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Paper text:
assessing susceptibility to diseases based on an individual 's genotype has long been a central theme of genetics studies . among inherited gene variations in humans , nonsynonymous single nucleotide polymorphisms ( nssnps )
that lead to an amino acid change in the protein product are most relevant to human inherited diseases ( 1 ) .
nssnps can be classified into two categories according to their phenotypic effects : those that cause deleterious effects on protein functions and are hence disease - associated and those that are functionally neutral .
given the huge number of nssnps already discovered ( 2,3 ) , a major challenge is to predict which of them are potentially disease associated .
computational tools have been developed to predict the nssnp 's phenotypic effect , e.g. the sift server ( 4 ) and the polyphen server ( 5 ) .
recently , studies have shown that combining information obtained from multiple sequence alignment and three - dimensional protein structure can increase the prediction accuracy ( 6 ) .
nssnpanalyzer server integrates multiple sequences alignment and protein structure analysis to identify disease - associated nssnps .
nssnpanalyzer takes a protein sequence and the accompanying nssnp as inputs and reports whether the nssnp is likely to be disease - associated or functionally neutral .
nssnpanalyzer also provides additional useful information about the nssnp to facilitate the biological interpretation of results , e.g. structural environment class and multiple sequence alignment .
briefly , on receiving the input sequence , nssnpanalyzer searches the astral database ( 7 ) for homologous protein structures .
nssnpanalyzer calculates three types of information from user 's input : ( i ) the structural environment of the snp , including the solvent accessibility , environmental polarity and secondary structure ( 8) ; ( ii ) the normalized probability of the substitution in the multiple sequence alignment ( 9 ) ; and ( iii ) the similarity and dissimilarity between the original amino acid and mutated amino acid .
nssnpanalyzer then uses a machine learning method called random forest ( 10 ) to classify the nssnps .
the random forest classifier was trained to optimally combine the heterogeneous sources of predictors using a curated training dataset prepared from the swissprot database ( 11 ) .
several recent studies have demonstrated the better performance of random forest over other machine learning approaches ( 1214 ) .
for the nssnp phenotypic effect prediction , we also found that random forest gave the best results on this training dataset . in a cross - validation test ,
the false positive rate is 38% and the false negative rate is 21% ( 15 ) .
the nssnpanalyzer web server is implemented on a linux redhat 8.0 platform with the common gateway interface scripts written in php .
two inputs are mandatory : protein sequence in fasta format and the nssnp identities to be analyzed . an nssnp is denoted as x#y , where x is the original amino acid in one letter , # is the position of the substitution ( starting from 1 ) , and y is the mutated amino acid in one letter .
in addition to the two mandatory inputs , users may also upload an accompanying protein structure file in pdb format if they want their own structure to be used . finally , because the calculation usually takes a while
users can use the sample data to learn the input format and perform a demo run .
the results of nssnpanalyzer are displayed on a web page and stored on the server for a week .
a link to the results page can also be sent to the user via email .
the output includes several calculated features of the nssnp : ( i ) predicted phenotypic class ( disease - associated versus neutral ) ; ( ii ) a hyperlink to the homologous structure with a scop identifier ( 7 ) ; ( iii ) the normalized probability of the substitution calculated by the sift program ( 4 ) ; ( iv ) area buried score , a measure of the solvent accessibility ; ( v ) fraction polar score , a measure of environmental polarity related to hydrogen bond formation ; ( vi ) secondary structure ( helix , sheet and coil ) ; and ( vii ) the structural environment class , a discrete environment class definition by combining features ( iv)(vi ) ( 8) .
the area buried score and fraction polar score are calculated by the environment program ( 8) , and the secondary structure is calculated by the stride program ( 16 ) .
the user can click the  view alignment button to see the local sequence alignment spanning the substitution sites and get a direct sight on the mutability of the substitution .
the original amino acid is highlighted in blue , and the mutated amino acid is highlighted in red .
briefly , on receiving the input sequence , nssnpanalyzer searches the astral database ( 7 ) for homologous protein structures .
nssnpanalyzer calculates three types of information from user 's input : ( i ) the structural environment of the snp , including the solvent accessibility , environmental polarity and secondary structure ( 8) ; ( ii ) the normalized probability of the substitution in the multiple sequence alignment ( 9 ) ; and ( iii ) the similarity and dissimilarity between the original amino acid and mutated amino acid .
nssnpanalyzer then uses a machine learning method called random forest ( 10 ) to classify the nssnps .
the random forest classifier was trained to optimally combine the heterogeneous sources of predictors using a curated training dataset prepared from the swissprot database ( 11 ) .
several recent studies have demonstrated the better performance of random forest over other machine learning approaches ( 1214 ) .
for the nssnp phenotypic effect prediction , we also found that random forest gave the best results on this training dataset . in a cross - validation test ,
the false positive rate is 38% and the false negative rate is 21% ( 15 ) .
the nssnpanalyzer web server is implemented on a linux redhat 8.0 platform with the common gateway interface scripts written in php .
two inputs are mandatory : protein sequence in fasta format and the nssnp identities to be analyzed . an nssnp is denoted as x#y , where x is the original amino acid in one letter , # is the position of the substitution ( starting from 1 ) , and y is the mutated amino acid in one letter .
in addition to the two mandatory inputs , users may also upload an accompanying protein structure file in pdb format if they want their own structure to be used . finally , because the calculation usually takes a while
users can use the sample data to learn the input format and perform a demo run .
the results of nssnpanalyzer are displayed on a web page and stored on the server for a week . a link to the results page
the output includes several calculated features of the nssnp : ( i ) predicted phenotypic class ( disease - associated versus neutral ) ; ( ii ) a hyperlink to the homologous structure with a scop identifier ( 7 ) ; ( iii ) the normalized probability of the substitution calculated by the sift program ( 4 ) ; ( iv ) area buried score , a measure of the solvent accessibility ; ( v ) fraction polar score , a measure of environmental polarity related to hydrogen bond formation ; ( vi ) secondary structure ( helix , sheet and coil ) ; and ( vii ) the structural environment class , a discrete environment class definition by combining features ( iv)(vi ) ( 8) . the area buried score and fraction polar score are calculated by the environment program ( 8) , and the secondary structure is calculated by the stride program ( 16 ) .
the user can click the  view alignment button to see the local sequence alignment spanning the substitution sites and get a direct sight on the mutability of the substitution .
the original amino acid is highlighted in blue , and the mutated amino acid is highlighted in red .
considering the remarkable cpu cost of calculation , we are planning to provide precalculated results for all human nssnps in the dbsnp ( 17 ) with homologous structures available .
we will also test the applicability of extracting structural predictors from predicted structures to eliminate the requirement of having experimentally determined structures available .
( a ) the main output page of nssnpanalyzer . the user can click the icon to see the interpretation of each field .
( b ) an example of local sequence alignment spanning the nssnp ( d7n ) . the original amino acid ( d ) is highlighted in blue , and the mutated amino acid ( n ) is highlighted in red .
nonsynonymous single nucleotide polymorphisms ( nssnps ) are prevalent in genomes and are closely associated with inherited diseases . to facilitate identifying disease - associated nssnps from a large number of neutral nssnps , it is important to develop computational tools to predict the nssnp 's phenotypic effect ( disease - associated versus neutral ) . 
 nssnpanalyzer , a web - based software developed for this purpose , extracts structural and evolutionary information from a query nssnp and uses a machine learning method called random forest to predict the nssnp 's phenotypic effect . 
 nssnpanalyzer server is available at .