relating experimental data to biological knowledge is a necessity to cope with the data avalanches emerging from recent developments in high - throughput technologies . automatic functional profiling has become the de facto approach for the secondary analysis of high - throughput data . a number of tools employing available gene functional annotations as well as pathway databases have been developed ( 118 ) . an important aspect of standard functional profiling methodology is inability to overcome the limits of employed annotation vocabularies . however , to control it one does not need an infinite number of functional terms . human language contains a limited number of words but through grammar rules these words can be transformed into an almost infinite number of sentences , which allow the expression of almost any idea . in our previous paper ( 20 ) , we proposed to construct new functional terms ( referred to as  complex functions ) . not ) play the role of grammar rules and resulting space of  complex functions covers an almost infinite number of possible biological functions . the present article describes profcom , a web tool for functional profiling based on the concept described previously ( 20 ) . profcom supports automatic analyses for several model organisms as well as provides a web service interface , which allows the submission of any kind of annotation data . for each organism , profcom provides analysis of different annotations , including gene ontology ( go ) ( 21 ) , funcat ( 22 ) and interpro motifs ( 23 ) . profcom currently offers automatic analyses for homo sapiens , mus musculus , rattus norvegicus , caenorhabditis elegans , drosophila melanogaster and saccharomyces cerevisiae . in addition , any organism and annotation can be analyzed by profcom using web service interface . a standard tool for automatic functional profiling accepts a query list of genes ( referred to as set a , usually the set of genes experimentally identified to be related to the studied biological phenomena ) and a reference set ( referred to as set b , usually the set of all genes from the analyzed organism ) . then , for each attribute f from the set f ( f is usually a functional term from the employed annotation vocabulary f , i.e. go , funcat , etc . ) the number af genes in set a and bf genes in set b that have been annotated with f is counted . in the next step , the null hypothesis h0 ( genes that belong to the set a are independent of having attribute f ) is tested . hypergeometric , binomial or -tests are usually employed to find over / under represented attributes ( 19 ) . unlike most currently available web tools for functional profiling , profcom implements different profiling paradigms . along with standard profiling of functional terms f ( referred to as  base categories ) from annotation vocabularies it also searches for the enrichment related to base categories ( for example , a new  complex function w may define the set of genes that belongs simultaneously to the for example , intersection of two categories f1 and f2 is formally defined as  complex function w = f1  f2 . in other words , w corresponds to the set of genes that belong to both categories f1 and f2 . the union of two categories f1 and f2 is formally defined as w = f1  f2 . in this case , w corresponds to the set of genes that belong either to category f1 or f2 . the difference between two categories f1 and f2 is formally defined as w = f1/f2 ;  complex function w corresponds to the set of genes from category f1 excluding those that simultaneously belong to category f2 . for example , the base categories can be defined as  complex functions of the first degree , the category w = f1  f2 is a complex functions leads to combinatorial complexity . to analyze enrichments for all possible combinations of degree higher than 2 greedy heuristics does not guarantee to find the optimal solution in every case but significantly reduce the computational complexity . to adjust p - values for multiple the estimated p - value corresponds exactly to the definition of an experiment - wise westfall and young p - value ( 3,20,24 ) . more details on the searching algorithm and p - value adjustment can be found in supplementary materials . as input profcom accepts several types of gene or protein identifiers . entrez gene ( 25 ) ,  uniprot / swiss - prot ,  gene symbol ( 25,26 ) ,  unigene ( 25 ) ,  ensembl ( 27 ) ,  refseq protein id ,  refseq transcript id ( 28 ) and  affymetrix probe codes ( 29 ) . additionally , a mixture of several identifier types is possible . in the first step , table 1.types of gene identifiers recognized by profcom and data sources used for i d mappingtype of idsfile usedgene symbol , ensembl ,  locustagftp://ftp.ncbi.nlm.nih.gov / gene / data / gene_info.gzrefseq protein id ,  refseq transcript idftp://ftp.ncbi.nlm.nih.gov / gene / data / gene2refseq.gzuniprot / swiss - protftp://ftp.ncbi.nlm.nih.gov / gene / data / gene_refseq_uniprotkb_collab.gzunigeneftp://ftp.ncbi.nlm.nih.go / gene / dat / gene2unigeneaffymetrix probe codeshttp://www.affymetrix.com / annotation files types of gene identifiers recognized by profcom and data sources used for i d mapping the user gets full information on mapping of the supplied gene ids . it includes the informational source used as well as a possible multiple mapping of the user supplied ids to table 3 reports the final mapping ( one - to - one mapping ) , which is used in subsequent analyses . profcom implements simple heuristics to resolve multiple mapping issues . if it is possible to map a particular gene i d to several  entrez gene ids , the i d which has the most abundant annotation is selected . however , if the user finds this mapping to be incorrect ( table 3 ) he / she can simply resubmit the data by substituting those ambiguous gene ids with  entrez gene ids considered to be correct . on the other hand , if several supplied gene ids are mapped to the same then they are considered as belonging to one gene and the ids are reported concatenated together by a semicolon (  ;  ) . we would like to point out that protein and gene identifiers can be highly ambiguous ( 30 ) with multiple synonymous variants . for this reason , the quality of the retrieved annotation can be different for different types of identifiers . several powerful recourses to map different type of gene ids exist ( http://beta.uniprot.org/ ) . to escape multiple mapping issues , we recommend submitting currently , they include go ( 21 ) , funcat ( 22 ) and interpro motifs ( 23 ) . detailed information on data sources used to retrieve each annotation is presented in the table 2 . the profcom web interface allows the user to use all annotations simultaneously or combine them . table 2data file used by profcom to automatically retrieve annotationsannotationfile usedgene ontologyftp://ftp.ncbi.nlm.nih.go / gene / dat / gene2unigeneinterpro motifsftp://ftp.ebi.ac.uk / pub / databases / interpro / protein2ipr.datfuncathttp://mips.gsf.de/ data file used by profcom to automatically retrieve annotations in addition to the interactive web - submissions , custom annotation data can be analyzed using the profcom web service . this allows the use of profcom for almost any problem domain , e.g. different annotation types or organisms . furthermore , web services enable one to run profcom analyses in pipelines or automated workflows from most systems . this ensures a fast and convenient usage for a broad range of use cases : starting from a quick hypothesis evaluation to detailed high - quality annotations . the computation is distributed on linux workstations utilizing a sun grid engine and thus ensures scalability . a profcom analysis starts by user - friendly dialog - driven web form . in the first step , the model organism is chosen and the list of gene or protein names of interest is uploaded . , the set of all annotated genes (  entrez gene ids ) from the chosen organism is used as the reference set . depending on the chosen organism the profcom web page automatically shows all available annotations . here , we present one example of analyses of real data by profcom to illustrate it novelties and utilities in comparison to existing related tools . more examples can be found in supplementary materials , where we bring together several independent studies that performed gene expression analyses to identify over / under expressed genes in different cancer types . we collect a set of differentially expressed genes originally identified in each study ( we refer to each of these sets as set a and the set of all human genes is referred to as set b ) . in ref . ( 31 ) , microarray experiments were done to compare gene expression in 50 ovarian cancer specimens , including all four histotypes to gene expression in five pools of normal ovarian surface epithelial cells . data were analyzed to determine whether changes in gene expression correlated with different histotypes , grade or stage . several set of genes that show the greatest ability to differentiate between considered cancer subtypes were originally identified . for example , 47 selected genes were 2-fold differentially expressed in mucinous ovarian cancers compared to other histotypes and with normal ovarian surface epithelial cells . it is widely known that the processes of ca homeostasis are often disordered in many cancer types ( 32 ) . therefore , the presence of go term  calcium - ion binding among top enriched go terms is of particular interest . eight genes ( mrc1 , efhd2 , pls1 , anxa10 , ldlr , mmp1 , s100p , thbs2 ) from the set a are related by this term ( figure 1 ) . on the other hand , there are 894 genes in the whole human genome classified as  calcium - ion binding. using conventional go terms vocabularies , standard profiling procedure is not able to supply evidences that would discriminate these eight genes ( from all human 894  calcium - ion binding ) and , thus , to clarify molecular mechanism involved . figure 1.profcom output table  top enriched categories of degree 1 for the considered example . the complex function  calcium - ion binding excluding integral to membrane excluding hydrolase activity inferred by profcom ( figure 2 ) relates all  calcium - ion binding genes from the set a and is more specific in comparison to a single go term , i.e. only 533 genes ( compared to 894 ) in the human genome are classified by this complex function . it is not only better from statistical viewpoint ( equal selectivity with 1-fold increase in specificity ) , but also supplies valuable biological information which can be helpful for making biological conclusions about molecular mechanisms involved in the considered cancer type . figure 2.profcom output table  top enriched categories of degree 3 for the considered example . a standard tool for automatic functional profiling accepts a query list of genes ( referred to as set a , usually the set of genes experimentally identified to be related to the studied biological phenomena ) and a reference set ( referred to as set b , usually the set of all genes from the analyzed organism ) . then , for each attribute f from the set f ( f is usually a functional term from the employed annotation vocabulary f , i.e. go , funcat , etc . ) the number af genes in set a and bf genes in set b that have been annotated with f is counted . in the next step , the null hypothesis h0 ( genes that belong to the set a are independent of having attribute f ) is tested . hypergeometric , binomial or -tests are usually employed to find over / under represented attributes ( 19 ) . unlike most currently available web tools for functional profiling , profcom implements different profiling paradigms . along with standard profiling of functional terms f ( referred to as  base categories ) from annotation vocabularies it also searches for the enrichment related to base categories ( for example , a new  complex function w may define the set of genes that belongs simultaneously to the for example , intersection of two categories f1 and f2 is formally defined as  complex function w = f1  f2 . in other words , w corresponds to the set of genes that belong to both categories f1 and f2 . the union of two categories f1 and f2 is formally defined as w = f1  f2 . in this case , w corresponds to the set of genes that belong either to category f1 or f2 . the difference between two categories f1 and f2 is formally defined as w = f1/f2 ;  complex function w corresponds to the set of genes from category f1 excluding those that simultaneously belong to category f2 . for example , the base categories can be defined as  complex functions of the first degree , the category w = f1  f2 is a complex functions leads to combinatorial complexity . to analyze enrichments for all possible combinations of degree higher than 2 greedy heuristics does not guarantee to find the optimal solution in every case but significantly reduce the computational complexity . to adjust p - values for multiple the estimated p - value corresponds exactly to the definition of an experiment - wise westfall and young p - value ( 3,20,24 ) . more details on the searching algorithm and p - value adjustment can be found in supplementary materials . as input profcom accepts several types of gene or protein identifiers . for example , for the human genome profcom supports identifiers from  entrez gene ( 25 ) ,  uniprot / swiss - prot ,  gene symbol ( 25,26 ) ,  unigene ( 25 ) ,  ensembl ( 27 ) ,  refseq protein id ,  refseq transcript id ( 28 ) and  affymetrix probe codes ( 29 ) . additionally , a mixture of several identifier types is possible . in the first step , table 1.types of gene identifiers recognized by profcom and data sources used for i d mappingtype of idsfile usedgene symbol , locustagftp://ftp.ncbi.nlm.nih.gov / gene / data / gene_info.gzrefseq protein id ,  refseq transcript idftp://ftp.ncbi.nlm.nih.gov / gene / data / gene2refseq.gzuniprot / swiss - protftp://ftp.ncbi.nlm.nih.gov / gene / data / gene_refseq_uniprotkb_collab.gzunigeneftp://ftp.ncbi.nlm.nih.go / gene / dat / gene2unigeneaffymetrix probe codeshttp://www.affymetrix.com / annotation files types of gene identifiers recognized by profcom and data sources used for i d mapping the user gets full information on mapping of the supplied gene ids . it includes the informational source used as well as a possible multiple mapping of the user supplied ids to table 3 reports the final mapping ( one - to - one mapping ) , which is used in subsequent analyses . profcom implements simple heuristics to resolve multiple mapping issues . if it is possible to map a particular gene i d to several  entrez gene ids , the i d which has the most abundant annotation is selected . however , if the user finds this mapping to be incorrect ( table 3 ) he / she can simply resubmit the data by substituting those ambiguous gene ids with entrez gene ids considered to be correct . on the other hand , if several supplied gene ids are mapped to the same entrez gene i d then they are considered as belonging to one gene and the ids are reported concatenated together by a semicolon (  ;  ) . we would like to point out that protein and gene identifiers can be highly ambiguous ( 30 ) with multiple synonymous variants . for this reason , the quality of the retrieved annotation can be different for different types of identifiers . currently , they include go ( 21 ) , funcat ( 22 ) and interpro motifs ( 23 ) . detailed information on data sources used to retrieve each annotation is presented in the table 2 . the profcom web interface allows the user to use all annotations simultaneously or combine them . table 2data file used by profcom to automatically retrieve annotationsannotationfile usedgene ontologyftp://ftp.ncbi.nlm.nih.go / gene / dat / gene2unigeneinterpro motifsftp://ftp.ebi.ac.uk / pub / databases / interpro / protein2ipr.datfuncathttp://mips.gsf.de/ data file used by profcom to automatically retrieve annotations in addition to the interactive web - submissions , custom annotation data can be analyzed using the profcom web service . this allows the use of profcom for almost any problem domain , e.g. different annotation types or organisms . furthermore , web services enable one to run profcom analyses in pipelines or automated workflows from most systems . this ensures a fast and convenient usage for a broad range of use cases : starting from a quick hypothesis evaluation to detailed high - quality annotations . the computation is distributed on linux workstations utilizing a sun grid engine and thus ensures scalability . a profcom analysis starts by user - friendly dialog - driven web form . in the first step , the model organism is chosen and the list of gene or protein names of interest is uploaded . the set of all annotated genes (  entrez gene ids ) from the chosen organism is used as the reference set . depending on the chosen organism the profcom web page automatically shows all available annotations . here , we present one example of analyses of real data by profcom to illustrate it novelties and utilities in comparison to existing related tools . more examples can be found in supplementary materials , where we bring together several independent studies that performed gene expression analyses to identify over / under expressed genes in different cancer types . we collect a set of differentially expressed genes originally identified in each study ( we refer to each of these sets as set a and the set of all human genes is referred to as set b ) . in ref . ( 31 ) , microarray experiments were done to compare gene expression in 50 ovarian cancer specimens , including all four histotypes to gene expression in five pools of normal ovarian surface epithelial cells . data were analyzed to determine whether changes in gene expression correlated with different histotypes , grade or stage . several set of genes that show the greatest ability to differentiate between considered cancer subtypes were originally identified . for example , 47 selected genes were 2-fold differentially expressed in mucinous ovarian cancers compared to other histotypes and with normal ovarian surface epithelial cells . it is widely known that the processes of ca homeostasis are often disordered in many cancer types ( 32 ) . therefore , the presence of go term  calcium - ion binding among top enriched go terms is of particular interest . eight genes ( mrc1 , efhd2 , pls1 , anxa10 , ldlr , mmp1 , s100p , thbs2 ) from the set a are related by this term ( figure 1 ) . on the other hand , there are 894 genes in the whole human genome classified as  calcium - ion binding. using conventional go terms vocabularies , standard profiling procedure is not able to supply evidences that would discriminate these eight genes ( from all human 894  calcium - ion binding ) and , thus , to clarify molecular mechanism involved . 
 figure 1.profcom output table the complex function  calcium - ion binding excluding integral to membrane excluding hydrolase activity inferred by profcom ( figure 2 ) relates all  calcium - ion binding genes from the set a and is more specific in comparison to a single go term , i.e. only 533 genes ( compared to 894 ) in the human genome are classified by this complex function . it is not only better from statistical viewpoint ( equal selectivity with 1-fold increase in specificity ) , but also supplies valuable biological information which can be helpful for making biological conclusions about molecular mechanisms involved in the considered cancer type . figure 2.profcom output table  top enriched categories of degree 3 for the considered example . automatic functional profiling becomes the de facto approach for the secondary analysis of high - throughput data . a number of tools employing available gene functional annotations have been developed . however , most of these tools are limited by available annotation vocabularies and may fail to provide full interpretation of biological relationships in a set of genes involved in complex biological phenomena . here , we present profcom , a web - based tool that implements the new profiling paradigm for the interpretation of functional relations between genes . not ) to provide complex functions that classify more specifically the biological role of a gene group . as been demonstrated , in many cases , complex functions provide better understanding of molecular mechanisms involved for the phenomena under study . on the other hand , in some cases , relative go terms can form many redundant complex functions and may complicate the manual analyses of the profcom results . one potential way to resolve redundancy problem is the inclusion of methodologies that group related sets of annotations before the analyses ( 18,33,34 ) , in the future . profcom provides technical support to the user that corresponds to the best currently available standards in the field . it has a dialog - driven web page for submission that covers several mostly exploited model organisms . in addition , the web service interface allows one submitting any kind of annotation data and is not limited to a particular organism or problem domain . this property significantly simplifies the procedure of data analyses and increases the spectrum of gene sets that can be analyzed .
profcom is a web - based tool for the functional interpretation of a gene list that was identified to be related by experiments . 
 a trait which makes profcom a unique tool is an ability to profile enrichments of not only available gene ontology ( go ) terms but also of  complex functions. a  complex function is constructed as boolean combination of available go terms . 
 the complex functions inferred by profcom are more specific in comparison to single terms and describe more accurately the functional role of genes . 
 profcom provides a user friendly dialog - driven web page submission available for several model organisms and supports most available gene identifiers . 
 in addition , the web service interface allows the submission of any kind of annotation data . 
 profcom is freely available at http://webclu.bio.wzw.tum.de / profcom/.