BACKGROUND
one of the central challenges in computational biology is the prediction of gene function  <cit> .
the inference of gene function typically starts with dna sequence analysis based on ortholog information  <cit> .
although this method has proven to be successful in many cases, considerable numbers of genes  in current genome annotations still are of unknown function.
complementary approaches are therefore required to characterize the function of these genes.
since the start of the dna microarray era, the "guilt-by-association"  methodology has been used to infer gene function  <cit> .
this concept is based on the assumption that genes involved in similar cellular functions are likely to display correlated expression behavior  <cit> .
in addition, this correlated behavior might identify common regulatory mechanisms.
ultimately, to understand the function of a new gene, one should exploit all available experimental data sources   <cit>  or even by the joint efforts of many scientists in a community annotation  <cit> .
previous work on gene function prediction has mainly been focused on higher organisms using multiple high-throughput data sources  <cit> .
on the other hand, genome organizational principles that are unique for prokaryotes supply valuable additional information about gene function.
however, it is expected that the gba method is particularly powerful for prokaryotes, due to their tight coupling of transcription and translation  <cit> .
in addition, for many prokaryotes, the available gene expression datasets greatly outnumber other experimental data sources.
to improve the analysis of the predictions, prosecutor provides additional information for each annotated gene, most notably in its genomic context, which is particularly useful for operons.
the occurrence of adjacent divergent co-expressed genes is also highlighted since these are expected to be co-regulated  <cit> .
finally, putative new members of transcriptional modules are examined for the presence of the same regulatory motif that is already known for the module.
our prosecutor software imposes no constraints on the biological annotations used; it generates hypotheses based on large variety of annotation sources e.g., gene ontology, metabolic pathways, uniprot keywords, etc.
this is in contrast to most other methods  <cit>  which, with few exceptions  <cit> , are focused on coupling genes to gene ontology sources only.
we discuss some of the functional assignments obtained by prosecutor, as well as a number of mining capabilities provided by the software.
we find that the increasing variety of experimental conditions used in dna microarray experiments has greatly improved the ability to identify the function of unknown genes using gba principles.
RESULTS
prosecutor software
prosecutor is a standalone application developed in java and shares its functional database structure with the fiva software  <cit> .
it features an iterative implementation of the gba method which is based on iterative group analysis algorithm   <cit> .
several characteristics of the software analysis modules are described below.
the iterative guilt-by-association  method
the igba method requires dna microarray datasets and functional categories from annotation sources to infer putative gene functions.
the rationale for our approach is the gba principle, i.e., genes that are functionally involved in, or linked to, the same function will in general show higher expression correlations than genes that are not functionally related.
the prediction algorithm of prosecutor calculates the significance of association for all pairs of genes and functional categories.
for n genes, expression profiles from dna microarrays  are used to create an n × n correlation matrix m .
each row j of this matrix represents the  expression correlation between gene gj and all other genes.
to annotate each gene gj, we sort all other genes by their correlation with gene gj, and subject the resulting sorted gene list to iga .
this results in a list of functional categories that are over-represented among the genes that are highly correlated with gene gj, with associated p-values.
the iga algorithm works iteratively and therefore does not require a fixed cutoff of the sorted correlation list, no minimum correlation has to be defined.
instead, iga determines the appropriate cutoff that yields the lowest p-value for each individual analysis of a gene to a functional category.
as a consequence, the function assignment by iga is very sensitive  <cit>  compared to methods which use a predefined correlation cut-off.
performance of functional categories
receiver operating characteristic curves
the performance on well-annotated genes was assessed to evaluate the sensitivity of the igba method.
this evaluation has to be specific for each functional category, because for some of them we expect that all members show close correlation, while others are so general that their members will not correlate and igba is expected to fail.
the category specific evaluation of expression coherence is done as follows: our igba algorithm yields a p-value for every pair of gene-functional category pair .
this p-value is indicative of the confidence of the assignment of a gene to a functional category.
for each category we sort the gene list by p-values and examine the positions of the p-values of its known members in this sorted list.
we are then able to calculate an "expression coherence value" for each functional category by plotting the true and false positive rates on receiver operating characteristic  curves   <cit> .
the corresponding area under the roc curve  is a quantitative measure of the expression coherence of the genes of a functional category.
a functional category in which all known members show strong co-expression will have an auc close to  <dig> , whereas a randomly predicting functional category  would yield auc values around  <dig> .
using the auc measure, we are now able to select the most promising functional categories for further analysis.
parameter free approach
various methods have been developed that specifically employ data from microarrays studies  <cit> .
some of these methods are designed for temporal gene expression profiles  <cit>  or calculate a functional enrichment for each dataset  <cit> .
other approaches require preprocessing of the annotation data, e.g., generating a set of validated and highly unlikely associations  used for training of the prediction model  <cit> .
our prosecutor application improves on previous methods by providing a parameter free approach for the inferral of gene function.
no trusted set of functional associations between proteins is required since prosecutor treats every functional category individually, thereby circumventing preselection toward particular processes.
additional layers of information
the strength of prosecutor comes also from its additional prokaryote-specific layers of information combined with a convenient visualization of the functional predictions.
this prioritizing of the results allows for the rapid identification of the most promising function predictions.
genomic context analysis
the function predictions generated by prosecutor are provided for individual genes.
genes co-transcribed to a polycistronic messenger rna are known as operons whose members typically share biological function.
predictions for genes of which other member of the same operon were already linked to the predicted function are highlighted in the visualization of the results.
the same procedure is applied to divergent genes which share the same upstream region .
this layer of information that is based on the genomic context of genes provides additional, and in some case cases vital, information concerning putative function predictions.
regulatory mechanism analysis
transcriptional modules represent genes that are regulated by a common regulator.
the regulatory mechanisms underlying the co-expression of members of a transcriptional module are used as additional evidence to prioritize the prosecutor results.
for some organisms, functional annotations based on curated knowledge of transcriptional modules are available  <cit> .
motif instances from all members of a transcriptional module are used to create a position specific scoring matrix.
this matrix is used to search for additional hits in the upstream and coding regions from the first gene of the operon as well as the gene of interest .
using this approach, we are able to predict putative new targets for transcriptional modules that exhibit significant co-expression with known members of the transcriptional module and a putative regulatory motif in their upstream regions .
graph visualization
functional predictions are represented by prosecutor as graphs using the prefuse toolkit  <cit>  to visualize the gene redundancy and overlap between the functional categories of different functional predictions.
this method allows to visually determine the uniqueness of each of the function predictions.
a force-directed layout from the prefuse visualization framework is used to position the different nodes  in the network .
performance compared to random microarray data
the performance of different annotation sources  was investigated by comparing auc results for real and random data using a two-sample kolmogorov-smirnov test.
this method was used to compare the distribution of auc values of our algorithm based on  <dig> microarrays from e. coli  as compared to results for which the genes were randomized  .
the null hypothesis that the true data do not significantly deviate from the random distribution is rejected with a p-value of 2e- <dig>  the real data yield significantly higher auc values than expected by chance.
this confirms that the coexpression enrichment of many functional categories is predictive of gene function.
additional analysis of the auc distribution across the annotation sources shows that the transcription module annotation source contains a large number of high scoring functional categories .
moreover, we found that applying a pearson correlation measure for calculating the correlation matrix outperforms spearman correlations, generating 16% more functional categories with an auc value of  <dig>  or higher .
prosecutor test-cases
most genome annotations deposited to genbank are rarely if ever updated  <cit> .
as research progresses, knowledge of many previously uncharacterized genes improves.
this annotation gap enables us to analyze results obtained by prosecutor by manual literature mining of genes for which no function was available in the original genome annotation.
for this validation, only functional categories exhibiting strong predictive properties, with auc values higher than  <dig> , were taken into account.
first test-case: validating results of prosecutor
the first analysis deals with results obtained from prosecutor for all tested organisms and was based on data from dual-dye microarrays.
prosecutor predicted a large number of gene functions for previously unannotated genes which could be validated using literature information .
the complete results of this analysis is available on the supplemental website.
analysis of the results for the model organisms e. coli and b. subtilis was facilitated by the large diversity of microarray perturbation studies available.
a detailed analysis for b. subtilis revealed that for 25% of the best  <dig> predictions sufficient literature data was available to positively confirm the predictions .
functional predictions identified by prosecutor for several organisms that are confirmed using literature information.
gene: the gene for which a validated functional prediction with a functional category  was found.
rank: the position of a gene in the prioritized list based on p-values.
these p-values describe the functional prediction significance for every individual gene with a specific functional category.
auc: the expression coherence value for a functional category with respect to its own members.
notice the examples of genes for which a particular annotation is assigned rank  <dig>  this means that this gene is more close associated with this functional category then any of the original known members of the category, indicating a very high confidence in the prediction.
second test-case: extending transcriptional modules in e. coli
the second analysis dealt with the detection of putative new members of existing transcriptional modules in e. coli .
we used gene expression data from  <dig> affymetrix genechips  <cit>  combined with functional annotations based on curated regulatory network information from regulondb  <cit> .
the results of prosecutor were supplemented with data obtained from the position specific scoring matrices.
these matrices were based on aligned motif sequences of the known dna binding sites from the members of every transcriptional module.
we found that some of the newly identified putative transcriptional module members had been confirmed in the literature, but are not yet catalogued in regulondb.
the remainder of the putative transcriptional module members which could not be verified using literature information are marked "putative" in table  <dig>  due to the exceptional predictive performance  and the additional analysis of the results using known regulatory mechanisms, we were able to reliably predict a large number of putative and validated members for transcription modules.
transcriptional module
transcriptional module member predictions identified by prosecutor applied to well-known transcriptional modules for e. coli.
gene: the gene for which a validated functional prediction with a transcriptional module  was found.
rank: represents the position of a gene in the sorted list with p-values.
these p-values describe the functional prediction significance for every individual gene with a specific functional category.
more significant p-values are matched with lower ranks.
auc: describes the association efficiency for a transcriptional module with respect to its own members.
in addition to the rank information provided by prosecutor, supplemental motif information is provided.
this data is obtained by applying a position specific scoring matrix  to the upstream sequences of all genes in the genome.
the pssm is derived from aggregating all known consensus target sequences .
the additional motif information allows users to concentrate on genes that exhibit coexpression with a transcriptional module as well as possessing a predicted consensus sequence.
this additional evidence contributes to the confidence in assigning a gene to a particular transcriptional module.
motif rank: based on the results for a pssm when matched to every upstream sequence in the genome.
for example, based on the pssm of the regulator lexa, the upstream region of gene dind contains the best ranking motif .
third test-case: performance of annotation sources for saccharomyces cerevisiae
the genome annotation of s. cerevisae is available in genbank as well as embl format, allowing our prosecutor software to perform an igba analysis.
for this third analysis we used two annotation sources .
the gene expression data was obtained from the stanford microarray database  <cit> .
the distribution of auc values of our algorithm  is compared to results for which the genes were randomized  the results based on the real data yield more large auc values than expected by chance.
the categories with high auc values will presumably allow our igba method to assign reliable functional predictions.
this demonstrates that prosecutor, while being specifically optimized for prokaryotes, will also be a useful tool for the general biologist community.
community resource
the complete results of the annotation efforts from our software for twelve organisms are available on the supplemental website  <cit> .
on this dedicated web-site functional couplings can be mined in three ways: 1) through a list of the best functional couplings for each functional category; this allows "browsing" through the most promising associations, 2) a sorted list of functional categories and their predictive power ; in case that one is interested in the genes that are associated with a specific functional category, and 3) a sorted list of genes; allows to identify to which functional categories a gene of interest is associated.
all data sources used for analysis are available, allowing researchers studying any of the analyzed organisms to perform a functional analysis for their expression dataset and/or functional categories.
CONCLUSIONS
prosecutor uses dna microarray data combined with functional annotations to infer putative gene functions.
we show that multiple annotation sources are informative and non-redundant and allow maximizing the use of all available dna microarray data.
for b. subtilis, we were able to confirm  <dig> out of the  <dig> best functional predictions generated by prosecutor, using published literature.
we therefore believe that the other functional assignments based on our analysis are also likely to be informative and reliable.
combined with regulatory motif information for the species b. subtilis and e. coli, prosecutor allows the identification of new transcriptional module members.
prosecutor can thus serve as a generic tool for a genome-wide annotation of gene functions in prokaryotes.
the results of such a re-annotation effort, for  <dig> widely studied bacterial species, is supplied as a community resource at the associated website  <cit> .

 one of the central challenges in computational biology is the prediction of gene function. the inference of gene function typically starts with dna sequence analysis based on ortholog information. although this method has proven to be successful in many cases, considerable numbers of genes in current genome annotations still are of unknown function. complementary approaches are therefore required to characterize the function of these genes. since the start of the dna microarray era, the "guilt-by-association" methodology has been used to infer gene function. this concept is based on the assumption that genes involved in similar cellular functions are likely to display correlated expression behavior. in addition, this correlated behavior might identify common regulatory mechanisms. ultimately, to understand the function of a new gene, one should exploit all available experimental data sources or even by the joint efforts of many scientists in a community annotation. previous work on gene function prediction has mainly been focused on higher organisms using multiple high-throughput data sources. on the other hand, genome organizational principles that are unique for prokaryotes supply valuable additional information about gene function. however, it is expected that the gba method is particularly powerful for prokaryotes, due to their tight coupling of transcription and translation. in addition, for many prokaryotes, the available gene expression datasets greatly outnumber other experimental data sources. to improve the analysis of the predictions, prosecutor provides additional information for each annotated gene, most notably in its genomic context, which is particularly useful for operons. the occurrence of adjacent divergent co-expressed genes is also highlighted since these are expected to be co-regulated. finally, putative new members of transcriptional modules are examined for the presence of the same regulatory motif that is already known for the module. our prosecutor software imposes no constraints on the biological annotations used; it generates hypotheses based on large variety of annotation sources e.g., gene ontology, metabolic pathways, uniprot keywords, etc. this is in contrast to most other methods which, with few exceptions, are focused on coupling genes to gene ontology sources only. we discuss some of the functional assignments obtained by prosecutor, as well as a number of mining capabilities provided by the software. we find that the increasing variety of experimental conditions used in dna microarray experiments has greatly improved the ability to identify the function of unknown genes using gba principles.  prosecutor software prosecutor is a standalone application developed in java and shares its functional database structure with the fiva software. it features an iterative implementation of the gba method which is based on iterative group analysis algorithm. several characteristics of the software analysis modules are described below. the iterative guilt-by-association method the igba method requires dna microarray datasets and functional categories from annotation sources to infer putative gene functions. the rationale for our approach is the gba principle, i.e., genes that are functionally involved in, or linked to, the same function will in general show higher expression correlations than genes that are not functionally related. the prediction algorithm of prosecutor calculates the significance of association for all pairs of genes and functional categories. for n genes, expression profiles from dna microarrays are used to create an n × n correlation matrix m. each row j of this matrix represents the expression correlation between gene gj and all other genes. to annotate each gene gj, we sort all other genes by their correlation with gene gj, and subject the resulting sorted gene list to iga. this results in a list of functional categories that are over-represented among the genes that are highly correlated with gene gj, with associated p-values. the iga algorithm works iteratively and therefore does not require a fixed cutoff of the sorted correlation list, no minimum correlation has to be defined. instead, iga determines the appropriate cutoff that yields the lowest p-value for each individual analysis of a gene to a functional category. as a consequence, the function assignment by iga is very sensitive compared to methods which use a predefined correlation cut-off. performance of functional categories receiver operating characteristic curves the performance on well-annotated genes was assessed to evaluate the sensitivity of the igba method. this evaluation has to be specific for each functional category, because for some of them we expect that all members show close correlation, while others are so general that their members will not correlate and igba is expected to fail. the category specific evaluation of expression coherence is done as follows: our igba algorithm yields a p-value for every pair of gene-functional category pair. this p-value is indicative of the confidence of the assignment of a gene to a functional category. for each category we sort the gene list by p-values and examine the positions of the p-values of its known members in this sorted list. we are then able to calculate an "expression coherence value" for each functional category by plotting the true and false positive rates on receiver operating characteristic curves. the corresponding area under the roc curve is a quantitative measure of the expression coherence of the genes of a functional category. a functional category in which all known members show strong co-expression will have an auc close to, whereas a randomly predicting functional category would yield auc values around. using the auc measure, we are now able to select the most promising functional categories for further analysis. parameter free approach various methods have been developed that specifically employ data from microarrays studies. some of these methods are designed for temporal gene expression profiles or calculate a functional enrichment for each dataset. other approaches require preprocessing of the annotation data, e.g., generating a set of validated and highly unlikely associations used for training of the prediction model. our prosecutor application improves on previous methods by providing a parameter free approach for the inferral of gene function. no trusted set of functional associations between proteins is required since prosecutor treats every functional category individually, thereby circumventing preselection toward particular processes. additional layers of information the strength of prosecutor comes also from its additional prokaryote-specific layers of information combined with a convenient visualization of the functional predictions. this prioritizing of the results allows for the rapid identification of the most promising function predictions. genomic context analysis the function predictions generated by prosecutor are provided for individual genes. genes co-transcribed to a polycistronic messenger rna are known as operons whose members typically share biological function. predictions for genes of which other member of the same operon were already linked to the predicted function are highlighted in the visualization of the results. the same procedure is applied to divergent genes which share the same upstream region. this layer of information that is based on the genomic context of genes provides additional, and in some case cases vital, information concerning putative function predictions. regulatory mechanism analysis transcriptional modules represent genes that are regulated by a common regulator. the regulatory mechanisms underlying the co-expression of members of a transcriptional module are used as additional evidence to prioritize the prosecutor results. for some organisms, functional annotations based on curated knowledge of transcriptional modules are available. motif instances from all members of a transcriptional module are used to create a position specific scoring matrix. this matrix is used to search for additional hits in the upstream and coding regions from the first gene of the operon as well as the gene of interest. using this approach, we are able to predict putative new targets for transcriptional modules that exhibit significant co-expression with known members of the transcriptional module and a putative regulatory motif in their upstream regions. graph visualization functional predictions are represented by prosecutor as graphs using the prefuse toolkit to visualize the gene redundancy and overlap between the functional categories of different functional predictions. this method allows to visually determine the uniqueness of each of the function predictions. a force-directed layout from the prefuse visualization framework is used to position the different nodes in the network. performance compared to random microarray data the performance of different annotation sources was investigated by comparing auc results for real and random data using a two-sample kolmogorov-smirnov test. this method was used to compare the distribution of auc values of our algorithm based on microarrays from e. coli as compared to results for which the genes were randomized. the null hypothesis that the true data do not significantly deviate from the random distribution is rejected with a p-value of 2e- the real data yield significantly higher auc values than expected by chance. this confirms that the coexpression enrichment of many functional categories is predictive of gene function. additional analysis of the auc distribution across the annotation sources shows that the transcription module annotation source contains a large number of high scoring functional categories. moreover, we found that applying a pearson correlation measure for calculating the correlation matrix outperforms spearman correlations, generating 16% more functional categories with an auc value of or higher. prosecutor test-cases most genome annotations deposited to genbank are rarely if ever updated. as research progresses, knowledge of many previously uncharacterized genes improves. this annotation gap enables us to analyze results obtained by prosecutor by manual literature mining of genes for which no function was available in the original genome annotation. for this validation, only functional categories exhibiting strong predictive properties, with auc values higher than, were taken into account. first test-case: validating results of prosecutor the first analysis deals with results obtained from prosecutor for all tested organisms and was based on data from dual-dye microarrays. prosecutor predicted a large number of gene functions for previously unannotated genes which could be validated using literature information. the complete results of this analysis is available on the supplemental website. analysis of the results for the model organisms e. coli and b. subtilis was facilitated by the large diversity of microarray perturbation studies available. a detailed analysis for b. subtilis revealed that for 25% of the best predictions sufficient literature data was available to positively confirm the predictions. functional predictions identified by prosecutor for several organisms that are confirmed using literature information. gene: the gene for which a validated functional prediction with a functional category was found. rank: the position of a gene in the prioritized list based on p-values. these p-values describe the functional prediction significance for every individual gene with a specific functional category. auc: the expression coherence value for a functional category with respect to its own members. notice the examples of genes for which a particular annotation is assigned rank this means that this gene is more close associated with this functional category then any of the original known members of the category, indicating a very high confidence in the prediction. second test-case: extending transcriptional modules in e. coli the second analysis dealt with the detection of putative new members of existing transcriptional modules in e. coli. we used gene expression data from affymetrix genechips combined with functional annotations based on curated regulatory network information from regulondb. the results of prosecutor were supplemented with data obtained from the position specific scoring matrices. these matrices were based on aligned motif sequences of the known dna binding sites from the members of every transcriptional module. we found that some of the newly identified putative transcriptional module members had been confirmed in the literature, but are not yet catalogued in regulondb. the remainder of the putative transcriptional module members which could not be verified using literature information are marked "putative" in table due to the exceptional predictive performance and the additional analysis of the results using known regulatory mechanisms, we were able to reliably predict a large number of putative and validated members for transcription modules. transcriptional module transcriptional module member predictions identified by prosecutor applied to well-known transcriptional modules for e. coli. gene: the gene for which a validated functional prediction with a transcriptional module was found. rank: represents the position of a gene in the sorted list with p-values. these p-values describe the functional prediction significance for every individual gene with a specific functional category. more significant p-values are matched with lower ranks. auc: describes the association efficiency for a transcriptional module with respect to its own members. in addition to the rank information provided by prosecutor, supplemental motif information is provided. this data is obtained by applying a position specific scoring matrix to the upstream sequences of all genes in the genome. the pssm is derived from aggregating all known consensus target sequences. the additional motif information allows users to concentrate on genes that exhibit coexpression with a transcriptional module as well as possessing a predicted consensus sequence. this additional evidence contributes to the confidence in assigning a gene to a particular transcriptional module. motif rank: based on the results for a pssm when matched to every upstream sequence in the genome. for example, based on the pssm of the regulator lexa, the upstream region of gene dind contains the best ranking motif. third test-case: performance of annotation sources for saccharomyces cerevisiae the genome annotation of s. cerevisae is available in genbank as well as embl format, allowing our prosecutor software to perform an igba analysis. for this third analysis we used two annotation sources. the gene expression data was obtained from the stanford microarray database. the distribution of auc values of our algorithm is compared to results for which the genes were randomized the results based on the real data yield more large auc values than expected by chance. the categories with high auc values will presumably allow our igba method to assign reliable functional predictions. this demonstrates that prosecutor, while being specifically optimized for prokaryotes, will also be a useful tool for the general biologist community. community resource the complete results of the annotation efforts from our software for twelve organisms are available on the supplemental website. on this dedicated web-site functional couplings can be mined in three ways: 1) through a list of the best functional couplings for each functional category; this allows "browsing" through the most promising associations, 2) a sorted list of functional categories and their predictive power; in case that one is interested in the genes that are associated with a specific functional category, and 3) a sorted list of genes; allows to identify to which functional categories a gene of interest is associated. all data sources used for analysis are available, allowing researchers studying any of the analyzed organisms to perform a functional analysis for their expression dataset and/or functional categories.  prosecutor uses dna microarray data combined with functional annotations to infer putative gene functions. we show that multiple annotation sources are informative and non-redundant and allow maximizing the use of all available dna microarray data. for b. subtilis, we were able to confirm out of the best functional predictions generated by prosecutor, using published literature. we therefore believe that the other functional assignments based on our analysis are also likely to be informative and reliable. combined with regulatory motif information for the species b. subtilis and e. coli, prosecutor allows the identification of new transcriptional module members. prosecutor can thus serve as a generic tool for a genome-wide annotation of gene functions in prokaryotes. the results of such a re-annotation effort, for widely studied bacterial species, is supplied as a community resource at the associated website. 
our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. furthermore, prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. we supply the complete results of our analysis for prokaryotic organisms on a dedicated website. we developed prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. the prosecutor software and supplementary datasets available at allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest.
 despite a plethora of functional genomic efforts, the function of many genes in sequenced genomes remains unknown. the increasing amount of microarray data for many species allows employing the guilt-by-association principle to predict function on a large scale: genes exhibiting similar expression patterns are more likely to participate in shared biological processes.  we developed prosecutor, an application that enables researchers to rapidly infer gene function based on available gene expression data and functional annotations. our parameter-free functional prediction method uses a sensitive algorithm to achieve a high association rate of linking genes with unknown function to annotated genes. furthermore, prosecutor utilizes additional biological information such as genomic context and known regulatory mechanisms that are specific for prokaryotes. we analyzed publicly available transcriptome data sets and used literature sources to validate putative functions suggested by prosecutor. we supply the complete results of our analysis for prokaryotic organisms on a dedicated website.  the prosecutor software and supplementary datasets available at allow researchers working on any of the analyzed organisms to quickly identify the putative functions of their genes of interest. a de novo analysis allows new organisms to be studied. 