BACKGROUND
cronobacter, formerly known as enterobacter sakazakii  <cit> , is a bacterial genus containing seven species  <cit>  in the family enterobacteriacae; c. sakazakii, c. malonaticus, c. muytjensii, c. turicensis, c. dublinensis, c. universalis, and c. condimenti.
the organism has received a lot of attention recently due to its association with neonatal infections, especially meningitis, necrotizing enterocolitis, septicaemia and subsequent death  <cit> .
these bacteria have been isolated from a wide range of food stuffs  <cit> , therefore it is important to be able to detect cronobacter species in food.
for this purpose several diagnostic tests exist.
however, most of these tests make no distinction as to the species of the bacteria.
not all cronobacter species are known to be pathogenic to infants and can cause asymptomatic colonisation.
the strict microbiological criteria for the presence of cronobacter in powdered infant formula  for intended age <  <dig> months  <cit>  means it is of great interest to differentiate between pathogenic and non-pathogenic strains.
although a range of possible virulence features  have been identified in cronobacter and reviewed elsewhere  <cit> , their presence does not correspond to clinical symptoms.
therefore, the identification of further discriminating factors would be useful.
currently, to differentiate between species, it is necessary to sequence either the 16s rna subunit  <cit>  or the mlst genes  <cit> ; the latter is required for searching the cronobacter mlst database  <cit> .
there are  <dig> isolates of cronobacter recorded in the mlst database  <cit>  at the time of analysis .
although it is known that type  <dig> strains  are associated with meningitis  <cit> , neither of the above methods is able to differentiate between pathogenic and non-pathogenic strains, they only identify individual species.
moreover, both methods are time consuming compared with the use of biochemical diagnostic test kits which take 4- <dig> hours to produce results that can easily be interpreted.
for this reason we aimed to develop methods for identifying which of the strains in the cronobacter genus are pathogenic based on data obtained from standard biochemical diagnostic tests.
these tests were those commonly used to identify cronobacter isolates and are used in their taxonomic description  <cit> .
here we used expectation maximization  clustering algorithm to divide the data on the basis of the biochemical test results.
since the precise pathogenic status of most cronobacter strains is unknown, we considered the resulting clusters as being pathogenic or not on the basis of  the source from which the strains were isolated and/or  mlst types previously associated with pathogenic or non-pathogenic strains  and reference  <cit> .
the clustering of the biochemical test results was also examined for traits associated with pathogenicity.
RESULTS
clustering the dataset for test  <dig> with the number of clusters being  <dig>  resulted in clusters  <dig>  and  <dig>  containing  <dig> and  <dig> strains respectively  where pi  is the probability of cluster membership for a randomly chosen strain and l is the maximum log likelihood .
according to our hypothesis cluster  <dig> was most likely to contain pathogenic strains since all st  <dig> strains were assigned to this cluster.
it is known that st  <dig> strains are associated with the most serious pathogenic states such as meningitis in infants  <cit> .
of the other mlst types, st  <dig> and  <dig> were placed exclusively with the potentially non-pathogenic strains in cluster  <dig>  st  <dig> was split between two clusters with  <dig> of  <dig> strains in the non-pathogenic grouping.
all except one st  <dig> strain were predicted to be in the pathogenic cluster, as were all of the st  <dig> strains .
the group with unspecified clinical source  was divided between the two clusters, indicating that not all clinical isolates are likely to be pathogenic and this feature  alone by no means allows us to infer pathogenicity of a strain.
for example, one clinical case, classified as non-pathogenic, was obtained from a breast abscess and it is plausible that this was a secondary infection although it is not known if another infectious agent was isolated.
thus this may indeed be a non-pathogenic strain.
two asymptomatic strains appeared in the pathogenic cluster; one of these strains is st  <dig> and the other st  <dig>  several st  <dig> strains are from clinical sources and it is likely that all st  <dig> strains will have similar pathogenic characteristics.
therefore, we can speculate that these strains could have caused an infection following a higher ingested dose or a lower immune status.
abbreviations: c: clinical, e: environmental, eft: enteral feeding tube, f: food, fuf: follow up formula, if: infant formula, mp: milk powder, u: unknown wf: weaning food.
sources of isolation and strain numbers are given in full in additional file  <dig>
clustering for the test  <dig> dataset gave two clusters in which  <dig> strains  were in cluster  <dig>  and eight strains  were in cluster  <dig> .
one strain of those in cluster  <dig> was associated with a clinical diagnosis  and was likely to be pathogenic, as well as one st  <dig> strain, with the remainder placed in cluster  <dig>  the heterogeneity of mlst types in both clusters, as well as the small number of strains in cluster  <dig>  suggests that the biochemical data in test  <dig> is not sufficient to differentiate between pathogenic and non-pathogenic strains.
to prove this, the em algorithm was allowed to automatically determine the number of clusters to assign the data to .
as a result, only a single cluster was produced indicating that the test  <dig> data is not sufficient to differentiate between cronobacter strains.
for abbreviations in this table see footnote to table  <dig>  sources of isolation and strain numbers are given in full in additional file  <dig>
clustering of the test  <dig> dataset  resulted in cluster  <dig> containing  <dig> instances  and cluster  <dig> containing  <dig> instances .
the majority of the st  <dig> strains were grouped in the second cluster, indicating that this cluster contains the potentially pathogenic strains.
however, all other mlst types  were split between the two clusters.
st  <dig> was mostly placed in the non-pathogenic cluster, with one strain in cluster  <dig>  st  <dig> was split evenly  between the two clusters.
most of the st  <dig> strains were found to be non-pathogenic with just one strain being pathogenic.
however, many strains indicated as pathogenic in the test  <dig> results  were placed in the larger potentially non-pathogenic grouping.
based on the division of strains of the same mlst type between clusters, it is likely that the results of test  <dig> are less accurate than test  <dig> and test  <dig> , although many st  <dig> and st  <dig> strains appeared to be correctly assigned.
note that this test has the fewest number of strains available; it is expected that the availability of more data will greatly improve the results of clustering using this diagnostic test data.
for abbreviations in this table see footnote to table  <dig>  sources of isolation and strain numbers are given in full in additional file  <dig>
for the fourth test, cluster  <dig> contained  <dig> strains  and cluster  <dig> contained  <dig> strains .
the clusters are shown in table  <dig> .
this clustering assignment was successful at differentiating between mlst types.
st  <dig> and  <dig> were placed entirely in the non-pathogenic grouping  and with two exceptions , the st  <dig> strains were placed in cluster  <dig>  allowing us to label the latter as the potentially pathogenic cluster.
all except two st  <dig> strains  were placed in the non-pathogenic cluster.
st  <dig> strains were placed in the pathogenic cluster as were all except one strain of st  <dig> .
a more detailed examination of the strains allocated to each cluster showed that all strains labelled as pathogenic were positive for the inositol fermentation  test, whilst the prospective non-pathogenic strains were negative for this test.
although this is not conclusively shown by the result of the inositol test in test  <dig> and test  <dig>  the test  <dig> data does indicate a bias towards strains with inositol fermentation in the pathogenic cluster.
this suggested that either inositol fermentation was a requirement for pathogenicity, or that the genetic locus conferring inositol fermentation was linked to genes conferring pathogenic traits.
this latter conclusion was supported by the two apparently pathogenic st  <dig> strains which were negative for inositol fermentation : strain  <dig> was isolated from infant formula, but strain  <dig> was associated with neonatal meningitis indicating pathogenesis.
it is probable that the inositol fermentation gene was lost from these strains, but the pathogenic traits acquired alongside it remained.
it should be noted that this test is different from the ino test in the test  <dig> dataset, which we removed from the analysis as it produces the same result for all cronobacter strains.
all strains in cluster  <dig>  are negative for inositol fermentation, all strains in cluster  <dig> are positive for inositol fermentation.
for abbreviations in this table see footnote to table  <dig>  sources of isolation and strain numbers are given in full in additional file  <dig>
consensus clustering
aggregating the clustering assignments based on the majority rule  for the  <dig> strains which have data available from all four tests resulted in the clusters shown in table  <dig>  the results showed the majority of st  <dig> strains were placed in cluster  <dig>  however, there was still splitting of st  <dig>   <dig> and  <dig> strains between the two clusters.
there were also only  <dig> of the  <dig> strains placed in the non-pathogenic category.
it was hypothesised that the results from test  <dig> could be skewing the results, as this test did not differentiate between strains of different mlst sequence types.
therefore we excluded this test from the consensus clustering allowing  <dig> strains for which data was available from tests  <dig>   <dig> and  <dig> to be analysed.
test  <dig> was retained since many st  <dig> and st  <dig> strains appeared to be correctly assigned.
the results  were similar to those for clustering with test  <dig> alone.
all strains of st  <dig>   <dig> and  <dig> appeared in cluster  <dig> .
with two exceptions , the st  <dig> strains were grouped in cluster  <dig>  along with the remainder of mlst types.
the consensus clustering of tests  <dig>   <dig> and  <dig> datasets also showed the same correlation with inositol fermentation as the results for test  <dig> alone.
all strains in cluster  <dig>  are negative for inositol fermentation, all strains in cluster  <dig> are positive for inositol fermentation.
for abbreviations in this table see footnote to table  <dig>  sources of isolation and strain numbers are given in full in additional file  <dig>
all strains in cluster  <dig>  are negative for inositol fermentation, all strains in cluster  <dig> are positive for inositol fermentation.
for abbreviations in this table see footnote to table  <dig>  sources of isolation and strain numbers are given in full in additional file  <dig>
the results of all four clustering analyses gave plausible assignments of the data into two clusters, one of which has the propensity of being pathogenic and the other one of being non-pathogenic.
the various mlst types were not divided equally between the clusters as one would expect by chance alone.
our hypothesis that strains with similar biochemical characteristics will have similar pathogenicity seems to hold since strains with pathogenic source isolates were grouped together throughout, although there were a small number of strains that were placed in the non-pathogenic cluster whilst having putative pathogenic status.
strains with the same mlst type were generally grouped together indicating, as might be expected, that strains with the same mlst type have similar biochemical characteristics.
to further investigate the association of inositol fermentation with pathogenicity, we examined the annotated genome of c. sakazakii baa- <dig>    <cit>  for genes associated with inositol fermentation.
whilst baa- <dig> is st  <dig> and negative for inositol fermentation, this strain was isolated from powdered formula associated with a clinical outbreak  <cit>  and therefore is likely to be a pathogenic strain.
the gene coding for inositol monophosphatase , which is annotated in the kegg database  <cit>  as part of the inositol phosphate metabolism pathway , was found in close proximity  to a predicted protein  which has been identified in the baa- <dig> genome and found in two other meningitic strains of c. sakazakii  by hybridization with the baa- <dig> genome  <cit> .
strains  <dig> and  <dig> are st  <dig> and were associated with fatal outbreaks, indicating this as a putative virulence factor.
this was also found to be in close proximity to the zinc-containing metalloprotease locus characterized by kothary et al  <cit> .
also at a distance of approximately  <dig> kb upstream, was a prophage fragment, gr <dig> , which contains genes homologous to the yersinia pseudotuberculosis adhesion pathogenicity island, as well as genes identified in strains  <dig> and  <dig> and the reference genome .
despite baa- <dig> being deficient for inositol fermentation, the proximity of these genes to inositol monophosphatase and their implication as putative virulence factors suggests that the inositol monophosphate gene is associated with pathogenesis and supports our hypothesis that inositol fermentation is linked to the pathogenicity of cronobacter species.
the lack of inositol fermentation in baa- <dig> may be explained by the loss of another gene, as yet unknown, which also plays a crucial role in the inositol phosphate metabolism pathway.
the genome of a c. turicensis strain  has also been sequenced  <cit> .
no biotyping data exists for c. turicensis strains.
however, the original characterisation of the c. turicensis species  <cit>  showed that c. turicensis is positive for inositol fermentation and the c. turicensis strain sequenced contains the inositol monophosphatase gene associated with pathogenesis.
the majority of c. turicensis strains were placed in the pathogenic cluster in tests  <dig> and  <dig>  but not in test  <dig> .
the sequenced strain  <dig> was pathogenic in tests  <dig> and  <dig> .
our clustering method has demonstrated that it is possible to quickly differentiate between pathogenic and non-pathogenic strains, and may lead to a quick and easy diagnostic test that can reliably identify pathogenic strains.
CONCLUSIONS
here we have used expectation maximization clustering to divide strains of cronobacter into groups of pathogenic and non-pathogenic strains based on the results of diagnostic biochemical tests.
the clustering assignments showed promise, clearly dividing the data into two clusters containing obviously pathogenic and non-pathogenic strains, based on the source of isolate and the mlst type of the strain.
however, further experiments characterising the pathogenicity of cronobacter strains are required to confirm the accuracy of the classification.
nevertheless, our results demonstrated a clear association between pathogenic strains and inositol fermentation, supported by genomic proximity of putative virulence factors to the gene coding for inositol monophosphatase.

 cronobacter, formerly known as enterobacter sakazakii, is a bacterial genus containing seven species in the family enterobacteriacae; c. sakazakii, c. malonaticus, c. muytjensii, c. turicensis, c. dublinensis, c. universalis, and c. condimenti. the organism has received a lot of attention recently due to its association with neonatal infections, especially meningitis, necrotizing enterocolitis, septicaemia and subsequent death. these bacteria have been isolated from a wide range of food stuffs, therefore it is important to be able to detect cronobacter species in food. for this purpose several diagnostic tests exist. however, most of these tests make no distinction as to the species of the bacteria. not all cronobacter species are known to be pathogenic to infants and can cause asymptomatic colonisation. the strict microbiological criteria for the presence of cronobacter in powdered infant formula for intended age < months means it is of great interest to differentiate between pathogenic and non-pathogenic strains. although a range of possible virulence features have been identified in cronobacter and reviewed elsewhere, their presence does not correspond to clinical symptoms. therefore, the identification of further discriminating factors would be useful. currently, to differentiate between species, it is necessary to sequence either the 16s rna subunit or the mlst genes; the latter is required for searching the cronobacter mlst database. there are isolates of cronobacter recorded in the mlst database at the time of analysis. although it is known that type strains are associated with meningitis, neither of the above methods is able to differentiate between pathogenic and non-pathogenic strains, they only identify individual species. moreover, both methods are time consuming compared with the use of biochemical diagnostic test kits which take 4- hours to produce results that can easily be interpreted. for this reason we aimed to develop methods for identifying which of the strains in the cronobacter genus are pathogenic based on data obtained from standard biochemical diagnostic tests. these tests were those commonly used to identify cronobacter isolates and are used in their taxonomic description. here we used expectation maximization clustering algorithm to divide the data on the basis of the biochemical test results. since the precise pathogenic status of most cronobacter strains is unknown, we considered the resulting clusters as being pathogenic or not on the basis of the source from which the strains were isolated and/or mlst types previously associated with pathogenic or non-pathogenic strains and reference. the clustering of the biochemical test results was also examined for traits associated with pathogenicity.  clustering the dataset for test with the number of clusters being resulted in clusters and containing and strains respectively where pi is the probability of cluster membership for a randomly chosen strain and l is the maximum log likelihood. according to our hypothesis cluster was most likely to contain pathogenic strains since all st strains were assigned to this cluster. it is known that st strains are associated with the most serious pathogenic states such as meningitis in infants. of the other mlst types, st and were placed exclusively with the potentially non-pathogenic strains in cluster st was split between two clusters with of strains in the non-pathogenic grouping. all except one st strain were predicted to be in the pathogenic cluster, as were all of the st strains. the group with unspecified clinical source was divided between the two clusters, indicating that not all clinical isolates are likely to be pathogenic and this feature alone by no means allows us to infer pathogenicity of a strain. for example, one clinical case, classified as non-pathogenic, was obtained from a breast abscess and it is plausible that this was a secondary infection although it is not known if another infectious agent was isolated. thus this may indeed be a non-pathogenic strain. two asymptomatic strains appeared in the pathogenic cluster; one of these strains is st and the other st several st strains are from clinical sources and it is likely that all st strains will have similar pathogenic characteristics. therefore, we can speculate that these strains could have caused an infection following a higher ingested dose or a lower immune status. abbreviations: c: clinical, e: environmental, eft: enteral feeding tube, f: food, fuf: follow up formula, if: infant formula, mp: milk powder, u: unknown wf: weaning food. sources of isolation and strain numbers are given in full in additional file clustering for the test dataset gave two clusters in which strains were in cluster and eight strains were in cluster. one strain of those in cluster was associated with a clinical diagnosis and was likely to be pathogenic, as well as one st strain, with the remainder placed in cluster the heterogeneity of mlst types in both clusters, as well as the small number of strains in cluster suggests that the biochemical data in test is not sufficient to differentiate between pathogenic and non-pathogenic strains. to prove this, the em algorithm was allowed to automatically determine the number of clusters to assign the data to. as a result, only a single cluster was produced indicating that the test data is not sufficient to differentiate between cronobacter strains. for abbreviations in this table see footnote to table sources of isolation and strain numbers are given in full in additional file clustering of the test dataset resulted in cluster containing instances and cluster containing instances. the majority of the st strains were grouped in the second cluster, indicating that this cluster contains the potentially pathogenic strains. however, all other mlst types were split between the two clusters. st was mostly placed in the non-pathogenic cluster, with one strain in cluster st was split evenly between the two clusters. most of the st strains were found to be non-pathogenic with just one strain being pathogenic. however, many strains indicated as pathogenic in the test results were placed in the larger potentially non-pathogenic grouping. based on the division of strains of the same mlst type between clusters, it is likely that the results of test are less accurate than test and test, although many st and st strains appeared to be correctly assigned. note that this test has the fewest number of strains available; it is expected that the availability of more data will greatly improve the results of clustering using this diagnostic test data. for abbreviations in this table see footnote to table sources of isolation and strain numbers are given in full in additional file for the fourth test, cluster contained strains and cluster contained strains. the clusters are shown in table. this clustering assignment was successful at differentiating between mlst types. st and were placed entirely in the non-pathogenic grouping and with two exceptions, the st strains were placed in cluster allowing us to label the latter as the potentially pathogenic cluster. all except two st strains were placed in the non-pathogenic cluster. st strains were placed in the pathogenic cluster as were all except one strain of st. a more detailed examination of the strains allocated to each cluster showed that all strains labelled as pathogenic were positive for the inositol fermentation test, whilst the prospective non-pathogenic strains were negative for this test. although this is not conclusively shown by the result of the inositol test in test and test the test data does indicate a bias towards strains with inositol fermentation in the pathogenic cluster. this suggested that either inositol fermentation was a requirement for pathogenicity, or that the genetic locus conferring inositol fermentation was linked to genes conferring pathogenic traits. this latter conclusion was supported by the two apparently pathogenic st strains which were negative for inositol fermentation: strain was isolated from infant formula, but strain was associated with neonatal meningitis indicating pathogenesis. it is probable that the inositol fermentation gene was lost from these strains, but the pathogenic traits acquired alongside it remained. it should be noted that this test is different from the ino test in the test dataset, which we removed from the analysis as it produces the same result for all cronobacter strains. all strains in cluster are negative for inositol fermentation, all strains in cluster are positive for inositol fermentation. for abbreviations in this table see footnote to table sources of isolation and strain numbers are given in full in additional file consensus clustering aggregating the clustering assignments based on the majority rule for the strains which have data available from all four tests resulted in the clusters shown in table the results showed the majority of st strains were placed in cluster however, there was still splitting of st and strains between the two clusters. there were also only of the strains placed in the non-pathogenic category. it was hypothesised that the results from test could be skewing the results, as this test did not differentiate between strains of different mlst sequence types. therefore we excluded this test from the consensus clustering allowing strains for which data was available from tests and to be analysed. test was retained since many st and st strains appeared to be correctly assigned. the results were similar to those for clustering with test alone. all strains of st and appeared in cluster. with two exceptions, the st strains were grouped in cluster along with the remainder of mlst types. the consensus clustering of tests and datasets also showed the same correlation with inositol fermentation as the results for test alone. all strains in cluster are negative for inositol fermentation, all strains in cluster are positive for inositol fermentation. for abbreviations in this table see footnote to table sources of isolation and strain numbers are given in full in additional file all strains in cluster are negative for inositol fermentation, all strains in cluster are positive for inositol fermentation. for abbreviations in this table see footnote to table sources of isolation and strain numbers are given in full in additional file the results of all four clustering analyses gave plausible assignments of the data into two clusters, one of which has the propensity of being pathogenic and the other one of being non-pathogenic. the various mlst types were not divided equally between the clusters as one would expect by chance alone. our hypothesis that strains with similar biochemical characteristics will have similar pathogenicity seems to hold since strains with pathogenic source isolates were grouped together throughout, although there were a small number of strains that were placed in the non-pathogenic cluster whilst having putative pathogenic status. strains with the same mlst type were generally grouped together indicating, as might be expected, that strains with the same mlst type have similar biochemical characteristics. to further investigate the association of inositol fermentation with pathogenicity, we examined the annotated genome of c. sakazakii baa- for genes associated with inositol fermentation. whilst baa- is st and negative for inositol fermentation, this strain was isolated from powdered formula associated with a clinical outbreak and therefore is likely to be a pathogenic strain. the gene coding for inositol monophosphatase, which is annotated in the kegg database as part of the inositol phosphate metabolism pathway, was found in close proximity to a predicted protein which has been identified in the baa- genome and found in two other meningitic strains of c. sakazakii by hybridization with the baa- genome. strains and are st and were associated with fatal outbreaks, indicating this as a putative virulence factor. this was also found to be in close proximity to the zinc-containing metalloprotease locus characterized by kothary et al. also at a distance of approximately kb upstream, was a prophage fragment, gr, which contains genes homologous to the yersinia pseudotuberculosis adhesion pathogenicity island, as well as genes identified in strains and and the reference genome. despite baa- being deficient for inositol fermentation, the proximity of these genes to inositol monophosphatase and their implication as putative virulence factors suggests that the inositol monophosphate gene is associated with pathogenesis and supports our hypothesis that inositol fermentation is linked to the pathogenicity of cronobacter species. the lack of inositol fermentation in baa- may be explained by the loss of another gene, as yet unknown, which also plays a crucial role in the inositol phosphate metabolism pathway. the genome of a c. turicensis strain has also been sequenced. no biotyping data exists for c. turicensis strains. however, the original characterisation of the c. turicensis species showed that c. turicensis is positive for inositol fermentation and the c. turicensis strain sequenced contains the inositol monophosphatase gene associated with pathogenesis. the majority of c. turicensis strains were placed in the pathogenic cluster in tests and but not in test. the sequenced strain was pathogenic in tests and. our clustering method has demonstrated that it is possible to quickly differentiate between pathogenic and non-pathogenic strains, and may lead to a quick and easy diagnostic test that can reliably identify pathogenic strains.  here we have used expectation maximization clustering to divide strains of cronobacter into groups of pathogenic and non-pathogenic strains based on the results of diagnostic biochemical tests. the clustering assignments showed promise, clearly dividing the data into two clusters containing obviously pathogenic and non-pathogenic strains, based on the source of isolate and the mlst type of the strain. however, further experiments characterising the pathogenicity of cronobacter strains are required to confirm the accuracy of the classification. nevertheless, our results demonstrated a clear association between pathogenic strains and inositol fermentation, supported by genomic proximity of putative virulence factors to the gene coding for inositol monophosphatase. 
therefore to be able to differentiate between the pathogenic and non-pathogenic strains is of interest to the food industry and regulators. pathogenicity of a strain was postulated on the basis of either pathogenic symptoms associated with strain source or corresponding mlst sequence types, allowing the clusters to be labelled as containing either pathogenic or non-pathogenic strains. an investigation of the genomes of cronobacter sakazakii and c. turicensis revealed the gene for inositol monophosphatase is associated with putative virulence factors in pathogenic strains of cronobacter. we demonstrated a computational approach allowing existing diagnostic kits to be used to identify pathogenic strains of cronobacter. cronobacter, formerly known as enterobacter sakazakii, is a food-borne pathogen known to cause neonatal meningitis, septicaemia and death. the results also revealed a potential association between pathogenicity and inositol fermentation. here we report the use of expectation maximization clustering to categorise strains of cronobacter as pathogenic or non-pathogenic based on biochemical test results from standard diagnostic test kits. the organism is ubiquitous, being found in the environment and in a wide range of foods, although there is variation in pathogenicity between cronobacter isolates and between species. the resulting clusters correlated well with mlst sequence types and revealed new information about the pathogenicity of cronobacter species.
 cronobacter, formerly known as enterobacter sakazakii, is a food-borne pathogen known to cause neonatal meningitis, septicaemia and death. current diagnostic tests for identification of cronobacter do not differentiate between species, necessitating time consuming 16s rdna gene sequencing or multilocus sequence typing. the organism is ubiquitous, being found in the environment and in a wide range of foods, although there is variation in pathogenicity between cronobacter isolates and between species. therefore to be able to differentiate between the pathogenic and non-pathogenic strains is of interest to the food industry and regulators.  here we report the use of expectation maximization clustering to categorise strains of cronobacter as pathogenic or non-pathogenic based on biochemical test results from standard diagnostic test kits. pathogenicity of a strain was postulated on the basis of either pathogenic symptoms associated with strain source or corresponding mlst sequence types, allowing the clusters to be labelled as containing either pathogenic or non-pathogenic strains. the resulting clusters gave good differentiation of strains into pathogenic and non-pathogenic groups, corresponding well to isolate source and mlst sequence type. the results also revealed a potential association between pathogenicity and inositol fermentation. an investigation of the genomes of cronobacter sakazakii and c. turicensis revealed the gene for inositol monophosphatase is associated with putative virulence factors in pathogenic strains of cronobacter.  we demonstrated a computational approach allowing existing diagnostic kits to be used to identify pathogenic strains of cronobacter. the resulting clusters correlated well with mlst sequence types and revealed new information about the pathogenicity of cronobacter species. 