmental sequence information15 using Pathway Tools16 and MetaCyc17–19. Pathway Tools is a production-quality software environment developed at SRI International that supports metabolic inference and flux balance analysis based on the MetaCyc database of metabolic pathways and enzymes representing all domains of life. Unlike KEGG, MetaCyc emphasizes smaller, evolutionarily conserved or co-regulated units of metabolism and contains the largest collection  of experimentally validated metabolic pathways20. Navigable and extensively commented pathway descriptions, literature citations, and enzyme properties combined within an ePGDB provide a coherent structure for exploring and interpreting predicted metabolic networks from the human microbiome across multiple levels of biological information . Over 9,800 PGDBs have been developed by researchers around the world, and thus ePGDBs represent a data format for metabolic reconstructions that exhibit a potential for reusability in further studies.In responding to the deficiencies of existing tools, we recently developed a modular annotation and analysis pipeline enabling reproducible researchutCyc, a compendium of over 418 ePGDBs constructed from public shotgun metagenome datasets generated by the HMP5, the MetaHIT inflammatory bowel disease study7, and the BGI diabetes study6. Relevant pipeline modules are summarized in utCyc provides consistent taxonomic and functional annotations, facilitates large-scale and reproducible comparisons between ePGDBs, and directly links into robust software and database resources for exploring and interpreting metabolic networks. This metabolic network reconstruction provides a multidimensional view of the microbiome that invites discovery and collaboration21.Here we present Gn=148)5, a MetaHIT 22, and a BGI  study6. See We collected 418 assembled human gut shotgun metagenomes from public repositories and etaPathways. ePGDBs were created for each sample by running the MetaPathways 2.5 pipeline and the Pathway Tools version 17.5, using the assembled metagenomes described above. The pipeline consists of five modular steps, including (1) quality control and ORF prediction, (2) homology-based functional and taxonomic annotation, (3) analyses consisting of tRNA and lowest common ancestor (LCA)23 identification, (4) construction of ePGDBs using Pathway Tools and, finally, (5) pathway export25 , COG32 (downloaded 2013-12-27), MetaCyc19 (downloaded 2011-07-03), RefSeq33 (downloaded 2014-01-18), and SEED34 (downloaded 2014-01-30). Protein sequence similarity searches were performed using the program FAST35 with standard alignment result cutoffs  greater than 0.4 . The ch39 (version 115) and GreengeneS40 (downloaded 2012-11-06) ribosomal RNA (rRNA) gene databases using BLAST version 2.2.25, with the same post-alignment thresholds applied as was previously described for the functional annotation. BLAST was applied for rRNA gene annotation because it has greater sensitivity for nucleotide-nucleotide searches than FAST, and the smaller reference database sizes make the relatively larger computational requirement justifiable.Quality-controlled contigs were also searched against the SILVA23 for taxonomic binning. In brief, the LCA in the NCBI Taxonomy Database33 was selected based on the previously calculated FAST hits from the RefSeq database. This effectively sums the number of FAST hits at the lowest shared position of the NCBI Taxonomy Database. The RefSeq taxonomic names often contain multiple synonyms or alternative spellings. Therefore, names that conform to the NCBI Taxonomy Database were selected in preference over unknown synonyms.Additionally, predicted ORFs were taxonomically annotated using the LCA algorithmetaPathways uses tRNAscan-SE version 1.4  an annotation file containing gene product information (0.pf), (2) a catalog of contigs and scaffolds (genetic-elements.dat), and (3) a PGDB parameters file (organism-params.dat). The PathoLogic module43 in the Pathway Tools software, was used to build the ePGDB and predict the presence of metabolic pathways based on functional annotations. Following ePGDB construction, the base pathways  were extracted from ePGDBs to generate a summary table of predicted metabolic pathways for each sample.Functional annotations were parsed and separated into three files that serve as inputs to PetaPathways 2.5 generates data in a consistent file and directory structure. The output for each sample is contained within a single directory, which in turn is organized into sub-directories containing relevant files  enables interactive exploration, visualization, and searches of individual sample results along with comparative queries of multiple samples, via a custom knowledge engine data structure. Input and output files are available for download from the GutCyc website (www.gutcyc.org) and may be readily explored in the MetaPathways GUI or Pathway Tools on Linux, Mac OS X and Windows machines.Miles see . The Metugaboo cluster part of Compute Canada's national platform of Advanced Research Computing resources. https://www.westgrid.ca/support/systems/bugaboo. The Hallam lab computers have a configuration profile of 2×2.4 GHz Quad-Core Intel Xeon processors with 64 GB 1,066 MHz DDR3 RAM. The Bugaboo cluster provides 4,584 cores with 2 GB of RAM per core on average. The average sample took 7–8 h to process on a single thread, and the span of the processing required to generate the GutCyc Collection was 135 days.Computational processing was performed using a local cluster of machines in the Hallam laboratory and on WestGrid's BetaPathways 2.5, including integrated third party software, is available on GitHub , licensed under the GNU General Public License, version 3), along with a companion tutorial  released under the Creative Commons Attribution License . Pathway Tools is available under a free license for academic use, and may be commercially licensed (www.biocyc.org/download-bundle.shtml). MetaPathways outputs were processed using Pathway Tools version 17.5 under default settings except for disabling of the PathoLogic taxonomic pruning step  as was described previously31, and an additional refinement step of running the Transport Inference Parser44 to predict transport reactions . FAST is freely available under the GNU General Public License, version 3 on our GitHub page .MutCyc project website (www.gutcyc.org), and at Figshare as described in (Data Citation 1). Each sample’s data records are contained within a single directory. Within this directory, sub-directories and files are located as depicted in utCyc Collection is presented in A list of each sample, its provenance, location and relevant data processing steps can be found in <sample_ID>, this directory contains two files: (1) <sample_ID>.fasta, which contains the renamed, quality-controlled sequences, and (2) <sample_ID>.mapping.txt, which maps the original sequence names to the new names assigned by MetaPathways. Sequences are renamed to <sample_ID>_X where X is the zero-indexed contig number pertaining to the order in which the contig appears in the input file .For a sample with an identifier of <sample_ID>.fna which contains nucleic acid sequences of all predicted ORFs, (2) <sample_ID>.faa which contains amino acid sequences of all predicted ORFs, (3) <sample_ID>.qced.faa which contains amino acid sequences of all predicted ORFs meeting user defined quality thresholds , and (4) <sample_ID>.gff, a general feature format (GFF) file45 containing all quality-controlled sequences and information about the strand (− or +) on which the ORF was predicted. ORFs are named <sample_ID>_X_Y, where X is the contig number pertaining to the order in which the contig appears and Y represents the order in which the ORFs were predicted on the contig.This directory contains four files, (1) annotation_table, (2) rRNA, (3) tRNA, and (4) pgdb. The annotation_table sub-directory contains (1) statistics files for each functional database used to annotate the ORFs (<sample_ID>.<DB>_stats_<index>.txt), (2) <sample_ID>.functional_and_taxonomic_table.txt detailing the length, location, strand and annotation  of each ORF, and (3) a file listing all ORFs and their functional annotations (<sample_ID>.ORF_annotation_table.txt). The prokaryotic small subunit ribosomal RNA (SSU or 16S rRNA) gene is a standard marker gene used for measuring taxonomic diversity46. The rRNA sub-directory contains files detailing statistics for each taxonomic database used to annotate the ORFs (named as <sample_ID>.<DB>.rRNA.stats.txt). The tRNA sub-directory contains (1) <sample_ID>.trna.stats.txt, detailing the type, anticodon, location and strand of each predicted tRNA and (2) <sample_ID>.tRNA.fasta containing all predicted tRNA sequences. The pgdb sub-directory contains a <sample_ID>.pwy.txt file describing metabolic pathways predicted in the ePGDB, specifically, each predicted pathway, the ORF identities involved in each pathway, the enzyme abundance, and the pathway coverage in a tabular format navigable via the MetaPathways GUI.This directory contains four sub-directories: (1) <sample_ID>.annotated.gff, a GFF file containing all quality-controlled sequences with their annotations.This directory contains a file named athway Tools: (1) genetic-elements.dat, (2) organism-params.dat, and (3) 0.pf which contains all functional annotations to be processed by Pathway Tools. A sub-directory called flat-files contains flat files describing database objects such as compounds, reactions, pathways (each of which is described in more detail in47) for individual ePGDBs.This directory contains the three files necessary to build a ePGDB using P<sample_ID>.run.stats, the parameters used to process the sample; (2) <sample_ID>.nuc.stats, the number and length of nucleic acid sequences before and after quality control filtering; and (3) <sample_ID>.amino.stats, the number and length of amino acid sequences before and after quality control filtering.This directory contains three files: (1) utCyc was derived from third-party sequence data from three publicly-available human gut microbiome sampling projects with metagenomic assemblies, with published details on their own technical validation steps: the HMP5, a MetaHIT study22, and a BGI study6. The technical validation of third-party software used in MetaPathways may be found in the corresponding publications for MetaProdigal27, BLAST48, and tRNAscan-SE41. GutCyc functional sequence similarity was computed using FAST, an aligner based on a version of LAST49, with multi-threading performance improvements and new support for generating BLAST-like E-values, with significant correlation with BLAST output  outputs of BLAST and LAST: R2=0.887, P<0.01)24. The protocols undertaken in the MetaCyc project for the ongoing manual curation of new metabolic pathways, and its subsequent implications for accurate pathway prediciton, may be found in the following MetaCyc publications50.GetaPathways pipeline may be found in previously published reports51 with specific emphasis on how changes in taxonomic pruning, read length and metagenomic assembly coverage impact the accuracy and sensitivity of pathway recovery. In brief, pathway prediction is affected by taxonomic distance, sequence coverage and sample diversity, nearing an asymptote of maximum accuracy for metagenomes with increasing coverage. Additionally, like any alignment-based analysis, annotation quality is a function of both the level of errors in the input sequence data and the selection of reference databases. Summary data generated for each ePGDB as presented in athway Tools PathoLogic module52, which has an accuracy of 91% as evaluated using organism pathway databases with high levels of manual curation43). The performance of the Pathway Tools PathoLogic module has also been evaluated using datasets with different complexity and coding potential, including simulated metagenomes, a symbiotic system, and the Hawaii Ocean Time-series31. The authors provide detailed information about the effects of read length, coverage and sample diversity on pathway recovery but found that performance specificity was high (>85%) using all three datasets. The authors also provide a list of `prediction hazards’ such as the identification of dissimilatory nitrate reduction pathways of which the user should be aware and conclude that despite being imperfect, Pathway Tools provides a powerful means with which to predict metabolic interactions31.Validation of the overall MutCyc Collection has been crafted into a format that is both comprehensible to domain experts, and interpretable by machines, there are myriads of uses that can be explored. For example, comparing ePGDBs with sets of microbial PGDBs from the same environment can aid in identifying ‘distributed pathways’ present in the metagenome metabolic reconstruction, but absent from any individual genomic metabolic reconstruction31. Annotations from each of the protein reference databases can also be explored individually using MetaPathways. In addition, a file for each sample, located at sample/results/annotation_table/sample.2.txt provides a detailed overview of the annotation for each ORF in each database as well as score reflecting the confidence of the alignment and annotation. The predicted transport proteins can be used to predict trophism patterns within a community. Furthermore, the Pathway Tools software allows for sophisticated comparative analyses between ePGDBs, at the level of compounds, reactions, enzymes, and pathways53. The MetaFlux54 module of Pathway Tools for performing flux balance analysis (FBA)55 can be used with GutCyc ePGDBs to generate quantitative simulations of microbiome growth and pathway flux. A set of microbiome metabolic models also facilitates the exploration of the impact of xenobiotics56, and provides a computational substrate for systems biology approaches to engineering the gut microbiome57. etaPathways and Pathway Tools, along with example data analysis use cases.Once a set of data such as GutCyc. In the first case, we demonstrate how to use a GutCyc ePGDB to determine the metabolic path between two small molecules of interest. In the second case, we use GutCyc to visualize different levels of biological information, e.g., metabolomics data, in the context of a microbiome metabolic network.In this section we motivate further two specific use cases for Gathway Tools software provides advanced biochemical querying capabilities for both PGDBs and ePGDBs. One such capability is energy-optimal metabolite tracing. To summarize, given both a starting and a terminal/target compound within an ePGDB, Pathway Tools is able to compute the shortest and most energetically-favorable route through the metabolic reaction network. While there is no guarantee that, in a complex milieu such as the gut microbiome, the syntrophic flux will necessarily follow a short and minimal energy path, these criteria allow us to narrow down a multiplicity of possible paths to a single parsimonious candidate path.The Pet al.58, they demonstrated a causal connection between the intestinal gut microbiota’s metabolism of red meat and the promotion of atherosclerosis. In brief, the gut microbiome is capable of transforming excess L-carnitine into trimethylamine (TMA), which is further processed by the liver to form the cardiovascular disease-associated metabolite trimethylamine N-oxide (TMAO). Using this biotransformation as a motivating case, we queried an arbitrarily selected ePGDB from the GutCyc Collection, SRS015217Cyc, for the biochemical reaction path from L-carnitine to TMA, which is not provided explicitly by Koeth et al.58 Utilizing the Pathway Tools Metabolic Route Search feature, we found an optimal path between L-carnitine to TMA for this ePGDB, using the MetaCyccarnitine degradation II pathway  along with a betaine reductase reaction , minimizing the number of enzymes involved and chemical bond rearrangements. Pathway Tools found the optimal path in seconds.In a study by Koeth L-carnitine and glycine betaine have known transporter families that facilitate their movement across the cell membrane59, as do TMA and TMAO60, and thus the metabolic route in this ePGDB may be a distributed pathway31. This demonstrates the power of ePGDBs in computing connections between nutritional or pharmaceutical inputs (such as L-carnitine) to identify potential interactions with known disease biomarkers (as TMAO is to cardiovascular disease).The metabolic route identified may also help generate mechanistic hypotheses from microbiome study observations. athway Tools is to visualize the results of high-throughput experiments mapped onto the Cellular, Genome, and Regulation Overviews, or as ‘Omics Pop-Ups’ when viewing a particular pathway61. Specifically, Pathway Tools provides support for the analysis of mass spectrometry data, by automatically mapping a list of monoisotopic masses to matching entries in MetaCyc, or in specific ePGDBs62. As a demonstration of this capability, we analyzed mass-spectrometry data from a metabolomic study of humanized mice microbiomes63. The dataset contained 867 unique masses, of which 453 masses were identified using MetaCyc by performing standard adduct monoisotopic mass manipulations64, followed by monoisotopic mass search using Pathway Tools. We mapped the identified compounds on the Cellular Overview65 of an arbitrarily-selected ePGDB from the GutCyc Collection for illustrative purposes, as seen in athway Tools62, we identified the pathway class of ‘Secondary Metabolites Degradation’ as enriched for identified compounds . By visually inspecting the pathways in the class, we can see that pathway P562-PWY, ‘myo-, chiro-, and scillo-inositol degradation pathway’, has four matched compounds from the metabolomics dataset.Another capability of PHow to cite this article: Hahn, A. S. et al. A geographically-diverse collection of 418 human gut microbiome pathway genome databases. Sci. Data 4:170035 doi: 10.1038/sdata.2017.35 (2017).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


New genomic and transcriptomic information shows that multi-level regulation of gene expression is required for the pathway to function, yet we know it to be one of the most dynamic examples of convergent evolution. Now, a focus on the molecular transition from C3–C4 intermediates, together with improved mathematical models, experimental tools and transformation systems, holds great promise for improving C4 photosynthesis in crops.It is now over half a century since the biochemical characterization of the C4 photosynthetic pathway by Hal Hatch and Roger Slack  and PEP carboxylase in the mesophyll cells. It is the gas-tight nature of the bundle sheath that allows the decarboxylation of C4 acids in this compartment to elevate CO2 partial pressure around Rubisco. This inhibits its oxygenase activity allowing it to operate close to its maximal rate.The C4 photosynthesis is recognized as one of the most dynamic examples of convergent evolution, arising multiple times over the last 60 million years in warm semi-arid regions, with early occurrences coinciding with low atmospheric CO2 in the late Oligocene  hold great promise for improving C4 photosynthesis in a crop environment.The C4 leaves, and leaves of closely related C3 plants, which is catalysing a new generation of research into the C4 mechanism and the genetic architecture underpinning it. A transcriptomics/genomics approach and a review of gene expression across multiple lineages of C4 plants  for all types of C3–C4 intermediates, but advantages related to improved water and nitrogen use efficiency are only expressed in intermediate plants possessing a degree of C4 acid fixation  report on phylogenetic searches for C3–C4 species. 3–C4 Cryptopetala clade and a diverse C4 Pilosa clade, while Schüßler et al. (2016) resolve the C3–C4 intermediate and C4 lineages in the Salsoleae family (Chenopodiaceae). 3 to the C4 state in the leaves of Salsola soda (Chenopodiaceae). 3 and C4Flaveria species to identify transcription factors. Moricandia species (Brassicaceae) from C3–C4 into the C4 pathway. Regardless of phylogenetic constraints, 3–C4 pathway brings intermediate species into C4-like environments facilitating C4 evolution.In this issue, both 4 photosynthesis made use of new physiological techniques such as gas exchange measurements. This led to the development of distinguishing gas exchange features of C4 CO2 assimilation rates. We now have a good understanding of how C4 photosynthesis responds to environmental variables such as light, temperature and CO2  and C18OO discrimination allows quantification of CO2 diffusion from intercellular airspace to the mesophyll cytosol in relation to carbonic anhydrase activity there. Recent technical advances have greatly facilitated the measurements of isotope discrimination concurrently with gas exchange (4 grass with short-term variation in atmospheric CO2 concentration. Setaria viridis plants with reduced carbonic anhydrase activity and used measurements of C18OO discrimination to show that carbonic anhydrase and mesophyll conductance are both limiting factors affecting CO2 assimilation rates at low CO2 partial pressures.Most C14C pulse chase which were used by Hatch and Slack to unravel the mysteries of C4 photosynthesis have been replaced by mass spectrometric measurements of 13CO2 labelling kinetics, which provide a wealth of information compared to past experiments. This technique was used for the first time by 4 metabolism in C3 species.The techniques of the 4 crops are grown in dense stands where most leaves are shaded compared to their wild progenitors. 4 crops lose photosynthetic efficiency in low light as they become shaded by new leaves, costing the crop up to 10% of its yield potential. The ancestors of maize and miscanthus appear to have existed in very open habitats, where water and nutrient deficiencies would have limited leaf area. There may therefore have been little evolutionary pressure for maintenance of photosynthetic efficiency in shade conditions. Improving C4 photosynthesis in a crop environment may be an important next step for increasing genetic yield potential in some of these most important crops (Today, major Cnt crops .4 research go next? In 50 years we have seen the expansion of the field from the examination of a rudimentary biochemical pathway in just a few species to the construction of complex evolutionary models and assembly of massive genomic and transcriptomic data sets from a large range of both crop and wild C4 species, as well as multiple efforts to engineer C4 traits into C3 crops and model species. As gene and transcript sequencing costs plummet with third-generation technologies, what will be the new technological driver of C4 research?Where will C4 system for ‘grind and find’ extraction of metabolites, transcripts and proteins has been a challenge. Recently, however, high resolution MALDI imaging mass spectrometry was used to examine the lipid composition of thylakoids of mesophyll and bundle sheath cells of maize (From a biochemical and modelling perspective, the confounding nature of the two-compartment C3 dicots, this is done in model systems like Arabidopsis or tobacco by gene inactivation or overexpression, but only recently have grass transformation systems become sufficiently routine for researchers to approach these experiments in their laboratories. Alternatives such as producing large panels of mutants by non-targeted mutagenic approaches or by crossing genetic material to develop near-isogenic lines with and without genetic polymorphisms is outside the scope of most small research laboratories. Future development of new and more efficient transformation systems for a range of C4 plants and the development of genetic stocks which can be ordered routinely for knockout lines and backcrossed mutants, sequenced populations and recombinant in-bred lines would see a rapid development in C4 research similar to that seen when Arabidopsis genetic resources became widely available.Gene discovery through genomics approaches reveals gene candidates and evidence for the importance of certain genes in evolution or for plant performance, but these must be experimentally validated. Commonly, for C4 engineering, synthetic biology has the potential to impact hugely on both basic and basic/strategic engineering approaches (4 rice project, the ability to make multiple gene constructs simplifies cloning strategies (4 grass transformation systems, production of allele mimics of potentially important genes occurring in nature and engineering of novel enzyme properties in C4 plants would advance rapidly.In the field of C4 anatomical and biochemical data together to underpin the knowledge of the C4 mechanism we have today.There is a vast array of information and new technology now at our fingertips. Nevertheless, we must still marvel at the achievements of researchers 50 years ago in assembling a completely new photosynthetic pathway from a collection of radiolabelling experiments and enzyme assays, and the rapidity with which these researchers brought C


Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases. Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide and has a strong genetic basis e.g., pathways or biological processes, associated with clinical phenotypes However, all these studies did not take into account the underlying genetic interplays or functional modules involved in CAD. Therefore, the genetic basis of CAD has remained largely unknown due to the limited understanding of just a small proportion of individual genetic variations. There is a developing consensus that genetic variations of CAD often function by sophisticated interactions through a modular fashion rather than individually e.g., KEGG pathways), or gene lists related to specific functions. Based on the differences in the theoretical models used, the pathway-based methods can be classified into three categories, i.e., functional enrichment analysis In general, the gene sets in GSA can be generated according to the manually-curated pathways . Finally, we identified the risk functional modules (subnetworks) for CAD by decomposing the built genetic network.i.e., pathway-based) contribute significantly to CAD risk. As a result, totally 6 pathways were identified to be significantly associated with CAD , non-alcoholic fatty liver disease (NAFLD), and Huntington’s disease.The raw WTCCC genotyping data for CAD contained 482,247 SNPs from 5000 individuals (3000 controls and 2000 CAD patients). After data processing, 101,822 SNPs from 4864 individuals (2938 controls and 1926 CAD patients) were retained for further analysis. We defined the SNPs annotated to the genes of the same KEGG pathway as a SNP set. Hence, 276 pathway-based SNP sets were generated in total. These SNP sets were tested by logistic kernel machine regression model to evaluate whether they jointly  were identified. We then mapped the involved SNPs onto genes. By integrating prior PPI knowledge, we constructed genetic networks using 121 unique genes and 149 gene−gene pairs. As shown in Epistasis analysis of all SNP−SNP pairs within or across the identified pathways was performed. Totally 186,640 SNP–SNP significant interactions (α = 3.023 (P = 0.884). Among all the genes, two genes, PIK3R1 and APP, showed the highest connectivity, which are connected with 11 (Bonferroni-adjusted P = 0.0041) and 12 other genes (Bonferroni-adjusted P = 0.00088), respectively. These two genes were thus defined as the hub genes.We focused on the largest sub-network for the following topological analysis. The connection degree distribution of the largest sub-network  indicatePIK3R1 encodes the regulatory subunit α of phosphatidylinositol 3-kinase (PIK3), which orchestrates a series of cell function regulation, such as cell proliferation, cell differentiation, apoptosis, and glucose transport 2+ entry APP, which encodes β-amyloid precursor protein, is generally recognized to be closely related to AD APP can lead to dysfunction of endothelial cells due to cytotoxicity and damage induced by long-term exposure to Aβ peptide PIK3R1 and APP on the development of CAD.P values ranging 0.8–1.0, except the smallest one which only contained 4 genes (α) of power law distribution, KS testing statistics, and some other topological properties for each module are also shown in To identify the most compact functional subnetworks, we further decomposed the largest network into smaller modular units. We obtained totally 10 modules, which consist of 4–14 genes . The cor 4 genes . The estTo understand the function involvements of each module, we performed a gene ontology (GO)-based enrichment analysis using the database for annotation, visualization and integrated discovery (DAVID). Only modules containing more than 10 genes were analyzed and the significantly-enriched GO terms for each module are listed in β, PLC-γ, and PLC-δ. These isozymes possess conserved and specific domains, and therefore PLC activation may be induced in various ways γ is mostly activated through protein tyrosine kinases (PTKs). As a result, activated PLC-γ would induce a series of signal transduction, which may lead to trans-activation of epidermal growth factor (EGF), a key element in inhibition of vascular wall deposition and thrombogenesis e.g., caused by injury, promote monocyte adhesion. During the migration and transformation to macrophages, monocyte may stimulate lymphocytes to produce multiple cytokines, thus promoting proliferation of smooth muscle and formation of fibrous plaque eventually Among all these enriched GO terms, phospholipase C activity (GO: 0004629) and cell adhesion molecule binding (GO: 0050839) took special attention. Phospholipase C (PLC), which is distributed widely in various tissues, is a key enzyme in phosphatidylinositol signaling pathway e.g., non-alcoholic fatty liver disease, Huntington’s disease, and AD, may share molecular mechanisms with CAD. Furthermore, 2 hub genes, PIK3R1 and APP, and 6 risk functional modules for CAD have been identified. Our findings are different from Liu’s study Studies of genetic variants in various molecular biological processes will greatly promote our understanding of CAD and its pathogenesis. In the present study, a pathway-based approach is applied to identify the risk pathways and functional modules for CAD. This study demonstrated that multiple pathways may be involved in the underlying molecular processes for CAD, such as cardiac muscle contraction, glycerolipid metabolism, and glycosaminoglycan biosynthesis. Meanwhile, this study reveals that some other diseases, Genetic studies are traditionally based on single gene analysis, which poses tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis allows a comprehensive understanding of the molecular mechanisms underlying complex diseases by considering the joint effect and integrality as function unit of multiple genes. Extensive studies utilizing pathway-based analysis have significantly advanced our capacity to explore large-scale omics data that have been rapidly accumulating in biomedical fields Pathway-based approach has some advantages over the conventional GWAS approach. The singe-locus analysis widely used in GWAS is only capable of capturing a small portion of susceptible SNPs with prominent marginal effects, leaving the important genetic component, such as epistasis or joint effects between multiple genes, undetected. Identifying the complex interplays among multiple genes in the genome-wide context is an essential task to systematically unravel the molecular mechanisms underlying complex diseases. In this study, we employed the newly-developed logistic kernel machine regression model in the pathway-level analysis to capture the joint effects of multiple genes involved in the pathways. In this way, we are not only able to avoid the curse of ‘high dimensionality and small sample size’ associated with analysis of GWAS data, but also able to estimate the missing genetic components and epistasis, thus helping elucidate the sophisticated molecular interplays between or across the risk pathways for CAD.P value of 0.05 to identify the putative epistatic gene pairs, which could lead to an inflated type I error. Correction for multiple tests is a very complicated and challenging issue for the analysis of large-scale GWAS data, especially for epistatic analysis because of correlation of gene pairs or correlations stemming from LD. To address this concern, we applied an additional criterion of experimentally-confirmed PPI support for gene pairs, which might alleviate the issue of the inflated type I errors to some extent. Finally, this study only integrated GWAS data with PPI data for genetic network analysis. Integrating more omics data such as epigenetic or epidemiological data would help illustrate the genetic, epigenetic, and environmental factors for CAD, which is the focus in our future studies.There are also limitations in this study. First, the whole pathogenic process of CAD may involve a long cascade of multiple biological pathways. Ideally, the accumulative effects of these risk pathways on CAD should be examined, which is practically difficult to achieve because of the unpredictability of joint action among different pathways and the possibility of over-fitting. Second, we replaced missing genotypes with the most frequent allele. Such simplification for data imputation could lead to expansion of major alleles, resulting in a decreased minor allele frequency (MAF). On the other hand, genotype imputation is a complex process in GWAS research, which can address the failures occurring during genotyping assay to some extent WTCCC genotyping data of 482,247 SNPs from 2000 CAD subjects and 3000 health control subjects P > 1 × 10−4) in the control group. Second, for loci with missing values after the filtering above, missing genotypes were replaced with the most frequent one. Third, to remove data redundancy due to LD, only tag SNPs that were representative in the corresponding genomic regions were utilized for the current analysis. For identification of these tag SNPs, each individual chromosome was scanned using the ‘moving window’ method in which the window size was set to 50 SNPs with step length of 5 SNPs. The cutoff of LD r2 was set as 0.8. Finally, SNPs were considered as mapped onto genes if these SNPs are situated in the flanking regions spanning from 5 kb upstream to 5 kb downstream of the genes, as described previously http://www.r-project.org/).To improve the data quality, several data preprocessing procedures were performed. First, SNPs were excluded if they did not meet all the following criteria: (1) genotype missing rate < 5%, (2) individual missing rate < 5%, and (3) MAF > 0.01. Furthermore, all the included loci must meet Hardy–Weinberg equilibrium (HWE) proportions . This model can be described as follows:p SNPs contained in the pathway, which is often defined as a positive, semi-definite kernel function K can be viewed as a function that measures the similarity between two individuals based on the genotypes of the SNPs in the SNP set. There are three options for K: the linear, Gaussian, and identical-by-state kernels. The null hypothesis for testing a pathway is that its overall effect is zero, i.e., H0: Q statistics that follows a P × N < 0.05 . More details about the methods used are described previously Logistic kernel machine regression model was applied to identify the susceptible pathways related to CAD. Suppose that a pathway contains P < 0.05 were retained and then translated into putative gene–gene interactions. However, the gene pairs that were finally used to construct the gene network must have support from the HPRD PPI knowledge database. After the gene network was built, its topological properties  were examined. Network hub genes were identified by testing whether the connectivity of a certain gene node was equal to or greater than that expected based on a Poisson distribution To further elucidate the underlying interplays between multiple genetic variants within a pathway or across different pathways, an expanded genetic network was constructed by integrating statistical gene−gene interactions involved in the identified susceptible pathways with their PPI knowledge. First, a pairwise epistatic analysis was performed using PLINK for the SNPs that were annotated to these susceptible pathways. To avoid possible loss of some meaningful interactions, all SNP pairs with To characterize the functional involvement of the putative modules for CAD, GO analysis was performed for each module using DAVID SQR conceived the project and wrote the manuscript. XZ and YZL performed the analysis and wrote the manuscript. XZ, YDC, JQ, LJ, YT, ML, NZ, YL participated in writing the computing codes and analyzing the public datasets. All authors read and approved the final manuscript.The authors declare no competing interests.


Chemotherapeutic agents cause many short and long term toxic side effects to peripheral nervous system (PNS) that drastically alter quality of life. Chemotherapy-induced peripheral neuropathy (CIPN) is a common and enduring disorder caused by several anti-neoplastic agents. CIPN typically presents with neuropathic pain, numbness of distal extremities, and/or oversensitivity to thermal or mechanical stimuli. This adverse side effect often requires a reduction in chemotherapy dosage or even discontinuation of treatment. Currently there are no effective treatment options for CIPN. While the underlying mechanisms for CIPN are not understood, current data identify a “dying back” axon degeneration of distal nerve endings as the major pathology in this disorder. Therefore, mechanistic understanding of axon degeneration will provide insights into the pathway and molecular players responsible for CIPN. Here, we review recent findings that expand our understanding of the pathogenesis of CIPN and discuss pathways that may be shared with the axonal degeneration that occurs during developmental axon pruning and during injury-induced Wallerian degeneration. These mechanistic insights provide new avenues for development of therapies to prevent or treat CIPN. Cancer therapies result in multiple toxic side effects that limit the doses used and cause long-lasting damage to patients. Chemotherapy-induced peripheral neuropathy (CIPN) is a severe and long lasting side effect caused by diverse anticancer agents that damage sensory and/or motor nerves. Symptoms of CIPN include numbness, pain, burning, tingling, heat/cold hyperalgesia, and mechanical allodynia, as well as reduced motor function. CIPN commonly presents with a “Glove-and-Stocking” distribution with the most distal portions of the limb exhibiting the greatest deficits  scale. These evaluation methods suffer from subjectivity and inconsistency  exert their antineoplastic activity by forming interstrand DNA adduct  through intraperitoneal or intravenous injection, followed by behavioral, electrophysiological, or morphological analysis. These models enable a rigorous assessment of the neuropathological features consistently observed following chemotherapeutic treatment. A recent review  whereby chemotherapies cause CIPN are not yet understood, nor is it clear whether distinct agents converge on a shared pathway to induce symptoms. While effects of chemotherapeutic agents on neurons, glial cells, and skin cells have each been suggested to initiate the neurological symptoms, studies in patients and in animal models implicate axonal degeneration as a common process in CIPN pathology. Specifically, chemotherapeutic agents either directly or indirectly trigger a “dying back” axon degeneration that proceeds in a distal-to-proximal manner. Potential mechanisms for initiating axon degeneration include defects in axon transport, altered mitochondrial function, or altered Cas Figure .in vitro vesicle motility assay and discovered that vincristine and paclitaxel inhibit anterograde axonal transport in axoplasm isolated from squid giant axons, and that vincristine inhibits retrograde transport as well  both function as intracellular stores of Ca2+, and paclitaxel treatment alters Ca2+ homeostasis, potentially by inducing Ca2+ release from the mitochondria  in the ER  may also contribute to the disorder. Central sensitization may occur as a direct or indirect consequence of chemotherapeutic agents. Low concentrations of chemotherapeutic drugs, oxaliplatin, and paclitaxel, can be detected in the CNS after systemic dosing that produce hyperalgesia in rats  γ neurons provide another system where stereotyped axon pruning occurs during development. Pruning of dorsal and medial axonal branches during metamorphosis requires signaling through a TGF-β receptor complex in MB γ neurons, and is initiated by myoglianin secreted from nearby glial cells  is initiated by semaphorin3F expressed in interneurons in the stratum oriens, and is achieved by signaling complexes containing neuropilin-2 and plexin-A3 , brain-derived neurotrophic factor (BDNF) and neurotrophins 3 and 4 (NT3 and NT4), which are released in a limited amount by target tissues and promote axon and cell body survival  is a retrograde response gene, and newly transcribed Bclw mRNA is transported to the axons, where it is locally translated and the resultant protein promotes axonal survival , which delays the process of WD. This mutation encodes a chimeric fusion protein of the N-terminal fragment of E4 ubiquitin ligase Ube4b and the enzyme nicotinamide mononucleotide adenylyltransferase 1  from nicotinamide mononucleotide  have identified gene variants associated with increased vulnerability to CIPN. Single nucleotide polymorphisms of FGD4 and EPHA5 were significantly associated with susceptibility to paclitaxel-induced peripheral neuropathy in both European and African ancestral groups (Baldwin et al., 2+ homeostasis. While each agent may initially affect the nervous system in a distinctive manner, the pathologies observed in CIPN converge to cause an axon degenerative process. Mechanistic studies of the phenotypically similar axon degeneration processes during developmental axon pruning or during injury-induced WD are likely to shed new light on the molecular basis of CIPN. Indeed, several signaling molecules on the SARM1, DLK/MAPK, and NMNAT pathways that protect axons from WD can also impact CIPN. Concurrently, human GWAS studies have identified pathways implicated in neurite outgrowth and/or axon maintenance that may be relevant for axon degeneration in CIPN. These findings suggest that axon degeneration is central to CIPN pathology. Enhanced understanding of the pathologic process of axon degeneration will be essential for developing an arsenal of effective therapies of CIPN.As summarized in this review, multiple chemotherapeutic agents that cause CIPN are directed against different molecular targets. Preclinical studies have generated several hypotheses to explain the pathogenesis of CIPN by these agents, including defective axon transport, mitotoxicity, and altered CaAll authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene–gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated. For instance, the rs11591147 SNP which maps to exon 12 of mericans . In the mericans  there ismericans , in whic as SIFT , Polyphe as SIFT , or CADD as SIFT  can be uLastly, this type of pathway analysis visualization is applicable for SNPs data originating from different types of studies , but it can also support the interpretation of specific phenomena such as epistasis or gene–gene interaction. Epistasis is yet another manner in which connections within a pathway are different in different individuals, where two alleles mapping to different loci associate in concert with a phenotype, but where those two alleles individually show no phenotype association . ConsideHowever, genetic variants currently cannot be combined easily in pathway representations because it is not clear how to visualize and interpret variation data once connected programmatically to pathway content. In this review, we sought to investigate how to dynamically visualize genetic variations in a pathway context using user-friendly tool. First, we performed a systematic review of articles that analyzed genetic variants using pathway based methods in order to identify and describe the visualization options of the tools resulting from this literature review. The purpose of the tool evaluation relates directly to the need to combine SNP data, such as those from GWAS results, with biological context in order to better understand results in a disease context. Second, we performed a use case with the tools identified, testing a computationally derived epistatic dataset of gene–gene interactions for 12 candidate genes in obesity risk, in order to evaluate how genetic variant analysis of epistasis is tackled by the tools. Taking a visualization point of view, we report the features and the potential of the different software. Reviewing the articles, we also collected current statistical methodologies that have been applied in pathway-based analysis of GWAS data, and we report those without discussing in detail.This review follows criteria developed by the PRISMA statement .Table 1 together with the other four tools previously identified. Details of the 65 relevant articles selected with the literature search are given in Supplementary Table In order to assemble an overview of visualization approaches used in studies that applied pathway-based analysis to genetic association studies fully reflecting current practices, a keyword search for “Pathway Analysis” in PubMed and Medline July 2014) was conducted. The literature research was performed using EndNote X7. The search yielded 2,231 articles from January 2005 through August 2014, 2,184 remained after removing duplicates, 15 others were added based on suggestions by experts in the field. Subsequently, these articles were screened manually by reading title and abstract. We retained only those 264 articles describing pathway-based analysis with genetic variation, and these articles were studied in detail. Retaining the 65 most relevant papers, all from 2007 through 2014, we aggregated the results with key features of the analysis, summarized in Supplementary Table 4 was con1) (TM from Thomson Reuters2 (3 (4 and it depicts only copy number variations (CNVs). We describe in the Section “Results,” the five tools mentioned here with a specific focus on the visualization options for the genetic variants. However, some relevant command line tools were also detected in the literature search, but we do not describe these because of the absence of user-friendly visualization features.Although some algorithms are available as web services or installable software, no generally accepted implementation for the visualization of SNP results exists. From the literature search, we found the following bioinformatics tools able to visualize the significant variants in a pathway: 1) , MetaCorReuters2 , Path3 . SNPs were extracted from the 12 genes following specific criteria: 500 kb window of the gene, exclusion of SNP with minor frequency allele <0.05, exclusion of SNP that shows LD of r2 > 0.8, and imputation of missing genotypes. The resulting SNP dataset in the study was 1,191 SNPs with genotype data available for 1,141 obese individuals (body mass index >30 kg/m2). Genotyping was performed with Affymetrix 500 K mapping array and the Affymetrix 50 K supplemental array. A statistical epistasis network (SEN) (An epistatic dataset from rk (SEN)  was utilThe evaluation of five pathway-based analysis tools—Caleydo, IPA, MetaCore, Path, and PathVisio—that support incorporation of genetic association data demonstrates: first, how polymorphism data can be visualized and analyzed in a pathway-based environment, and second, how different information and experimental data can be combined for analysis and visualization. Pathway content provides the biological processes in which GWAS-identified genes are known to be involved and shows other genes related by common function that may not pass GWAS significance thresholds. Integration of other types of genomics data as accepted by these tools, often in combination with bioinformatic pipelines for data processing, permit evaluation of different transcriptomics outcomes in subjects with a specific genotype or phenotype, and some tools allow also integration of metabolomics results.5  file, we formatted the input data accordingly. The results of this analysis recognized 13 objects, limited to just one SNP per gene. Different outputs such as pathway maps, gene ontology (GO) processes, process networks, and diseases (symbolized by biomarkers) are listed as part of the result  corrections. The FDR calculation considers the p-value of each network map and its rank given the total number of maps in the entire set of pathway maps. The list also contains the ratio of significant genes in the dataset over the number of genes in the pathway. If one pathway in the list is selected, a pathway map is displayed. In our example, the first pathway of the list is “retinal ganglion cell damage in glaucoma” in which two genes appear to be colored bright and illustrated that they present the input SNPs with a red colored bar  with rs1108923. It is remarkable to notice that ASIC1 is not in the list of the 12 obesity genes of the study selected. Indeed, the SNPs from the obesity-epistasis dataset , our objective is to highlight aspects of pathway analysis. After uploading the list of 58 SNPs with the significant epistasis interactions value, the program automatically displays an overview page with information such as the number of SNPs recognized by the tool, in this case 22 SNPs of 58 were mapped. In addition, a table is shown with Entrez gene IDs and affiliated information such as cellular location, type of gene, and interacting drug. Clicking on one of the gene names listed, it displays a link to a description gene page for human, mouse and rat, in which additional information about the gene functionality are provided. In this overview page, there is a possibility to perform different analysis as was mentioned above. We opted to the Core Analysis that includes the enrichment pathway analysis. However, such analysis takes into account the genes in which the 22 SNPs were mapped and not the SNPs themselves. The result page, as in MetaCore, lists several output such as: canonical pathways, diseases and function, regulators, and networks. The canonical pathway visualization is a list of enriched pathways ranked by 7 and RegInt plugin8, add functionalities related to genetic variants. BiomartConnect enables visualization of biological information in the backpage, retrieved with the Ensembl BioMart tool9, with which variants also are accessible. With this plugin the variants, stored in the Ensembl database and located in any gene selected from a pathway diagram, are visualized in the backpage. Moreover, additional SNP information like SIFT and Polyphen predictive scores is available and possible to display in the backpage. The RegInt plugin enables one to upload and visualize user data on the pathway, in the form of an interaction file. This file contained a data column listing the 58 SNPs and another listing the genes in which those SNPs are located. For the detailed input format check plugin instructions in Github10. We used the RegInt plugin to display the 58 epistatic SNPs. First, in the main panel, we opened a pathway diagram presenting at least one of the genes related to the 58 SNPs from the WikiPathways collection (see text footnote 6), a pathway database linked to the software. Then, in the backpage the SNPs related to the gene selected in the pathway are displayed. The number of SNPs visualized depends on the data uploaded. In our case we selected from WikiPathways the “brain-derived neurotrophic factor signaling pathway” (WP2380), that presents two (BDNF and SH2B1) of the 12 genes of the epistatic dataset  and Pathway/Ontology association analyses . This version of the tool is still relevant for specific pathway analysis with GWAS data, but the type of visualization provided no longer presents an interactive pathway diagram in which data are shown.Path is specifically designed for GWAS analysis, connects GWAS results with information retrieved from nine common bioinformatics resources , and supports visualization of the integrated data. Path uses UNPHASED  for statp-value of the variant from the genotype–phenotype association  the number of genes with a significant SNP that participate in that process and (ii) the total number of genes that are known to be assigned to that process in the selected reference set. Further details on how IPA identifies pathways reaching significance were not provided .From the tools analyzed, MetaCore, IPA, and PathVisio present a statistical analysis of the data provided. Instead Caleydo and Path provide only data visualization on pathway graph and not statistical methods for pathway analysis. MetaCore, PathVisio, and IPA perform pathway analysis in an automated fashion. The first tool uses an over-representation method on the gene list annotated from the variants present in the VCF provided as input. MetaCore employs a hyper-geometric model to determine the significance of the enrichments. PathVisio also uses an over-representation analysis and it is based on methods adopted in the MAPPFinder tool  with set pathway . In the ric test . This p-Table 1). A mock visualization of the beneficial and applicable features observed in the different tools (green highlight), and the new characteristics that enhance the visualization and analysis of SNP data in pathway-based analysis tools (red highlight) is shown in Figure 1. The five investigated tools share some similar and effective visualization approaches, such as depicting significant pathways that contain genes in the analyzed data by list view. These lists are generally ranked by enrichment ratios, p-values or FDR scores. Another common and useful strategy is to highlight genes for which pathway data are uploaded by the user, with an option to uncover gene details via hyperlinks.Comparing the five tools described above makes evident that each uses different interactive ways to combine experimental data with information about genes, metabolites, and pathway relationships . It would be a large improvement if this same approach were used to automatically select the relevant genes in the pathways based on the GWAS statistical parameters such as SNP A strength of PathVisio, on the other hand, is its enabling of this feature to permit visualization of variants in pathways sourced either from a public repository like Ensembl or from user data. However, PathVisio lacks the interactive visualization that links entities of different pathways, as it described in Caleydo. In this context, MetaCore depicts related experimental effects of genes known to be connected via membership in a pathway, protein–protein interactions, co-citation, or co-expression in other experimental datasets with network visualization. MetaCore’s network settings can be used to view or hide specific interaction mechanisms, such as binding, influence on expression, phosphorylation, or cleavage. IPA’s approach is similar to that of MetaCore. After running the enrichment analysis, IPA lists the most represented processes, such as canonical pathways, networks, upstream regulators, diseases, and biological functions. In this way, the user subjectively decides which information to use and how to integrate it. Finally, Path has some methods to integrate GWAS data in pathway analysis. Path’s basic data visualization of pathways uses the common strategies described above, and data integration focuses specifically on genetic information and on gene–gene interactions. Path’s representation also includes an LD plot, useful and important support for GWAS interpretation.As early as 2005, the importance of effective approaches to visualization was noted through interviews and observations of current work practices . That reAdditional characteristics regarding genetic variant interactions currently are rarely depicted in pathway visualizations: edgetics, gene–environment (G × E), and epistatic interactions. Edgetics is a new term referring to network perturbation models focusing on specific alterations of the molecular interactions resulting from genetic variants . This peAnother area in which pathway visualization of genetic associations can be improved involves G × E, where the genotype–phenotype association exists only under certain environmental conditions. A recently published catalog of G × Es for numerous cardiometabolic phenotypes showed the wide extent under which the genotype–phenotype association can be modified by factors such as diet, exercise, sleep, and many other exposures and lifestyle factors . For ideFigure 1). Two, improving strategies for combined visualization of genetic data with other omics data in a pathways context will vastly facilitate interpretation of results. For the first point, as indicated in Section “Results” and listed in Supplementary Table What is especially needed regarding the SNP data visualization in pathway-based analysis tools are two important items. One, there must be development and integration in the tools of specific statistical methods for GWAS pathway analysis . We also have reported the necessity to include in these tools statistical methods for the pathway-based analysis in GWAS, aiming to define features for more comprehensive pathway-based analysis tools.The main aim of this review is to give an overview of the current state of the tools that visualize SNP data in a pathway context. We attempted to identify and describe the visualization options of the tools that resulted from a literature review in order to provide suggestions for improvements in this developing field (EC performed the analysis and wrote the paper. LP and CE revised critically the work, and provided final approval with agreements on the content.EC, LP, and CE state that there are no conflicts of interest and there is no goal to endorse a commercial entity. EC and CE are part of the team that developed PathVisio, one of the visualization tools evaluated in this review.