the mouse functional genome database ( mfungd ) aims to provide a high - quality information resource for the research community incorporating manual annotation of gene products , in particular with respect to the cellular function in the context of their interaction . mus musculus is one of the most thoroughly studied mammalian model organisms . for thousands of mouse proteins , functional properties have been predicted or experimentally investigated and part of this information is stored in databases like uniprot and mgi ( 1,2 ) . due to its exceptional importance as a model organism , the genome sequence of mouse was the second mammalian genome that has been sequenced ( 3 ) . mouse is genetically tractable and large collections of mouse mutants exist which yield invaluable insights into the function of mammalian genes ( 4 ) . unfortunately , the detection of the genotype of mouse mutants that are obtained by treatment with chemical compounds such as enu is extremely time - consuming and labour intensive . in order to understand the function of mammalian genes in context and to identify the causes of complex diseases having a genetic background in mammals , bridging the gap between genotype and phenotype will be one of the most important and challenging tasks for the future . to achieve this goal , the knowledge about the function of isolated proteins needs to be extended to their functional context in the cellular environment . such an endeavour requires the integration of different sources of information like protein  protein interactions , genetic interactions as well as co - expression data . the integration of these data results in distinct but interconnected networks of proteins responsible for defined functional tasks in cells , so - called functional modules ( 5 ) . however , so far no reliable data set of functional modules for a mammalian organism exists . as an important step towards this goal , we combine computational methods with manual annotation to the mouse proteome with strong emphasis on the cellular context . a comprehensive genome resource must not only be capable to store and display information on gene products but also needs to support manual and semi - automatic annotation . to fulfil these requirements , we implemented mfungd within the mips genome research environment ( genre ) . this allows seamless integration of database management systems as well as various components required for a flexible annotation pipeline . genre is a j2ee - based component - oriented multi - tier architecture hiding the complexity of the procedures from the user . for example , the manual annotation process requires not only the access to various data sources , but also its support needs the integration of different algorithms such as clustering of protein family members in a structured way . we developed an integration tier capable to level the differences between the underlying resources by conversion into so - called data access objects ( daos ) . the main advantage of the dao design pattern within mfungd is the uniform access of any resource on a java object level . for databases , we used daos based on hibernate a high - performance object / relational persistence and query service , whereas for applications the daos were explicitly designed . on top of the integration tier , we implemented a so - called business tier based on enterprise java beans ( ejbs ) . ejbs are the core components for any kind of application ( business ) logic related to complex information processing within the annotation pipeline and advanced queries . for further unification of information , the xml format is not only used in the completely separated web - tier for rendering html output with xsl style sheets ( see figure 1 ) , but also for the communication with rich - clients for manual annotation hence reducing the time - consuming multiple invocation of ejb methods by the transmission of only one comprehensive xml document . a further advantage of the component - oriented approach is the extension of the system with minimal effort . for example , mfungd has been extended with a configurable advanced query interface component used also by different resources within mips . this interface provides the possibility to query the database using logical combinations of terms in a similar way to the entrez service . customizable full - text searches across the database are possible without any knowledge of the underlying data structure . querying indexed information is done by simple expressions allowing wildcards and the combination with logical operators . an example query for searching all mitochondrial proteins ( functional category 70.16 ) with > 1000 amino acids 70.16*[fcc ] > 1000[pil] instead of a complicated native database query involving several table joins . an inherent problem in the analysis of mammalian genomes is the lack of a complete and stable set of all exant full - length transcripts . new transcripts and splice variants are published regularly requiring continuous updating of datasets such as refseq . compared to the september 2004 refseq mouse release , the dataset of march 2005 contained 1331 new entries , 542 with changes in the transcript sequence whereas 196 that were removed . the mfungd will also allow updates of genome assembly , transcript data and gene models . the basis of the mfungd dataset is a complement of gene products that was obtained by softberry inc . , which used the fgenesh++c software as gene predictor . those include 13 259 gene products which were identical or highly similar to refseq cdnas , 18 330 gene models with significant similarity to a non - redundant ( nr ) database and 10  460 gene models without significant similarity ( > 90% identity ) to the nr database . transcripts of this dataset are currently mapped to the curated refseq dataset from the mouse strain c57bl/6j by a mapping procedure based on the blat software ( 6 ) . known transcripts from external resources which are not yet present in our dataset are added . the softberry gene models were based on the build 30 assembly of the mouse genome ( mm3 , feb . the ucsc genome browser ( 7 ) allows visualization of the mfungd transcripts , gene models and refseq data . general protein and gene features like interpro domains , 3d structure and physical properties are precalculated by the pedant system ( 8) . interpro domains and predicted transmembrane domains are shown on the mfungd web page , other features can be accessed via hyperlinks . results from fasta sequence similarity searches against > 3  000  000 protein sequences can be retrieved from the simap database ( 9 ) . attributes like gene names , protein names and synonyms are retrieved from public resources like uniprot ( 1 ) , mgd ( 2 ) or refseq ( 10 ) . in addition , mfungd contains information about literature that was used for manual annotation as well as protein i d , funcat annotation , comments , update information and cross - references to refseq , uniprot and mgd . a central part of the annotation process is the assignment of functional categories to protein entries . at mips , the functional catalogue ( funcat ) this annotation scheme has been applied to the manual annotation of several model organisms ( 11 ) . funcat is a hierarchically structured , organism - independent , flexible , controlled and scalable ( structured ) classification system enabling the functional description of proteins from any organism ( 11 ) . the capabilities of the funcat are not only limited to the functional annotation of genomes , but also provide a powerful tool in order to analyse genome- and proteome - wide data generated by large - scale transcriptome / proteome experiments ( 1214 ) as well as the computational analysis of functional networks ( 15,16 ) . the versatile application makes funcat a powerful and intensively used tool for integration of protein function data from different sources and thus fulfils the needs of bioinformatics approaches in systems biology . the assignment of functional categories in the manual annotation process depends primarily on the experimental evidence given in literature . here , the hierarchical structure of funcat allows adjusting the specificity of the level of the functional category to the information content of the experiments . in addition to information from literature , data from other sources like interpro ( 17 ) and funcatdb ( 11 ) as well as external resources like swissprot ( 1 ) genbank ( 18 ) and mgi ( 2 ) are used in order to obtain a comprehensive overview of the cellular function of respective proteins . evaluation of the information and the resulting assignment of functional categories lie in the responsibility of trained annotators . the high number of gene products in mammals requires supporting manual annotation by automated prediction of protein functions . the relation between sequence similarity and functional conservation has been well established for protein domains and complete proteins . since transfer of functional annotation given high sequence conservation is reliable , mfungd data sources for human , mouse and rat as well as other mammalian proteins annotated in swissprot were used . for the human genome , manual using conservative thresholds , funcat information has been transferred to 13  193 mouse proteins . if any of these protein entries is subsequently subjected to manual inspection , information of the automated process is supplemented with literature information and modified , if necessary . available in - house manual annotation was complemented by an automated mapping of available manual go - annotations to funcat categories . based on sequence similarity data and interpro domains , further sequence - associated information was compiled . gspc stands for  global superparamagnetic clustering ( 19 ) in which sequences are clustered in a monte carlo process according to a sequence similarity score . functional annotation is then transferred within a cluster from known to unclassified proteins by a consensus process among the known proteins in the same cluster . an internal parameter of the process determines the granularity , specificity and coverage of clusters . spc has been further developed into globalspc by systematic variation of the parameter settings ( 19 ) . the hrmn method ( heterogeneous relational markov network ) generates confidence values for the assignment of functional classification . hrmn is based on a network graph able to employ any parameter that can be assigned to a pair of sequences such as sequence similarity , interpro domains or quantitative data such as correlation of transcript regulation . independent graphs formed by independent data sources are connected to form a markov network , taking advantage of the synergistic effects between them ( 20 ) . in the graph , nodes represent proteins and the edges are weighted according to the strength of the relation between adjacent nodes . nodes may have funcat labels as attributes whose propagation from known to unknown proteins is assessed utilizing belief propagation ( 19)/generalized bp ( 20 ) . note that belief propagation does not require the sources to be uncorrelated or have similar distribution . moreover , bp allows us to simultaneously calculate the marginal beliefs for all ( not only one ) attributes , and solves conflicts incurred by the inherent property of funcat to allow more than one functional label for each protein , which confronts classifiers with a non - standard , soft classification task . a central part of the annotation process is the assignment of functional categories to protein entries . at mips , this annotation scheme has been applied to the manual annotation of several model organisms ( 11 ) . funcat is a hierarchically structured , organism - independent , flexible , controlled and scalable ( structured ) classification system enabling the functional description of proteins from any organism ( 11 ) . the capabilities of the funcat are not only limited to the functional annotation of genomes , but also provide a powerful tool in order to analyse genome- and proteome - wide data generated by large - scale transcriptome / proteome experiments ( 1214 ) as well as the computational analysis of functional networks ( 15,16 ) . the versatile application makes funcat a powerful and intensively used tool for integration of protein function data from different sources and thus fulfils the needs of bioinformatics approaches in systems biology . the assignment of functional categories in the manual annotation process depends primarily on the experimental evidence given in literature . here , the hierarchical structure of funcat allows adjusting the specificity of the level of the functional category to the information content of the experiments . in addition to information from literature , data from other sources like interpro ( 17 ) and funcatdb ( 11 ) as well as external resources like swissprot ( 1 ) genbank ( 18 ) and mgi ( 2 ) are used in order to obtain a comprehensive overview of the cellular function of respective proteins . evaluation of the information and the resulting assignment of functional categories lie in the responsibility of trained annotators . the high number of gene products in mammals requires supporting manual annotation by automated prediction of protein functions . the relation between sequence similarity and functional conservation has been well established for protein domains and complete proteins . since transfer of functional annotation given high sequence conservation is reliable , mfungd data sources for human , mouse and rat as well as other mammalian proteins annotated in swissprot were used . for the human genome , manual annotation was obtained from biomax informatics ag . using conservative thresholds , funcat information has been transferred to 13  193 mouse proteins . if any of these protein entries is subsequently subjected to manual inspection , information of the automated process is supplemented with literature information and modified , if necessary . available in - house manual annotation was complemented by an automated mapping of available manual go - annotations to funcat categories . based on sequence similarity data and interpro domains , further sequence - associated information was compiled . gspc stands for  global superparamagnetic clustering ( 19 ) in which sequences are clustered in a monte carlo process according to a sequence similarity score . functional annotation is then transferred within a cluster from known to unclassified proteins by a consensus process among the known proteins in the same cluster . an internal parameter of the process determines the granularity , specificity and coverage of clusters . spc has been further developed into globalspc by systematic variation of the parameter settings ( 19 ) . the hrmn method ( heterogeneous relational markov network ) generates confidence values for the assignment of functional classification . hrmn is based on a network graph able to employ any parameter that can be assigned to a pair of sequences such as sequence similarity , interpro domains or quantitative data such as correlation of transcript regulation . independent graphs formed by independent data sources are connected to form a markov network , taking advantage of the synergistic effects between them ( 20 ) . in the graph , nodes represent proteins and the edges are weighted according to the strength of the relation between adjacent nodes . nodes may have funcat labels as attributes whose propagation from known to unknown proteins is assessed utilizing belief propagation ( 19)/generalized bp ( 20 ) . note that belief propagation does not require the sources to be uncorrelated or have similar distribution . moreover , bp allows us to simultaneously calculate the marginal beliefs for all ( not only one ) attributes , and solves conflicts incurred by the inherent property of funcat to allow more than one functional label for each protein , which confronts classifiers with a non - standard , soft classification task . while the primary goal of any sequencing effort is the identification of the genetic elements of an organism , the ultimate perspective in the functional analysis is a better understanding of the molecular function to uncover the molecular cause of human diseases . with the first mammalian genomes at hand , it becomes obvious that a gene - centric view is fundamentally insufficient to understand complex cellular processes such as signal transduction , gene regulation or cell differentiation . an understanding of life processes requires the integration of genome as well as transcriptome , metabolome and other -omics sciences . protein interaction database ( 21 ) and the mammalian protein complex database ( 22 ) with public protein protein interaction data , gene expression data and text mining results will form the basis for the compilation of functional modules from mouse . this data set will be manually curated in order to serve as a reference data set for functional modules for a mammalian model organism and moreover provide a useful resource on the way to close the gap between genotype and phenotype .
mfungd ( ) provides a resource for annotated mouse proteins and their occurrence in protein networks . 
 manual annotation concentrates on proteins which are found to interact physically with other proteins . 
 accordingly , manually curated information from a protein  protein interaction database ( mppi ) and a database of mammalian protein complexes is interconnected with mfungd . 
 protein function annotation is performed using the functional catalogue ( funcat ) annotation scheme which is widely used for the analysis of protein networks . 
 the dataset is also supplemented with information about the literature that was used in the annotation process as well as links to the simap fasta database , the pedant protein analysis system and cross - references to external resources . 
 proteins that so far were not manually inspected are annotated automatically by a graphical probabilistic model and/or superparamagnetic clustering . 
 the database is continuously expanding to include the rapidly growing amount of functional information about gene products from mouse . 
 mfungd is implemented in genre , a j2ee - based component - oriented multi - tier architecture following the separation of concern principle .