BACKGROUND
the phenotype of an organism is governed by the interplay between genome content and cellular regulatory mechanisms.
recent high-throughput technologies such as microarrays, next-generation sequencing, rna-seq, proteomics and metabolomics have the potential to profile presence, expression, and/or abundance of components involved in these regulatory mechanisms.
these technologies assess simultaneously thousands of features  generating large and often noisy data.
the inherent noisiness results, for instance, in inaccurate genotype calling and/or inaccurate phenotypic measurements.
additionally, these features could have intrinsic relations , which makes identifying links to phenotypes a non-trivial task.
several methods have been devised to identify genetic links to phenotypes, which include a) correlating variation in snp presence to phenotypes  <cit> , b) correlating large-scale prokaryotic genomic data, obtained by integrating data from various sources like pathways and protein domains, with their phenotypes  <cit> , c) combining closely related quantitative traits to identify genetic markers that jointly affect  these traits  <cit> , d) selecting gene sets based on their expression levels under different experimental conditions and then building phenotype-specific gene networks  <cit> .
though these methods allow finding relations between features and phenotypes, correlation based methods  are not suitable for finding partial relations, i.e.
a feature that is important only for a subset of samples of a certain phenotype.
classification algorithms might capture such relations.
in addition, ~omics data sets often have many more features  than samples .
classifying such data sets leads to the small n  large p  problem, where estimating the true contribution of a feature becomes more difficult.
~omics data also needs to be preprocessed for highly correlated features, as the contribution of such features in classification would be wrongly estimated in tree-based classifiers  <cit> , and for features with homogeneous values across all observations as they decrease classification accuracy  <cit> .
additionally, in some experiments an imbalanced phenotype distribution could occur when most observations are of the same dominant phenotype.
many classification algorithms favor dominant phenotypes resulting in a biased classification  <cit> , in turn resulting in fewer and weaker relations identified between features and minority phenotypes.
thus, to decrease noise both ~omics and phenotype data should be preprocessed before using these data in classification.
identifying links to phenotypes from ~omics data is an essential part of an association analysis, yet prioritizing important links is often difficult without adequate visualization.
furthermore, some of the links could be spurious due to methodological reasons  and/or technological reasons .
therefore, visualization that integrates different information sources in a single figure such as networks  <cit>  or color encoded figures allow identifying gene to phenotype links more straight-forward.
therefore, we encoded different levels of gene to phenotype or gene to strain relationships with color codes.
in summary, determining links to phenotype from large data sets requires an approach that is robust against noise, handles imbalanced phenotype data and provides a comprehensive yet general visualization of links to all phenotypes.
we developed a method, phenolink, that facilitates associating phenotypes to ~omics data , is robust against noise, and decreases imbalance in phenotype data by a bagging procedure.
the random forest classification algorithm is extensively used in ~omics data analysis because it is less resource-intensive than many classification algorithms , it makes no implicit assumption regarding data properties and it is specifically suited to deal with the small n large p problem due to the use of many weak classifiers .
we use random forest to identify features that are relevant for phenotypes.
the identified links are visualized to allow better mining of relations between phenotype and ~omics data.
phenolink was implemented as a web-tool, which was applied to identify relations between genes  and phenotypes  of  <dig> lactobacillus plantarum strains.
although l. plantarum strain wcfs <dig> has been extensively studied in the past  <cit> , we identified novel gene-to-phenotype relations in addition to already known ones.
RESULTS
phenolink algorithm
phenolink is a web-tool developed to link phenotypes to ~omics data.
we use the random forest classification algorithm  <cit> , which builds an ensemble of decision trees, to find relations between features derived from ~omics data  and phenotypic readouts.
imbalance in phenotype data is decreased by bagging, and only one of the highly correlated and none of the homogeneous features  <cit>  in ~omics data is used in classification.
based on the contribution score, only relevant features are used in subsequent round of classification.
iterative feature selection allows the identification of only relevant relations between features and phenotypes.
applying phenolink to l. plantarum genotype and phenotype data
we used phenolink to identify genes that are important for phenotypes of  <dig> l. plantarum strains .
the genotype data consists of the presence/absence of  <dig> genes in these strains as determined by cgh arrays.
the phenotype data contain measurements of these strains for sugar utilization , and nitrogen-dioxide production .
from  <dig> phenotypic experiments only  <dig> were usable in association analysis .
a: numbers in parenthesis show number of strains with given phenotype, for instance there are  <dig> strains that could grow on sugar d-turanose and  <dig> strains that could not grow on this sugar.
phenotype and ~omics data are available at the web address of phenolink.
b: for some strains phenotypes could not be determined accurately resulting in an ambiguous phenotype "maybe".
using the web-interface of phenolink such phenotypes can be discarded from the association analysis .
once homogeneous genes were removed only  <dig> genes remained and of these  <dig> remained after eliminating all but one of the highly-correlated genes .
the default variance of 5% leads to discarding all features that have different values in at most two strains for the l. plantarum dataset .
decrease in phenotype imbalance using bagging  often allowed classifying four additional phenotypes.
though there were no big differences in classification accuracy for dominant phenotypes with or without bagging, phenotypes with the fewest strains mostly had a classification accuracy less than 30% and some of them having 0% classification accuracy without bagging.
bagging increased classification accuracy for some of these phenotypes up to 69%.
there was not a significant decrease in median classification accuracy of all accurately classified phenotypes .
visualizing identified relations allowed capturing genes and gene clusters that are related to single or multiple phenotypes, which are described below.
identifying novel and known links to phenotypes
only a few gene clusters were found that relate to a single phenotype.
for instance, a cluster of five genes  is found as important for methyl-d-mannopyranoside utilization .
in contrast, we found several gene clusters that are related to multiple phenotypes, and these include novel and well-described gene clusters such as those for l-arabinose  <cit>   and l-rhamnose metabolism  <cit>  .
most of the genes were related to growth on sugars encode enzyme , transport  or regulatory  functions.
many genes were related to multiple phenotypes , which is partly due to the fact that some transporters and enzymes are promiscuous and can utilize several structurally related sugars.
however, more importantly this demonstrates that for a manifestation of any phenotype often more than a single gene is important.
a very large gene cluster of  <dig> genes was found to be related to nitrate reduction and nitrogen dioxide production  by l. plantarum strains; this gene cluster was already experimentally validated to be involved in nitrogen dioxide production  <cit> .
another gene cluster containing  <dig> genes  was found to be related to utilization of multiple sugars and to absence of nitrogen-dioxide production.
comparison of phenolink and correlation-based methods
for all usable l. plantarum phenotypic tests, genes that were found to be related to phenotypes were the same using both pearson and spearman correlation metrics .
on average 69% of these genes were also found by phenolink.
however, on average 37% of the genes that were found by phenolink to be related to any phenotype were found by correlation.
the remaining genes identified by phenolink could not be found by these two conventional correlation methods, mostly due to the partial relations to phenotypes or they are related to phenotypes present in fewer strains, and phenolink effectively deals with such phenotype imbalance by using bagging.
nevertheless, most of the clear relations between phenotypes and genes can be identified by both methods.
for phenotypes of strains assessed by three api tests  no gene was found to be related by any of the correlation methods.
for the remaining phenotype data on average  <dig> and  <dig> genes were found for phenotypes of each experiment, respectively by the correlation methods and by phenolink.
however, some relations that were identified by correlation methods could not be identified by phenolink, because correlation-based methods often found genes that were only related to the dominant phenotype regardless of the gene's presence/absence values in strains of other phenotypes.
such biased relations are largely decreased by bagging and these genes are less likely to get higher contribution scores by the random forest algorithm as they could not be used to categorize strains of different phenotypes accurately.
most of the genes that were only identified by correlation methods have hypothetical, regulatory or transport and binding functions.
though the genes that were only identified by phenolink have similar functions, there were many protein encoding genes which were not identified by correlation.
both methods identified some phage proteins or transposases to be related to phenotypes and using a p-value of  <dig>  decreases such spurious links.
however as a consequence it would be impossible to identify any relation for many phenotypes based on the correlation-based methods.
applying phenolink to s. pneumonia genomic array footprinting based gene essentiality data
we also used phenolink with s. pneumoniae gene essentiality data to identify  <dig> genes that were found to be significant from a genomic array footprinting experiment  <cit>  and that were experimentally proven to be significantly attenuated during meningitis infection.
a ratio-based analysis of signals at initial time point  and at time point n was used by the authors to prioritize genes affected by transposon insertions .
a ratio-based analysis is in general more suited for data from microarray experiments where two dyes were used, due to that signals from both dyes of the same hybridization share many systematic errors occurring in microarray experiments, e.g., position on the slide surface and scanning effects  <cit> .
we analyzed this noisy dataset with phenolink using a default classification-based analysis.
the classification was based on signals for the respective time points.
as this dataset is very noisy, a signal based classification is expected to be less suited than a ratio-based analysis.
the phenolink based classification accuracy was high for all classes  except for experiments at time point  <dig> hours.
therefore, experimental results at time point  <dig> hour were not used in phenolink; however, the full dataset is available at the phenolink's web-site.
we found  <dig>  of the validated  <dig> genes  by using phenolink with similar parameters as it was used for l. plantarum dataset .
neighbours for some of these genes were also found and, as expected, some of them are in the same operon.
additionally, we also found a few new genes to be related to different time-points identified operon members and new targets are now being evaluated by a co-author of the genomic array footprinting study .
discussion
linking phenotypes to ~omics datasets is difficult due to size and noisiness of the data and lack of easily accessible tools that can  handle large and noisy data,  find links to phenotypes and  visualize links.
we developed a web-tool - phenolink - to identify links to phenotypes using classification-based feature selection .
the presence/absence of  <dig> genes in  <dig> l. plantarum strains and phenotypes of these strains was used in phenolink to identify links to phenotypes assessed in  <dig> experiments.
we tried different visualization techniques such as graph and tree structures for enhanced visualization of identified relations.
in visualization as much as possible information should be embedded in a single figure, while still it should be easy to interpret.
visualization of identified links using different colours for each relationship type allowed capturing relations not only between genes and phenotypes, but also relations among genes and among phenotypes.
additionally, visualization allowed identifying partial relations between genes and phenotypes , where different genes are essential for certain strains of a phenotype.
for instance, among correctly classified polysaccharide  utilization experiments, only for d-raffinose additional polysaccharide biosynthesis genes  were found to be related .
all strains that can't grow on raffinose lack these genes and most of the growing strains have either these genes or other polysaccharide biosynthesis genes  .
possibly the growing strains can utilize raffinose degradation product for polysaccharide biosynthesis.
the l. plantarum strains used in this study often showed similar phenotypes, or some of them had ambiguously defined phenotypes such as "maybe" due to either mild expression of a trait or possibly experimental error.
strains with this phenotype are, as expected, often misclassified.
therefore in this study, we discarded all strains with this phenotype; however it can be configured in our web tool to include them.
using ambiguous phenotypes in certain cases could be beneficial to validate input data as strains with similar gene content should have similar or the same phenotype.
phenolink can be configured to generate classification accuracy plots for each experiment , which shows how accurately strains were classified.
reasons for consistent misclassification of strains are:  ambiguous or wrong phenotype,  noisy ~omics data, and/or  these strains could belong to a minority phenotype .
in the present example, the presence/absence of genes was determined based on hybridization to an array containing probes to only a single reference strain  and its three plasmids.
because l. plantarum is a versatile species living in various environments, the gene content of many of these strains will be in part different from that of wcfs <dig>  <cit> .
strain nizo <dig> is exceptional, as it was always misclassified to be not growing on the sugar l-arabinose .
based on cgh data,  <dig> % of the genes of strain wcfs <dig> are predicted to be absent in strain nizo <dig>  <cit> ; however other strains that lack even more wcfs <dig> genes have correctly been classified.
probably this strain either does not grow on l-arabinose  or it uses different sets of genes to grow on l-arabinose, which differ too much in sequence compared to wcfs <dig> genes in order to be detected by cgh.
pan-genome arrays specifying probes based on the genomic content of multiple strains and plasmids within the same species, should provide a better estimate of species-level genomic divergence.
however, cross-hybridization of probes is the general disadvantage of the microarray technology, which leads to inaccurate gene calling.
with prices decreasing continuously, next-generation sequencing techniques are becoming better alternatives due to their accuracy and recall of new or divergent genes, that using microarrays would have been missed.
gene presence/absence determined by sequencing would allow phenolink to determine links to phenotypes more accurately.
phenolink allows decreasing huge combinations of possible experimental tests by pruning input data and prioritizing identified links.
though many phenotypes  were classified with accuracy above 80%, we used a 60% classification accuracy cutoff to accommodate noise in input data such as wrong gene calling or imbalance in phenotype data.
identifying partial relations is inherently difficult even with classification-based association analysis.
thus such findings, which are visualized in a black colour, should first be corroborated with available literature and/or databases before performing follow-up lab-experiments.
phenolink allows finding links to many phenotypes of several strains.
the input data should contain information about at least a few strains  with at least two different phenotypes .
however, most of the public data sets often lack either ~omics or phenotype data.
most of the ~omics and/or phenotype data sets are from studies of only a few strains, posing the small sample size problem preventing their use in phenolink, and yet many others had a phenotype imbalance problem  <cit> .
in this study, we describe the use of phenolink on two different datasets:  lactobacillus plantarum genotype and phenotype data and  streptococcus pneumoniae gene essentiality data.
these datasets are publicly available .
in phenolink, redundant and noisy features are removed before association analysis.
therefore an increase in the number of features would not increase proportionally the total run-time.
we tested the increase in phenolink's run-time as a function of an increase in the number of features.
to this end we created two datasets by increasing total number of features for  <dig> l. plantarum strains from  <dig> to  <dig> and  <dig>  an increase in the number of features exponentially increased phenolink run-time.
one has to note that this is likely due to that unlike with the actual l. plantarum data, most features in the randomly generated data had very high variances and were not often correlated.
this in turn substantially increased the number of features used in classification, and phenolink's run-time.
we developed a web-tool - phenolink - to link phenotypes to ~omics data.
it is a flexible and versatile tool.
phenolink can be used to effectively prioritize links from different ~omics datasets, such as genotype, transcriptome, metabolome, proteome to phenotypes.
it is a tool with enhanced visualization of links to phenotypes, is more accurate than correlation-based method and less resource-intensive than bayesian-based methods.
it has already been used in several studies to identify leads to phenotypes from diverse sets of ~omics data such as genotype, transcriptome and metabolome data.
thus, phenolink facilitates screening large ~omics and phenotype data sets, allowing to effectively capture known relations to phenotypes as well as novel relations.
CONCLUSIONS
linking phenotypes to large ~omics data sets is essential for generating leads to understand the underlying mechanism of a phenotype.
often such analysis is hindered by the scale of data and lack of easy-to-use tools.
we present an easily-accessible web-tool, phenolink incorporates well-studied techniques such as feature selection, bagging, removing redundant or noisy features and a feature selection stability criterion into the single tool.
using an enhanced visualization, this tool can be used to address different problems with different data types and data sizes.
it preprocesses input data to decrease noise and uses classification-based feature selection to accurately find features that are related to phenotypes.
it identifies links to phenotypes more accurately than correlation-based methods, efficiently handles large data sets and is robust against noise  <cit> .
visualization allows quick identification of relations  between features and phenotypes,  among features,  among phenotypes,  features and samples, which use different feature sets to exhibit the same phenotype, and also  outliers in phenotype data, which would allow detecting possible experimental errors.
identified links might be used to improve feature annotations  in selected cases with limited experimental validation.
phenolink therefore allows researchers to quickly screen large data sets for new leads to phenotype associations.

 the phenotype of an organism is governed by the interplay between genome content and cellular regulatory mechanisms. recent high-throughput technologies such as microarrays, next-generation sequencing, rna-seq, proteomics and metabolomics have the potential to profile presence, expression, and/or abundance of components involved in these regulatory mechanisms. these technologies assess simultaneously thousands of features generating large and often noisy data. the inherent noisiness results, for instance, in inaccurate genotype calling and/or inaccurate phenotypic measurements. additionally, these features could have intrinsic relations, which makes identifying links to phenotypes a non-trivial task. several methods have been devised to identify genetic links to phenotypes, which include a) correlating variation in snp presence to phenotypes, b) correlating large-scale prokaryotic genomic data, obtained by integrating data from various sources like pathways and protein domains, with their phenotypes, c) combining closely related quantitative traits to identify genetic markers that jointly affect these traits, d) selecting gene sets based on their expression levels under different experimental conditions and then building phenotype-specific gene networks. though these methods allow finding relations between features and phenotypes, correlation based methods are not suitable for finding partial relations, i.e. a feature that is important only for a subset of samples of a certain phenotype. classification algorithms might capture such relations. in addition, ~omics data sets often have many more features than samples. classifying such data sets leads to the small n large p problem, where estimating the true contribution of a feature becomes more difficult. ~omics data also needs to be preprocessed for highly correlated features, as the contribution of such features in classification would be wrongly estimated in tree-based classifiers, and for features with homogeneous values across all observations as they decrease classification accuracy. additionally, in some experiments an imbalanced phenotype distribution could occur when most observations are of the same dominant phenotype. many classification algorithms favor dominant phenotypes resulting in a biased classification, in turn resulting in fewer and weaker relations identified between features and minority phenotypes. thus, to decrease noise both ~omics and phenotype data should be preprocessed before using these data in classification. identifying links to phenotypes from ~omics data is an essential part of an association analysis, yet prioritizing important links is often difficult without adequate visualization. furthermore, some of the links could be spurious due to methodological reasons and/or technological reasons. therefore, visualization that integrates different information sources in a single figure such as networks or color encoded figures allow identifying gene to phenotype links more straight-forward. therefore, we encoded different levels of gene to phenotype or gene to strain relationships with color codes. in summary, determining links to phenotype from large data sets requires an approach that is robust against noise, handles imbalanced phenotype data and provides a comprehensive yet general visualization of links to all phenotypes. we developed a method, phenolink, that facilitates associating phenotypes to ~omics data, is robust against noise, and decreases imbalance in phenotype data by a bagging procedure. the random forest classification algorithm is extensively used in ~omics data analysis because it is less resource-intensive than many classification algorithms, it makes no implicit assumption regarding data properties and it is specifically suited to deal with the small n large p problem due to the use of many weak classifiers. we use random forest to identify features that are relevant for phenotypes. the identified links are visualized to allow better mining of relations between phenotype and ~omics data. phenolink was implemented as a web-tool, which was applied to identify relations between genes and phenotypes of lactobacillus plantarum strains. although l. plantarum strain wcfs has been extensively studied in the past, we identified novel gene-to-phenotype relations in addition to already known ones.  phenolink algorithm phenolink is a web-tool developed to link phenotypes to ~omics data. we use the random forest classification algorithm, which builds an ensemble of decision trees, to find relations between features derived from ~omics data and phenotypic readouts. imbalance in phenotype data is decreased by bagging, and only one of the highly correlated and none of the homogeneous features in ~omics data is used in classification. based on the contribution score, only relevant features are used in subsequent round of classification. iterative feature selection allows the identification of only relevant relations between features and phenotypes. applying phenolink to l. plantarum genotype and phenotype data we used phenolink to identify genes that are important for phenotypes of l. plantarum strains. the genotype data consists of the presence/absence of genes in these strains as determined by cgh arrays. the phenotype data contain measurements of these strains for sugar utilization, and nitrogen-dioxide production. from phenotypic experiments only were usable in association analysis. a: numbers in parenthesis show number of strains with given phenotype, for instance there are strains that could grow on sugar d-turanose and strains that could not grow on this sugar. phenotype and ~omics data are available at the web address of phenolink. b: for some strains phenotypes could not be determined accurately resulting in an ambiguous phenotype "maybe". using the web-interface of phenolink such phenotypes can be discarded from the association analysis. once homogeneous genes were removed only genes remained and of these remained after eliminating all but one of the highly-correlated genes. the default variance of 5% leads to discarding all features that have different values in at most two strains for the l. plantarum dataset. decrease in phenotype imbalance using bagging often allowed classifying four additional phenotypes. though there were no big differences in classification accuracy for dominant phenotypes with or without bagging, phenotypes with the fewest strains mostly had a classification accuracy less than 30% and some of them having 0% classification accuracy without bagging. bagging increased classification accuracy for some of these phenotypes up to 69%. there was not a significant decrease in median classification accuracy of all accurately classified phenotypes. visualizing identified relations allowed capturing genes and gene clusters that are related to single or multiple phenotypes, which are described below. identifying novel and known links to phenotypes only a few gene clusters were found that relate to a single phenotype. for instance, a cluster of five genes is found as important for methyl-d-mannopyranoside utilization. in contrast, we found several gene clusters that are related to multiple phenotypes, and these include novel and well-described gene clusters such as those for l-arabinose and l-rhamnose metabolism. most of the genes were related to growth on sugars encode enzyme, transport or regulatory functions. many genes were related to multiple phenotypes, which is partly due to the fact that some transporters and enzymes are promiscuous and can utilize several structurally related sugars. however, more importantly this demonstrates that for a manifestation of any phenotype often more than a single gene is important. a very large gene cluster of genes was found to be related to nitrate reduction and nitrogen dioxide production by l. plantarum strains; this gene cluster was already experimentally validated to be involved in nitrogen dioxide production. another gene cluster containing genes was found to be related to utilization of multiple sugars and to absence of nitrogen-dioxide production. comparison of phenolink and correlation-based methods for all usable l. plantarum phenotypic tests, genes that were found to be related to phenotypes were the same using both pearson and spearman correlation metrics. on average 69% of these genes were also found by phenolink. however, on average 37% of the genes that were found by phenolink to be related to any phenotype were found by correlation. the remaining genes identified by phenolink could not be found by these two conventional correlation methods, mostly due to the partial relations to phenotypes or they are related to phenotypes present in fewer strains, and phenolink effectively deals with such phenotype imbalance by using bagging. nevertheless, most of the clear relations between phenotypes and genes can be identified by both methods. for phenotypes of strains assessed by three api tests no gene was found to be related by any of the correlation methods. for the remaining phenotype data on average and genes were found for phenotypes of each experiment, respectively by the correlation methods and by phenolink. however, some relations that were identified by correlation methods could not be identified by phenolink, because correlation-based methods often found genes that were only related to the dominant phenotype regardless of the gene's presence/absence values in strains of other phenotypes. such biased relations are largely decreased by bagging and these genes are less likely to get higher contribution scores by the random forest algorithm as they could not be used to categorize strains of different phenotypes accurately. most of the genes that were only identified by correlation methods have hypothetical, regulatory or transport and binding functions. though the genes that were only identified by phenolink have similar functions, there were many protein encoding genes which were not identified by correlation. both methods identified some phage proteins or transposases to be related to phenotypes and using a p-value of decreases such spurious links. however as a consequence it would be impossible to identify any relation for many phenotypes based on the correlation-based methods. applying phenolink to s. pneumonia genomic array footprinting based gene essentiality data we also used phenolink with s. pneumoniae gene essentiality data to identify genes that were found to be significant from a genomic array footprinting experiment and that were experimentally proven to be significantly attenuated during meningitis infection. a ratio-based analysis of signals at initial time point and at time point n was used by the authors to prioritize genes affected by transposon insertions. a ratio-based analysis is in general more suited for data from microarray experiments where two dyes were used, due to that signals from both dyes of the same hybridization share many systematic errors occurring in microarray experiments, e.g., position on the slide surface and scanning effects. we analyzed this noisy dataset with phenolink using a default classification-based analysis. the classification was based on signals for the respective time points. as this dataset is very noisy, a signal based classification is expected to be less suited than a ratio-based analysis. the phenolink based classification accuracy was high for all classes except for experiments at time point hours. therefore, experimental results at time point hour were not used in phenolink; however, the full dataset is available at the phenolink's web-site. we found of the validated genes by using phenolink with similar parameters as it was used for l. plantarum dataset. neighbours for some of these genes were also found and, as expected, some of them are in the same operon. additionally, we also found a few new genes to be related to different time-points identified operon members and new targets are now being evaluated by a co-author of the genomic array footprinting study. discussion linking phenotypes to ~omics datasets is difficult due to size and noisiness of the data and lack of easily accessible tools that can handle large and noisy data, find links to phenotypes and visualize links. we developed a web-tool - phenolink - to identify links to phenotypes using classification-based feature selection. the presence/absence of genes in l. plantarum strains and phenotypes of these strains was used in phenolink to identify links to phenotypes assessed in experiments. we tried different visualization techniques such as graph and tree structures for enhanced visualization of identified relations. in visualization as much as possible information should be embedded in a single figure, while still it should be easy to interpret. visualization of identified links using different colours for each relationship type allowed capturing relations not only between genes and phenotypes, but also relations among genes and among phenotypes. additionally, visualization allowed identifying partial relations between genes and phenotypes, where different genes are essential for certain strains of a phenotype. for instance, among correctly classified polysaccharide utilization experiments, only for d-raffinose additional polysaccharide biosynthesis genes were found to be related. all strains that can't grow on raffinose lack these genes and most of the growing strains have either these genes or other polysaccharide biosynthesis genes. possibly the growing strains can utilize raffinose degradation product for polysaccharide biosynthesis. the l. plantarum strains used in this study often showed similar phenotypes, or some of them had ambiguously defined phenotypes such as "maybe" due to either mild expression of a trait or possibly experimental error. strains with this phenotype are, as expected, often misclassified. therefore in this study, we discarded all strains with this phenotype; however it can be configured in our web tool to include them. using ambiguous phenotypes in certain cases could be beneficial to validate input data as strains with similar gene content should have similar or the same phenotype. phenolink can be configured to generate classification accuracy plots for each experiment, which shows how accurately strains were classified. reasons for consistent misclassification of strains are: ambiguous or wrong phenotype, noisy ~omics data, and/or these strains could belong to a minority phenotype. in the present example, the presence/absence of genes was determined based on hybridization to an array containing probes to only a single reference strain and its three plasmids. because l. plantarum is a versatile species living in various environments, the gene content of many of these strains will be in part different from that of wcfs. strain nizo is exceptional, as it was always misclassified to be not growing on the sugar l-arabinose. based on cgh data, % of the genes of strain wcfs are predicted to be absent in strain nizo; however other strains that lack even more wcfs genes have correctly been classified. probably this strain either does not grow on l-arabinose or it uses different sets of genes to grow on l-arabinose, which differ too much in sequence compared to wcfs genes in order to be detected by cgh. pan-genome arrays specifying probes based on the genomic content of multiple strains and plasmids within the same species, should provide a better estimate of species-level genomic divergence. however, cross-hybridization of probes is the general disadvantage of the microarray technology, which leads to inaccurate gene calling. with prices decreasing continuously, next-generation sequencing techniques are becoming better alternatives due to their accuracy and recall of new or divergent genes, that using microarrays would have been missed. gene presence/absence determined by sequencing would allow phenolink to determine links to phenotypes more accurately. phenolink allows decreasing huge combinations of possible experimental tests by pruning input data and prioritizing identified links. though many phenotypes were classified with accuracy above 80%, we used a 60% classification accuracy cutoff to accommodate noise in input data such as wrong gene calling or imbalance in phenotype data. identifying partial relations is inherently difficult even with classification-based association analysis. thus such findings, which are visualized in a black colour, should first be corroborated with available literature and/or databases before performing follow-up lab-experiments. phenolink allows finding links to many phenotypes of several strains. the input data should contain information about at least a few strains with at least two different phenotypes. however, most of the public data sets often lack either ~omics or phenotype data. most of the ~omics and/or phenotype data sets are from studies of only a few strains, posing the small sample size problem preventing their use in phenolink, and yet many others had a phenotype imbalance problem. in this study, we describe the use of phenolink on two different datasets: lactobacillus plantarum genotype and phenotype data and streptococcus pneumoniae gene essentiality data. these datasets are publicly available. in phenolink, redundant and noisy features are removed before association analysis. therefore an increase in the number of features would not increase proportionally the total run-time. we tested the increase in phenolink's run-time as a function of an increase in the number of features. to this end we created two datasets by increasing total number of features for l. plantarum strains from to and an increase in the number of features exponentially increased phenolink run-time. one has to note that this is likely due to that unlike with the actual l. plantarum data, most features in the randomly generated data had very high variances and were not often correlated. this in turn substantially increased the number of features used in classification, and phenolink's run-time. we developed a web-tool - phenolink - to link phenotypes to ~omics data. it is a flexible and versatile tool. phenolink can be used to effectively prioritize links from different ~omics datasets, such as genotype, transcriptome, metabolome, proteome to phenotypes. it is a tool with enhanced visualization of links to phenotypes, is more accurate than correlation-based method and less resource-intensive than bayesian-based methods. it has already been used in several studies to identify leads to phenotypes from diverse sets of ~omics data such as genotype, transcriptome and metabolome data. thus, phenolink facilitates screening large ~omics and phenotype data sets, allowing to effectively capture known relations to phenotypes as well as novel relations.  linking phenotypes to large ~omics data sets is essential for generating leads to understand the underlying mechanism of a phenotype. often such analysis is hindered by the scale of data and lack of easy-to-use tools. we present an easily-accessible web-tool, phenolink incorporates well-studied techniques such as feature selection, bagging, removing redundant or noisy features and a feature selection stability criterion into the single tool. using an enhanced visualization, this tool can be used to address different problems with different data types and data sizes. it preprocesses input data to decrease noise and uses classification-based feature selection to accurately find features that are related to phenotypes. it identifies links to phenotypes more accurately than correlation-based methods, efficiently handles large data sets and is robust against noise. visualization allows quick identification of relations between features and phenotypes, among features, among phenotypes, features and samples, which use different feature sets to exhibit the same phenotype, and also outliers in phenotype data, which would allow detecting possible experimental errors. identified links might be used to improve feature annotations in selected cases with limited experimental validation. phenolink therefore allows researchers to quickly screen large data sets for new leads to phenotype associations. 
~omics datasets are often very large and noisy with many features. thus, associating phenotypes to ~omics data requires an approach that is robust to noise and can handle large and diverse data sets. we developed a web-tool phenolink that links phenotype to ~omics data sets using well-established as well new techniques. phenolink is an easily accessible web-tool to facilitate identifying relations from large and often noisy phenotype and ~omics datasets. phenolink can be used to uncover phenotype links to a multitude of ~omics data, e.g., gene presence/absence, gene expression, or metabolite abundance. we applied phenolink to identify gene-phenotype relations based on the presence/absence of genes in lactobacillus plantarum strains and phenotypic measurements of these strains in several experimental conditions, including growth on sugars and nitrogen-dioxide production. visualization of links to phenotypes offered in phenolink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data. phenolink imputes missing values and preprocesses input data to decrease inherent noise in the data and to counterbalance pitfalls of the random forest algorithm, on which feature selection is based. preprocessed data is used in feature selection to identify relations to phenotypes. in addition to known gene to phenotype relations we also found novel relations.
 linking phenotypes to high-throughput molecular biology information generated by ~omics technologies allows revealing cellular mechanisms underlying an organism's phenotype. ~omics datasets are often very large and noisy with many features. thus, associating phenotypes to ~omics data requires an approach that is robust to noise and can handle large and diverse data sets.  we developed a web-tool phenolink that links phenotype to ~omics data sets using well-established as well new techniques. phenolink imputes missing values and preprocesses input data to decrease inherent noise in the data and to counterbalance pitfalls of the random forest algorithm, on which feature selection is based. preprocessed data is used in feature selection to identify relations to phenotypes. we applied phenolink to identify gene-phenotype relations based on the presence/absence of genes in lactobacillus plantarum strains and phenotypic measurements of these strains in several experimental conditions, including growth on sugars and nitrogen-dioxide production. genes were ranked based on their importance to correctly predict the phenotype of a given strain. in addition to known gene to phenotype relations we also found novel relations.  phenolink is an easily accessible web-tool to facilitate identifying relations from large and often noisy phenotype and ~omics datasets. visualization of links to phenotypes offered in phenolink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data. phenolink can be used to uncover phenotype links to a multitude of ~omics data, e.g., gene presence/absence, gene expression, or metabolite abundance. 