Both computational and experimental approaches have been used to determine the minimal gene set required to sustain a bacterial cell. Such studies have provided clues to the minimal cellular-function set needed for life. We evaluate a minimal cellular-function set directly, instead of a geneset.We estimated the essentialities of KEGG pathway maps as the entities of cellular functions, based on comparative genomics and metabolic network analyses. The former examined the evolutionary conservation of each pathway map by homology searches, and detected "conserved pathway maps". The latter identified "organism-specific pathway maps" that supply compounds required for the conserved pathway maps. We defined both pathway maps as "autonomous pathway maps". Among the set of autonomous pathway maps, the one that could synthesize all of the biomass components , and that was composed of a minimal number of pathway maps, was determined for each of E. coli and B. subtilis, as "minimal pathway maps". We consider that they correspond to a minimal cellular-function set. The network of minimal pathway maps, composed of 20 conserved pathway maps and 21 organism-specific pathway maps for E. coli, starts a sequence of catabolic processes from carbohydrate metabolism. The catabolized compounds are used for anabolism, thus creating materials for cell components and for genetic information processing.Our analyses of these pathway maps revealed that those functioning in "genetic information processing" are likely to be conserved, but those for catabolism are not, reflecting an evolutionary aspect of cellular functions. Minimal pathway maps were compared with a systematic gene knockout experiment, other computational results and parasitic genomes, and showed qualitative agreement, with some reasonable exceptions due to the experimental conditions or differences of computational methods. Our method provides an alternative way to explore the minimal cellular function set. Advances in sequencing technology have allowed the complete genome sequences of more than 750 prokaryotes and 20 eukaryotes to be determined thus far. One of the possible subjects to be solved using this advance of data is the identification of a minimal gene set i.e. an estimation of the genes that are necessary and sufficient for sustaining a functional cell under certain conditions . This tyHaemophilus influenzae [Mycoplasma genitalium [In the computational approach, it is assumed that the genes shared by distantly related organisms are likely to be essential, and that a catalogue of these genes might comprise a minimal gene set for cellular life . Soon affluenzae  and Myconitalium  were seqnitalium . After tnitalium -14. In gBacillus subtilis, and found that six of them were lethal. Based on this ratio, he estimated the minimal genome size could be 318~562 kbp . Many subsequent experimental reports utilized individual knockouts [In the experimental approach, the essential genes that are indispensable for cell growth are determined by large-scale gene disruption, and they are considered to comprise a minimal gene set. The first experimental attempt along this line was performed by Itaya, before the advent of comparative genomics . He invenockouts -18, RNA nockouts , transponockouts -25, antinockouts ,27 and hnockouts . Becausenockouts . In addiConsidering these difficulties in detecting a minimal gene set by both the computational and experimental approaches, we adopted a different strategy. Instead of a minimal gene set, we computationally explored a minimal cellular-function set. The cellular functions are functional modules composed of a group of genes, for example, glycolysis, TCA cycle and aminoacyl-tRNA biosynthesis. Since one of the final aims of minimal gene set determination is to reveal the functional components of a living cell, and these components are sometimes debated in terms of the combination of the cellular functions, detection of the minimal cellular-function set is a more direct method. In addition, this approach is more robust, because the acceptance of a given cellular function could be possible regardless of NOGD (see Methods). In this work, we regard the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway maps as the entities of the cellular functions. The KEGG database classifies the genes of sequenced genomes into more than 100 functional modules, named pathway maps, in which the reactions, substrates and products of the corresponding genes (proteins) are shown. Based on this information, not only the network of genes and compounds, but also the network of pathway maps (cellular-functions) can be illustrated.Figure As of December 2008, more than 700 bacterial genomes have been completely sequenced. We divided them into three lineages: proteobacteria, firmicutes, and others, with reference to the classical phylogenetic classification (data from KEGG). We selected 10 representative genomes from each lineage, taking the genome size, the phylogenetic distance, and the annotation availability into account  was constructed by highlighting the genes assigned to the pathway map. We evaluated the evolutionary conservation of each pathway map, based on the conserved orthologous genes derived as above. For the KEGG pathway maps of a given genome in a triplet, all of the conserved orthologous genes were assigned. Since the total number of genes assigned to a pathway map depends on the genome (organism), we defined the ortholog fraction (OF) of pathway map Ni, orth is the number of orthologous genes detected in pathway map i using a given triplet, and Nai, min is the minimum total-number of genes appearing in pathway map i of the three genomes. OFi was averaged over 1,000 triplets. The pathway maps with high OF values are considered to be the "conserved pathway maps", and they will be the "core" of a minimal cellular-function set. Referring to the descending order of OF values, we chose γ pathway maps and classified them as the conserved pathway mapsγ.where Escherichia coli (E. coli) and Bacillus subtilis (B. subtilis) as the model organisms in this work, because there are many experimental and computational data that can be compared. In the network reconstruction process, we regarded the same compounds as one node, and a substrate-product relation as an edge. Therefore a set of reactions in a pathway map was transformed into a unique network  in a pathway map, and tried to connect it to the same compound in another pathway map, regardless of the dead end material. If it was possible, two pathway maps were linked with a directed edge, and a new large network with a new set of initial substrates and products was created. Repeating this procedure for all conserved pathway mapsE. coli and the B. subtilis pathway maps, this selection and connection process was repeated until there were no more pathway maps to be connected. The resultant network defined the network of "autonomous pathway maps", because it was expected to synthesize most of the necessary compounds inside the network. The initial substrates of the autonomous pathway maps were defined as nutrients imported from the extracellular environment. We generated 10,000 autonomous pathway maps from the initial pathway-map construction process using different random seeds. As the autonomous pathway-map network depends on the γ parameter (the number of conserved pathway maps), we started from the conserved pathway mapsγ and constructed 10,000 patterns of the autonomous pathway mapsγ, at each γ.A pathway map that can provide the initial substrates of the pathway-map network was chosen randomly from those other than the components of the pathway-map network, and was connected into the pathway-map network to create a new one. In this process, only a part of the pathway map, i.e., minimal sequential chemical reactions necessary to synthesize the initial substrate, was connected, so that the extra reactions for the initial-pathway map network were excluded. Since the added pathway maps are non-conserved and depend on the organism, we called them the organism-specific pathway maps. Referring to each of the E. coli [B. subtilis [E. coli, they were determined quantitatively using the dry weight composition data for an average E. coli B/r cell, which grew exponentially at 37°C under aerobic conditions in a glucose minimal medium [We assumed the autonomous pathway maps have to synthesize at least indispensable compounds for the organism by themselves. The attained autonomous pathway maps were assessed to determine whether they satisfied this condition. As the indispensable compounds, we employed the biomass components estimated by Feist et al. for E. coli  and by Osubtilis , the numl medium . Among tThe ortholog fraction was calculated for each pathway map . In Figure γ * at each γ is plotted against the number of the conserved maps, γ. The least numbers of autonomous pathway mapsγ * are at a minimum when γ s are 20 (E. coli) and 25 (B. subtilis). Therefore, minimal pathway maps for E. coli and B. subtilis were determined from the autonomous pathway maps20 * and the autonomous pathway maps25 *, respectively, as those composed of the minimal number of the pathway maps.We defined γ pathway maps in the descending order of OFs as the conserved pathway mapsγ * for B. subtilis only increases gradually. This indicates that the first time for the autonomous pathway maps to synthesize all the biomass components is at γ = 25. Note that synthesizing all the biomass components is easy if the total number of autonomous pathway maps is large, because the larger the total number is, the greater the products. By contrast, if the initial conserved pathway maps to be extended are not appropriately prepared, the extension process will be terminated before the pathway map network develops. Thus, the conserved pathway maps25 of B. subtilis provide a good starting point so that the resultant autonomous pathway maps can synthesize all the biomass components. In the case of E. coli, the autonomous pathway mapsγ are able to synthesize all the biomass components at γ = 10, but the least number of the autonomous pathway mapsγ * decreases by the increment of γ, and reaches a minimum value at γ = 20. After that, it increases as in B. subtilis. It also indicates that the conserved pathway maps20 of E. coli provide a good starting point to be the autonomous pathway maps*, composed of a necessary and sufficient number of the pathway maps.In Figure The components of minimal pathway maps for each of the organisms are shown in Table E. coli and B. subtilis, whereby in B. subtilis, "alanine and aspartate metabolism" and "arginine and proline metabolism" were identified as the conserved pathway maps, whereas they were chosen as the organism-specific pathway maps in E. coli. This indicates that even though their degrees of conservation are marginal, they are necessary to constitute the minimal pathway maps. In the "other amino acids" category, β-amino acid (only in E. coli) and D-amino acid metabolisms were required for the minimal pathway maps. In addition, the pathway maps in the "energy", "lipid", "cofactors and vitamins" and "secondary metabolites" (only in B. subtilis) categories were also selected. In the "carbohydrate" and "energy" categories, all of the pathway maps other than "aminosugars metabolism" were selected as the organism-specific pathway map, not as the conserved pathway map. This result means that catabolism is essential for cellular life, but is not conserved throughout the bacterial genomes, probably because bacteria have evolved to adapt to a variety of environments. During the evolutionary process, bacteria that succeeded in obtaining or adjusting genes to effectively utilize the nutrients around them could survive. Consequently, their catabolic pathway maps have diverged.The organism-specific pathway maps in the minimal pathway maps are denoted as "OS" (Organism Specific) in the "MPM" columns in Table E. coli and B. subtilis for each were determined for every γ, to decide the minimal pathway maps. The autonomous pathway maps thus obtained show various combinations of the pathway maps, reflecting the variety of the organism-specific pathway maps selected. Only a part of them, named "autonomous pathway maps*", can synthesize all biomass components required to be an autonomous cell. To characterize the organism-specific pathway maps included in the minimal pathway maps, we calculated the percentage appearance of each pathway map in about 54 sets of autonomous pathway maps20 * in E. coli and 3,391 sets of autonomous pathway maps25 * in B. subtilis " is almost equivalent (reverse reaction) to the "citrate cycle (TCA cycle)" from the viewpoint of logistics of compounds, and they were alternatively selected in many cases (the sum of their percentages of appearance is around 100%). But, there are dead ends in "reductive carboxylate cycle (CO2 fixation)" (at least ATP citrate synthase and 2-oxoglutarate synthase are missing in E. coli). Subsequently, this pathway map requires more initial substrates than the "citrate cycle (TCA cycle)". Furthermore, we noticed that 64 pathway maps (excluding the conserved pathway maps) appeared at least once for the autonomous pathway maps20 * in E. coli (Table B. subtilis (16/52). These results indicate that there are many possibilities to synthesize the biomass components from extracellular nutrients, i.e., many solutions to realize the autonomous pathway maps*, if the total number of the pathway maps is unlimited.As described in the Methods, 10,000 sets of autonomous pathway maps of E. coli is shown in Figure The network of minimal pathway maps of E. coli with the data on the "Profiling of E. coli Chromosome" (PEC) database that presents 302 essential genes [B. subtilis were compared with the result of a systematic gene knockout experiment of B. subtilis that detected 271 essential genes [E. coli genome are multiply assigned to more than or equal to two pathway maps. Therefore, the pathway map that includes at least one essential gene does not necessarily represent the experimentally-derived essential pathway map. We also employed the definition by which we could achieve the highest correlation between our results and the experimental results. If both results were different, there would be no mutual relation, even though we employed a biased threshold. We discuss other definitions in the "Remarkable features of network construction" section.We compared minimal pathway maps of al genes . Also mial genes . We defiE. coli. Among those, 30 are in common with minimal pathway maps in E. coli. Also 37 were determined to be experimentally essential for B. subtilis. They include 27 pathway maps that are in common with the minimal pathway maps of B. subtilis , but "energy", "lipid", "other amino acids" and "secondary metabolites" are less consistent (< 0.5). The Jaccard coefficients for whole pathway maps  are 0.61 for E. coli and 0.53 for B. subtilis  might be explained as follows. We noticed that the computationally-derived essential pathway maps for B. subtilis were abundant in catabolism ("carbohydrate" and "energy" categories). Such pathway maps occupy 35% of all computationally-derived essential pathway maps. On the other hand, the occupancies of pathway maps of catabolism in our results and in the experimental results, are only 17% and 22%, respectively. This is probably because the genes used in catabolism are likely to be under codon usage, to be expressed abundantly and ubiquitously, so that the bacteria adapt a specific environment in which they should survive.42 and 46 computationally-derived essential pathway maps were defined for Buchnera aphidicola (B. aphidicola) strains  and Wigglesworthia glossinidia (W. glossinidia) are parasitic organisms and phylogenetically close to E. coli [E. coli with their genomes. The pathway maps that the genomes hold were taken from the KEGG database. In Table B. aphidicola APS are shown in the "Par" (Parasite) column. Against these data, the Jaccard coefficients of minimal pathway maps of E. coli are calculated and shown in the "Parasite" column in Table  E. coli . We compB. aphidicola APS, Sg, Bp, Cc, 5A, Tuc7 and W. glossinidia, were 0.59, 0.59, 0.59, 0.56, 0.59, 0.60 and 0.52, respectively, indicating minimal pathway maps show better consistency with the data of B. aphidicola strains than with the data of W. glossinidia. The minimal pathway maps were also compared with the KEGG pathway maps of Mycoplasma genitalium. This organism is not a close relative of E. coli. The Jaccard coefficient was 0.49 in total.In the "carbohydrate" and "lipid" categories, the coefficients were lower than those against the experimental and the computational results, but in the "energy" and "other amino acids" categories, the coefficients were higher than those against the experimental and the computational results. The total Jaccard coefficients against the data of E. coli and the pathway maps of several parasitic genomes imply that the minimal cellular functions are represented in the minimal pathway maps of E. coli. Also the slight differences seen in the Jaccard coefficients may reflect the phylogenetic distances between E. coli and the parasites.The high Jaccard coefficients between the minimal pathway maps of E. coli, the fractions of essential genes for all pathway maps in "genetic information processing" were higher than 30% (data not shown). However, the fractions of essential genes for all pathway maps in catabolism ("carbohydrate" and "energy" categories) were lower than 30%, except the fraction for "C5-Branched dibasic acid metabolism" in the computational results (50%). This is because the computationally-determined essential genes are the conserved genes, and genes in "genetic information processing" are strongly conserved. Also these genes tend to code proteins that have no substitutions, e.g., each of the ribosomal proteins. Disrupting them is likely to be lethal. Apparently, the computational method referring only to the gene conservation can hardly clarify the significance of catabolism. On the other hand, we first identify the conserved pathway maps that include conserved orthologous genes. Subsequently, the pathway maps that supply the substrates for the conserved pathway maps are identified. In this case, the pathway maps in catabolism are naturally introduced. Although this method relies on the reliability of chemical reactions or pathway-map network data employed, the framework is very simple. The significance of catabolism may be still under discussion and one may consider only the genes in "genetic information processing" and a small number of additional genes are enough to constitute a living cell in very rich media, even though there are no genes in catabolism. We cannot argue for the possibility of such a virtual organism at this stage, but we can point out that in each bacterial genome the portion of genes for catabolism is considerable (in E. coli 7%), and to shed light on their significance, our method is effective. We consider this methodology provides an alternative way to explore a minimal cellular-function set, other than current experimental and computational approaches.We demonstrated that there were good consistencies in the comparisons between the minimal pathway maps and the experimental, computational and parasitic data. However, we noticed that these results depended on how the essential genes to the essential pathway maps were converted. We used "number" of essential genes in the pathway maps to define the essential pathway maps. When we employed the "fraction" of essential genes in the pathway maps, as was applied to define the conserved pathway maps , instead of the number, the results were slightly different. In both experimental and computational results for E. coli, and 41 pathway maps, including 25 conserved pathway maps and 16 organism-specific pathway maps for B. subtilis. The conserved pathway maps include many pathway maps classified as "genetic information processing", whereas the organism-specific pathway maps mainly include pathway maps for catabolism, reflecting evolutionary aspects. The consistencies between minimal pathway maps and the experimental, computational, and parasitic data indicate that our procedure is realistic.The method to evaluate the minimal KEGG pathway maps proposed here identified 41 pathway maps, including 20 conserved pathway maps and 21 organism-specific pathway maps for E. coli, iAF1260 [B. subtilis, iYO844 [In the case of KEGG data analysis, since our method to detect organism-specific pathway maps is applicable to only the enzymatic reactions, it is insufficient to estimate the essentialities of "membrane transport", "signal transduction" or "cell motility", for which the chemical reactions are not provided in the reaction_mapformula.lst file. However, our method can be applied to the other data for biochemical reactions, instead of the KEGG data, e.g., a genome-scale metabolic model of  iAF1260  and that, iYO844 . By analAs mentioned in the Background, a minimal genome and a minimal cellular-function set depend on the environment or nutrients, and their general definitions are very difficult. However, an estimation of minimal cellular-functions to realize a specific biological system is useful to design an efficient biological process, from the viewpoints of synthetic biology and cell engineering. For instance, we could design a bacterial genome that will degrade harmful chemicals, such as dioxin, or synthesize beneficial materials, such as ethanol, in large quantities through photosynthesis, by considering only minimal pathway maps related to their efficient catalysis.YA performed the computational experiments, analyzed the data and wrote the manuscript. MO supervised the study, analyzed the data and wrote the manuscript.Table S1 and S2.. The 30 representative genomes (Table S1) and the pathway maps with high OF values recalculated using 30 genomes that were partly different from the original 30 genomes (Table S2).Click here for fileTable S3. Compounds exchanged between the pathway maps and nutrients imported from extracellular environment in Figure Click here for file


Ethnobotanical studies generally describe the traditional knowledge of a territory according to a "hic et nunc" principle. The need of approaching this field also embedding historical data has been frequently acknowledged. With their long history of civilization some regions of the Mediterranean basin seem to be particularly suited for an historical approach to be adopted. Campania, a region of southern Italy, has been selected for a database implementation containing present and past information on plant uses.A relational database has been built on the basis of information gathered from different historical sources, including diaries, travel accounts, and treatises on medicinal plants, written by explorers, botanists, physicians, who travelled in Campania during the last three centuries. Moreover, ethnobotanical uses described in historical herbal collections and in Ancient and Medieval texts from the Mediterranean Region have been included in the database.1672 different uses, ranging from medicinal, to alimentary, ceremonial, veterinary, have been recorded for 474 species listed in the data base. Information is not uniformly spread over the Campanian territory; Sannio being the most studied geographical area and Cilento the least one. About 50 plants have been continuously used in the last three centuries in the cure of the same affections. A comparison with the uses reported for the same species in Ancient treatises shows that the origin of present ethnomedicine from old learned medical doctrines needs a case-by-case confirmation.The database is flexible enough to represent a useful tool for researchers who need to store and compare present and previous ethnobotanical uses from Mediterranean Countries. In the last decade, several information systems have been developed to collect and manage the traditional knowledge (TK) on the use of plants from different geographical areas. As far as the Mediterranean basin is concerned, networks like MEDUSA  have beecorpus of traditional knowledge. Before the Roman colonization, Greek and Etrurian colonies occupied this region, together with indigenous Italic people, as Osci and Samnites [It has been recently stressed that one of the unresolved problems of the present ethnobotanical and ethnopharmacological studies is the limited contribution of disciplines like anthropology and historical sciences ,6. As a Samnites . Later oA relational database has been elaborated with the goal of providing a standard for the storage of historical series of ethnobotanical data in the Mediterranean region, allowing the temporal exploration of links and meanings.The database prototype has been implemented as a local relational database. Different implementation solutions were tested, according to the requirements and use cases. Present and ancient ethnobotanical data were organized in the same table structure, allowing some costs of redundancy in favor of the homogenization of data and queries. Because flexibility was a major issue for our archive, object oriented concepts were implemented, such as unlimited hierarchical structures (self-joined tables), particularly for categorized look-up tables and species taxonomy, and inheritance concepts, allowing e.g. information sources to be a reference, a person, an herbal or an archeological remain. Those techniques allowed us to develop a minimal data structure fitting different kind of information, and to perform queries at different levels of detail.The structure of the developed repository is summarized in Figure The archive currently allows the storage and management of following information:• ethnobotanical information from interviews (informants), historical herbarium specimens and documents , according to a hierarchical catalogue of plant uses and preparations . It is also possible to include ethnobotanical information from archeological remains, although the relationships to the relative databases have not yet been implemented.• plant names information: plant popular names , and their relationship to species taxonomy . Informa• information on specimens in historical herbals.Ethnobotanical data were organized in a relational structure: a relevée can group information on multiple plants, which can be used for different scopes, each being reached with one or a sequence of preparation steps. Traditional knowledge cannot be easily categorized and therapeutic uses are often intermingled with ceremonial and symbolic ones. Since attempts to categorize these kinds of uses are not always meaningful, the storage of a description that reports in detail a specific application of a plant is recommended. However, to allow a more effective search within the database, beliefs and practices dealing with folk uses of a species were grouped in use categories, ranging over medicinal (including affection and mode of administration of the plant), ceremonial , alimentary, domestic , veterinary uses. These categories have been organized in a hierarchical structure and may be assigned according to the detail level of the information gathered. A reliability degree of those use assignments is given, particularly for data from the ancient treatises and from archeological remains. The principle of storing information with different levels of accuracy has been implemented also for the preparation steps  and for the plant parts involved .This level of semantic detail allows fine tuned query possibilities and is needed if a future improvement to a multilingual application is planned.Corpus Hippocraticum [De Materia Medica" [Naturalis Historia [Herbarium [Corpus Hippocraticum is a largely overcome challenge, whereas Raven [The database is intended to store and manage data from pre-Linnean times to present. At the moment the oldest entries stored in the database, dealing with plant uses in Mediterranean region during Antiquity, come from studies carried out in the last decade on craticum  treatise Medica" -15, PlinHistoria , and Pseerbarium . Since aerbarium . For exaerbarium , the ideas Raven  is clearas Raven  and Stiras Raven . Thus thSince a consistent number of our entries come from plant labels in historical herbals, also detailed information on specimen and herbal collections was stored in the database.Information sources can be of multiple types, namely bibliographic references, collectors, informants, historical herbals or archeological remains. All or subsets of these types can be referenced, according to the tables where a source information is needed.As already mentioned, reliability of the information stored in the database is handled at multiple levels, namely: phytonym identification (Plant table), which in the case of informants or collectors is useful, tentative identifications (Taxon assignment table), used mainly for data from the ancient treatises, and use assignments (Plant use table), for data from ancient sources or archeological remains.Figures Different query features allow the user to get a temporal drawing of the importance of a species within the life of a community. A query for a species or a genus returns all the available information, either from published works, unpublished interviews, labels of historical herbals or archeological remains. The hierarchical features implemented for uses, plant parts, preparations and sites  allows both generic or fine-grained queries for those parameters, according to the requirements of the user. Particular query settings can be specified, in order to extend the queries to rougher levels of detail, e.g. querying for a genus instead of a species, thus including all the species of that genus, or querying for neurology instead of headache, thus including all neurological affections. Such latter use comparisons can only be performed in the frame of the specified hierarchical structure, which implicitly defines the degree of similarity of the uses. In the future, different hierarchical classifications of the stored uses could be defined and added to the use table, thus enhancing comparative capabilities of the archive.The performed queries return the appropriate reliability information. This is particularly important for the case of multiple putative identifications of ancient phytonyms, where a query for a given use produces multiple report lines for the same ethnobotanical information, according to the taxon assignments. The tuning of reliability settings allows restricting the query from all the record combinations  to only records with a certain reliability degree or to only record combinations with the highest reliability.Also historical summary information can be retrieved , matching a plant binomial with a specific use, and showing for example how many times a given medicinal use of a species is reported in the database for a defined period.® Access 2000 [The repository has been implemented with PostgreSQL8.3 , while eess 2000 .At present time only data on flowering plants have been stored in the database, but it is possible to extend the archive also to non-vascular plants, algae and fungi. The interval of time was chosen because plant nomenclature from XVIII century onward is based on Linnean classification, which allows a more reliable approach to species identification. At the moment, all the data from modern (1950 – present days) ethnobotanical studies carried out in Campania have been stored in the database, together with the available information from different historical sources dating back from the first half of 1900's to XIX century. The majority of the records were extracted from hundreds of published works, both popular and scientific, which belong to three main types of sources:I) floristic description of a territory, which often contained ethnobotanical information  the labels of the historical herbaria samples of Herbarium Porticense (PORUN), that in same case reported some indications about the use of a plant in the site where it was collected (collection Vincenzo and Francesco Briganti of Herbarium Porticense – PORUN)III) medical treatises of physicians of past centuries (i.e. Petagna ; LimonceThe database currently includes 474 different species, which have been used in Campania between the end of XVIII century and the 2007.1672 different uses have been recorded for the species listed in the database, ranging from medicinal, to alimentary, ceremonial, domestic and veterinary. Table From the analysis of the information stored in the database, it is possible to make some generalizations on the evolution of ethnobotanical knowledge in Campania. Several trends remain unchanged over the last two centuries, such as the prevalence of data about medicinal plants, included those used in the so-called food medicine, which was often mentioned in XIX century sources. The occurrence of a relatively high number of medicinal citations is also due to the interests of the authors of that time, generally physicians or chemists.The data were analyzed for both content and geographical distribution. In Figure Olea europea L. and Vitis vinifera L., followed by Brassica oleracea L. ranked highest in usage, and consequently in cultural significance, in the Campanian territory. The domestication and cultivation of these species was of pivotal importance in the Mediterranean civilization since Neolithic times, and represent still now an important economic resource for the Campania and the whole Mediterranean Region. The treatment of a range of internal affections with the so called food medicine often relies upon cultivated plants; in general, the collection of wild greens, although still practiced, has been considerably reduced in present times. This is probably due to the continuous decrease of people able to recognize plants in the wild, which, in turn, has been determined by the decline of agriculture in the region. However, cultivated plants correspond to about 30% of the species recorded in Table Adiantum capillus-veneris L., Parietaria judaica L., and Artemisia absinthium L. frequently used in Campania (Table Table ia Table .It is evident that even though the general significance of these plants, based on the number of usages, remained unmodified, many present uses differ from those of XIX Century. The analysis of data suggests that in XIX, and in the first decade of the following Century, the selected plants were primarily used as pain-killer, and as febrifuge, whereas their reliance for these scopes is now less frequently reported -41. The The Campanian historical database encompasses also information on plant uses in ancient Mediterranen cultures, particularly of Greek and Roman phytotherapy. This inclusion needs some methodological considerations: in a straightforward assay, the anthropologist Di Nola  categoriA. capillus-veneris, A. absinthium and P. judaica have been reported in table A. capillus-veneris was administred mainly against pulmonar and kidney affections, and to treat gynecological diseases, A. absinthium was highly reputed as a tonic and digestive, and as a powerful antiworming agent and P. judaica had application as a diuretic, in the therapy of skin affections and to help the resolution of traumatic accidents. Similar therapeutic uses are also descripted by Petagna [Rosmarinus officinalis in Latin America [Paul  suggeste Petagna . A compa Petagna  and the  America .In this respect, the comparison of the TK of a restricted territory as that of Campania with the corpus of Ancient phytotherapy can not be exhaustive and needs a more detailed scrutiny carried out on a larger set of historical data from other territories of the Mediterranean region.The Mediterranean region, with its complex history, still needs historical studies dealing on traditional knowledge of plants. This database revealed to be a useful tool to store and to compare present and previous ethnobotanical uses in the Campanian region. The collection of data is in progress. Further information could be gathered in small libraries and local archives present almost in an Campanian towns, where it could be possible to find forgotten sources dealing with local traditions.In the next future it is necessary to finalize the structure of the database for publishing it online, adding multilingual support and administration of user privileges, in order to be contributed by other researchers, thus enhancing the Mediterranean ethnobotanical network.The authors declare that they have no competing interests.ADN was involved in setting up the historical database and data analysis. GBP was involved in the design and implementation of the relational database. AP was involved in data collection, archiviation from ancient sources and data analysis.


It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need.Our results show a low level of consistency, comprehensiveness and compatibility among three popular pathway databases . The level of consistency for genes in similar pathways across databases ranges from 0% to 88%. The corresponding level of consistency for interacting genes pairs is 0%-61%. These three original sources can be assumed to be reliable in the sense that the interacting gene pairs reported in them are correct because they are curated. However, the lack of concordance between these databases suggests each source has missed out many genes and interacting gene pairs.http://www.pathwayapi.com.Researchers will hence find it challenging to obtain consistent pathway information out of these diverse data sources. It is therefore critical to enable them to access these sources via a consistent, comprehensive and unified pathway API. We accumulated sufficient data to create such an aggregated resource with the convenience of an API to access its information. This unified resource can be accessed at  It is challenging to draw biological conclusions from today's microarray experiments. The main source of the difficulty is that the number of samples available for analysis is usually very small relative to the number of genes to be considered. It is often the case that many genes are statistically significant according to the wide variety of computational and statistical analysis algorithms. Yet there is little concurrence between the genes selected by different algorithms. Furthermore, the genes selected by these algorithms do not always provide an insight that is biologically consistent or biologically interpretable.In order to obtain results that are more biologically meaningful, it is important to incorporate information from biological repositories into the analysis of microarray data . Indeed,Examples of the new generation of microarray data analysis algorithms that incorporate biological pathway information into the analysis process include ORA  ,5, FCS  for gene selection.The importance of the accuracy and comprehensiveness of the biological pathway information used should be clear from the short review above of modern microarray data analysis algorithms. For instance, clinicians may potentially end up with different results and conclusions depending on the database they group their genes by!Therefore we study the following issues in this paper:+ Are various selected biological pathway data sources consistent with each other?+ Are they sufficiently comprehensive individually?+ Are the databases easily accessible to researchers who wish to use their data for their analysis?Although there are many commonly used pathway databases  we have selected three data sources  for our analysis. These sources are chosen because they are representative of three very different kinds of curation effort. For instance, Wikipathways is maintained by a community of professional users via the free and open wiki platform. KEGG database is curated independently by a single lab from published literature. Ingenuity is a commercial product.Our results show a low level of consistency, comprehensiveness and compatability among these three selected pathway databases. We addressed these issues with a unified easy-to-use API which allows access to biological pathway information from KEGG, Ingenuity, and Wikipathways. This common API allows researchers to gain instant and updated access to data from the different repositories.Pathway databases  have always been assumed to be consistent because they share a common data source: published literature . We show here that this assumption is not true.We define the following metrics to illustrate the diversity across databases. The first metric, the "Gene Agreement Count" of a pathway, counts the number of genes that are common to that pathway in all the databases. The second metric, the "Gene Pair Agreement Count" of a pathway counts the number of "interacting gene pairs" that are common to that pathway in all the databases. An interacting gene pair is a pair of genes (or their products) that are directly interacting in a pathway. In the case of metabolic pathways, however, we define an interacting gene pair as proteins that catalyze adjacent steps in the pathway.When calculating the "Gene Agreement Percentage" of a pathway, we first find the total number of genes within that pathway for each individual database. We next select the gene count from the database that has the least number of genes for that pathway. Finally we divide the Gene Agreement Count by this mininum gene count to obtain the Gene Agreement Percentage. The same technique is employed to calculate the Gene Pair Agreement Percentage.The three databases represent some of their pathway entries not as genes but as proteins or symbols depicting protein families or classes. In such instances we replace all such proteins and symbols with the genes they represent. For example, suppose that A activates B within a pathway, where A and B are symbols representing protein classes that are products of 3 genes and 2 genes respectively. We replace A activates B by 6 new activating relationships. We claim the validity of this replacement method because it exactly captures all the genes and relationships the original curator had intended. All statistics calculated here are based on the expanded relationships.Our investigation into database consistency began with a manual comparison on the agreement of the apoptosis pathway across databases. Results indicate a range of 11%-16% (Gene Pair Agreement Percentage) and 32%-46% (Gene Agreement Percentage). This is an extremely low level of agreement given a pathway as pervasive as the apoptosis pathway. Full results are seen in Table The next step involved an automated extraction for the apoptosis pathway between the databases. The results are shown in Table The pathways across separate databases were matched via the following longest-common-substring-based technique (LCS): Given a pathway × in database 1, we generate a list of pathways Y in database 2. This list Y is ranked according to the length of the longest common substrings with pathway X. This list is next manually scanned to obtain the pathway which has the closest nomenclatural match to pathway X.We compare three possible algorithms which could have been used to find pathway matches and they are1. LCS-based algorithm: pathways are matched by matching the names of the pathways to the pathway with the closest name followed by a manual verification.2. Gene pair overlap: pathways are matched by finding pairs of pathways with the maximum number of matched interacting gene pairs.3. Gene overlap: pathways are matched by finding pairs of pathways with the maximum number of matched genes.We carry out experiments to determine which of the three approaches above is most suitable for finding the best pathway matches. The quality of these algorithms can be judged according to two different aspects:1. The percentage accuracy of matching pathways found: how accurate are the matching pathways found by each individual algorithm (finding the maximum number of true positives).2. The completeness of the algorithm in finding all matching pathways: is the algorithm able to match up all pathways that should be matched and not pair up pathways that should not be matched .Intuitively, the pathway pairs based on gene overlap would have a lower accuracy than that of the gene pair based overlap. The reason being it is much easier to have spurious overlaps in genes as compared to gene pairs. Therefore there will be a greater number of false positives using the gene overlap algorithm. Hence we perform quantitative analysis mainly on the LCS and gene pair algorithms.LCS Pand the matched pathway pairs found by the gene pair algorithm be defined as RP P. For the sake of comparison .Let the matched pathway pairs found by the LCS algorithm be defined as n Figure , in bothIn total, LCS produces 24 pathway pairs while the gene overlap algorithm produces 40 pathway pairs. 16 pathway pairs are common among these two algorithms so they naturally do not affect any comparative studies between the two algorithms. We thus ignore them in this analysis. The 8 pathway pairs exclusively singled out by our LCS technique are seen in Table Comparing Table An example of a false positive found by comparing gene pairs is the pathway pair "Long-term potentiation (LTP)" and "Calcium signaling pathway" . PPAR-alpha is a ligand activated transcription factor that belongs to the family of nuclear receptors. After binding with its partner RXR-alpha, PPAR-alpha plays essential roles in the regulation of cellular differentiation, development, metabolism, and tumorigenesis of higher organisms. On the other hand, TGF-beta acts as antiproliferative factor in normal cells at early stages of oncogenesis. It phosphorylates smad2/3, which consequently binds with smad4 to form an antitumorigenesis transcription factor. The formed smad2/3-smad4 factor is mutually inhibited with PPAR-alpha-RXR-alpha complex. Thus the two pathways are independent since both factors are not involved in the key processes of the other pathway. The reason they are paired is that they may have a mutual inhibition.Furthermore, we realize that because some pathway pairs that should be matched have low levels of gene pair overlap. Consequently the gene-pair-based matching technique is unable to match them . An example of such pathway pairs is the TGF-Beta Signaling pathway between KEGG and Ingenuity (gene pair overlap ≤ 4%), Wnt Signaling (gene pair overlap ≤ 8%) and Cell cycle (gene pair overlap ≤ 10%). These pathway pairs however are successfully matched by our LCS technique thus providing us with more opportunities to merge and reconcile data resulting in pathways that are more complete.For the sake of completeness of analysis, we perform three further checks that compare the LCS, gene-overlap and gene-pair-overlap methods.We carry out a comparison between the LCS algorithm and the gene overlap algorithm via the following technique.P1, find a ranked list of pathways PL such that each pathway i PLwithin PL is ranked according to the gene overlap between P1 and iPL.1. For each pathway P1 and obtain its matching pathway, PLCS1.2. Execute the LCS algorithm on pathway PLCS1.3. Compare the ranked list PL with pathway PLCS1 obtained using the LCS algorithm is found in the top three pathways within PL. The LCS algorithm disagrees with the gene-overlap algorithm on the remaining 6% of pathways. These 6% of pathways have a large number of genes. Leading to a higher probability of spurious overlapping of genes.4. 94% of the time, the pathway P1 and LCS1 Pand that of P1 and the top pathways in PL. It turns out that the former is always higher than that of the top three pathways in PL. This shows that the accuracy of the LCS algorithm is sufficiently good.5. To confirm, for these remaining 6% of pathways, we calculate the percentage gene pair overlap between We carry out a second comparison between the LCS algorithm and the gene pair overlap algorithm via the following technique.PU that the LCS algorithm could not find a match.1. Compile a list of pathways i PUon this list, find a corresponding matching pathway i PUMby using the pathway with the highest gene overlap percentage.2. For each of the pathways i PUand iPUM.3. We calculate the gene pairs percentage overlap between each pathway pair P1 that the LCS algorithm could find a matching pathway PLCS1, we calculate the gene pairs percentage overlap between pathway P1 and its matching pathway PLCS1.4. Similarly, for each pathway 5. The percentage overlap found in Step (3) is generally significantly lower than that in Step (4). Evidently, our LCS technique manages to match up pathways that should be matched and does not pair up those that should not be matched.This is illustrated in Figure Our final analysis involves taking the LCS algorithm as the reference algorithm and comparing the results of the gene pair overlap and gene overlap approaches with it. The results are reproduced in Figure The graph shows the gradual decreasing of percentage overlap, with the gene pair overlap graph always staying above the gene overlap graph. This shows that the gene pair overlap algorithm generally has more accurate results than the gene overlap algorithm (which fits into our earlier postulation).Therefore, our LCS technique achieves superior performance (accuracy and completeness) compared to the method of matching pathways according to their gene overlap. We can safely conclude that the LCS algorithm is most suitable for our purpose.This section conducts an independent audit on the comprehensiveness of individual pathway databases. We use two metrics to determine the comprehensiveness of individual pathway databases. The first metric known as the "Pathway Comprehensive Score" determines the comprehensiveness of pathways within databases. The second metric is known as the "Gene Pair Coverage Score" and it determines how comprehensively each database covers the number of unique gene pairs within the databases. This is accomplished by dividing the number of gene pairs within each database by the total number of gene pairs within our database. We will elaborate further within this section.The "Pathway Comprehensive Score" metric first counts the total number of unique pathways present within the three databases . A score for each database is next calculated by dividing the number of pathways a database hosts by the total number of unique pathways. A score of 0 indicates that the database hosts nil pathways while a score of 1 indicates it hosts all the pathways. Thus databases having a small number of very large pathways will score low on this metric because it is missing out many pathways completely.KEGG achieves the highest score of 0.59. This is followed by Wikipathways 0.42) and Ingenuity (0.13). This short study indicates that KEGG Pathways remains the most comprehensive of all databases in terms of number of pathways contained. This is illustrated by a Venn diagram in Figure 2 and IngThe second metric known as the "Gene Pair Coverage Score" calculates the percentage of gene pairs each individual database has. It first counts the total number of unique gene pairs present within the three databases . A score for each database is next calculated by dividing the number of gene pairs a database hosts by the total number of unique gene pairs. Here, KEGG achieves the highest score of 0.65. This is followed by Wikipathways (0.27) and Ingenuity (0.16). In interpreting these numbers, it is important to bear in mind that KEGG contains both regulatory pathways and metabolic pathways. The latter pathways are not the focus of Wikipathways and Ingenuity. As a result, KEGG contains many gene pairs which catalyze adjacent steps in metabolic pathways that are not found in Wikipathways and Ingenuity.The two preceding sections demonstrate the inadequacy of using only one selected database for data analysis. Cross-database queries are the intuitive solution to harness the required information across these databases. However incompatibilities between different databases makes cross-database accesses extremely challenging to execute. We investigate these incompatibilities across different databases and present them here.Different databases use different methods of data access. Some databases only allow data to be downloaded via web access. Others provide flexible access to their databases through their API.A lot of human intervention is required to download the required information for databases with no public API. This creates tedious challenges for software to obtain information from such databases.For databases whose API is public, there is no guarantee that all such API would use the same programming languages. This causes developers to incorporate clumsy wrappers within their applications to adhere to the API of the databases.All databases release their pathway information via some non-standard graphical format. Such a graphical representation is useful for visual manual analysis. However, it is inconvenient for large-scale computational analysis.Some repositories do release their data in formats such as their proprietary markup languages or API data structures. These are more convenient for large-scale analysis. Indeed there are some efforts to make data exchange formats compatible with one another. An example of such an effort is BioPax . We haveIt has been demonstrated that JSON (as compared to XML) uses significantly less server computational resources and is capable of delivering content within a much shorter time in . E.g., oWe would like to highlight the fact that although compression techniques are available to reduce the amount of data to be transferred to 2-23% of their original size , these tHence for the above reasons, the flat xml file formats used by BioPax might not be the best choice for large-scale computational manipulation especially when dealing with large-scale data of tens of thousands of genes over hundreds of pathways. We have therefore opted for using JSON as the data exchange format. This lack of a consistent data format means that different databases use different formats to represent their data. Hence dedicated codes have to be written to parse, understand and integrate data from each individual database. Furthermore, note that the KEGG no longer supports the BioPax format. From the WWW, even the KEGG data in BioPax Level 1 (from January 2006) are no longer available. The current method of doing so is to convert the KEGG KGML data format first into PID (Pathway Interaction Database) format and then converting it to BioPax Level 2.Different repositories assign different naming conventions to their pathway nodes. These nodes can be described as proteins, genes or symbols depicting protein families. For example, KEGG describes most of their elements as genes, Ingenuity describes them as proteins, while wikipathways uses a combination of both.Hence it is possible to miss crucial genetic relationships because of such inconsistent representation. To obtain all relationships represented within pathways, algorithms are required to convert all nodes to a common representation.Common biological pathways in different databases are often given names with limited indication of how pathways are related to one another. For instance, KEGG may refer to a pathway as "Wnt signaling and pluripotency" and the Wikipathways might refer to it simply as "Wnt signaling". Other than the fact that both pathways have the common terms "Wnt signaling", there is no way of knowing that the "Wnt signaling" pathway is a subset of the "Wnt signaling and pluripotency" pathway other than through human intervention.This makes it difficult to determine pathways that refer to similar biological processes . It is difficult to match and compare similar pathways across different repositories.Sometimes there is contradictory information across different biological sources. When such a scenario occurs, the algorithm has to decide which contradictory information to discard and which to keep. For instance within the KEGG's Cell Cycle Pathway, gene RB1 activates gene TFDP1. Ingenuity's Cell Cycle Pathway however states that gene RB1 inhibits gene TFDP1.In view of these issues mentioned above, we provide a single common API to access the different databases. This common API works in this manner: A local database serves as a cache, storing data from the other repositories. Requests for information from the different repositories are directed to this cache to obtain the required information. To ensure that our interface is always kept up to date, automatic incremental updates are run periodically to extract the latest information from the different repositories. This process creates a unified interface for the different databases, as well as a unified database where graphs of the same pathway are merged.The database cache currently stores a total of 397 gene pathways, 21,314 genes and 60,900 gene pairs. From this API, access to pathways from both the integrated and the individual sources are provided. Further details of this interface can be found at . The API+ Pathway Formalization: Key features within pathways+ Database Cache: How we store, extract and update the data+ API Implementation: Short specification of the APIPathway databases supply many informative features that are useful for the purposes that these databases were originally intended for. However, for use in gene expression analysis algorithms such as ORA, FCS and GSEA it is sufficient to capture only two key features in these pathway databases.One feature defines all the genes within the pathway while the other defines gene-gene relationship within the pathway. Here we only consider two relationships between genes: activation and inhibition.  This formalization helps organize and streamline information within pathways.For illustration, we redraw a KEGG pathway in Figure As mentioned earlier, one of the inconsistencies across different databases is the inconsistent usage of proteins, genes or protein lists within pathway data. To address this issue, all gene or protein representations are converted to their corresponding NCBI Gene ID. The NCBI Gene ID is obtained by issueing and parsing the results of the query:Webquery 1http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=search&term=Y+homo+sapiensY refers to the gene name. Executing this query iteratively across all the genes/proteins within the pathway provides us with the Gene IDs within the pathway. This common terminology reconciles gene naming inconsistencies across the different repositories.The symbol There are only two types of relationships present between genes: inhibition and activation. These two relationships are illustrated in Figure By constructing such inhibitor-inhibitee/activator-activatee relationships, investigators explicitly know the exact relationship of genes within pathways. This allows them to analyze the adherence of these relationships in their experimental data.We maintain a database cache to store information from the other pathway repositories. To ensure fast response to users, all queries submitted are directed to this database cache. Our database cache is kept up to date with a set of automated scripts written to do periodic incremental updates from the other databases.Data from wikipathways are publicly available via their proprietary file format known as the GPML format . Hence wWebquery 2 http://www.wikipathways.org/index.php/Special:BrowsePathwaysPageThe corresponding GPML file for each pathway is obtained with this query:Webquery 3 http://www.wikipathways.org//wpi/batchDownload.php?species=Homo%20sapiens&fileType=gpml&tag_excl=Curation:TutorialX refers to the name of the pathway.Here The GPML format is designed towards the visual display of pathway information. Hence it contains detailed coordinate information about the spatial location of genes and arrows/t-bars (which depict activating/inhibiting relationships). Yet how these genes are related is not described in the GPML specifications. A parser is therefore needed to understand these spatial descriptions and extract the relevant genes and associations. The different components of the parser are:Gene Extraction: Extraction of genes from the GPML file requires the identification all occurrences of the GPML DOM attribute name: "DataNode". This enables the parser to obtain the Gene Name, Gene NCBI ID and the spatial coordinate locations associated to this datanode.+ Spatial Clustering: Activating/inhibiting relationships are described across gene clusters spatially. Therefore the genes have to be spatially clustered to determine spatially the activator/activatee or inhibitor/inhibitee relationships.+ An example is reproduced in Figure Using the coordinates from the genes obtained above, a nearest neighbour technique is employed to organize the genes into their respective clusters. Basically this nearest neighbour algorithm groups genes together if their distance apart is below a threshold .Relationship Extraction: Relationships within the GPML files are represented by the attribute keywords: "Arrow" for activating and "T-Bar" for inhibiting. These attributes provide their spatial coordinate information of activating and inhibiting relatioships. The challenge here is associating the correct gene clusters to each relationship.+ By representing relationships as a straight line, this relationship line in the spatial space is extended until it intersects with the nearest gene clusters on both sides of it. This technique assigns the activator/activatee or the inhibitor/inhibitee gene clusters to both sides of the relationship.For metabolic pathways (because the gene relationship is neither activating or inhibiting), the GPML attribute keyword is simply a "Solid" line attribute. In such instances, the relationship type attribute to the gene pair would be "neutral".Data is obtained from KEGG via a series of API calls and processing the data (SOAP format) returned. An API call is issued to obtain all the pathways first. This returns all the relevant pathway IDs stored within KEGG. Separate API calls are made for each pathway ID to obtain the genes and gene pairs present for each specific pathway.The API call to obtain all the pathways for homo sapiens is:API 1 -pathways("hsa")serv.listwhere "serv" refers to the created wdsl object to communicate with KEGG and hsa refers to the "homo sapiens species".The API call for gene and gene pair extraction from a KEGG pathway is:API 2 -genes-by-pathway(X)serv.getAPI 3 -element-relations-by-pathway(X)serv.getwhere × refers to the pathway ID within KEGG.Most pathway information available from Ingenuity is in a pictorial format. This forces pathway data extraction to be done manually, an extremely painful and time consuming process.An expiry date is assigned to all information stored within our database cache. Upon reaching the expiry date, scripts are triggered to run, automatically extracting information from the reference databases (KEGG and Wikipathways) and populating it into our database cache. .The API was written in PHP, and data transfer in JSON format. We have chosen JSON over SOAP or XML because:+ JSON is lighter in weight, transmitting less information over the internet. Client applications therefore executes faster.+ JSON has the ability to easily represent most general data structures such as records, lists and trees.+ With SOAP or XML, dedicated parsers are always required on the client. JSON is innately supported by most programming languages, eliminating the need for client parsers.The implemented functions of the API include:http://www.pathwayapi.com/api/API_GetDatabase.php and the sample results returned is: .+ GetDatabase: Returns all repositories supported by our API. No parameters are required for this function. The usage example is: + GetGene: Returns the NCBI GeneID of the gene. This function takes the name of the gene as the parameter. An usage example is:http://www.pathwayapi.com/api/API_GetGeneID.php?SearchGene=MDM1. The format returned is: ,]. In this case, there are two separate gene ids that are returned.+ GetDBPathways: Returns the all pathway names and IDs of a specific repository. Only the database name needs to be submitted to the function. For instance,http://www.pathwayapi.com/api/API_GetDBPathways.php?DatabaseName=KEGG. Here, the following will be returned:  where "1" refers to the Pathway ID, "KEGG" refers to the name of the database and "Glycolysis Gluconeogenesis..." refers to the name of the pathway.http://www.pathwayapi.com/api/API_GetPathway.php?Pathway=Apoptosis will return the jason format like: , ].+ GetPathway: Returns the pathway ID of a specific pathway of a repository. Posting the name of the pathway in this manner: In this instance, this implies that there are at least two pathways with the "Apoptosis" keyword witin their pathway names. The two pathways occurs in the KEGG databases and in the Ingenuity databases. The pathway id associated to each is 140 and 210 respectively.http://www.pathwayapi.com/api/API_GetPathwayGenes.php?Pathway = 7 Resulting in:  where "231" refers to the gene ID and "AKR1B1" refers to the name of the gene.+ GetPathwayGenes: Returns all the GeneID of a specific pathway of a repository. Providing the pathway ID to this function will return you the genes within this pathway in this manner: http://www.pathwayapi.com/api/API_GetGenePathways.php?SearchGene = 7157 We obtain the following database pathway pairs:  In this example, "128" refers to the Pathway ID and "MAPK signaling pathway - Homo sapiens (human)" refers to the name of the pathway.+ GetGenePathways: Returns all the pathways which a gene occurs. In the opposite note, this function will return all the pathways which a supplied gene occurs in. http://www.pathwayapi.com/api/API_GetPathwayInteractions.php?Pathway = 7 will result in ,] In the example above: "231" and "2584" refers to the IDs of the gene pair "AKR1B1" and "GALK1" refers to the corresponding genes of the ID.+ GetPathwayInteractions: Returns all interactions within a pathway of a database. Passing in the ID of the pathway, the API will return all the interactions within the pathway. http://www.pathwayapi.com/api/API_GetPathwayDiff.php?Pathway1 = 7&Pathway2 = 8+ GetPathwayDiff: Get the differences in genes and gene interactions across pathways. This function requires you to supply the IDs of the two pathways you wish to check on the difference in. The call below shows the difference in genes and gene interactions between pathway 7 and pathway 8. This gives the following results where: , , ,] Where  refers to the genes within pathway 7 not in pathway 8. refers to the genes within pathway 8 not in pathway 7. refers to the gene interactions within pathway 7 not in pathway 8. refers to the gene interactions within pathway 8 not in pathway 7. This set is empty because all interactions in pathway 8 are in pathway 7.It is widely accepted that analyzing microarray experiments with biological information provides biological inferences of a greater detail. Examples of such analysis are -24.However, such techniques run into issues if the data source used is not consistent or comprehensive. For example, using the same technique on a different database yields a differing analysis result.Faced with such an issue, the solution is to integrate biological information across different data sources to obtain a more wholesome analysis. Yet the incompatibility of the different data sources renders this option extremely challenging.Furthermore, we investigated and discovered low levels of consistency, comprehensiveness and compatibility among three popular pathway databases .Our strategy of addressing this issue was to create an API (freely available) which gives researchers access to various pathway databases of their choice as well as to an integrated database. This integrated database resolves various incompatibility issues between databases such as:1. Incompatible methods of data access2. Incompatible data formats3. Incompatible molecular representations4. Incompatible pathway namesHowever we understand the limitations faced by such systems. For instance, in the event that two databases provide conflicting definitions of the same pathway, both pieces of conflicting definitions will be included within the pathway information itself. The integrated database is also more comprehensive because it is the union of the data sources. Every gene/edge in any of the three data sources is also in the integrated database. Furthermore, the integrated database is equipped with a API to allow the user to conveniently identify inconsistencies and to resolve them in accordance to his specific application needs. To ensure fast responsiveness, API connections are made towards a central unified database which keeps a cached copy of the records of the other databases. To make certain that the cached entries are always kept up to date, entries from the cache are flushed periodically and automatically updated again for the reference databases wikipathways and KEGG. As data from Ingenuity is obtained in a manual fashion, updates from Ingenuity can only be achieved laboriously by curators importing new data physically from Ingenuity. Note that data from Ingenuity will not be released.There are many efforts on the aggregation of pathways data   where usTwo of the projects that are similar to this work are Pathway Commons  and BioWOne issue we have with most data aggregators is their lack of explanation on how their data is kept updated. For instance, little mention is made on how the aggregated data is updated from the various repositories. In fact this issue is acknowledged in . Here weOur final point deals with the aggregator's inaction to develop integrated pathway data from their diverse data sources. By standardizing gene references and key features within pathways, we have the ability to integrate similar pathways together. As a result our integrated pathways are more comprehensive. Contrasting to prior available methods, researchers can easily use our API to obtain data for each pathway either from the integrated database or from a specific database of their choice. This gives researchers a straightforward mechanism for incorporating pathway information into their microarray analysis.http://www.pathwayapi.com. Data from the sources can be accessed via the API as described. In addition, the data will be available for download as a CSV or SQL file. The database schema and instructions on their usage are stated in the downloaded file. All information will be provided free of charge. Information from Ingenuity will however not be released as it is proprietary . The latest number of pathways in each database are shown in Figure The database is available at DS developed the software and database. DD provided the data from Ingenuity and pathway analysis. DS and LW wrote the manuscript. All authors contributed to design of analytical algorithms. All authors read and approved the final manuscript. This research is supported in part by an A*STAR AIP scholarship (DS), a A*STAR grant SERC 072 101 0016 .


We report from the second ESF Programme on Functional Genomics workshop onData Integration, which covered topics including the status of biological pathwaysdatabases in existing consortia; pathways as part of bioinformatics infrastructures;design, creation and formalization of biological pathways databases; generatingand supporting pathway data and interoperability of databases with other externaldatabases and standards. Key issues emerging from the discussions were the need forcontinued funding to cover maintenance and curation of databases, the importanceof quality control of the data in these resources, and efforts to facilitate the exchangeof data and to ensure the interoperability of databases.


The integration of information present in many disparate biological databases represents a major challenge in biomedical research. To define the problems and needs, and to explore strategies for database integration in mouse functional genomics, we consulted the biologist user community and implemented solutions to two user-defined use-cases.We organised workshops, meetings and used a questionnaire to identify the needs of biologist database users in mouse functional genomics. As a result, two use-cases were developed that can be used to drive future designs or extensions of mouse databases. Here, we present the use-cases and describe some initial computational solutions for them. The application for the gene-centric use-case, "MUSIG-Gen" starts from a list of gene names and collects a wide range of data types from several distributed databases in a "shopping cart"-like manner. The iterative user-driven approach is a response to strongly articulated requests from users, especially those without computational biology backgrounds. The application for the phenotype-centric use-case, "MUSIG-Phen", is based on a similar concept and starting from phenotype descriptions retrieves information for associated genes.The use-cases created, and their prototype software implementations should help to better define biologists' needs for database integration and may serve as a starting point for future bioinformatics solutions aimed at end-user biologists. At present, we are just beginning to appreciate the complexity of genotype-phenotype association in humans, but more detailed and comprehensive analyses in basic research are urgently needed. Although studies in humans are important, they are limited because of the size of cohorts, strong but often unknown environmental influences, poor and inconsistently coded diagnosis, and lack of repeatability. Therefore, animal models are absolutely essential to complement human studies; they allow the investigation of underlying biological mechanisms in well-controlled experimental systems.In particular, the mouse is an ideal model system for studying genetic factors that contribute to diseases because genetic reference populations (GRPs) with a large number of allelic variants in many genes, combinations thereof, and many knock-out mouse lines with deletions in single genes are available . ResearcConsequently, funding agencies around the world have supported an increasing number of functional genomics projects focused on the use of the laboratory mouse as a model for human disease. The results obtained have been collected in various databases. However, in most cases, these databases represent single project outputs and are maintained at different sites. Exceptions are, for example, the mouse genome database (MGD) database of MGI , the moue.g. via a website, downloadable files or web services). Several resources do not adopt common standards e.g. using the same identifier for a given gene or protein [However, there is a large volume of data in distributed databases that is not contained in MGI (Mouse Genome Informatics) or GeneNetwork and which are important for functional genomics studies (see the Mouse Resource Browser MRB ). Ad-hoc protein . In thisAs a first step towards new concepts for database integration, we have established a network of scientists from Europe, North America, Japan and Australia. The network is funded as a Coordination Action by the European Commission and called CASIMIR  . The CooMuch can already be achieved using query tools that ease selection and joining of distributed data, such as BioMart , and/or In order to gather the perspective of the end-users, the biologists, who will perform the actual data mining we designed use-cases together with biologists. Subsequently, two software implementations were developed on the basis of these use-cases to provide tools which could carry out the tasks requested by the users in the most practical format. Here, we describe two use-cases that arose as a result of our discussions with biologist-users during workshops, meetings and via a questionnaire. Furthermore, we demonstrate the first steps towards their implementation.During the first sessions with different user groups, some principle needs for data mining became apparent. These needs were further confirmed in subsequent meetings and demonstrations of development steps to biologist users. A user-friendly interface should not only query multiple databases but also allow for multiple search terms, allow iterative interactions, and contain a tool that allows storage of the results. Furthermore, most of the currently performed data mining in functional mouse genomics concerns genes, their functions and variants on one side; and phenotype descriptions on the other side. Based on these discussions, we designed two generic use-cases that should be suitable to a larger scientific community: a gene-centric and a phenotype-centric use-case.etc. Since the searching of all these web databases by hand is very laborious and time-consuming, our user groups decided to describe a gene-centric use-case starting with an input of a limited number of gene names and aiming to facilitate easy and automatic collection of information about these genes from different sources. This process should be performed in an interactive fashion and allow storage and export of the results obtained.The advent of high-throughput technologies in biology, such as gene expression microarrays, makes it now possible to identify, with the help of statistical and bioinformatics tools, large groups of candidate genes changing their expression levels in different experimental conditions. However, of the genes identified in this way, usually a few hundred, only a limited number of genes (in the order of 20-50) can feasibly be studied experimentally in the laboratory. Therefore, researchers prioritize the gene lists based on their own knowledge, literature, and additional information from many different web accessible databases, such as gene and protein descriptions, genetic diversity information, expression patterns in different tissues, An iterative user-driven strategy was developed based on the principles of an "online shop" Fig. . Here, aFollowing the above principle, the integration of mouse databases via a gene-centric use-case should allow candidate gene symbols to be entered into a query form which then automatically collects basic information like synonyms, gene IDs, descriptions and genome locations for the entries Fig. . Based oRANTES (correct gene symbol Ccl5), MIP1a (Ccl3) and IP-10 (Cxcl10). For other genes, it may be not known to the researcher that they represent members of large gene families, and one has to choose one or all to proceed with the analysis. Examples are Hox, Fgf, Inhibin, and interferon genes. Here, we consider as the "correct gene name" the name which is given by the international nomenclature committees: Mouse . Examplfor Mice ), human for Mice ), and rafor Mice ).etc., but still makes sure that the correct genes will be found. For this, entries will be searched in a first step against the MGI database for disambiguation [It is thus important that the use-case allows entering any gene name, synonyms, incomplete names, iguation . For eacIn a second step, it is possible to collect additional information from different databases for the genes in the cart list. Examples of databases are MGI and ENSEMBL/mouse for information on gene structure and links to other resources; Eurexpress , SymAtlaThe list of collected genes in a shopping cart can then be used to perform meta-analyses. For example, an analysis of GO-terms will allow finding out if certain GO-categories are over-represented in the particular gene list, indicating that the genes may belong to a specific pathway or biological process. Similarly, an analysis of expression patterns may reveal if there is a certain tissue in which the genes from the list are preferentially expressed.At present, only few of the currently existing databases offer some of the above-described functionalities, the most comprehensive one being MGI. And thus far, only BioMart represents an initiative which aims to allow the user to design queries on information from otherwise disparate databases. Also, BioMart allows refining searches and filtering out relevant information. However, Biomart is currently aimed at the advanced and trained user and is not yet designed for simple querying and collection of results in a shopping cart to which new genes and information can be added.A second use-case was defined through the interaction with the user groups. It should allow researchers to begin their search with a phenotype description Fig. . In thisAt present, the most extensive and well structured phenotype ontology for the mouse is the Mammalian Phenotype (MP) ontology , accessiIn the future, cross-referencing mouse MP terms with ontologies that describe diseases (such as the Disease Ontology - DO ) and pheThe results from the phenotype-driven searches should then be linked to gene names associated with a given phenotype. These genes are presented as a list from which the user can choose the genes of interest and save them in a shopping cart. It is then possible to feed the genes into the gene-centric use-case and perform a more detailed data mining or meta-analysis.C. elegans phenotype ontology [The description and further development of the phenotype-driven use-case may represent a very useful concept for scientists and clinicians outside the mouse community. For example the Human Phenotype Ontology HPO is based on OMIM  and a seontology  could beontology . Gene syA prerequisite for computer-supported data integration is programmatic access to select and retrieve data from distributed resources. As described by  there arFor Europhenome and Mugen  SOAP/WSDUsers may want to integrate their local database or other databases. To demonstrate how this can be achieved, we generated web services for accessing GNF SymAtlas expression data. For this, we first saved the SymAtlas data locally. We then defined the Entrez Gene ID's as a common field which could be retrieved from the MGI Biomart and matched to the records in the local SymAtlas database. We then used MOLGENIS to create the relevant SOAP web services to retrieve the data from the local database, to subsequently load and display them in the shopping cart interface.http://www.casimir.org.uk/usecase1/. In the following, we describe this tool from the perspective of the scientific user.After having defined the use-cases we wanted to provide users and developers with a first implementation which may then be tested and further revised in the future. Thus, certain parts of the use-case scheme outlined in Fig. RANTES" and "IP-10", the correct gene names are displayed together with the search term and all other synonyms. If, for example, "Fgf" is used as query, all Fgf gene family members are displayed. The user may now decide which members to follow further. The genes selected in this process via the check box may then be saved in a shopping cart.Fig. The gene list can subsequently be retrieved from the cart Fig.  and addiNew genes can be easily added to an existing cart by calling up the entry form from within a cart and follow the same procedure as described above.Because the genes listed in a cart contain a correct and unique identifier (MGI and/or Ensembl IDs) they can be directly used to query other databases. Such features and searches could be easily added to the existing MUSIG-Gen application. But even more important, it may now possible to perform an analysis on the entire group of genes in the cart. In the current version of the use-case, we implemented a GO term count as a proof-of-concept for the user interface. GO terms can be associated with all genes of the list using the 'load more data' feature and the representation of different GO-terms across the whole gene list be displayed Fig. . These aAs a final step, we added an export function to the shopping cart which allows the user to export his data in CSV format and then perform highly customized analysis locally.The application layer of the shopping cart was developed in PHP. PHP proved to be a good choice for the development of the user interfaces, but did create some problems for the development of the web service client scripts because of a lack of multi-threading. The latter makes it impossible to retrieve data from different web services at the same time. The major problem is that some web pages access multiple services and depending on the network speed and the kind of query some web services are slow to respond. This operation would thus stop the page from loading in the browser. We managed to mitigate this problem by creating an AJAX (Asynchronous JavaScript and XML) based loading system using the PHP PEAR AJAX  librarieThe shopping cart system uses a Postgresql database to store user data. The data stored comprises the user's personal data  as well as the data retrieved from the different web services. The system imposes no limits as to how many data fields or data values a user can download and store in his shopping carts.http://www.casimir.org.uk/biomart/martview/. Other data from the MGI BioMart can also be loaded, such as MGI, Ensembl, EntrezGene IDs as well as GO and MP ontologies. The Ensembl BioMart can also be queried at this stage for Uniprot IDs. Both BioMarts are accessed using the default BioMart XML-REST services. For this, we developed and used a generic BioMart XML-REST PHP client class which can be used to query any BioMarts.The application initially retrieves gene nomenclature and genome location data based on gene symbol: By default, nomenclature and genome location data is loaded from our MGI BioMart e.g. the GO and MP ID fields. The user can choose to load the ontology term names which are loaded from the OLS SOAP web service.Data may also be loaded from the Eurexpress BioMart or from the GNF and INTACT SOAP web services (using generic PHP SOAP libraries). There are also some fields which have the option of loading additional information, http://www.casimir.org.uk/sourcecode/The source code and documentation for the MUSIG-Gen prototype may be downloaded form the following web server: http://www.casimir.org.uk/usecase2/. The MUSIG-Phen prototype starts from a phenotype description, collects the genes associated with this phenotype in a cart and then performs all the analyses described above for MUSIG-Gen.Based on the scheme outlined in Fig. e.g. add more information, perform meta-analysis, export lists. Alternatively, the user may start his query by browsing the hierarchical list of MP terms, select one and then retrieve the genes associated to the MP term  and represents a very useful platform to test new developments which aim to connect mouse and human phenotype databases.At the present state, the MUSIG-Phen software was not designed for more sophisticated queries, because discussions with users revealed that further detailed queries very soon become highly specialized and complex for certain user subgroups. However, the present use-case implementation may already serve to query nascent databases  ontology is loaded from the OLS web service. The user-selected MP term is sent as query input to the MGI, EuroPhenome and MUGEN web services and matching gene symbols are returned. Gene symbols can then be selected and sent to the gene-centric use-case shopping cart.etc. which would make automatic matching impossible. We therefore favor the idea to develop a web service field ontology which should be integrated into MRB or Biocatalogue to provide a look-up service for field names. Currently developments are ongoing within the Biocatalogue project to create a web service ontology to which web service developers annotate their fields which may provide a suitable solution to this problem.Basic information about web services, such as type (for example BioMart or SOAP) and location URL is currently stored in a separate table. However, a larger web service catalogue such as BioMoby , Biocatahttp://www.casimir.org.uk/sourcecode/The source code and documentation for the MUSIG-Phen prototype may be downloaded form the following web server: The aim of generating the MUSIG-Gen and MUSIG-Phen applications was to provide a first set of solutions to user-defined use-cases and thereby generate a test environment for a fully distributed integration strategy. We also presented the applications to various user groups and collected their feed-back. All users appreciated the tools which were able to integrate data from several databases, and they especially liked the principle of the shopping cart. An additional, often mentioned suggestion was to link the genes in MUSIG-Gen to mouse mutants and phenotypes as well as gene expression information. We are planning to add these functionalities to future prototypes.Our plan for a third use-case is to define the needs for an integration of mouse and human functional genomics databases. Here, we believe that the phenotype-centric use case may serve as a valuable basis to provide an entry point for clinical researchers. The concept would be to enter descriptions of human disease phenotypes as queries and to obtain mouse phenotype descriptions which relate to these terms. However, for such a query, it will first be necessary to relate the human phenotype descriptions with MP terms or with more detailed EQ-based phenotype descriptions.The authors declare that they have no competing interests.KLS conceived the study, organised the user workshops, developed the use-cases and wrote the manuscript. DS deployed Biomart for the various resources used for the use-case implementations. MS was involved in developing the use-cases and drafting the manuscript. RA developed the use-cases, set-up the Symatlas web service and drafted the manuscript. MG developed the prototypes, conducted the user demonstrations and wrote the manuscript. PNS coordinates the CASIMIR project and was involved in developing the use-cases and drafting the manuscript. All authors read and approved the final manuscript.


Although molecular pathway information and the International HapMap Project data can help biomedical researchers to investigate the aetiology of complex diseases more effectively, such information is missing or insufficient in current genetic association databases. In addition, only a few of the environmental risk factors are included as gene-environment interactions, and the risk measures of associations are not indexed in any association databases.) that includes both the genetic associations and the environmental risk factors available in PubMed database. Each genetic risk factor is linked to a molecular pathway database and the HapMap database through human gene symbols identified in the abstracts. And the risk measures such as odds ratios or hazard ratios are extracted automatically from the abstracts when available. Thus, users can review the association data sorted by the risk measures, and genetic associations can be grouped by human genes or molecular pathways. The search results can also be saved to tab-delimited text files for further sorting or analysis. Currently, PADB indexes more than 1,500,000 PubMed abstracts that include 3442 human genes, 461 molecular pathways and about 190,000 risk measures ranging from 0.00001 to 4878.9.We have developed a published association database (PADB; PADB is a unique online database of published associations that will serve as a novel and powerful resource for reviewing and interpreting huge association data of complex human diseases. The importance of association databases is ever increasing and several association databases have been published. The Genetic Association Database (GAD)  is an ar), which can help biomedical researchers to review genetic and environmental risk factors more effectively along with molecular pathway and HapMap information. PADB indexes sentences containing keywords such as 'case-control', 'cohort', 'meta-analysis', 'systematic review', 'odds ratio', 'hazard ratio', 'risk ratio', 'relative risk', or 'associat*' from PubMed [Here we describe the development of a published association database  in al locus . Althougal locus . Indeed,l allele . Becauseophrenia ,44, and ophrenia .The results of association studies have been reported using different risk values such as odds ratios, relative risks, hazard ratios or risk ratios, according to the design and analysis method of the study, and each of them is expressed exclusively against other measures. Accordingly, the search results of various keywords related to association studies in PubMed rarely overlap Table . For exaThe GAD and the HPLD contain only a few environmental risk factors as gene-environment interactions, as discussed above. Furthermore, the pathway database links provided by the GAD seldom work and the HPLD does not provide any links to the pathway databases. Because the HPLD uses a controlled vocabulary, the search results for 'stroke' successfully include related terms such as 'cerebrovascular disease' and 'transient ischemic attack'. However, the users cannot search for terms such as 'rs4680' or 'Chinese' in the abstract because they are not included in the standard vocabulary. The PharmGKB database specifically focuses on the pharmacogenetic association data only and also lacks environmental factors. The Cochrane Database of Systematic Reviews offers only systematic reviews, and genetic associations are seldom reviewed. Although the AlzGene database and the T1Dbase have many excellent features, including meta-analysis or microarray data, each database covers only one specific disease. Recently, genome-wide association databases such as CGEMS, NINDS and dbGaP have been introduced. Because these databases store the results of genome-wide association studies on a specific disease, they also differ from general association databases such as PADB, GAD and HPLD Table .PADB is a unique online database of published association data. As it automatically collects and updates association data directly from PubMed database, it is comprehensive and up-to-date. PADB covers both genetic and environmental risk factors, along with molecular pathway and HapMap information. PADB will thus serve as a novel and powerful resource for reviewing and interpreting disease association data.Project name: Published Association DatabaseProject home page: Operating system: LinuxProgramming language: Perl and PascalLicence: the database is freely accessible for academic users under the GNU GPL.Restrictions to use by non-academics: commercial users are referred to the developers of PubMed, Entrez Gene, CGAP Gene, UCSC Genome Browser, HapMap Project, BioCarta, KEGG, GAD and HPLD databases for more details on access.HR developed the database and takes responsibility for the integrity of the data and the accuracy of the data analyses. JL participated in the design of the database and helped to draft the manuscript. All authors read and approved the final manuscript.