future releases of tcsnp will integrate new t. cruzi sequences as they become available . many of these are expected to come from a resequencing effort that is underway in our lab .
planned updates to the website functionality include the development of a standardized api and web services through which other databases can consume the information provided by tcsnp and the development of functionality to design primers by interfacing with primer3 ( 19 ) , while using the snp information in this process .
the tcsnp database is composed of a web application written in perl , running against a postgresql database .
the database schema is based on the genomics unified schema ( gus , http://gusdb.org ) with local customizations .
the web application has been developed using a model - view - controller architectural pattern , where the work of each layer is performed by a specialized perl component [ all components are available from cpan ( 20 ) ] .
access to the database from perl ( i.e. the model ) is done through a hybrid combination of ( i ) an abstraction of the database schema using perl 's dbix::class package and ( ii ) custom sql queries executed using perl 's database access interface ( dbi ) .
the controller component managing user 's requests and dispatching calls to other components is perl 's catalyst ( 21 ) .
a number of custom controller modules were developed , which contain the business logic of the tcsnp application . finally , the viewer component is perl 's template toolkit , which uses custom templates to render web pages using information provided by the controller .
the database runs on dedicated freebsd servers , with the catalyst web application running under the apache web server .
the reference genome sequence of the cl brener strain of t. cruzi was obtained from genbank using the umbrella accession number aahk00000000 ( july , 2005 ) . other t. cruzi sequences ( mrnas and ests )
were also obtained from genbank using custom entrez queries ( may , 2007 ) . before loading into the database
this was necessary because of the variations in how different authors write the names of strains in genbank submissions and publications .
cl brener strain appears in different genbank sequences as cl brener , cl - brener , clbrener , cl - brenner , cl brenner , cl brenner , or cl brener ( note the different capitalization , use of middle dash , spaces and the writing of the strain name using a single or a double  n ) . because the database allows users to perform searches using strain names , it was important to reduce redundancy where possible .
in some cases , however , it was important to keep the distinction between similar strain names , for example to discriminate between cloned stocks and their parental uncloned strains .
as an example , we have kept caniii and caniii cl1 ( cl1 stands for  clone 1 ) , and sc43 and sc43 cl1 as different 
redundancy is still present , however , and further curation of strain names can be done .
we also encourage users of the database to send feedback about this issue . before clustering all sequences
were masked against a library of vector sequences and t. cruzi repetitive elements , as described previously ( 22 ) .
annotated coding sequences from the reference genome , and other publicly available sequences were mapped against the genome scaffolds using blat .
sequences mapping to the same genomic regions were clustered together and multiple sequence alignments were obtained using phrap .
this initial clustering allowed us to group mrnas , ests and reference coding sequences to the reference genome assembly sequences .
but because allelic variants in the cl - brener genome were separated during assembly ( 12 ) , those initial clusters showed many instances of allelic variants separated into different alignments ( i.e. each mapping to its own contig ) . to obtain alignments between allelic variants , we merged alignments with highly similar consensus sequences ( by blast analysis ) . afterwards , and based on user feedback , we have also merged , splitted and re - analyzed many alignments .
users of tcsnp should also be aware of the fact that many sequences from the cl - brener genome assembly are located in contigs with assembly problems or may represent assembly artifacts . for sequences containing assembly warnings in the original genbank records ,
we have attached similar notes to the corresponding alignments in tcsnp to help users in the interpretation of the snp data .
multiple sequence alignments were scanned to identify polymorphic columns . to calculate the probability of these sites being true polymorphisms
as opposed to sequencing errors , we have used the software package polybayes , version 5 ( 13 ) . polybayes uses a bayesian statistical framework that relies on allele frequency , alignment depth and base quality values amongst other attributes to calculate a probability score .
because chromatogram trace data are not available for many of the sequences in this release , we have devised a scoring strategy that uses arbitrary base quality values .
sequence bases obtained from the t. cruzi cl - brener genome ( 19x shotgun coverage ) were arbitrarily assigned a base quality value of 40 ; those from genbank records , a value of 30 ( individual submissions ) ; and those from dbest , a value of 20 ( single - pass , unedited ) . as an example , using this scoring scheme , a single base from an est differing from two allelic variants of cl - brener reference sequence ( depth = 3 ) would give a probability of 0.22 of being a true snp ( see for example : http://snps.tcruzi.org/snps/view/4028216 ) . to analyze the effect of each snp on the corresponding protein product
, we noted the codon position of the snp in each reference coding sequence and evaluated the change introduced by the polymorphic base .
also , for a subset of the alignments ( those containing coding sequences of similar length , with indels being a multiple of 3 ) , we calculated dn and ds values ( 14 ) using bioperl 's population genetics modules ( 23 ) .
the tcsnp database is composed of a web application written in perl , running against a postgresql database .
the database schema is based on the genomics unified schema ( gus , http://gusdb.org ) with local customizations .
the web application has been developed using a model - view - controller architectural pattern , where the work of each layer is performed by a specialized perl component [ all components are available from cpan ( 20 ) ] .
access to the database from perl ( i.e. the model ) is done through a hybrid combination of ( i ) an abstraction of the database schema using perl 's dbix::class package and ( ii ) custom sql queries executed using perl 's database access interface ( dbi ) .
the controller component managing user 's requests and dispatching calls to other components is perl 's catalyst ( 21 ) .
a number of custom controller modules were developed , which contain the business logic of the tcsnp application . finally , the viewer component is perl 's template toolkit , which uses custom templates to render web pages using information provided by the controller .
the database runs on dedicated freebsd servers , with the catalyst web application running under the apache web server .
the reference genome sequence of the cl brener strain of t. cruzi was obtained from genbank using the umbrella accession number aahk00000000 ( july , 2005 ) . other t. cruzi sequences ( mrnas and ests )
were also obtained from genbank using custom entrez queries ( may , 2007 ) . before loading into the database ,
this was necessary because of the variations in how different authors write the names of strains in genbank submissions and publications .
cl brener strain appears in different genbank sequences as cl brener , cl - brener , clbrener , cl - brenner , cl brenner , cl brenner , or cl brener ( note the different capitalization , use of middle dash , spaces and the writing of the strain name using a single or a double  n ) . because the database allows users to perform searches using strain names , it was important to reduce redundancy where possible .
in some cases , however , it was important to keep the distinction between similar strain names , for example to discriminate between cloned stocks and their parental uncloned strains .
as an example , we have kept caniii and caniii cl1 ( cl1 stands for  clone 1 ) , and sc43 and sc43 cl1 as different 
redundancy is still present , however , and further curation of strain names can be done .
before clustering all sequences were masked against a library of vector sequences and t. cruzi repetitive elements , as described previously ( 22 ) .
annotated coding sequences from the reference genome , and other publicly available sequences were mapped against the genome scaffolds using blat .
sequences mapping to the same genomic regions were clustered together and multiple sequence alignments were obtained using phrap .
this initial clustering allowed us to group mrnas , ests and reference coding sequences to the reference genome assembly sequences .
but because allelic variants in the cl - brener genome were separated during assembly ( 12 ) , those initial clusters showed many instances of allelic variants separated into different alignments ( i.e. each mapping to its own contig ) . to obtain alignments between allelic variants , we merged alignments with highly similar consensus sequences ( by blast analysis ) . afterwards , and based on user feedback , we have also merged , splitted and re - analyzed many alignments .
users of tcsnp should also be aware of the fact that many sequences from the cl - brener genome assembly are located in contigs with assembly problems or may represent assembly artifacts . for sequences containing assembly warnings in the original genbank records ,
we have attached similar notes to the corresponding alignments in tcsnp to help users in the interpretation of the snp data .
multiple sequence alignments were scanned to identify polymorphic columns . to calculate the probability of these sites being true polymorphisms
as opposed to sequencing errors , we have used the software package polybayes , version 5 ( 13 ) .
polybayes uses a bayesian statistical framework that relies on allele frequency , alignment depth and base quality values amongst other attributes to calculate a probability score .
because chromatogram trace data are not available for many of the sequences in this release , we have devised a scoring strategy that uses arbitrary base quality values .
sequence bases obtained from the t. cruzi cl - brener genome ( 19x shotgun coverage ) were arbitrarily assigned a base quality value of 40 ; those from genbank records , a value of 30 ( individual submissions ) ; and those from dbest , a value of 20 ( single - pass , unedited ) . as an example , using this scoring scheme , a single base from an est differing from two allelic variants of cl - brener reference sequence ( depth = 3 ) would give a probability of 0.22 of being a true snp ( see for example : http://snps.tcruzi.org/snps/view/4028216 ) . to analyze the effect of each snp on the corresponding protein product , we noted the codon position of the snp in each reference coding sequence and evaluated the change introduced by the polymorphic base .
also , for a subset of the alignments ( those containing coding sequences of similar length , with indels being a multiple of 3 ) , we calculated dn and ds values ( 14 ) using bioperl 's population genetics modules ( 23 ) .
national agency for the promotion of science and technology ( anpcyt , argentina ) ( grant pict 38209 ) ; and the university of san martn ( grant s05/22 ) . funding for open access charge : anpcyt .
 the tcsnp database ( http://snps.tcruzi.org ) integrates information on genetic variation ( polymorphisms and mutations ) for different stocks , strains and isolates of trypanosoma cruzi , the causative agent of chagas disease . 
 the database incorporates sequences ( genes from the t. cruzi reference genome , mrnas , ests and genomic sequences ) ; multiple sequence alignments obtained from these sequences ; and single - nucleotide polymorphisms and small indels identified by scanning these multiple sequence alignments . 
 information in tcsnp can be readily interrogated to arrive at gene sets , or snp sets of interest based on a number of attributes . 
 sequence similarity searches using blast are also supported . 
 this first release of tcsnp contains nearly 170 000 high - confidence candidate snps , derived from the analysis of annotated coding sequences . 
 as new sequence data become available , tcsnp will incorporate these data , mapping new candidate snps onto the reference genome sequences . 
FUTURE WORK
METHODS
Database and web application
Data sources
Sequence clustering and alignment
Candidate SNP identification and analysis
FUNDING