This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

BhanuPrakash, A

Jami, Anusha

Jayashree, B

Nayak, Spurthi

Reddy, P Srinivasa

Varshney, Rajeev K

English

PubMed

B Jayashree

A BhanuPrakash

Anusha Jami

P Srinivasa Reddy

Spurthi Nayak

Rajeev K Varshney

Springer - Publisher Connector

Perl module and PISE wrappers for the integrated analysis of sequence data and SNP features

BioMed CentralBMC Research Notes
ssOpen AcceTechnical Note
Perl module and PISE wrappers for the integrated analysis of 
sequence data and SNP features
B Jayashree*1, A BhanuPrakash1, Anusha Jami2, P Srinivasa Reddy3, 
Spurthi Nayak3 and Rajeev K Varshney*3
Address: 1Bioinformatics Unit, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, 
India, 2Bioinformatics Centre, University of Hyderabad, Gachibowli, 500046, Hyderabad, Andhra Pradesh, India and 3Centre of Excellence in 
Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India
Email: B Jayashree* - b.jayashree@cgiar.org; A BhanuPrakash - a.bhanuprakash@cgiar.org; Anusha Jami - anushajami@yahoo.co.in; P 
Srinivasa Reddy - pallamsri88@gmail.com; Spurthi Nayak - s.nayak@cgiar.org; Rajeev K Varshney* - r.k.varshney@cgiar.org
* Corresponding authors    
Abstract
Background: There is a need for software scripts and modules for format parsing, data
manipulation, statistical analysis and annotation especially for tasks related to marker identification
from sequence data and sequence diversity analysis.
Results: Here we present several new Perl scripts and a module for sequence data diversity
analysis. To enable the use of these software with other public domain tools, we also make available
PISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. This
enables the user to generate pipelines for automated analysis, since PISE is a web interface
generator for bioinformatics programmes.
Conclusion: A new set of modules and scripts for diversity statistic calculation, format parsing and
data manipulation are available with PISE wrappers that enable pipelining of these scripts with
commonly used contig assembly and sequence feature prediction software, to answer specific
sequence diversity related questions.
Background
Single Nucleotide Polymorphisms (SNPs) are commonly
found throughout the genome and provide dense maps
over small chromosomal regions. The recent advances in
sequencing and genotyping have made large scale SNP
diversity analysis possible in several crop species. This
helps assess genome variation that can then be harnessed
for crop improvement. Sequence diversity information
may be desirable across defined groups of sequences, such
as candidate gene transcripts from different genotypes, or
assembled transcripts for a particular marker from more
than one genotype. The grouping could be based on the
objective of the study – across race, location, genes or
regions within genes. Sequence data analysis usually
involves steps such as clustering of sequence data, to
determine redundancy levels. Sequence assembly is car-
ried out to generate consensus sequences or contigs and
singlets. The user then processes this output to determine
presence of microsatellites or SNPs. Along with SNP iden-
tification it is also desirable to obtain other aspects from
the alignment; such as SNP and indel (insertion-deletions)
frequency, the type of variant and haplotypes, PIC value
for the SNP and haplotype besides nucleotide diversity
(π). Validation of predicted SNP(s) through wet lab exper-
Published: 24 May 2009
BMC Research Notes 2009, 2:92 doi:10.1186/1756-0500-2-92
Received: 2 March 2009
Accepted: 24 May 2009
This article is available from: http://www.biomedcentral.com/1756-0500/2/92
© 2009 Jayashree et al; licensee BioMed Central Ltd. 
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 5
(page number not for citation purposes)
BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92iments is the next step to convert the identified SNP into
a genetic marker. Although more than 30 SNP genotyping
platforms are currently available, these are both expensive
and demand considerable expertise. One solution for val-
idating the identified SNP(s) through cost effective SNP
genotyping platform is development of CAPS (cleaved
amplified polymorphic sequences) marker by predicting
the restriction enzyme that can use the identified SNP as a
recognition site.
There are several available software solutions for sequence
clustering, and a few popular ones for assembly. The pop-
ular group of Clustal programmes [1], d2-cluster [2] for
EST clustering and cap3 or PCAP [3,4], the TIGR assem-
bler [5] or Phrap [6]http://www.phrap.org/phredphrap
consed.html are used for sequence assembly. Similarly,
there are many freely available software programmes for
the identification of SNPs[7-10]. DnaSP reports on nucle-
otide polymorphism features from aligned sequence data
[11]. None of them however automate group wise identi-
fication and reporting of polymorphism statistics and
more importantly consider the presence of heterozygous
loci in the sequence data. Many available programs read
heterozygous SNPs as missing/bad quality sequence data
and thus do not consider them for analysis. As a result fea-
tures such as sequence diversity, PIC of SNP and haplo-
types, etc. may be underestimated. The need for a module
that could report SNP features for any number of user
defined groups coupled with the need to be able to calcu-
late statistics taking into consideration the presence of
heterozygous loci led to the development of the SNP
DIVersity ESTimator module (divest.pm).
Sequence analysis involves pipelining of data from one
software to another and often also includes branched
flows such as when annotation of sequences with putative
function is also a requirement. Format conversion scripts
to convert output of one program to input of another are
needed when the user wants to pipeline several tools and
modules. Along with output parsing scripts, some degree
of automation can be achieved in data analysis tasks. The
availability of software environments for pipelining and
workflow management help the user to create custom
analysis pipelines.
The PISE programme [12,13] is a robust environment that
has been around for many years now and allows integra-
tion of internal scripts/tools that are part of the target exe-
cution environment as well as external tools that a user
runs. Ease of use is achieved through the creation of a
graphical user interface (GUI) for all of the programmes/
scripts available in the environment and the chaining
together of scripts to facilitate automation and analysis.
So rather than reinvent a workflow environment; we
implemented PISE locally and provided PISE XML wrap-
pers for the Perl scripts and modules developed by us,
besides making them available as web services. The avail-
ability of the programs and wrapper sripts allow users to
implement versatile pipelines either in the familiar
browser environment or in the Taverna workbench. The
modules and scripts are available to all interested users.
Implementation
The source code for the programs has been written in Perl
5.8. For the diversity estimator module, Bioperl modules
have been used. The scripts have been tested within the
PISE environment and are being used independently as
well, on machines running either Linux or Windows OS.
An example pipeline that makes use of the scripts written
is indicated (Figure 1A). The modules allow a large
amount of data to be processed; we have tested it to work
with up to 99 user defined groups consisting of up to 225
sequences per group. Interface development involves writ-
ing the XML specification for the programme/script/mod-
ule using the grammar provided http://www.pasteur.fr/
~letondal/XML/pise.dtd, where file manipulations and
redirection of output files to another programme are also
specified. The scripts are also available as a soaplab service
and can so be called through the Taverna workflow tool.
Results and discussion
Functionality of Perl modules and scripts
The module divest.pm is the diversity estimator module
that reads an input file derived from assembly pro-
grammes like cap3 or the alignment file output from mul-
tiple sequence alignment programmes like ClustalW or
BioEdit. The file contains the assemblies of reads for each
user defined group. The user can choose to calculate diver-
sity statistics for all groups or one particular group. The
statistics calculated include nucleotide diversity, number
of SNPs, and type of variant whether transition or trans-
version, SNP frequency, number of haplotypes, the PIC
(polymorphism information content) of SNP and haplo-
type, haplotype frequency and also number and fre-
quency of indels.
SNP and indel frequency are simply calculated as the total
length of the sequence in base pairs/number of SNPs or
indels. The count of number of haplotypes and haplotype
frequency is based on the standard methods [14]. An
important addition in these calculations is the considera-
tion of heterozygous loci. The user can replace an N with
an H (in case of true heterozygote) based on two peaks in
chromatograms obtained from sequencing machines. The
algorithm assigns 'H' two alleles (A/T or C/G each with a
value of 0.5) and this is used in haplotype analysis and
sequence diversity calculations.
The nucleotide diversity π is calculated using the formula
[15]Page 2 of 5
(page number not for citation purposes)
BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92where k is the number of SNPs identified in an alignment
of 'n' genotypes, L is the number of basepairs and
.
The PIC of SNP is calculated using the formula [16]
where pi is the frequency of the ith allele at a given SNP
locus.
The haplotype diversity is calculated using the formula
[17,18]
where K is the number of haplotypes, pi is the frequency
of haplotypes and n is the total number of reads.
The statistics generated is exported in Excel format,
besides the script also generates input file to Network
4.502 http://www.fluxus-technology.com/sharenet.htm.
Network helps generate evolutionary trees from various
data including haplotype and haplotype frequency data.
Scripts that enable the conversion of a multiple sequence
alignment into the diversity estimator input file have been
π = k/aL
1 1
2
/( )i
i
n −=∑
PIC pi
2
i
k
= − ∑1
H n n pi
i
k
= − ⋅ −⎡
⎣⎢
⎤
⎦⎥∑[ /( )]1 1 2
A. An example pipeline that can be executed through the PISE environmentFigur  1
A. An example pipeline that can be executed through the PISE environment. Blue boxes represent inputs and out-
puts in the pipeline. B. The PISE interface to the diversity estimator module. C. Screenshot of divest.pm ouput.Page 3 of 5
(page number not for citation purposes)
BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92currently written for the ClustalW and cap3 programmes.
Another script that enables concatenating cap3 contig and
singlets file is also available, the output of this file gener-
ates a 'unigene' file that can serve as input to the MISA
microsatellite analysis programme. An example analysis
pipeline is provided in Figure 1 along with the PISE inter-
face to the diversity estimator programme and its output.
Accessibility of modules and scripts
Figure 2 illustrates the XML specification for a module like
divest.pm. Chaining together of programmes is made fea-
sible through the parameter setting in the XML file, piping
output files to input file of the next programme. In a PISE
implementation available at http://hpc.icrisat.cgiar.org/
Pise/5.a/, all mentioned scripts are available under assem-
bly, format parsing, statistics and data manipulation
directories. A sample ACD file for the divest module is also
given, this provides meta-data regarding the input and
output files for this module and can be used by soaplab
for deploying divest.pm as a web service.
Intended use
The modules and scripts being provided through the PISE
environment can be used for various purposes (a) identi-
fying microsatellites in a set of unigenes, (b) calculating
the diversity between two or more groups of data based on
geographical location of the genotypes (e.g. cultivated,
wild, landraces, etc.), regions of the gene (e.g. intronic and
exonic regions) being sampled, (c) sequence diversity fea-
tures in a candidate gene across several genotypes and (d)
identifying SNPs and finding out how many of these are
convertible to CAPS, (e) functionally annotate sequences.
The ability to chain scripts to generate pipelines reduces
the file management burden for the user. A part of the
pipeline indicated in Figure 1 can also be executed
through the Taverna workflow environment [19], which
PISE XML wrapper and ACD file for the diversity estimator moduleFigure 2
PISE XML wrapper and ACD file for the diversity estimator module.Page 4 of 5
(page number not for citation purposes)
BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92provides web service access besides local service access.
The advantage to the user would be the accessibility and
integration of databases through web services. Some of
the scripts are available as soaplab web services at http://
220.227.242.214:8080/soaplab2/; under the heading
sequence analysis.
As the modules and scripts are being released as open
source code, interested users can continue to use as well as
make improvements to them.
Conclusion
Modules and scripts that facilitate sequence diversity anal-
ysis, format parsing and data manipulation are being
made available along with source code. The module/
scripts can be chained together with other freely available
sequence analysis software to generate pipelines for auto-
mated data analysis. The pipelining has been currently
made feasible through the PISE web interface generator
and a part of it can also be executed on the Taverna work-
bench. The module and scripts are available to interested
users.
Availability & requirements
Modules available from: http://www.icrisat.org/gt-bt/
softwares_downloads.htm.
Can be accessed and used at http://hpc.icrisat.cgiar.org/
Pise/5.a/
Soaplab service: http://220.227.242.214:8080/soaplab2/
Operating System: platform independent
Programming language: Perl
PISE http://www.pasteur.fr/recherche/unites/sis/Pise/
SNP2CAPS http://pgrc.ipk-gatersleben.de/snp2caps/
MISA, cap3 (optional; http://pgrc.ipk-gatersleben.de/
misa/misa.html; http://seq.cs.iastate.edu/down
load.html)
Any restrictions to use by non-academics: none
Abbreviations
ACD: Ajax Command Definition; SNP: Single Nucleotide
Polymorphism; PIC: Polymorphism Information Con-
tent; XML: Extensible Markup Language; PISE: Pasteur
Institute Software Environment; CAPS: Cleaved Amplified
Polymorphic markers; EST: Expressed Sequence Tag;
PCAP: Parallel Contig Assembly Program; CPAN: Com-
prehensive Perl Archive Network; MISA: MIcroSAtellite
Identification tool
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BA, AJ coded for the Perl modules and scripts, PSR and SN
used the programmes on their datasets, RKV articulated
the use cases, indices for the diversity estimator module
while JB developed the pipeline architecture and wrote
the manuscript.
Acknowledgements
The authors gratefully acknowledge support through the Generation Chal-
lenge Programme (GCP). The authors gratefully acknowledge Martin Sen-
ger's(IRRI) help with implementing the pipeline in Taverna.
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Background: There is a need for software scripts and modules for format parsing, data manipulation, statistical analysis and annotation especially for tasks related to marker identification from sequence data and sequence diversity analysis. Results: Here we present several new Perl scripts and a module for sequence data diversity analysis. To enable the use of these software with other public domain tools, we also make available PISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. This enables the user to generate pipelines for automated analysis, since PISE is a web interface generator for bioinformatics programmes.Conclusion: A new set of modules and scripts for diversity statistic calculation, format parsing and data manipulation are available with PISE wrappers that enable pipelining of these scripts with commonly used contig assembly and sequence feature prediction software, to answer specific sequence diversity related questions

Prakash, A B

Jami, A

Reddy, P S

Nayak, S N

Varshney, R K

ICRISAT Open Access Repository

BioMed CentralBMC Research NotesssOpen AcceTechnical NotePerl module and PISE wrappers for the integrated analysis of sequence data and SNP featuresB Jayashree*1, A BhanuPrakash1, Anusha Jami2, P Srinivasa Reddy3, Spurthi Nayak3 and Rajeev K Varshney*3Address: 1Bioinformatics Unit, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India, 2Bioinformatics Centre, University of Hyderabad, Gachibowli, 500046, Hyderabad, Andhra Pradesh, India and 3Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, IndiaEmail: B Jayashree* - b.jayashree@cgiar.org; A BhanuPrakash - a.bhanuprakash@cgiar.org; Anusha Jami - anushajami@yahoo.co.in; P Srinivasa Reddy - pallamsri88@gmail.com; Spurthi Nayak - s.nayak@cgiar.org; Rajeev K Varshney* - r.k.varshney@cgiar.org* Corresponding authors    AbstractBackground: There is a need for software scripts and modules for format parsing, datamanipulation, statistical analysis and annotation especially for tasks related to marker identificationfrom sequence data and sequence diversity analysis.Results: Here we present several new Perl scripts and a module for sequence data diversityanalysis. To enable the use of these software with other public domain tools, we also make availablePISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. Thisenables the user to generate pipelines for automated analysis, since PISE is a web interfacegenerator for bioinformatics programmes.Conclusion: A new set of modules and scripts for diversity statistic calculation, format parsing anddata manipulation are available with PISE wrappers that enable pipelining of these scripts withcommonly used contig assembly and sequence feature prediction software, to answer specificsequence diversity related questions.BackgroundSingle Nucleotide Polymorphisms (SNPs) are commonlyfound throughout the genome and provide dense mapsover small chromosomal regions. The recent advances insequencing and genotyping have made large scale SNPdiversity analysis possible in several crop species. Thishelps assess genome variation that can then be harnessedfor crop improvement. Sequence diversity informationmay be desirable across defined groups of sequences, suchas candidate gene transcripts from different genotypes, orassembled transcripts for a particular marker from morethan one genotype. The grouping could be based on theobjective of the study – across race, location, genes orregions within genes. Sequence data analysis usuallyinvolves steps such as clustering of sequence data, todetermine redundancy levels. Sequence assembly is car-ried out to generate consensus sequences or contigs andsinglets. The user then processes this output to determinepresence of microsatellites or SNPs. Along with SNP iden-tification it is also desirable to obtain other aspects fromthe alignment; such as SNP and indel (insertion-deletions)frequency, the type of variant and haplotypes, PIC valuefor the SNP and haplotype besides nucleotide diversity(π). Validation of predicted SNP(s) through wet lab exper-Published: 24 May 2009BMC Research Notes 2009, 2:92 doi:10.1186/1756-0500-2-92Received: 2 March 2009Accepted: 24 May 2009This article is available from: http://www.biomedcentral.com/1756-0500/2/92© 2009 Jayashree et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92iments is the next step to convert the identified SNP intoa genetic marker. Although more than 30 SNP genotypingplatforms are currently available, these are both expensiveand demand considerable expertise. One solution for val-idating the identified SNP(s) through cost effective SNPgenotyping platform is development of CAPS (cleavedamplified polymorphic sequences) marker by predictingthe restriction enzyme that can use the identified SNP as arecognition site.There are several available software solutions for sequenceclustering, and a few popular ones for assembly. The pop-ular group of Clustal programmes [1], d2-cluster [2] forEST clustering and cap3 or PCAP [3,4], the TIGR assem-bler [5] or Phrap [6]http://www.phrap.org/phredphrapconsed.html are used for sequence assembly. Similarly,there are many freely available software programmes forthe identification of SNPs[7-10]. DnaSP reports on nucle-otide polymorphism features from aligned sequence data[11]. None of them however automate group wise identi-fication and reporting of polymorphism statistics andmore importantly consider the presence of heterozygousloci in the sequence data. Many available programs readheterozygous SNPs as missing/bad quality sequence dataand thus do not consider them for analysis. As a result fea-tures such as sequence diversity, PIC of SNP and haplo-types, etc. may be underestimated. The need for a modulethat could report SNP features for any number of userdefined groups coupled with the need to be able to calcu-late statistics taking into consideration the presence ofheterozygous loci led to the development of the SNPDIVersity ESTimator module (divest.pm).Sequence analysis involves pipelining of data from onesoftware to another and often also includes branchedflows such as when annotation of sequences with putativefunction is also a requirement. Format conversion scriptsto convert output of one program to input of another areneeded when the user wants to pipeline several tools andmodules. Along with output parsing scripts, some degreeof automation can be achieved in data analysis tasks. Theavailability of software environments for pipelining andworkflow management help the user to create customanalysis pipelines.The PISE programme [12,13] is a robust environment thathas been around for many years now and allows integra-tion of internal scripts/tools that are part of the target exe-cution environment as well as external tools that a userruns. Ease of use is achieved through the creation of agraphical user interface (GUI) for all of the programmes/scripts available in the environment and the chainingtogether of scripts to facilitate automation and analysis.So rather than reinvent a workflow environment; weimplemented PISE locally and provided PISE XML wrap-pers for the Perl scripts and modules developed by us,besides making them available as web services. The avail-ability of the programs and wrapper sripts allow users toimplement versatile pipelines either in the familiarbrowser environment or in the Taverna workbench. Themodules and scripts are available to all interested users.ImplementationThe source code for the programs has been written in Perl5.8. For the diversity estimator module, Bioperl moduleshave been used. The scripts have been tested within thePISE environment and are being used independently aswell, on machines running either Linux or Windows OS.An example pipeline that makes use of the scripts writtenis indicated (Figure 1A). The modules allow a largeamount of data to be processed; we have tested it to workwith up to 99 user defined groups consisting of up to 225sequences per group. Interface development involves writ-ing the XML specification for the programme/script/mod-ule using the grammar provided http://www.pasteur.fr/~letondal/XML/pise.dtd, where file manipulations andredirection of output files to another programme are alsospecified. The scripts are also available as a soaplab serviceand can so be called through the Taverna workflow tool.Results and discussionFunctionality of Perl modules and scriptsThe module divest.pm is the diversity estimator modulethat reads an input file derived from assembly pro-grammes like cap3 or the alignment file output from mul-tiple sequence alignment programmes like ClustalW orBioEdit. The file contains the assemblies of reads for eachuser defined group. The user can choose to calculate diver-sity statistics for all groups or one particular group. Thestatistics calculated include nucleotide diversity, numberof SNPs, and type of variant whether transition or trans-version, SNP frequency, number of haplotypes, the PIC(polymorphism information content) of SNP and haplo-type, haplotype frequency and also number and fre-quency of indels.SNP and indel frequency are simply calculated as the totallength of the sequence in base pairs/number of SNPs orindels. The count of number of haplotypes and haplotypefrequency is based on the standard methods [14]. Animportant addition in these calculations is the considera-tion of heterozygous loci. The user can replace an N withan H (in case of true heterozygote) based on two peaks inchromatograms obtained from sequencing machines. Thealgorithm assigns 'H' two alleles (A/T or C/G each with avalue of 0.5) and this is used in haplotype analysis andsequence diversity calculations.The nucleotide diversity π is calculated using the formula[15]Page 2 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92where k is the number of SNPs identified in an alignmentof 'n' genotypes, L is the number of basepairs and.The PIC of SNP is calculated using the formula [16]where pi is the frequency of the ith allele at a given SNPlocus.The haplotype diversity is calculated using the formula[17,18]where K is the number of haplotypes, pi is the frequencyof haplotypes and n is the total number of reads.The statistics generated is exported in Excel format,besides the script also generates input file to Network4.502 http://www.fluxus-technology.com/sharenet.htm.Network helps generate evolutionary trees from variousdata including haplotype and haplotype frequency data.Scripts that enable the conversion of a multiple sequencealignment into the diversity estimator input file have beenπ = k/aL1 12/( )iin−=∑PIC pi2ik= − ∑1H n n piik= − ⋅ −⎡⎣⎢⎤⎦⎥∑[ /( )]1 1 2A. An example pipeline that can be executed through the PISE environmentFigur  1A. An example pipeline that can be executed through the PISE environment. Blue boxes represent inputs and out-puts in the pipeline. B. The PISE interface to the diversity estimator module. C. Screenshot of divest.pm ouput.Page 3 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92currently written for the ClustalW and cap3 programmes.Another script that enables concatenating cap3 contig andsinglets file is also available, the output of this file gener-ates a 'unigene' file that can serve as input to the MISAmicrosatellite analysis programme. An example analysispipeline is provided in Figure 1 along with the PISE inter-face to the diversity estimator programme and its output.Accessibility of modules and scriptsFigure 2 illustrates the XML specification for a module likedivest.pm. Chaining together of programmes is made fea-sible through the parameter setting in the XML file, pipingoutput files to input file of the next programme. In a PISEimplementation available at http://hpc.icrisat.cgiar.org/Pise/5.a/, all mentioned scripts are available under assem-bly, format parsing, statistics and data manipulationdirectories. A sample ACD file for the divest module is alsogiven, this provides meta-data regarding the input andoutput files for this module and can be used by soaplabfor deploying divest.pm as a web service.Intended useThe modules and scripts being provided through the PISEenvironment can be used for various purposes (a) identi-fying microsatellites in a set of unigenes, (b) calculatingthe diversity between two or more groups of data based ongeographical location of the genotypes (e.g. cultivated,wild, landraces, etc.), regions of the gene (e.g. intronic andexonic regions) being sampled, (c) sequence diversity fea-tures in a candidate gene across several genotypes and (d)identifying SNPs and finding out how many of these areconvertible to CAPS, (e) functionally annotate sequences.The ability to chain scripts to generate pipelines reducesthe file management burden for the user. A part of thepipeline indicated in Figure 1 can also be executedthrough the Taverna workflow environment [19], whichPISE XML wrapper and ACD file for the diversity estimator moduleFigure 2PISE XML wrapper and ACD file for the diversity estimator module.Page 4 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92provides web service access besides local service access.The advantage to the user would be the accessibility andintegration of databases through web services. Some ofthe scripts are available as soaplab web services at http://220.227.242.214:8080/soaplab2/; under the headingsequence analysis.As the modules and scripts are being released as opensource code, interested users can continue to use as well asmake improvements to them.ConclusionModules and scripts that facilitate sequence diversity anal-ysis, format parsing and data manipulation are beingmade available along with source code. The module/scripts can be chained together with other freely availablesequence analysis software to generate pipelines for auto-mated data analysis. The pipelining has been currentlymade feasible through the PISE web interface generatorand a part of it can also be executed on the Taverna work-bench. The module and scripts are available to interestedusers.Availability & requirementsModules available from: http://www.icrisat.org/gt-bt/softwares_downloads.htm.Can be accessed and used at http://hpc.icrisat.cgiar.org/Pise/5.a/Soaplab service: http://220.227.242.214:8080/soaplab2/Operating System: platform independentProgramming language: PerlPISE http://www.pasteur.fr/recherche/unites/sis/Pise/SNP2CAPS http://pgrc.ipk-gatersleben.de/snp2caps/MISA, cap3 (optional; http://pgrc.ipk-gatersleben.de/misa/misa.html; http://seq.cs.iastate.edu/download.html)Any restrictions to use by non-academics: noneAbbreviationsACD: Ajax Command Definition; SNP: Single NucleotidePolymorphism; PIC: Polymorphism Information Con-tent; XML: Extensible Markup Language; PISE: PasteurInstitute Software Environment; CAPS: Cleaved AmplifiedPolymorphic markers; EST: Expressed Sequence Tag;PCAP: Parallel Contig Assembly Program; CPAN: Com-prehensive Perl Archive Network; MISA: MIcroSAtelliteIdentification toolCompeting interestsThe authors declare that they have no competing interests.Authors' contributionsBA, AJ coded for the Perl modules and scripts, PSR and SNused the programmes on their datasets, RKV articulatedthe use cases, indices for the diversity estimator modulewhile JB developed the pipeline architecture and wrotethe manuscript.AcknowledgementsThe authors gratefully acknowledge support through the Generation Chal-lenge Programme (GCP). The authors gratefully acknowledge Martin Sen-ger's(IRRI) help with implementing the pipeline in Taverna.References1. Ramu C, Hideaki S, Tadashi K, Rodrigo L, Gibson TJ, Higgins DG,Thompson JD: Multiple sequence alignment with the Clustalseries of programs.  Nucleic Acids Res 2003, 31:3497-3500.2. Burke J, Davison D, Hide W: d2_cluster: A Validated Method forClustering EST and Full-Length cDNA Sequences.  GenomeRes 1999, 9:1135-1142.3. Huang X, Madan A: CAP3: A DNA sequence assembly pro-gram.  Genome Res 1999, 9:868-77.4. Huang X, Wang J, Aluru S, Yang S-P, Hillier L: PCAP: A Whole-Genome Assembly Program.  Genome Res 2003, 13:2164-2170.5. Sutton G, White O, Adams M, Kerlavage A: TIGR assembler: Anew tool for assembling large shotgun sequencing projects.Genome Sci Technol 1995, 1:9-19.6. Phrap documentation   [http://www.phrap.org/phredphrap/phrap.html]7. Batley J, Jewell E, Edwards D: Automated discovery of singlenucleotide polymorphism and simple sequence repeatmolecular genetic markers.  Methods Mol Biol 2007, 406:473-94.8. Savage D, Batley J, Erwin T, Logan E, Love CG, Lim GA, Mongin E,Barker G, Spangenberg GC, Edwards D: SNPServer: a real-timeSNP discovery tool.  Nucleic Acids Res 2005, 33:W493-5.9. Tang J, Leunissen JA, Voorrips RE, Linden CG van der, Vosman B:HaploSNPer: a web-based allele and SNP detection tool.BMC Genet 2008, 9:23.10. Tang J, Vosman B, Voorrips RE, Linden CG van der, Leunissen JA:QualitySNP: a pipeline for detecting single nucleotide poly-morphisms and insertions/deletions in EST data from diploidand polyploid species.  BMC Bioinformatics 2006, 7:438.11. Rozas J, Sánchez-DelBarrio JC, Messeguer X, Rozas R: DnaSP, DNApolymorphism analyses by the coalescent and other meth-ods.  Bioinformatics 2003, 19:2496-2497.12. Letondal C: A web interface generator for molecular biologyprograms in Unix.  Bioinformatics 2001, 17:73-82.13. Gilbert D: Pise: Software for building bioinformatics webs.Brief Bioinform 2002, 3:405-409.14. Rafalski A: Applications of single nucleotide polymorphisms incrop genetics.  Curr Opin Plant Biol 2002, 5:94-100.15. Halushka MA, Fan JB, Bentley K, Hsie L, Shen N, et al.: Patterns ofsingle nucleotide polymorphisms in candidate genes forblood-pressure homeostasis.  Nat Genet 1999, 22:239-247.16. Kota R, Rudd S, Facius A, Kolesov G, Thiel T, Zhang H, Sten N, MayerK, Graner A: Snipping polymorphisms from large EST collec-tions in barley (Hordeum vulgare L.).  Mol Gen Genomics 2003,270:24-33.17. Beaty TH, Fallin MD, Hetmanski JB, McIntosh I, Chong SS, Ingersoll R,Sheng X, Chakraborty R, Scott F: Haplotype Diversity in 11 Can-didate Genes Across Four Populations.  Genetics 2005,171:259-267.18. Sekino M, Hara M, Taniguchi N: Genetic diversity within andbetween hatchery strains of Japanese flounder Paralichthysolivaceus assessed by means of microsatellite and mitochon-drial DNA sequencing analysis.  Aquaculture 2002, 213:101-122.19. Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, OinnT: Taverna: a tool for building and running workflows of serv-ices.  Nucleic Acids Res 2006:W729-W732.Page 5 of 5(page number not for citation purposes)

Abstract Background There is a need for software scripts and modules for format parsing, data manipulation, statistical analysis and annotation especially for tasks related to marker identification from sequence data and sequence diversity analysis. Results Here we present several new Perl scripts and a module for sequence data diversity analysis. To enable the use of these software with other public domain tools, we also make available PISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. This enables the user to generate pipelines for automated analysis, since PISE is a web interface generator for bioinformatics programmes. Conclusion A new set of modules and scripts for diversity statistic calculation, format parsing and data manipulation are available with PISE wrappers that enable pipelining of these scripts with commonly used contig assembly and sequence feature prediction software, to answer specific sequence diversity related questions.</p

Nayak Spurthi

Reddy P Srinivasa

Jami Anusha

BhanuPrakash A

Jayashree B

Varshney Rajeev K

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BMC Research Notes

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Background



There is a need for software scripts and modules for format parsing, data manipulation, statistical analysis and annotation especially for tasks related to marker identification from sequence data and sequence diversity analysis.



Results



Here we present several new Perl scripts and a module for sequence data diversity analysis. To enable the use of these software with other public domain tools, we also make available PISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. This enables the user to generate pipelines for automated analysis, since PISE is a web interface generator for bioinformatics programmes.



Conclusion



A new set of modules and scripts for diversity statistic calculation, format parsing and data manipulation are available with PISE wrappers that enable pipelining of these scripts with commonly used contig assembly and sequence feature prediction software, to answer specific sequence diversity related questions

Jayashree, B.

Bhanuprakash, A.

Jami, A.

Reddy, P.S.

Nayak, S.

Varshney, R.K.

Research Repository

BioMed CentralBMC Research NotesssOpen AcceTechnical NotePerl module and PISE wrappers for the integrated analysis of sequence data and SNP featuresB Jayashree*1, A BhanuPrakash1, Anusha Jami2, P Srinivasa Reddy3, Spurthi Nayak3 and Rajeev K Varshney*3Address: 1Bioinformatics Unit, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, India, 2Bioinformatics Centre, University of Hyderabad, Gachibowli, 500046, Hyderabad, Andhra Pradesh, India and 3Centre of Excellence in Genomics, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Andhra Pradesh, IndiaEmail: B Jayashree* - b.jayashree@cgiar.org; A BhanuPrakash - a.bhanuprakash@cgiar.org; Anusha Jami - anushajami@yahoo.co.in; P Srinivasa Reddy - pallamsri88@gmail.com; Spurthi Nayak - s.nayak@cgiar.org; Rajeev K Varshney* - r.k.varshney@cgiar.org* Corresponding authors    AbstractBackground: There is a need for software scripts and modules for format parsing, datamanipulation, statistical analysis and annotation especially for tasks related to marker identificationfrom sequence data and sequence diversity analysis.Results: Here we present several new Perl scripts and a module for sequence data diversityanalysis. To enable the use of these software with other public domain tools, we also make availablePISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. Thisenables the user to generate pipelines for automated analysis, since PISE is a web interfacegenerator for bioinformatics programmes.Conclusion: A new set of modules and scripts for diversity statistic calculation, format parsing anddata manipulation are available with PISE wrappers that enable pipelining of these scripts withcommonly used contig assembly and sequence feature prediction software, to answer specificsequence diversity related questions.BackgroundSingle Nucleotide Polymorphisms (SNPs) are commonlyfound throughout the genome and provide dense mapsover small chromosomal regions. The recent advances insequencing and genotyping have made large scale SNPdiversity analysis possible in several crop species. Thishelps assess genome variation that can then be harnessedfor crop improvement. Sequence diversity informationmay be desirable across defined groups of sequences, suchas candidate gene transcripts from different genotypes, orassembled transcripts for a particular marker from morethan one genotype. The grouping could be based on theobjective of the study – across race, location, genes orregions within genes. Sequence data analysis usuallyinvolves steps such as clustering of sequence data, todetermine redundancy levels. Sequence assembly is car-ried out to generate consensus sequences or contigs andsinglets. The user then processes this output to determinepresence of microsatellites or SNPs. Along with SNP iden-tification it is also desirable to obtain other aspects fromthe alignment; such as SNP and indel (insertion-deletions)frequency, the type of variant and haplotypes, PIC valuefor the SNP and haplotype besides nucleotide diversity(π). Validation of predicted SNP(s) through wet lab exper-Published: 24 May 2009BMC Research Notes 2009, 2:92 doi:10.1186/1756-0500-2-92Received: 2 March 2009Accepted: 24 May 2009This article is available from: http://www.biomedcentral.com/1756-0500/2/92© 2009 Jayashree et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92iments is the next step to convert the identified SNP intoa genetic marker. Although more than 30 SNP genotypingplatforms are currently available, these are both expensiveand demand considerable expertise. One solution for val-idating the identified SNP(s) through cost effective SNPgenotyping platform is development of CAPS (cleavedamplified polymorphic sequences) marker by predictingthe restriction enzyme that can use the identified SNP as arecognition site.There are several available software solutions for sequenceclustering, and a few popular ones for assembly. The pop-ular group of Clustal programmes [1], d2-cluster [2] forEST clustering and cap3 or PCAP [3,4], the TIGR assem-bler [5] or Phrap [6]http://www.phrap.org/phredphrapconsed.html are used for sequence assembly. Similarly,there are many freely available software programmes forthe identification of SNPs[7-10]. DnaSP reports on nucle-otide polymorphism features from aligned sequence data[11]. None of them however automate group wise identi-fication and reporting of polymorphism statistics andmore importantly consider the presence of heterozygousloci in the sequence data. Many available programs readheterozygous SNPs as missing/bad quality sequence dataand thus do not consider them for analysis. As a result fea-tures such as sequence diversity, PIC of SNP and haplo-types, etc. may be underestimated. The need for a modulethat could report SNP features for any number of userdefined groups coupled with the need to be able to calcu-late statistics taking into consideration the presence ofheterozygous loci led to the development of the SNPDIVersity ESTimator module (divest.pm).Sequence analysis involves pipelining of data from onesoftware to another and often also includes branchedflows such as when annotation of sequences with putativefunction is also a requirement. Format conversion scriptsto convert output of one program to input of another areneeded when the user wants to pipeline several tools andmodules. Along with output parsing scripts, some degreeof automation can be achieved in data analysis tasks. Theavailability of software environments for pipelining andworkflow management help the user to create customanalysis pipelines.The PISE programme [12,13] is a robust environment thathas been around for many years now and allows integra-tion of internal scripts/tools that are part of the target exe-cution environment as well as external tools that a userruns. Ease of use is achieved through the creation of agraphical user interface (GUI) for all of the programmes/scripts available in the environment and the chainingtogether of scripts to facilitate automation and analysis.So rather than reinvent a workflow environment; weimplemented PISE locally and provided PISE XML wrap-pers for the Perl scripts and modules developed by us,besides making them available as web services. The avail-ability of the programs and wrapper sripts allow users toimplement versatile pipelines either in the familiarbrowser environment or in the Taverna workbench. Themodules and scripts are available to all interested users.ImplementationThe source code for the programs has been written in Perl5.8. For the diversity estimator module, Bioperl moduleshave been used. The scripts have been tested within thePISE environment and are being used independently aswell, on machines running either Linux or Windows OS.An example pipeline that makes use of the scripts writtenis indicated (Figure 1A). The modules allow a largeamount of data to be processed; we have tested it to workwith up to 99 user defined groups consisting of up to 225sequences per group. Interface development involves writ-ing the XML specification for the programme/script/mod-ule using the grammar provided http://www.pasteur.fr/~letondal/XML/pise.dtd, where file manipulations andredirection of output files to another programme are alsospecified. The scripts are also available as a soaplab serviceand can so be called through the Taverna workflow tool.Results and discussionFunctionality of Perl modules and scriptsThe module divest.pm is the diversity estimator modulethat reads an input file derived from assembly pro-grammes like cap3 or the alignment file output from mul-tiple sequence alignment programmes like ClustalW orBioEdit. The file contains the assemblies of reads for eachuser defined group. The user can choose to calculate diver-sity statistics for all groups or one particular group. Thestatistics calculated include nucleotide diversity, numberof SNPs, and type of variant whether transition or trans-version, SNP frequency, number of haplotypes, the PIC(polymorphism information content) of SNP and haplo-type, haplotype frequency and also number and fre-quency of indels.SNP and indel frequency are simply calculated as the totallength of the sequence in base pairs/number of SNPs orindels. The count of number of haplotypes and haplotypefrequency is based on the standard methods [14]. Animportant addition in these calculations is the considera-tion of heterozygous loci. The user can replace an N withan H (in case of true heterozygote) based on two peaks inchromatograms obtained from sequencing machines. Thealgorithm assigns 'H' two alleles (A/T or C/G each with avalue of 0.5) and this is used in haplotype analysis andsequence diversity calculations.The nucleotide diversity π is calculated using the formula[15]Page 2 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92where k is the number of SNPs identified in an alignmentof 'n' genotypes, L is the number of basepairs and.The PIC of SNP is calculated using the formula [16]where pi is the frequency of the ith allele at a given SNPlocus.The haplotype diversity is calculated using the formula[17,18]where K is the number of haplotypes, pi is the frequencyof haplotypes and n is the total number of reads.The statistics generated is exported in Excel format,besides the script also generates input file to Network4.502 http://www.fluxus-technology.com/sharenet.htm.Network helps generate evolutionary trees from variousdata including haplotype and haplotype frequency data.Scripts that enable the conversion of a multiple sequencealignment into the diversity estimator input file have beenπ = k/aL1 12/( )iin −=∑PIC pi2ik= − ∑1H n n piik= − ⋅ −⎡⎣⎢⎤⎦⎥∑[ /( )]1 1 2A. An example pipeline that can be executed through the PISE environmentFigur  1A. An example pipeline that can be executed through the PISE environment. Blue boxes represent inputs and out-puts in the pipeline. B. The PISE interface to the diversity estimator module. C. Screenshot of divest.pm ouput.Page 3 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92currently written for the ClustalW and cap3 programmes.Another script that enables concatenating cap3 contig andsinglets file is also available, the output of this file gener-ates a 'unigene' file that can serve as input to the MISAmicrosatellite analysis programme. An example analysispipeline is provided in Figure 1 along with the PISE inter-face to the diversity estimator programme and its output.Accessibility of modules and scriptsFigure 2 illustrates the XML specification for a module likedivest.pm. Chaining together of programmes is made fea-sible through the parameter setting in the XML file, pipingoutput files to input file of the next programme. In a PISEimplementation available at http://hpc.icrisat.cgiar.org/Pise/5.a/, all mentioned scripts are available under assem-bly, format parsing, statistics and data manipulationdirectories. A sample ACD file for the divest module is alsogiven, this provides meta-data regarding the input andoutput files for this module and can be used by soaplabfor deploying divest.pm as a web service.Intended useThe modules and scripts being provided through the PISEenvironment can be used for various purposes (a) identi-fying microsatellites in a set of unigenes, (b) calculatingthe diversity between two or more groups of data based ongeographical location of the genotypes (e.g. cultivated,wild, landraces, etc.), regions of the gene (e.g. intronic andexonic regions) being sampled, (c) sequence diversity fea-tures in a candidate gene across several genotypes and (d)identifying SNPs and finding out how many of these areconvertible to CAPS, (e) functionally annotate sequences.The ability to chain scripts to generate pipelines reducesthe file management burden for the user. A part of thepipeline indicated in Figure 1 can also be executedthrough the Taverna workflow environment [19], whichPISE XML wrapper and ACD file for the diversity estimator moduleFigure 2PISE XML wrapper and ACD file for the diversity estimator module.Page 4 of 5(page number not for citation purposes)BMC Research Notes 2009, 2:92 http://www.biomedcentral.com/1756-0500/2/92provides web service access besides local service access.The advantage to the user would be the accessibility andintegration of databases through web services. Some ofthe scripts are available as soaplab web services at http://220.227.242.214:8080/soaplab2/; under the headingsequence analysis.As the modules and scripts are being released as opensource code, interested users can continue to use as well asmake improvements to them.ConclusionModules and scripts that facilitate sequence diversity anal-ysis, format parsing and data manipulation are beingmade available along with source code. The module/scripts can be chained together with other freely availablesequence analysis software to generate pipelines for auto-mated data analysis. The pipelining has been currentlymade feasible through the PISE web interface generatorand a part of it can also be executed on the Taverna work-bench. The module and scripts are available to interestedusers.Availability & requirementsModules available from: http://www.icrisat.org/gt-bt/softwares_downloads.htm.Can be accessed and used at http://hpc.icrisat.cgiar.org/Pise/5.a/Soaplab service: http://220.227.242.214:8080/soaplab2/Operating System: platform independentProgramming language: PerlPISE http://www.pasteur.fr/recherche/unites/sis/Pise/SNP2CAPS http://pgrc.ipk-gatersleben.de/snp2caps/MISA, cap3 (optional; http://pgrc.ipk-gatersleben.de/misa/misa.html; http://seq.cs.iastate.edu/download.html)Any restrictions to use by non-academics: noneAbbreviationsACD: Ajax Command Definition; SNP: Single NucleotidePolymorphism; PIC: Polymorphism Information Con-tent; XML: Extensible Markup Language; PISE: PasteurInstitute Software Environment; CAPS: Cleaved AmplifiedPolymorphic markers; EST: Expressed Sequence Tag;PCAP: Parallel Contig Assembly Program; CPAN: Com-prehensive Perl Archive Network; MISA: MIcroSAtelliteIdentification toolCompeting interestsThe authors declare that they have no competing interests.Authors' contributionsBA, AJ coded for the Perl modules and scripts, PSR and SNused the programmes on their datasets, RKV articulatedthe use cases, indices for the diversity estimator modulewhile JB developed the pipeline architecture and wrotethe manuscript.AcknowledgementsThe authors gratefully acknowledge support through the Generation Chal-lenge Programme (GCP). The authors gratefully acknowledge Martin Sen-ger's(IRRI) help with implementing the pipeline in Taverna.References1. Ramu C, Hideaki S, Tadashi K, Rodrigo L, Gibson TJ, Higgins DG,Thompson JD: Multiple sequence alignment with the Clustalseries of programs.  Nucleic Acids Res 2003, 31:3497-3500.2. Burke J, Davison D, Hide W: d2_cluster: A Validated Method forClustering EST and Full-Length cDNA Sequences.  GenomeRes 1999, 9:1135-1142.3. Huang X, Madan A: CAP3: A DNA sequence assembly pro-gram.  Genome Res 1999, 9:868-77.4. Huang X, Wang J, Aluru S, Yang S-P, Hillier L: PCAP: A Whole-Genome Assembly Program.  Genome Res 2003, 13:2164-2170.5. Sutton G, White O, Adams M, Kerlavage A: TIGR assembler: Anew tool for assembling large shotgun sequencing projects.Genome Sci Technol 1995, 1:9-19.6. Phrap documentation   [http://www.phrap.org/phredphrap/phrap.html]7. Batley J, Jewell E, Edwards D: Automated discovery of singlenucleotide polymorphism and simple sequence repeatmolecular genetic markers.  Methods Mol Biol 2007, 406:473-94.8. Savage D, Batley J, Erwin T, Logan E, Love CG, Lim GA, Mongin E,Barker G, Spangenberg GC, Edwards D: SNPServer: a real-timeSNP discovery tool.  Nucleic Acids Res 2005, 33:W493-5.9. Tang J, Leunissen JA, Voorrips RE, Linden CG van der, Vosman B:HaploSNPer: a web-based allele and SNP detection tool.BMC Genet 2008, 9:23.10. Tang J, Vosman B, Voorrips RE, Linden CG van der, Leunissen JA:QualitySNP: a pipeline for detecting single nucleotide poly-morphisms and insertions/deletions in EST data from diploidand polyploid species.  BMC Bioinformatics 2006, 7:438.11. Rozas J, Sánchez-DelBarrio JC, Messeguer X, Rozas R: DnaSP, DNApolymorphism analyses by the coalescent and other meth-ods.  Bioinformatics 2003, 19:2496-2497.12. Letondal C: A web interface generator for molecular biologyprograms in Unix.  Bioinformatics 2001, 17:73-82.13. Gilbert D: Pise: Software for building bioinformatics webs.Brief Bioinform 2002, 3:405-409.14. Rafalski A: Applications of single nucleotide polymorphisms incrop genetics.  Curr Opin Plant Biol 2002, 5:94-100.15. Halushka MA, Fan JB, Bentley K, Hsie L, Shen N, et al.: Patterns ofsingle nucleotide polymorphisms in candidate genes forblood-pressure homeostasis.  Nat Genet 1999, 22:239-247.16. Kota R, Rudd S, Facius A, Kolesov G, Thiel T, Zhang H, Sten N, MayerK, Graner A: Snipping polymorphisms from large EST collec-tions in barley (Hordeum vulgare L.).  Mol Gen Genomics 2003,270:24-33.17. Beaty TH, Fallin MD, Hetmanski JB, McIntosh I, Chong SS, Ingersoll R,Sheng X, Chakraborty R, Scott F: Haplotype Diversity in 11 Can-didate Genes Across Four Populations.  Genetics 2005,171:259-267.18. Sekino M, Hara M, Taniguchi N: Genetic diversity within andbetween hatchery strains of Japanese flounder Paralichthysolivaceus assessed by means of microsatellite and mitochon-drial DNA sequencing analysis.  Aquaculture 2002, 213:101-122.19. Hull D, Wolstencroft K, Stevens R, Goble C, Pocock MR, Li P, OinnT: Taverna: a tool for building and running workflows of serv-ices.  Nucleic Acids Res 2006:W729-W732.Page 5 of 5(page number not for citation purposes)

A web interface generator for molecular biology programs in Unix. Bioinformatics

A: CAP3: A DNA sequence assembly program. Genome Res

Applications of single nucleotide polymorphisms in crop genetics.

Automated discovery of single nucleotide polymorphism and simple sequence repeat molecular genetic markers. Methods Mol Biol

D: SNPServer: a real-time SNP discovery tool. Nucleic Acids Res

DNA polymorphism analyses by the coalescent and other methods. Bioinformatics

et al.: Patterns of single nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat Genet

F: Haplotype Diversity in 11 Candidate Genes Across Four Populations. Genetics

Genetic diversity within and between hatchery strains of Japanese flounder Paralichthys olivaceus assessed by means of microsatellite and mitochondrial DNA sequencing analysis. Aquaculture

Graner A: Snipping polymorphisms from large EST collections in barley (Hordeum vulgare L.). Mol Gen Genomics

Hide W: d2_cluster: A Validated Method for Clustering EST and Full-Length cDNA Sequences. Genome Res

JD: Multiple sequence alignment with the Clustal series of programs.

Kerlavage A: TIGR assembler: A new tool for assembling large shotgun sequencing projects. Genome Sci Technol

L: PCAP: A WholeGenome Assembly Program. Genome Res

Leunissen JA: QualitySNP: a pipeline for detecting single nucleotide polymorphisms and insertions/deletions in EST data from diploid and polyploid species.

Oinn T: Taverna: a tool for building and running workflows of services.

Pise: Software for building bioinformatics webs. Brief Bioinform

Vosman B: HaploSNPer: a web-based allele and SNP detection tool.

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.359.1780

Perl module and PISE wrappers for the integrated analysis of sequence data and SNP features

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