GOTax, a novel web-based platform that integrates protein annotation with protein family classification and taxonomy, allows for an extensive assessment of functional similarity between proteins and for comparing and analyzing the distribution of protein families and protein functions over different taxonomic groups. We describe GOTax, a comparative genomics platform that integrates protein annotation with protein family classification and taxonomy. User-defined sets of proteins, protein families, annotation terms or taxonomic groups can be selected and compared, allowing for the analysis of distribution of biological processes and molecular activities over different taxonomic groups. In particular, a measure of functional similarity is available for comparing proteins and protein families, establishing functional relationships independent of evolution. New opportunities for understanding biology at the molecular level have been created as a result of the complete sequencing and extensive annotation of different genomes. The scientific community is just starting to uncover the agents and mechanisms taking part in the molecular biology of different organisms as we identify their genes and gene products and the corresponding biological and molecular roles of these. The comparison of two different genomes allows for identifying the common and unique characteristics of each of the genomes and provides a way for transferring annotation from well characterized to less well characterized genomes. As more and more genomes from organisms of different species across the whole taxonomic tree are characterized, it becomes possible to compare not only two genomes, but also sets of genomes. Therefore, differences and similarities in the molecular biology between different taxonomic groups can be investigated in a systematic and objective way. For example, it becomes possible to identify the distribution of particular protein families or particular biological processes and molecular activities along the taxonomic or phylogenetic tree.The comparison of different sets of genomes allows for identifying the processes, activities, and families unique to certain taxonomic groups or shared between taxonomic groups. A concrete application is the comparison between pathogenic and non-pathogenic bacteria, which provides insight into the mechanisms of pathogenicity. Another application is the comparison between human and different pathogens in order to identify features unique to the pathogens, a first step in the discovery of new drug targets. Comparative methods often rely on homology relationships, which are identified based on sequence similarity. More recently, new methods became available for comparing genes and gene products according to their functional annotation. In particular, measures of functional similarity of gene products have been proposed ,2 that rWe describe GOTax, a platform for investigating and comparing proteins, protein families, their activities and biological roles over the taxonomic tree. The platform includes GOTaxDB, a database integrating protein annotation , functioThe GOTax platform consists of four basic components Figure . GOTaxDBWe developed an intergrated database schema that was implemented using a MySQL  databaseGOTaxExplorer, the main tool for querying GOTaxDB, has been implemented in Java 1.5, providing a platform-independent software. The stand-alone version of GOTaxExplorer was successfully tested on Debian Linux, Red Hat Linux, Solaris 9, Windows 2000, and Windows XP. It comprises two user interfaces, a CLI and a GUI. The GUI is available either as a stand-alone application or as a Java Web Start application. Additionally, a SOAP  server iGOTaxExplorer supports four basic types of queries: selection of sets of entities; comparison of two sets of protein families; semantic comparison of two sets of GO terms; and functional comparison of two sets of proteins or protein families. Sets are selected by searching the different data sources integrated in the database and establishing relationships between the different data types  with a user-defined condition. This way the relationship between protein families and taxonomic groups can be investigated. A concrete example is determining the distribution of the PHP domain (PF02811) over the taxonomic tree. Moreover, GOTaxExplorer allows for finding the lowest common ancestor of species satisfying a query.The relationship between protein families and functional annotation can also be investigated. An example is, "Which biological processes are annotated to proteins with a PHP domain?" Furthermore, queries relating proteins to a specific family and functional term are possible: "Select all proteins with the PHP domain that are involved in DNA replication." More complex queries can relate all integrated data sources to each other. This allows queries like "Which Pfam domains involved in DNA replication are present in bacteria and not in archaea?" The comparison of two sets of Pfam families shows which families occur in both sets or are unique to one of these sets. This also allows for a general functional comparison of these two sets according to the GO annotation of the Pfam families. This can serve as a guide for a more detailed functional comparison between the genes of two species. An example is the identification of the Pfam families associated with both yeast and human proteins.Saccharomyces cerevisiae but not in human?" results in a list of GO terms. Let BP1 be a GO term mapped to a yeast protein and BP2 a GO term mapped to a human protein. Then the following cases can occur: BP1 equals BP2, BP1 is an ancestor of BP2, BP1 is a descendant of BP2, or BP1 and BP2 are both descendants of the same GO term. In the first case, the two terms are identical and, therefore, BP1 is not included in the result set. If BP1 is an ancestor of BP2, the true path rule implies that BP1 is also not included in the results. The true path rule states that a gene product annotated with one GO term can also be annotated with the term's ancestors in the graph. In the third case, the situation is not clear. In this case, the human gene product is annotated with the less detailed GO term, which can have several reasons. One possible explanation is that there is less knowledge about the human protein. However, it could also be the case that the fungal protein is a subclass of the more generic human protein. Alternatively, it is possible that there is more knowledge about the specific function of the human protein but it is not reflected by the annotation, or the exact functional term is still missing in GO. It remains unclear whether the human gene product is involved in the same process as the gene product from fungi or not. The fourth case presents a similar problem where the relationship between terms BP1 and BP2 is not clearly evident. In the latter two cases, the semantic similarity provides a basis for deciding whether the two terms are similar or not.The semantic comparison of two sets of GO terms uses a semantic similarity measure for finding similar and dissimilar terms in two sets of GO terms. The simple query "Which biological processes are present in MFscore and the BPscore assess the similarity of the molecular functions and biological processes, respectively, annotated to two proteins or protein families. The funSim score and the rfunSim score combine MFscore and BPscore into one measure. These scores allow for identifying the differences in the molecular biology of two closely related species, for example: "How functionally related are the proteins from Saccharomyces cerevisiae and Schizosaccharomyces pombe?" The functional comparison of protein families provides a way to compare species or groups of species whose proteins are not properly annotated to GO, and instead relies on the GO annotation of Pfam. Since GOTaxDB has a rather complicated database schema, the direct SQL queries to the database are quite complex. Therefore, we implemented a new simple query language in GOTaxExplorer. This query language was designed to be as easy to use and as flexible as possible. This greatly simplifies the use of the platform. More details about the query language can be found in Additional data file 1.The functional comparison of two sets of proteins or protein families allows for identifying functionally similar proteins or protein families from different species. For this comparison, we use semantic similarity scores that have been previously described . The MFsThe GOTaxExplorer CLI provides a light-weight interface with all query capabilities, but without input aids and result visualization. In addition to the query language, this version provides the option of using SQL for directly querying GOTaxDB. All results are printed onto the screen in a tabular format. The major advantage of this interface is that it can be integrated into scripts that automatically generate queries and further process their results. A batch file with commands can be used to query GOTaxDB or, alternatively, the query commands are read from the standard input. Furthermore, it is possible to integrate the CLI into other programs that access GOTaxDB in order to take advantage of the simple query language.The GUI provides a query interface that helps users to formulate queries without knowing the exact syntax of the query language Figure . It alsoMFscore. The visualization of the results from a Pfam family query provides for quickly classifying found Pfam families into functional categories. In case of the comparison of two Pfam family sets, the families are colored according to the set they are contained in. This permits a functional classification of the families unique to one set, shared between both sets, and missing in both sets.GOTaxExplorer provides the possibility to visualize taxonomy and GO results in the taxonomic tree or the GO tree Figure . AdditioGOTaxExplorer is also available as a Java Web Start client application . Java WeThe SOAP server provides a standard interface for remote access to the online version of GOTaxDB. This interface allows for integrating the GOTax platform in other web services without the need for a locally installed version. The SOAP server provides the same query functionality as the Web Start version. We created a Web Services Description Language (WSDL)  file desBPscore, MFscore, funsim score, and rfunSim score. Additionally, it is possible to obtain a similarity matrix for one of the scores from an all-against-all comparison. Different applications might require distances rather than similarities. Therefore, FSST is capable of transforming the scores into distances according to the formulaThe FSST was implemented for comparing user defined sets of gene products or annotations. It supports all similarity measures and different output formats and its multi-threaded implementation takes advantage of symmetric multi-processing computers, decreasing runtime considerably. FSST is configurable using command line arguments and a configuration file. As input to FSST, the user can provide a database file in plain text format giving the reference entities and their GO annotation, and a query file with the same format defining the query entities with their annotation. It is possible to perform either an all-against-all or a one-to-one comparison of query entities against database entities. The results are written to a text file containing distX = 1 - X   (1)X is either the BPscore, the MFscore, the funsim score, or the rfunSim score of the two gene products A and B. FSST is distributed with an embedded Apache Derby [where he Derby  databasefunSim score [simRel) takes into account how close the two terms are to their lowest common ancestor as well as how detailed this lowest common ancestor is. It ranges from 0 for not related GO terms to 1 for highly similar terms. A simRel score below 0.5 indicates that the two GO terms are only distantly related [GOA and GOB, respectively, is then calculated as follows. For each term in GOA, find the most similar term in set GOB, and calculate the average of their similarities as rowScore. Then, for each term in GOB find the term with the highest similarity from set GOA, and calculate the average as columnScore. The GOscore is defined as:We developed a method for assessing the functional similarity of two gene products, the im score . This scim score ,16,17. T related . The simGOscore = max, columnScore)   (2)GOscore is calculated for BP (BPscore) and for MF (MFscore), respectively. Finally, the funSim score is calculated as:A max(BPscore) and max(MFscore) denote the maximum possible score for biological process and molecular function, respectively. The funSim score ranges between 0 for completely unrelated gene products and 1 for gene products with identical functionality. Due to its definition, the funSim score is lower than the average of BPscore and MFscore in most cases. In order to obtain a more intuitive score, we define the rfunSim score for two gene products aswhere rfunSim ranges from 0 to 1 as funSim, but the values are up to 25% larger. Although the square root is a simple transformation, it changes the performance of the score. We tested how well the scores differentiate between protein pairs without sequence similarity and orthologous protein pairs. The ROCR [rfunSim score achieves a better calibration error than the funSim score. The detailed analysis with examples of protein pairs can be found in Additional data file 1.The ROCR  package The ROCR  was usedThe ROCR  for the In this section, we demonstrate the selection and comparison functionality of GOTax. First, the relationship between the PHP family domain and the other types of data is investigated. Comparisons are then performed at three levels with different examples: comparison of Pfam families, the semantic comparison of functional terms, and the functional comparison of proteins. For this analysis, the following databases where used: UniProt version 8.4, Pfam version 20, SMART extracted from InterPro  version Escherichia coli. It has been shown that the active site of this protein contains three zinc ions [The PHP domain (PF02811) is a putative phosphoesterase domain and belongs to the Pfam clan 'Amidohydrolase superfamily'. This family includes bacterial DNA polymerase III proteins as well as histidinol phosphatases and uncharacterized proteins. The only member of this family with known three-dimensional structure is the hypothetical protein Ycdx from inc ions . The putStaphylococcus aureus. The list of results includes essential proteins from other pathogenic organisms, indicating that the PHP domain is a promising drug target.First, we looked at the distribution of the domain over the taxonomic tree. Figure S. cerevisiae. The complete table with the results can be found in Additional data file 4. The search took less than 2 seconds, and the results show that human and yeast share 1,580 families, and that 1,478 Pfam families are unique to human and 252 are unique to yeast. The map of the functional space of Pfam families showing common families and families unique to one of the species is presented in Figure We used GOTaxExplorer for the comparison of Pfam families found in human with Pfam families found in Another example is the comparison of Pfam families found in proteins from human and different human viruses. GOTaxDB contains proteins and annotation from 18 human viruses, including the hepatitis C virus (HCV), HIV, and influenza viruses. The comparison shows that 24 Pfam families are shared between the viral and human proteins, 132 families are unique to viruses, and 3,170 families are unique to human. A map of the functional space with the comparison results is included in Additional data file 1 . The FtsJ-like methyltransferase domain (PF01728) occurs in human and in viral proteins for example. This domain occurs at the amino terminus of flaviviral NS5 protein and is hypothesized to be involved in viral RNA capping . All shaViridiplantae and fungi. Fungi and Viridiplantae share 1,898 Pfam families while 487 families are unique to fungi and 700 are unique to Viridiplantae. The results of this comparison can be found in Additional data file 6.It is also possible to compare the Pfam families of two different taxonomic groups. One example is the comparison of Pfam families found in proteins from S. cerevisiae proteins and biological processes annotated to human. First, we identified 382 biological processes found in yeast and not in human. Then, a semantic comparison was performed between the processes unique to yeast and the processes found in human. We found 100 yeast processes with a simRel score above 0.9 to a human process, indicating that there is a very similar process in human for each of these yeast processes. The biological process with the lowest semantic similarity of 0.14 is 'plasmid partitioning' (GO:0030541). There are 38 processes in yeast with a semantic similarity below 0.6 to any process occurring in human. Such low similarity values suggest that the best matching process in human is only distantly related to the yeast process. These 38 processes were grouped into the more general GO categories 'cellular physiological process' (GO:0050875), 'localization' (GO:0051179), 'metabolism' (GO:0008152), and 'regulation of physiological process' (GO:0050791)  and S. cerevisiae (NCBI Taxonomy id: 4932). UniProt contains 47,498 proteins from A. thaliana; out of these, 20,261 and 15,470 are annotated with MF and BP, respectively. From the 7,498 S. cerevisiae proteins in UniProt, 4,070 and 4,467 are annotated with MF and BP terms, respectively. The complete output can be found in the Additional data file 9. Figure A. thaliana proteins. The NA column contains proteins for which the corresponding score could not be computed because of a lack of molecular function or biological process annotation. More than half of A. thaliana proteins have either no molecular function or biological process annotation. Most of the annotated proteins have a high functional similarity to a S. cerevisiae protein. However, there are some proteins with a rfunSim score between 0.4 and 0.6, indicating only distant functional similarity. One such example is the cytokinin dehydrogenase 6 precursor (Q9LY71) from A. thaliana. It is annotated with the process 'stomatal complex morphogenesis' (GO:0010103) and the function 'cytokinin dehydrogenase activity' (GO:0019139). The most similar protein from yeast is the dihydrofolate reductase, which is annotated with the process 'folic acid and derivative metabolism' (GO:0006760) and with the functions 'dihydrofolate reductase activity' (GO:0004146) and 'protein binding' (GO:0005515). These two proteins have a rfunSim score of 0.47. Both proteins have oxidoreductase activity, which translates into a MFscore of 0.664. However, the processes they are part of are not related at all (BPscore = 0.0).We used FSST for a functional comparison of all proteins from There are other databases and programs that allow for selecting and comparing sets of entities and for performing functional similarity comparisons. However, none of these tools offers the same degree of functionality as the GOTax platform. On the one hand, the Sequence Retrieval System at EBI , Entrez The GOTax platform integrates proteins, protein families, Gene Ontology, and taxonomy into one database. It provides several interfaces for the selection and comparison of different sets of entities, and for performing functional comparisons of proteins and protein families. The major limitation of our approach is the lack of complete annotation for many genomes and especially of the proteins unique to certain taxa. Although several completely sequenced genomes are annotated, this annotation is not complete and is still changing. Restricting GOTaxDB to completely sequenced genomes is one way of trying to prevent such problems, but the user should also take into account that more reliable results are obtained for the genomes that are extensively annotated. The extent to which the genomes are annotated is provided on the GOTax web site . The userfunSim score. Additional data file Viridiplantae and fungi. Additional data file Viridiplantae and fungi. Additional data file Arabidopsis thaliana and S. cerevisiae.The following additional data are available with the online version of this paper. Additional data file rfunSim scoreFigure S1: Description of the query language used by the GOTax platform and an analysis of the Click here for fileTable columns are, in the following order: numbering, GO term accession number, GO term name, GO term type.Click here for fileTable columns are, in the following order: numbering, UniProt accession number, UniProt name, database.Click here for fileTable columns are, in the following order: numbering, Pfam accession number, domain name. Set 1 corresponds to yeast Pfams and set 2 to human Pfams.Click here for fileTable columns are, in the following order: numbering, Pfam accession number, domain name. Set 1 corresponds to virus Pfams and set 2 to human Pfams.Click here for fileViridiplantae and set 2 to fungal Pfams.Table columns are, in the following order: numbering, Pfam accession number, domain name. Set 1 corresponds to Pfams from Click here for filesimRel, matched GO term accession number, matched GO term name, semantic similarity according to Lin, matched GO term accession number, matched GO term name.Table columns are, in the following order: numbering, GO term accession number, GO term name, GO term type, Click here for filesimRel, matched GO term accession number, matched GO term name, semantic similarity according to Lin, matched GO term accession number, matched GO term name.Table columns are, in the following order: numbering, GO term accession number, GO term name, GO term type, Click here for fileArabidopsis thaliana (NCBI Taxonomy id: 3702) and S. cerevisiae (NCBI Taxonomy id: 4932)Complete FSST output for the comparison of proteins from Click here for file


GFINDer, a Web system we previously developed that dynamically aggregates functional and phenotypic annotations of user-uploaded gene lists and allows performing their statistical analysis and mining.The increasing protein family and domain based annotations constitute important information to understand protein functions and gain insight into relations among their codifying genes. To allow analyzing of gene proteomic annotations, we implemented novel modules within GFINDer original modules specifically devoted to the exploration and analysis of functional signatures of gene protein products. They allow annotating numerous user-classified nucleotide sequence identifiers with controlled information on related protein families, domains and functional sites, classifying them according to such protein annotation categories, and statistically analyzing the obtained classifications. In particular, when uploaded nucleotide sequence identifiers are subdivided in classes, the Statistics Protein Families&Domains module allows estimating relevance of Pfam or InterPro controlled annotations for the uploaded genes by highlighting protein signatures significantly more represented within user-defined classes of genes. In addition, the Logistic Regression module allows identifying protein functional signatures that better explain the considered gene classification.Exploiting protein information in Pfam and InterPro databanks, we developed and added in GFINDer modules provide genomic protein family and domain analyses supporting better functional interpretation of gene classes, for instance defined through statistical and clustering analyses of gene expression results from microarray experiments. They can hence help understanding fundamental biological processes and complex cellular mechanisms influenced by protein domain composition, and contribute to unveil new biomedical knowledge about the codifying genes.Novel  A great quantity of biomolecular information about genes and proteins is increasingly accumulating in form of textual annotations within heterogeneous and widely distributed databanks , which aThe gene Ontology (GO)  is curreIn the last years, several databanks have been creating to collect and provide proteomic information in organized form, including protein family and domain controlled annotations. Among them, the Pfam  and InteGFINDer [GFINDer independent and interconnected modules use several controlled vocabularies describing gene related biomolecular processes and functions.In order to allow comprehensive evaluation of gene annotations sparsely available in numerous different databanks accessible through the Internet, we previously developed GFINDer . It is aGFINDer  that dynGFINDer we implemented new specific modules dedicated to exploring and analyzing functional signatures of gene protein products. They allow annotating user-classified nucleotide sequence identifiers with controlled information on codified protein families, domains, and functional sites; classifying them according to such protein annotation categories; and statistically analyzing the classifications defined. The novel GFINDer modules support a genomic approach in the biological interpretation of high-throughput experimental results and hence in the understanding of fundamental biological processes and complex cellular patho-physiological mechanisms.With the aim of effectively take advantage of the valuable protein information present in Pfam and InterPro databanks, in GFINDer database and found that 3,495 InterPro entries  were grouped in 927 hierarchical trees of parent/child relations . We reconstructed such parent/child hierarchical trees within the GFINDer database and noticed that protein family trees had a maximum of 6 levels, with an average of 431 entries per level; protein domain trees had a maximum of 5 levels, with an average of 175 entries per level; and functional site trees had a maximum of 2 levels, with an average of 14 entries per level. Thus, only a total of 26.95% of the protein family (28.54%), domain (24.45%) and functional sites (9.84%) controlled annotations available in InterPro are hierarchically organized in many unrelated and very large, but not deep, trees of parent/child hierarchical relations.Pfam databank version 19.0 contained 8,183 protein family domain entries and InterPro databank release 12.1 included 12,967 entries . We structured all these entries in a GFINDer Web system new original modules specifically devoted to the exploration and analysis of functional signatures of gene protein products. They first enable the annotation of numerous user-classified nucleotide sequence identifiers with controlled information on related protein families, domains, and functional sites available in Pfam and InterPro databanks. Then, they allow classifying the annotated nucleotide sequence identifiers according to their protein annotation categories, and statistically analyzing the obtained classifications. In particular, the Exploration Protein Families&Domains module . After selecting a specific gene class, for each protein family or domain annotation category in that class the module provides the observed number of input genes, their expected number, and the significance p-value for that category with its histogram  or tyrosine phosphatase proteins (52 genes) [GFINDer Exploration and Statistics Protein Families&Domains modules to evaluate the relevant presence of genes associated with specific protein families or domains within the tyrosine kinase genes (TK) versus the tyrosine phosphatase genes (TP), or within the apoptosis genes (APO) versus the growth factor genes (GF) considered.In order to test effectiveness of our approach and assess capabilities of implemented 2 genes) -13. The 2 genes) . We usedExploration module we observed the distribution of protein families or domains within the two considered TK and TP classes of genes  and "Receptor tyrosine kinase, class II" (p = 0.02122) categories for the TK class; "Protein-tyrosine phosphatase, non-receptor 1/2" (p = 0.01641) and "KIM-containing protein tyrosine phosphatase" (p = 0.01641) categories for the TP class.First, with the s Figure . Then, uGFINDer statistical analysis properly highlighted as most relevant in each gene class protein domains logically pertaining to that class , "Protein kinase-like" (p < 0.00001), "Tyrosine protein kinase" (p < 0.00001), "Src homology-3" (p = 0.00016), and "SH2 motif" (p = 0.01459) protein domains for the TK gene class; "Tyrosine specific protein phosphatase and dual specificity protein phosphatase" (p < 0.00001), "Tyrosine specific protein phosphatase" (p < 0.00001), and "Protein tyrosine phosphatase, catalytic region" (p < 0.00001) for the TP class.Finally, we analyzed the protein domains most represented in the TK versus TP class. Focusing only on genes with protein domain annotations and on protein domain categories associated with at least five of the considered genes, s Figure . In factStatistics module to evaluate the protein domains most represented in the APO versus GF class. Also in this case, GFINDer statistical analysis properly selected as most relevant in each gene class protein domains correctly pertaining to that class , "TNFR/CD27/30/40/95 cysteine-rich region" (p = 0.00066), "Caspase Recruitment" (p = 0.00085), and "Apoptosis regulator Bcl-2, BH" (p = 0.00296) for the APO class; "Transforming growth factor beta" (p < 0.00001), "Transforming growth factor beta (TGFb), N-terminal" (p < 0.00001), and "Growth factor, cystine knot" (p = 0.00002) for the GF class.Similarly, we used the s Figure . In factGFINDer Web system.Obtained results demonstrate validity of the approach for the analysis of protein families, domains and related genes that we developed, implemented and made available within the GFINDer Protein Families&Domains modules we developed allow exploring and analyzing the available protein family and domain annotations by exploiting such relations. In the Exploration module, the user can select the level of detail at which exploring the protein family and domain annotations associated with a considered set of genes, or explore all levels at the same time  and tyrosine phosphatase (TP) proteins  and "Immunoglobulin-like" (p = 0.234) for the TK class, or "Fibronectin, type III-like fold" (p = 0.16395) and "Fibronectin, type III" (p = 0.20151) for the TP class).Information about protein families and domains is paramount to understanding gene functional characteristics. As the most reliable source of such information we selected Pfam, the curated databank specifically concerning protein families and domains. In addition, we also considered InterPro databank, since it is the most comprehensive source of annotations on the subject. In fact, it contains data from many member databases such as UniProt, PROSITE, PRINTS, ProDom, SMART, TIGRFAMs, PIRSF, SUPERFAMILY, Gene3D, PANTHER, and also Pfam. Furthermore, InterPro provides parent/child relations among many of its entries. Such relations indicate hierarchical levels of detail between a parent entry and a more specific child entry. The e Figure . In the  Figures , 3 and 6s Figure . In thisLogistic Regression module, the user can select the level of detail of the protein annotations to be included in the regression, or focus only on those independent annotations that are not linked by parent/child relations. Although logistic regression requires several samples  to provide reliable results [GFINDer Logistic Regression module are promising. They show that this module can support better interpretation of gene classes identified through gene expression experiments by finding the functional characteristics that better explain the considered gene classification. Furthermore, it can aid in better characterizing genes whose functional role is not yet completely understood, or even unknown, by estimating the probability of a gene to be functionally similar to a set of genes with certain functional characteristics. Thus, the implemented logistic regression analysis could contribute to unveil new biomedical knowledge about the considered genes.In the  results , applicaGFINDer does. Therefore, the novel implemented modules for evaluating protein family and domain annotations considerably enrich GFINDer, which is freely available on-line for non-profit use [Even if several tools are available for the analysis of gene annotations according to the Gene Ontology, at present to our knowledge only DAVID  and Fatiofit use , and makGFINDer modules for the evaluation of protein family and domain annotations of sets of genes can help in better interpreting high-throughput gene lists and in unveiling new functional knowledge about the considered genes, as our validation demonstrated. Thus, they can support a genomic approach in the understanding of fundamental biological processes and complex cellular mechanisms influenced by protein structural and functional characteristics related to domain composition of the codified proteins.The novel uniprot sprot.dat text file, which contains the whole protein information in the UniProt databank [GFINDer database table that we related to a previously created gene2accession correspondence table. This table contains nucleotide sequence and gene IDs, and the associated accession numbers of their protein products, which are freely available from the FTP site [By using standard text-parsing procedures, we extracted protein accession numbers, Pfam IDs and protein family domain categories from the databank  and is fdatabank . Then, wFTP site  of the EFTP site .GFINDer multi-database system we imported those InterPro tables containing controlled annotations of protein families, domains and functional sites and their hierarchical relations, and related them to the gene2accession correspondence table. Furthermore, we reconstructed the trees of parent/child hierarchical relations defined among the InterPro entries and structured them within the GFINDer/InterPro database in order to use them more efficiently during user protein annotation analysis.From the InterPro FTP site  we freelGFINDer Web system is built as three-tier architecture based on a multi-database structure. In the first tier, the data tier, a MySQL DBMS manages all considered biomolecular annotations stored in different relational databases. In this tier, we added a specifically designed relational database where we stored and hierarchically structured all annotations retrieved about protein families and domains. To associate a protein family or domain with their codifying genes we considered the protein codes associated with a gene, as provided by the Entrez Gene database. Using Java programming language, we implemented procedures able to automatically import and keep updated, in GFINDer data tier, protein family and domain information and related gene annotations when new releases of them become available in UniProt, InterPro and Entrez Gene databanks. Particular procedures automatically reconstruct protein family and domain hierarchies according to the hierarchical information provided by InterPro, and structure them in the specific GFINDer database.GFINDer processing tier we used Microsoft ActiveX Data Object technology and Standard Query Language to interact with the MySQL DBMS server on the data tier, and created new management and analysis routines in Javascript and Active Server Page scripts. In the analysis routines categorical and logistic regression statistical approaches were implemented to evaluate protein family and domain category terms. They employ different tests  to assess statistical significance of the over- and under-representation of categorical protein family and domain annotations in a group of user-classified genes.In GFINDer user tier, which is composed of any client computer connected to the Web server on the processing tier through an Internet/intranet communication network, we employed Hyper Text Markup Language and Javascript to implement a Web graphic interface for the new developed Protein Families & Domains modules.In GFINDer to analyze protein family and domain controlled annotations. Because a gene may or may not be associated with a certain protein annotation category, the number of genes and their frequency, distribution, and probability of occurrence is calculated for each protein family and domain associated with the user-considered gene set. Several different statistical tests can be used to calculate a probability value of having x genes or fewer associated with a given annotation category. In GFINDer we implemented the hypergeometric test (more time-consuming), the binomial test (which is an asymptotic limit of the first for high number of genes), and the exact Fisher test (based on a two-way table crossing gene classes and protein family or domain categories) [p-values relate to relevant protein family or domain categories for a certain class of genes. However, depending on the number of considered genes and their associated protein families or domains, the number of performed statistical tests can be high. This can greatly increase the Type-I error associated with the tests, i.e. the probability of obtaining a significant p-value by chance when the null hypothesis is true . This requires corrections on the calculated p-values in order to obtain proper significances.Categorical statistical techniques were implemented in egories) ,25,26. AGFINDer several correction methods for multiple tests are available. The simplest and strictest is the Bonferroni method that can be applied if the performed tests are independent [α of each single test, from which every corresponding p-value is considered significant, in such a way that the Type-I error of the whole set of tests is maintained. The correction is the following: αcorrected = α / N, where N is the number of performed tests. From a practical point of view this is equivalent to keep the usual threshold α for the performed tests and apply a correction to the observed p-values such that: pcorrected = N * p. However, the Bonferroni method greatly reduces the power of detecting a specific hypothesis when the number of tests increases. False Discovery Rate (FDR) and Family-Wise Error-rate (FWE), an extension of Bonferroni method, are milder corrections and they are even suitable when independence among tests does not hold [p-values such that the maximum has rank N and the minimum has rank 1. Then, the correction to be applied is: pcorrected = p * N / rank(p). The latter instead, in the implementation proposed by Benjamini and Hochberg [p-value correction: pcorrected = p * (N - rank(p) + 1). All three methods above illustrated have been included in GFINDer. Among them the FDR, which is the mildest of the three and practically consists of defining the maximum acceptable number of obtained false-positive tests, is considered the most suitable correction method to be applied to genomic data.In ependent . It consnot hold . The forHochberg , uses th0 + b1x1 + b2x2 + ... + bixi + ... + bnxn) that predicts the probability (p) of the occurrence of an outcome event (y) as a function of certain predictor variables (xi). It fits a special s-shaped curve by using the non linear function:Logistic regression  is a varp-values between 0 (as y approaches minus infinity) and 1 (as y approaches plus infinity). In our implementation, we considered the following corresponding non linear equation, called logit(p):which produces p is the proportion of considered classified genes in the two evaluated classes; xi are the proportions of considered genes in the two evaluated classes that present the i characteristic; bi (with 1 ≤ i ≤ n) are the regression coefficients for the i characteristic; and b0 is the intercept, or constant term, of the regression. The absolute value of each bi coefficient indicates the importance of the corresponding i characteristic in contributing to the considered gene classification.where null model was used as starting guess for the iterations, i.e. all bi coefficients are zero and the b0 intercept is the logarithm of the ratio of the number of considered genes belonging to the first of the two evaluated classes to the number of considered genes belonging to the second class.In order to solve the non linear equation, we used a straightforward Active Server Page and Javascript implementation of a standard iterative method  to minimp-value of bi coefficients, our logistic regression implementation also produces an odds ratio associated with each predictor variable xi and its 95% confidence interval. The odds of an event is defined as the probability of the event occurring divided by the probability of the event not occurring. The odds ratio for a predictor variable xi denotes the relative amount by which the odds of belonging to the first of the two evaluated classes, for those considered genes that present the i characteristic, increases (odds ratio greater than 1.0) or decreases (odds ratio less than 1.0) when the value of the predictor variable xi is increased by 1.0 unit.In addition to providing value, standard error and GFINDer software modules, and wrote this manuscript. EB developed GFINDer software module devoted to logistic regression, and was involved in their design and testing. AF developed the routines devoted to structuring and updating protein family and domain annotations within GFINDer database. FP provided supervision and funding of the project.MM was responsible for the overall conception and project coordination, was involved in design, development and testing of


Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from  They belong to the Apicomplexa phylum, which contains many other parasitic protists of medical and veterinary importance [Malaria is a life-threatening disease affecting half a billion humans in underdeveloped and developing countries. Its global heartland is Africa, with an appalling death toll of 1 to 2 million people every year [portance .i.e. quinine and chloroquine [Plasmodium to the very few drugs that have been developed [Artemisia annua, and derivatives which can be produced efficiently and cheaply. However, the scientific community is worried that plans for the extensive use of artemisinin might be ruined by emergence of the parasitic resistance it will almost certainly trigger, sooner or later [Malaria was eradicated from temperate regions following concerted preventative sanitary actions and after important insecticide campaigns and systematic treatments with available drugs, oroquine -6. The poroquine . Resistaoroquine ,9. Subseeveloped . The proeveloped . Currentor later -15. GiveP. falciparum [Plasmodium yoelii yoelii [Plasmodium berghei, Plasmodium chabaudi, and P. vivax, and the genomic sequencing of Plasmodium gallinaceum, Plasmodium knowlesi and Plasmodium reichenowi still in progress [Plasmodium DNA  accounted for the instability of genomic fragments in bacteria [Plasmodium genus is, therefore, the best documented at the genomic sequence level, with well-established syntenic relations. At the level of the Apicomplexa phylum, additional complete genomes of Cryptosporidium, Theileria and Toxoplasma have been either released or announced [A major landmark in the history of malaria was the launch of a collaborative genomic sequencing programme in 1996 -21. In Nlciparum  and a whi yoelii  were relprogress . This unbacteria -19 and cnnounced ,26.Plasmodium molecular data have been collected and organized in the PlasmoDB public database as early as sequencing outputs were made available [i.e. the "gene to mRNA to protein" dogma, using the Gemomics Unified Schema [Plasmodium species at one glance, based on syntenies. Predictions of protein domains, post-translational modifications, subcellular targeting sequences, etc. are included. Furthermore, PlasmoDB is currently the only site where molecular data are (1) tentatively clustered based on homology, (2) linked to generic schemes designed to view metabolic pathways, and (3) linked to X-omic functional information . Any biologist can exploit these integrated data with basic or combined queries [i.e. appropriate biological knowledge that can accelerate the design and introduction of new therapeutic strategies. PlasmoDB operates inside ApiDB, a master web portal for apicomplexan genomes [All vailable -30. The d Schema ,29, and  queries -30, and  genomes .P. falciparum genes could be assigned a function, based on detected sequence homology with characterized genes from other organisms [P. falciparum genome was estimated to code for 5,268 proteins, out of which 3,208 did not have any significant similarity to proteins in other organisms to justify provision of functional assignment. This proportion of uncharacterized genes was further increased by 257 additional sequences, which had significant similarity to proteins, described as hypothetical, in other organisms [in silico exploration of the malaria biology.Contrasting with this integrated and user-friendly access to molecular and functional data, the proportion of genes for which a biological function has been inferred appears like a curse. Only 34 % of the rganisms ,33,34. Trganisms . The 200Plasmodium sequences and distantly related organisms.Since absence of evidence is not evidence of absence, the scientific community faces a serious epistemological problem when trying to derive conclusions from a small genome (the number of genes is similar to that of yeast), in which two thirds cannot be used in meaningful analyses. A sustained effort is required to improve functional annotation methods and to contribute to the PlasmoDB gene descriptions. Different teams in the world have attempted to address this problem. This paper provides an overview of some of the theoretical and practical developments that were introduced to improve detection of similarities between Plasmodium cellular organization is the result of multiple endosymbioses involving an ancestral alga [i.e. the apicoplast [In silico analyses combining molecular phylogeny and targeting sequence prediction allowed a first rationalized mining of the apicoplast function [A second difficulty, also related to sequence homology detection, is the reconstruction of molecular phylogenies for malarial genes. Accurate molecular phylogenies are particularly important since the ral alga ,35,36, aicoplast -43. Compicoplast . In silifunction . The idefunction  and TULIfunction ,47 approin silico management (and mining) of information. Genes coding for enzymes can further be linked to metabolic scheme(s) designed using generic methods, i.e. KEGG [Plasmodium, i.e. the Malaria Parasite Metabolic Pathways (MPMP) [A third difficulty is the representation of knowledge of malaria parasite biology. Description of gene function follows the guidelines of the Gene Ontology (GO) structured vocabulary . GO is a.e. KEGG  or MetaC.e. KEGG , or specs (MPMP) ,51. PathFourthly, the difficulty of organizing molecular data into knowledge representations becomes more pronounced in the analysis of global datasets arising from X-omic  experiments. The strategies, methods and tools, which have been used or designed in order to link malarial molecular and functional data in the most versatile ways, were examined. In particular the MADIBA tool, developed for local analyses of transcriptomic outputs, is described.in silico experiments can be a useful tool to help characterize a possible new target. These include determination of protein three-dimensional structure and determination of possible binding ligands through in silico protein-ligand docking. To date, the structures of less than 70 malaria proteins have been determined and made available via the Protein Data Bank [A fifth difficulty, once a target gene has been identified for possible antimalarial intervention, is the process of reaching a decision on the entry into costly drug- or vaccine-development programmes. Besides experimental validation criteria, ata Bank . This reata Bank , and onci.e., 1) comparisons of the compositionally atypical malaria gene and protein sequences, 2) high throughput molecular phylogeny assessment, 3) usable and interoperable representations of metabolic and other biological process, 4) versatile and local integration of molecular and functional data obtained from X-omic experiments, and 5) determination and prediction of protein structure and subsequent virtual ligand screening on candidate therapeutical targets. Linking protein structures with ligand structures is a pivotal step for a chemogenomics knowledge base, in which functional and structural knowledge deriving from malaria genomics and post-genomics might be connected with the space of small molecules containing known and potential drugs.This review does not pretend to provide any complete panorama on malaria molecular, functional and therapeutical genomics or to introduce panaceas. Important difficulties for global analyses and high throughput approaches were addressed here, as five major challenges for the future, Plasmodium DNA has been a recurrent problem for malarial genomics and post-genomics. It was responsible for instability of genomic segments in E. coli and difficult assembly during the sequencing process [P. falciparum [Plasmodium genus, like P. vivax, do not show such a strong compositional bias at the DNA level, but their protein sequences appear to be also compositionally divergent from average.The extreme A+T bias in  process . It impl process ,55. The lciparum -58. SomeE-value , i.e. the number of alignments one expects to find in the database by chance, with equivalent or better scores. It can be determined from the complete distribution of scores. The BlastP associated statistics defined by Karlin and Altschul [E-value computation model requires two restrictive conditions: first, individual residue distributions for the two sequences should not be 'too dissimilar' and second, sequence lengths 'should grow at roughly equal rates' [P. falciparum fall outside of the validity domain for a BlastP comparison with unbiased sequences [Plasmodium sequences did not have any apparent homology with sequences from other genomes may not be that most malarial genes are unique in the living world, but that the BlastP semi-automatic annotation procedure was technically limited. Some missed sequences could be retrieved by adding protein structural information , but thnalysis, , providiZ-value was shown to be independent of sequence length and amino acid distribution [Z-values can be used as a statistical test and a single-linkage clustering criterion. In practice, a Z-value table can be analysed using the TULIP theorem to detect pairs of proteins that are probable homologues following a Z-value confidence cutoff. For instance, a Z-value above 10 allows an estimate that the alignment is significant with a statistical risk of 1/Z-value2, i.e. 0.01. A version of the BlastP algorithm, implemented with Z-value statistics, should be helpful to refine malaria sequence comparisons.An alternative method to assess the relevance of a pairwise alignment was introduced by Lipman and Pearson . It usesribution  and is fribution  demonstre.g. GOtcha; [e.g. Ontology-based pattern identification – OPI – following the guilt-by-association principle; [Additional improvement of automatic annotation procedure are expected, in particular by combining sequence comparisons with GO term associations  a complinciple; ). In theThe amino acid compositional bias and high insert content of malaria proteins is also a disturbing factor when attempting to reconstruct phylogenies. Conventional methods used for phylogeny reconstructions based on multiple alignments can be used in conjunction with visual judgment,- , with qun protein sequences is an n × n table. According to the output table processing, it can be either totally recomputed at each database update, or stored and updated by computing complementary tables. Information is extracted from the output table to help reduce complexity and diversity at the sequence level. Sets of sequences sharing features are named "clusters" [The output of an all-by-all comparison of lusters" .E-value statistics to build the all-against-all comparison table [E-values, i.e. COG [E-value table can be converted into a rigorous and stable metric. The handling of the output n × n table of E-values requires pragmatic post-processing normalization, including asymmetric corrections of E-values obtained after permutation of the two aligned sequences or consensus E-value computation after alignment with different algorithms. The E-value table can be converted into a Markov matrix , or a close graphic equivalent, i.e. graphs connecting protein entries with E-values as weights for graph edges , a representation that has been exploited in the OrthoMCL project. The protein sets are organized either by detecting graphs and sub-graphs following pragmatic rules, with granularities depending on E-value thresholds, or by distance clustering using E-value as a pseudo-metrics, or by Markov-random-field clustering. None of the organization of the protein sequences obtained through these methods can be named a spatial projection, and none of the obtained clusters can be represented as a phylogenetic reconstruction. Eventually, the economy of computing E-values in an all-by-all comparison experiment is lost in the updating process that requires a complete re-calculation. In spite of these drawbacks, massive comparisons based on BlastP/E-values have been undertaken because they were less CPU-demanding than other methods. The OrthoMCL clustering method based on BlastP/E-value represents therefore a pragmatic reduction of the protein diversity, and phylogeny reconstruction require post treatments within each clusters. OrthoMCL flags probable orthologous pairs identified by BlastP as reciprocal best hits across genomes. Access to OrthoMCL groups is linked to the PlasmoDB GUS underlying database, allowing multiple queries with other PlasmoDB data and information, and allowing additional cross-species/cross-phylum profiling of the BlastP/E-value-supported orthologues.A first massive comparison project, OrthoMCL, was designed to cluster malaria genes based on their sequence similarity with genes of 55 other genomes , using the BlastP algorithm and Karlin-Altschul on table ,70. As mon table  for massi.e. COG , Tribe [i.e. COG , ProtoMai.e. COG , ProtNeti.e. COG , SIMAP [i.e. COG  and SYSTi.e. COG , there iZ-values rather than BlastP/E-values. This method has been initiated for the ClusTR protein sequence clustering, underlying the UniProt/Integr8 knowledge base at the European Bioinformatics Institute, EBI [Z-value statistics detailed above, it is the solution of choice when comparing compositionally biased and high-insert containing sequences. Additionally, for any set of homologous proteins, it is possible to measure a table of pair-wise divergence times and build phylogenetic trees using distance methods [Z-value and divergence time tables that serve to reconstruct the TULIP trees are the result of a Monte Carlo simulation, which is a convergent process at the level of the pair-wise comparison and is not altered by database updates. As a result, whereas a phylogenetic database computed from multiple alignments would require a complete and increasing computation for any update, the TULIP tree calculation simply requires the calculation of the {new}-by-{old} and {new}-by-{new} Z-values and divergence times. A mapping of each Plasmodium sequence can be obtained and updated following all-against-all pairwise comparisons based on Z-value statistics. The CPU-cost required by the Smith-Waterman comparison method and by the Monte-Carlo simulation used to compute Z-values will be compensated in the future by implementing Z-values on the BlastP heuristics. A high-throughput assessment of molecular phylogenies of Plasmodium genes based on BlastP/Z-value, including all recorded genes in public databases, will therefore be feasible and upgraded at the pace of public database updates.A more CPU-demanding alternative method for massive all-against-all protein sequence comparison uses Smith-Waterman/ute, EBI . Because methods . These t methods  or PUZZL methods  methods  methods . An advae.g. apicoplast lipid – fatty acid, isoprenoid -syntheses, plant-like targets localized outside plastids – folate metabolism, tubulin -, etc.). Criteria for confirmation of a plant/algal sub-genome in P. falciparum include molecular similarities with plant genes, and will therefore benefit from future progress in high-throughput molecular phylogenies. Information on sequence and sequence similarity are not sufficient to highlight functions that are, for instance, unique to plants: they have to be linked to appropriate knowledge representation of the biological function of each gene and each process in which gene products play their roles.User access to molecular phylogenies, which has been designed in the OrthoMCL project in a very practical and user-friendly way, is essential to mine the genome for clues to therapeutic opportunities. The most obvious approach is to detect protein sequences that are excluded or diverge strongly from the mammalian proteome. More subtly, the question of the plant/algal sub-genome of Apicomplexans has been demonstrated to be a source of therapeutic targets for herbicidal drugs (i.e. based on the fact that some levels of knowledge can be reconstructed by the integration of parts of knowledge of lower levels) is a difficult epistemological question and can hardly be debated here. Current data integration strategies are dependent on available consensus methods, with their compromises and imperfections that have been slowly adopted by the scientific communities. Biological  knowledge is primarily produced in the form of research publications, books, patents and other un-structured texts, and since two decades in structured databases (biologists are used to fill Genbank forms prior to paper submissions). Data and information can be organized as semantic networks, following combinations of ontologic hierarchies and praxeologic schemes [senso lato.Having in hand a table of gene entries, with summarized annotations is not sufficient to handle genomic information. Data can be organized based on biological principles so as to reflect current knowledge at best, and to be viewed at a glance in global X-omic experiments, or for comparative purposes. The question of knowledge representation in reductionist terms  consortium , malariai.e. KEGG [Plasmodium, i.e. the Malaria Parasite Metabolic Pathways (MPMP) coordinated by Hagai Ginsburg at the Hebrew University of Jerusalem [Concerning enzymes, entries can be linked to graphical representations of the metabolic reactions they catalyze. To that end, in the same way genes were defined following the GO procedure, enzyme substrates and products are defined following a chemical ontology (CO), and the reaction itself has to be defined following an enzymatic reaction ontology. A reaction can be viewed as a small graph in which the enzyme is associated with the line that connects the nodes corresponding to the substrates and products. Metabolic pathways are connected by shared nodes, and can be viewed at different scales. Current representations of malaria metabolism have been made available using generic methods, .e. KEGG ,81 or Me.e. KEGG ,82,83, oerusalem ,51.P. falciparum. Each scheme provides links to metabolite  information, and enzyme descriptions following the Enzyme Commission (EC) classification. EC numbers give access to multiple sequence alignments, protein motifs, genomic mapping, links to Genbank, UniProt, PDB, etc. Tilling KEGG schemes from different organisms highlights metabolic similarities and differences, and could be of help for anti-malarial purposes, highlighting for instance metabolic reactions occurring in Plasmodium and not in humans. However, the maps are designed based on the enzymatic reactions and, for instance, the fatty acid synthesis due to the type 1 fatty acid synthase  from the human cell cytosol, strictly overlaps with the type 2 fatty acid synthase (FASII dissociated enzymes) from the Plasmodium apicoplast. Information on the protein structure and cell compartmentalization of the enzymes would have been sufficient to distinguish between FASI and FASII. Furthermore, metabolites that are generated in one compartment (i.e. diacylglycerol generated in one of the numerous cell membranes) may not be available for an apparent downstream reaction occurring in another compartment. Thus, used without caution, the KEGG schemes may seem to be fully valid for the entire living world, with a misleadingly clear and fully cross-connected global overview of metabolism, and can lead to unrealistic representations. The missing enzymes, which have been experimentally assayed, are not shown, and it is unclear whether gaps within pathways are due to absent enzymes or to incomplete data. Additionally, the KEGG representation is not intended for the design of schemes other than those pre-defined. KEGG outputs need to be reexamined for accuracy of interpretation (see below).The Kyoto Encyclopedia of Genes and Genomes (KEGG) resource provides a set reference of metabolic schemes, manually designed so as to represent all possible primary metabolic reactions, and formatted with the KEGG Markup Language (KGML) . These rPlasmodium, called PlasmoCyc, has been initiated [P. falciparum gene annotation and information of the MetaCyc reference database, the PathoLogic module of the Pathway Tool Software [P. falciparum. As in KEGG, each graph gives access to metabolite, reaction and enzyme information. The Gene-Reaction Schematic (GRS) representation allows a visualization of the relation between the genes, the enzymes, the catalyzed reactions, even in the case of complex or multienzymatic proteins. This model is useful to distinguish for instance FASI and FASII proteins. It is further useful for Plasmodium proteins that are often multienzymatic. For each reference pathway, the occurrence or absence of enzyme homologues in the Plasmodium genome is documented. As in the case of KEGG, compartmentalization information is missing from the pathway model, in spite of some effort to highlight some specific pathways (for instance the apicoplast fatty acid synthesis).As an alternative, the design of MetaCyc schemes for nitiated ,82. As fSoftware  allows tPlasmodium biological processes can further be viewed at the Malaria Parasite Metabolic Pathways web portal [in silico graph-based treatments.A synthetic representation of b portal . Among 1From this short overview, it is clear that metabolic representations should be carefully used. On one hand, generic approaches  do not include cell compartmentalization data and the chemical ontology for lipids is not finished. On the other hand, the curated MPMP representations are based on high-quality data but they are static, with no graph management tools, giving click-access to remote information. Future challenge will be to design underlying models for building graphs from genomic data (following the GO), metabolite data , reaction connections and compartmentalization information for both gene products and metabolites. Construction of PlasmoCyc benefited from information of the MPMP ,82, and Plasmodia, includes the understanding of the transcriptome, proteome and interactome of the parasite to elucidate mode-of-action of inhibitory compounds, allow optimization of such inhibitor activities, explain resistance mechanisms to known drugs, chemically validate potential drug targets and ultimately identify and/or functionally describe new drug targets  combining interaction information with co-expression data and GO annotations indicated unique interactions, identified groups of interacting proteins implicated in various biological processes, and predicted novel functions to previously uncharacterized proteins [Plasmodium interactome with that from other organisms indicated a marked divergence with very little conservation with other protein complexes [In-depth understanding of the protein-protein interaction network (defined as interactome) can provide insights into the function of proteins, regulatory mechanisms and functional relationships of these Figure . An exteproteins . Interesomplexes .in silico data mining approaches. As such, data mining of the transcriptome using an extensive sequence similarity search identified 92 putative proteases in the P. falciparum genome, 88 of which are actively transcribed [The abovementioned X-ome datasets are therefore now available for nscribed . This stnscribed . The transcribed . PlasmoDnscribed .in situ synthesized short oligos, longer oligos, spotted oligos or cDNAs) are so high that it is almost impossible for the moment to compare results from different platforms. How can a transcriptomic profile be compared to a proteomic profile, given the errors, the linearity of signals and the magnitude of variations of each method, and the biological stability and turnover of RNA transcripts and of proteins? Is the enzyme profile correlated with the metabolite profile [An important question in mining different global X-omic datasets is how they can be compared. Draghici et al.  made cle profile ? A pragm profile ,105-107. profile . This qu profile ,109. In Plasmodium-specific genes without human homologues. MADIBA analysis of the transcriptome dataset from Le Roch et al. [Plasmodium genes (41% vs. 37%) and characterization of 6 additional clusters with GO annotations, of which one exclusively contained glycolysis in its entirety  and another identifying gene as potential as drug targets due to their Plasmodium-specific characteristics. Therefore, MADIBA allows versatile analyses of a vast variety of transcriptomic profiles. These analyses can highlight potential drug targets by providing functionality to co-clustered expressed genes in a guilt-by-association manner, including those of un-annotated proteins, by predicting co-regulated expression via chromosomal localization as well as the identification of motifs for cis-regulatory elements and lastly by identifying unique Plasmodium-specific genes involved in specific biological mechanisms.Various tools aim to provide biological interpretation of gene clusters but these mostly specialize in only one or two types of analyses. FatiGO , GeneLynh et al.  resultedP. falciparum interactome [in silico and experimental functional genomics data within a Bayesian framework to create the network of pairwise functional linkages. This resulted in predicting functionality based on associations between characterized and uncharacterized proteins for 95% of the currently annotated hypothetical proteins in the P. falciparum proteome. Only 107 hypothetical proteins show interaction with other hypothetical proteins potentially representing new pathways or previously uncharacterized components of known pathways.With the advent of integrative investigations of datasets from the transcriptome, proteome, interactome etc. new analysis tools are being developed including a Partial Least Squares (Projection to Latent Structures-PLS) method which has been used to integrate yeast transcriptome and metabolome data . Linear eractome  to eluciThis overview shows that the integration and mining of global functional genomic experiments are in the frontline in drug discovery processes ,122. MalIn the case of drug treatments, the sought effects on the metabolism of targeted tissues or organisms include up- or downregulation of the protein target(s), the upregulation of detoxification pathways (cytotoxic responses) and the upregulation of alternative or compensatory pathways of the affected organism that can be reflected in changes in the transcriptome/proteome of the organism. The characterization of the transcriptional response induced by drug challenge has been applied with success in the antibacterial field, creating reference compendia of expression profiles after drug challenge that provide insight into a drugs' MOA -125. TraP. falciparum, whether it is a new/additional functionality that is ascribed to a known protein or the characterization of novel function of a previously 'unknown' protein. As the hypothetical proteins represent more than half (~60%) of the malarial proteome (see above), these are some of the most attractive areas to the drug target discovery effort. The basic rationale behind using expression profiles to assign functionality to genes is based on the principle of guilt-by-association [P. falciparum.Any of the above-mentioned strategies are potential starting points to the discovery of unsuspected drug targets in ociation ,134 in wociation . Improveociation . OPI useociation  warns agin silico experiments can be achieved to connect these biological targets with the pharmacological space of small compounds [Vital malaria proteins may have no counterpart in humans or sequence dissimilarity with their human homologues that may be sufficient to become a therapeutical target without disturbing essential function in the human host. The literature on potential protein targets for anti-malaria treatments is already crowded, and will hopefully be enriched and better documented in the future. Once a target has been identified, an important decision is whether or not to enter into costly drug- or vaccine-development programmes. Determination or prediction of the target three-dimensional structure and ompounds , and assE. coli is notoriously difficult, however. A number of problems are typically encountered. The A+T richness results in substantially different codon usage compared to E. coli. Plasmodium genes are also typically much longer than their homologues in other organisms, as are the resulting proteins. Increased protein size is due mostly to long protein inserts with generally little homology to cognate enzymes. These inserts tend to be disordered and of low complexity, resulting in proteins that are not amenable to expression and crystallization. Further problems include sporadic mutations of low complexity sequences introduced by E. coli, and cryptic prokaryotic translation start sites within malarial genes. Some level of protein expression may be obtained by fine control of expression conditions, often a change of strain or of complete expression system, addition of rare codon tRNAs, and more and more often by production of synthetic genes coding for identical protein sequences but with a codon usage optimized for bacteria [E. coli proteins were all negatively correlated with soluble expression.The rationalized identification of new inhibitors depends on possession of structural information. As for any other organism, the primary problem is obtaining high and pure protein yields for crystallization trials. Recombinant expression of malarial proteins in bacteria -142. Mehbacteria  recentlyPlasmodium protein structures in the PDB [see Plasmodium structures have been made. Whether this trend will continue beyond the "low hanging fruit" remains to be seen.The difficulty of expressing malarial proteins is reflected by the paucity of structures in the Protein Data Bank . At the  PDB see . In cont PDB see ; the Str PDB see ) has incPlasmodium-specific inserts make it difficult to correctly identify core-conserved regions. The presence of inserts often confuses multiple and structural-alignment programmes. A number of techniques have been used to circumvent this problem  and Plam Figure . From a species . As an  system; ,150) canmologues ,151. Furmologues  and secomologues ,152.Despite the difficulties with homology modelling of malarial proteins there have been some notable successes. Malarial DHFR forms part of a bifunctional protein that also carries thymidylate synthase. A number of existing drugs such as cycloguanil and pyrimethamine target the DHFR domain, and have been used effectively in the past. However drug resistance has evolved that reduces the usefulness of this important class of drugs. Hence malarial DHFR has been a popular target for homology modelling efforts -158. ToyPlasmodium protein structures. Both publicly available versions of PlasmoDB (4.4 and 5) are still incomplete in this regard. PlasmoDB 4.4 only lists P. falciparum structures, and there is incomplete overlap between versions 4.4 and 5. PlasmoDB 4.4 includes a section for >440 modeled structures based on a wholesale modelling attempt of >5,000 open reading frames documented at [Plasmodium structures therefore requires some curating. A dedicated resource for deposition of structures from the Plasmodium structural community would be useful. The resource should also include model structures subjected to the same rigor as experimental structures in the PDB, including quality criteria and scoring. Thus "experimental" details for modelling  should be included. This resource should also allow easy comparison with corresponding solved structures enabling evaluation of the communities' ability to model this difficult organism.Apart from the PDB, there is currently no resource that includes all ented at . Howeverin silico prediction of the families of ligands that can interfere with protein function.Protein structures provide invaluable information for drug or vaccine discovery. First, compliance of the structure with known properties of recorded pharmaceutical targets (termed "druggable" genes) -167 or pAdvances in combinatorial chemistry have broken limits in organic synthetic chemistry and accelerated the production throughput. Thus, millions of chemical compounds are currently available in private and academic laboratories and recorded in 2D or 3D electronic databases. It is often technically impossible and very expensive to screen such a high number of compounds using wet high throughput screening techniques. An alternative is high throughput virtual screening by molecular docking, a technique which can screen millions of compounds rapidly and cost effectively. Molecular docking is a computer-based method which predicts the ligand conformations inside the active site of the target as well as an estimate of the binding affinity between protein and ligand. It also gives insight about the interactions between protein and ligand and allows to generate mode-of-action hypotheses. Screening each compound, depending on its structural complexity, requires from a few seconds to hours of computation time on a standard PC workstation depending on the chosen docking algorithm. Consequently, screening all compounds in a single database would require years. However, the problem is embarrassingly parallel and the computation time can be reduced very significantly by distributing data to process over a grid gathering thousands of computers ,171.in silico docking pipeline on grids of clusters [Recently, virtual screening projects on grids have emerged with the purpose of reducing cost and time. They focused on the development of an clusters  but alsoclusters . Pharmacclusters . Other pclusters .in silico docking on the European public EGEE grid infrastructure [Plasmodium responsible for the initial cleavage of human haemoglobin [P. falciparum  [In mid-2005, the WISDOM  initiative successfully deployed large scale tructure . The biomoglobin . There aand HAP) . High leand HAP) . This anin silico experimentation environment. Having the necessary computing power at hands, scientists from a virtual organization can design new large-scale test systems for generating new hypotheses. The benefit is that a large number of targets can be combined with a very large number of potential hit molecules, using different docking algorithms and allowing to chose different parameter settings. As discussed above for the X-omic experiments in post-genomic platforms, in silico methods pose the same important question in order to mine the data, i.e. how they can be compared. The careful input data preparation is a crucial step in the process, which has to be performed by experts and be made available for the whole scientific community. By sharing the results of virtual screenings in a common knowledge space, different experts coming from different fields can jointly derive a rational for selecting appropriate combinations of targets, ligands and virtual screening methods.The main goal of the WISDOM project has been to make use of the EGEE grid infrastructure to set up an in silico docking deployment saw over 46 million docked ligand-protein solutions, resulting from two docking tools, five targets, one million compounds and four parameter settings, the equivalent of 80 years on a single PC in about six weeks. Up to 1,700 computers were simultaneously used in 15 countries around the world. Post-processing of the huge amount of data generated was a very demanding task as millions of docking scores had to be compared. At the end of the large scale docking deployment, the best compounds were selected based on the docking score, the binding mode of the compound inside the binding pocket and the interactions of the compounds to key residues of the protein.The WISDOM large scale Several promising scaffolds have been identified among the 100 compounds selected for post processing. Among the most significant ones are urea-, thiourea-, and guanidino analogs, as these scaffolds are most repeatedly identified in the top 1,000 compounds Figure . Validatin silico virtual screening before in vitro testing.The developed and established protocols can be used for coming scenarios. Several teams have expressed interest to propose targets for a second computing challenge called WISDOM II that should be carried out in late 2006. While docking methods have been significantly improved in the last years, docking results need to be post-processed with more accurate modelling tools before biological tests are undertaken. The major challenges for docking methods are prediction and scoring. Molecular dynamics (MD) has great potential at this stage: firstly, it enables a flexible treatment of the ligand/target complexes at room temperature for a given simulation time, and therefore is able to refine ligand orientations by finding more stable complexes; secondly, it partially solves conformation and orientation search deficiencies which might arise from docking; thirdly, it allows the re-ranking of molecules based on more accurate scoring functions. Efforts are now devoted to deploy on the grid both docking and Molecular Dynamics calculations to further accelerate in silico storage, organization and mining of data from malaria genomics and post-genomics, were examined in the context of the prediction and characterization of targets and drugs: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogeny, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments, 5) the determination and prediction of protein structures and their virtual docking with drug candidate structures. Data management that should be at the genomic scale, including multiple species, and should therefore be as reliable as possible. A "biological space" should be formatted so as to represent scientific knowledge and to connect genomic data and post-genomic functional profiles in the most versatile way, allowing the mining of information with diverse methods . Basic analytical tools like MADIBA and sophisticated mining approaches will be needed to understand and compare the biological responses to anti-malarial drugs. The other door to connect the biological space and the chemical space follows the "direct chemical genetic" way, i.e. "from known biological target to drug candidate". In addition to crystal structures of malaria proteins, the automated structural annotation of the malaria proteome should be initiated . A chem models; , but it  models; . The mod models; ,185 and  models; ,187, how models; . Is thisBeyond virtual screening, the grid technology provides the collaborative IT environment to enable the coupling between molecular biology research and goal-oriented field work . It propin silico information must be supported and validated by data gathered in vitro and in vivo. As other X-omic strategies approach, the necessity of in silico mining is unquestionable; it is also susceptible to generate a huge amount of theoretical data that will need years to be confronted to the experimentation. The challenges in this domain remain severe. Difficulties of cloning and expressing parasite proteins in heterologous systems, the validation of druggable targets using siRNA etc, and the experimental assignment of functionality to the many hypothetical proteins, will occupy scientists in parallel for some time to come. Data generated from in silico analysis leads to a need for further laboratory work, such as the in vitro testing of ligands identified as potential drug leads through the WISDOM project. A strong linkage between scientists undertaking in vitro research and in silico researchers is therefore essential to support an iterative approach to knowledge generation and analysis, in the context of malaria chemogenomics.Finally, present and future in silico storage and organization of malaria genomics, post-genomics data and antimalaria chemical information, as made available in the literature and public websites. All authors contributed to the writing of the current review manuscript.All authors contributed to the analysis of the current status on the Plasmodium protein structures in the Protein Data Bank at the date of writing.Non-redundant malarial structures in the Protein Data Bank (PDB). The table compiles non redundant entries for Click here for file