it is widely accepted that mutation is the main causal factor for diverse tumorigenesis [ 1 , 2 ] .
the recent development of next - generation sequencing ( ngs ) technologies has revolutionized the speed and throughput of dna sequencing , which facilitates the discovery of new driver mutations .
for example , the arid1a gene is mutated in 83% of gastric cancers with microsatellite instability and the sf3b1 gene is somatically mutated in 9.7% of chronic lymphocytic leukemia patients [ 4 , 5 ] . in colorectal cancers , although the majority of recurrent mutations have been previously known , some novel mutations , such as the mutations in the sox9 and fam123b genes , were also identified by ngs technology , which may have implications for colorectal tumorigenesis . in melanoma , approximately 40% was found to have causal mutations affecting b - raf function .
along with the well - known driver mutations , the novel mutations identified by ngs will be useful for predicting the prognosis and molecular characterization of cancers [ 8 , 9 ] .
recently , a number of mutation calling tools , such as varscan , genome analysis toolkit , and mutect , have been developed to detect single - nucleotide variants ( snvs ) or indels from ngs data . however , snv calling from a single cancer case is not the final step for defining clinically meaningful mutations . to define the pathogenic snvs in human cancers ,
a commonly used approach is to identify the phenotype - associated recurrent mutations in the study group . due to the data size burden of ngs output , profiling the mutations associated with a certain phenotype or prognosis in a study group by a researcher who does not have a bioinformatics background or facilities
is very difficult . currently , there is no user - friendly tool supporting methods for the detection of recurrent or phenotype - specific mutations . in this study , in order to support the definition of recurrent mutations or phenotype - specific mutations from ngs data of a group of cancers with diverse phenotypes , we aimed to develop a user - friendly tool , named mutation arranger for defining phenotype - related snv ( map ) .
map software was designed as a standalone program , compatible in microsoft windows environments with a user - friendly graphic interface , and compiled codes of map can be easily installed .
both variant call format   and its annotation files , such as annovar output , can be used as input files for map .
when a user loads the input file , called sample descriptor file ( gsf ) , map software displays the sample set information and filter options ( fig .
1 ) . once data are loaded , the user can filter the data using the ' analysis options ' tab in map software based on annotation information . by using the sample set and analysis options
summary metrics include the mutation status for each sample , total mutation frequency , p - values for differences between phenotype groups based on clinical information , genomic position , reference / observed sequence , and additional information , such as read depth by default . map software also displays more detailed information in summary metrics based on the user - selected ' analysis options . ' in the ' analysis result ' tab , map software provides the frequency- or p - value - based filter function .
this functionality supports detection of the recurrent mutations in study subjects and identification of the phenotype - specific mutation that occurs significantly in a certain phenotypic subclass .
then , map software represents the user - selected mutations graphically in the ' visualization ' tab . to sort the variants by genomic position , p - value , or frequency , users can use the sort function in the ' analysis result ' tab .
details of the program download , installation , and analysis procedures are available in the user manual .
the major steps of map are as follows . 
 - generating mutation summary metrics in study subjects- determination of recurrent mutations by frequency- determination of phenotype - specific mutations by association test- graphical illustration of user - selected mutations 
  - generating mutation summary metrics in study subjects - determination of recurrent mutations by frequency - determination of phenotype - specific mutations by association test - graphical illustration of user - selected mutations
mutation calling files from genome analysis toolkit   can be used directly as input files for ' sample descriptor . ' alternatively , manually prepared standard variant call format   is also available .
annotation information files , such as annovar outputs , can be used as input files for sample descriptor .
gsf is a comma - separated values text file that describes the input files , such as gatk output files and annovar output files , and phenotype information .
when users load the input file , map software displays the sample set information and filter options in the ' source ' tab .
if any file is not prepared properly , map software informs the user in the ' sample set files ' tab .
after data are loaded , the user can filter the data using the ' analysis option ' tab based on annotation information .
based on user - selected filters in the ' analysis options ' tab , summary metrics represent additional annotation information , such as gene name , function , known variant , and amino acid change .
the additional annotations can be hidden using the snp filter option in the ' analysis option ' tab .
these mutation metrics are exported into a comma - separated values text file for further investigation .
in the ' analysis result ' tab , map software defines the recurrent mutations in study subjects by frequency .
the frequency threshold can be determined arbitrarily by the user in the ' frequency filter ' function .
map also defines the phenotype - specific mutations using the mutation metrics and phenotype information .
phenotype - specific mutations that occur more frequently in one of two clinical conditions ( e.g. , drug - responder vs. non - responder ) are determined by chi - square test under the user - defined significance level .
the graphic user interface is implemented in the software for users to easily handle large - scale data or access the analysis result ( fig . 2b and 2c ) .
the recurrent or phenotype - specific mutations can also be demonstrated visually in heat - map style .
for example , the left and right of the heat - map represent the sample name and mutational spectrum , respectively .
the bottom of the heat - map represents the gene name by color for mutational spectrum . frequency or p - value plots
are represented on top of the heat - map ( fig . 2b and 2c ) .
map is a user - friendly program with multiple functions that supports the determination of recurrent or phenotype - specific mutations and provides graphic illustration images to the users .
its operation environment , microsoft windows , enables more researchers who can not operate linux to define clinically meaningful mutations with ngs data from cancer cohorts .
 next - generation sequencing ( ngs ) is widely used to identify the causative mutations underlying diverse human diseases , including cancers , which can be useful for discovering the diagnostic and therapeutic targets . currently , a number of single - nucleotide variant ( snv)-calling algorithms are available ; however , there is no tool for visualizing the recurrent and phenotype - specific mutations for general researchers . in this study , in order to support defining the recurrent mutations or phenotype - specific mutations from ngs data of a group of cancers with diverse phenotypes , we aimed to develop a user - friendly tool , named mutation arranger for defining phenotype - related snv ( map ) . 
 map is a user - friendly program with multiple functions that supports the determination of recurrent or phenotype - specific mutations and provides graphic illustration images to the users . 
 its operation environment , the microsoft windows environment , enables more researchers who can not operate linux to define clinically meaningful mutations with ngs data from cancer cohorts . 
Introduction
Description
Prerequisites
Generation of summary metrics 
Determination of recurrent or phenotype-specific mutations 
Visualization 
Conclusion