New softwares for automated microsatellite marker development

Microsatellites are repeated small sequence motifs that are highly polymorphic and abundant in the genomes of eukaryotes. Often they are the molecular markers of choice. To aid the development of microsatellite markers we have developed a module that integrates a program for the detection of microsatellites (TROLL), with the sequence assembly and analysis software, the Staden Package. The module has easily adjustable parameters for microsatellite lengths and base pair quality control. Starting with large datasets of unassembled sequence data in the form of chromatograms and/or text data, it enables the creation of a compact database consisting of the processed and assembled microsatellite containing sequences. For the final phase of primer design, we developed a program that accepts the multi-sequence ‘experiment file’ format as input and produces a list of primer pairs for amplification of microsatellite markers. The program can take into account the quality values of consensus bases, improving success rate of primer pairs in PCR. The software is freely available and simple to install in both Windows and Unix-based operating systems. Here we demonstrate the software by developing primer pairs for 427 new candidate markers for peanut.


INTRODUCTION
A molecular marker is an identifiable DNA region whose inheritance can be followed along generations. Molecular markers have become a powerful tool in many areas of biology. Microsatellites, also known as simple sequence repeats (SSR) or short tandem repeats (STR), are repeated sequence motifs, whose unit of repetition is between 1 and 6 bp. They are highly abundant in the genomes of eukaryotes, polymorphic and usually co-dominant and transferable between different mapping populations. Microsatellite markers can also be used in automated genotyping techniques. Thus, they have become one of the most useful molecular markers for a large number of organisms.
Researchers working on the development of microsatellite markers need an efficient way to go from usually hundreds or thousands of trace and/or text sequence files to the identification of new potential markers. However, the softwares currently available for data-mining microsatellites are either restricted to small datasets, not available for Windows, or are not targeted towards marker development. In addition, they do not offer base quality control and with one exception (Repeat-Masker when used with Staden), they are not integrated into a sequence analysis software, and as such require already assembled data as input (1)(2)(3), http://www.maizemap.org/ bioinformatics/SSRFINDER/README.ssrfinder and http:// www.repeatmasker.org/.
We have developed a Staden module that integrates the program TROLL (4) to the Staden package. TROLL is an open source program based on the Aho-Corasick algorithm for the detection of perfect microsatellites. The Staden Package is a free, open source, software tool used for sequence assembly and analysis (5) (http://staden.sourceforge.net/). The Staden package, working with the module and TROLL, enables efficient processing of a large number of sequences in chromatogram and/or text form to produce a small database with only microsatellite containing sequences, processed, assembled in contigs and that have not previously been used for marker development. For the primer design we have developed a program that uses Primer3 (6) to design primers flanking the SSR located by the module. It is available as a stand-alone program and through a Web service. This program accepts multiple sequences in the 'experiment file' format of Staden as input and produces a list of primer pairs *To whom correspondence should be addressed. The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org for amplification of microsatellite markers. The program can take into account the quality values of consensus bases, improving success rate of primer pairs in PCR. The Staden Package, TROLL and the module are freely available for both Linux and Windows platforms.

General details of the software
The TROLL module was developed in the Tcl/Tk scripting language, following the Staden Package module development specifications. It works through Pregap4 reading the Staden Experiment Files one by one. These files contain, among other information, the DNA sequences, the corresponding quality values and any vector contamination masking information. All sequences are then concatenated and a special character (#) is used as a separator. TROLL then locates the SSRs, these are then filtered, disregarding those in low quality or vector contaminated regions and the result is written back on the Experiment Files. A file containing the names of experiment files with microsatellites is generated (pregap.SSR.passed), and this can be used for entering into the Gap4 interface of Staden to create a database of entirely microsatellite containing sequences. The tags added to the experiment files may be used by Gap4 to aid in assembly by masking and to visualize the microsatellites. Additionally the tags are used by the primer design program to define the target region around which the Primer3 designs primers (see below).
The following module configuration options are available: (i) Tag type: used to control Tag type used to mark microsatellite repeats. (ii) Motif length: used to set the motif lengths (mono, di, tri, tetra, penta and/or hexa) that will be used in the search for SSRs. (iii) Repeat min: used to set the minimum number of motif repetitions for SSRs of each motif length. (iv) Motif file: the location of the file containing the motif list required by the TROLL program. (v) Create SSR.passed file?: for the creation of a file with the names of all sequences containing SSRs. This file is used to enter into Gap4 to create a database of microsatellite containing sequences. (vi) Create no_SSR_passed file?: used to create a file with the name of all sequences that do not contain SSRs. (vii) Filter quality: used to set the base quality threshold which will be used when analyzing SSR regions.
The program for primer design is responsible for parsing the experiment format file exported from Gap4 and calling the Primer3 program repeatedly to produce primer pairs flanking the SSRs. For each sequence present in the input file, the program analyses the tags added by the TROLL module (default is 'REPT') and produces a list of the repeats found. Since the input file may contain assembled readings, overlapping repeats are discarded. Based on the list of repeats produced, and on the input parameters chosen by the user (e.g.: GC content, TM, primer length etc.), input Primer3 files are created and processed. The program then analyses Primer3 output to cluster repeats being flanked by the same primer pairs. When all sequences have been analysed, the program outputs a tab spaced file (primers.passed) that includes the contig name, primer names, microsatellite sequence(s), primer sequences, annealing temperatures and the quality of the contig sequences with the primers annealed. In addition, the program provides three other output files, marker_seqs.fasta containing the sequences for which the primer design was successful, not_marker_seqs.fasta with sequences for which primer design was not successful, and primers.failed, which also contains the sequences for which primer design was not successful, but in the experiment file format of Staden. The primers.failed file can be used for another round of analysis using the primer design program with less stringent conditions for the primer design.
Downloading and using the programs net/ (Follow the link to Resources). The primer design program is available as a Web service, requiring no program installation. Alternatively, the primer design program can be downloaded as a Perl script and installed locally. For the Perl script to work you will need to install Primer3, which is available from http://frodo.wi.mit.edu/ primer3/primer3_code.html. (iv) The Step-by-step protocol for use of the module from http://finder.sourceforge.net/ (Follow the link to Resources). The Step-by-step protocol for use of the module is written for novice users of the Staden Package. However, to use the TROLL module to its full potential, the user needs to be familiar with the Staden. The course documentation that is downloaded with the software is highly recommended (for Windows users in C:\Program files\Staden package\course\).

Examples of marker development from three datasets
TC-repeat enriched genomic library. Peanut (Arachis hypogaea) genomic DNA library enriched for TC repeats was made using genomic DNA cut with the restriction enzyme Sau3AI, oligonucleotides and magnetic beads according to a standard protocol (7). Two hundred and eighty genomic clones were sequenced using the forward primer, and plasmids that contained microsatellites were identified using the module. These plasmids were sequenced using the reverse primer, and the resulting 338 chromatograms were processed using the module, as described in the Step-by-Step protocol (Supplementary Data), but also using a naming scheme allowing Staden to identify forward and reverse read pairs from individual clones. After assembly in Gap4, where necessary, forward and reverse reads were joined manually using the 'Find read pairs', 'Find internal joins' and 'Join Contigs' commands. Consensus sequences were exported in experiment file format from the Gap4 interface, and used as input file for the Web interface of the primer design program. ; and the TROLL module was run to identify and select di-, tri-, tetra-and pentamicrosatellites with six or more motif repetitions. The resultant pregap.SSR.passed file was entered into the Staden Gap4 interface, using the Gap4 assembler with masking, to create a database of microsatellite containing sequences that have not yet been used for marker development. Consensus sequences were exported in experiment file format from the Gap4 interface, and used as input file for the primer design program.
Text data from GenBank. Three thousand eight hundred and ninety Arachis sequences from GenBank in simple text format were processed essentially as described in the Step-by-step protocol for text data with the addition of the use of Cross-Match using the UniVec database to remove contaminating vector sequences, and the use of the BLAST module for the elimination of already known Arachis markers (as described for the processing of ESTs).

RESULTS AND DISCUSSION
The number of molecular markers available for Arachis, and many other species, is still a limiting factor for genetic mapping. When we had begun working with microsatellite marker development we could not find a software that dealt in an integrated way with the problems that marker development presented, most importantly: the identification of microsatellite containing sequences, the removal of vector sequences and known marker sequences, the processing of sequences into non-redundant contigs, and a large-scale interface with Primer3. To overcome this we developed the softwares published here. Recently we published new microsatellite markers developed using this software in various stages of development, together with the first microsatellite-based genetic map for Arachis. In total 271 new microsatellite markers were developed, 69 data-mined from GenBank, 121 from microsatellite enriched genomic libraries and 81 from ESTs (12). The software in the final stage of development described here solves the problems we encountered and allows the efficient data-mining of new markers. The TROLL module integrates simply and completely into the Staden Package and performed its task extremely fast. By concatenating the input sequences and calling the TROLL program only once, the module finds all SSRs in a single pass, thus maintaining TROLL's linear processing time. TROLL's simplicity and specialized function is targeted exactly at the types of repeats that are most useful for microsatellite marker development: repeats with di-, tri-, tetra-and penta-nucleotide motif sizes. Although TROLL does not find imperfect repeats, we did not find this to be a problem. Long imperfect repeats largely consist of shorter perfect ones, and isolated short imperfect repeats are of very limited use for marker development. The primer design program worked well and, when used with chromatogram data and quality control, effectively eliminated primers being designed to low quality regions of the consensus sequences (for an example output see Table 1). Here we illustrate the use of the module and primer design program with three different datasets, each of which presenting slightly different difficulties for marker development. Staden working with the module proved effective in all cases.

TC-repeat enriched genomic library
Processing the 338 chromatograms produced 43 microsatellite containing contigs. This type of data where every sequence contains a microsatellite, and many of the sequence reads 'dieout' at the end of the microsatellite is difficult to assemble correctly. Incorrect joining of reads that end in microsatellites can easily be a problem. The use of masking in the assembly avoided this problem. The primer design program produced primer pairs for 30 microsatellite markers. The processing of these sequences is included just for demonstration, markers from these sequences are already published (12). Only one of the primer pairs (3.3%) failed to amplify an identifiable PCR product. Raw output from the program is a tab-spaced file with, in addition to the information above, primer names and base qualities of the contig regions that bind to the primers.

ESTs
Of the 8800 ESTs, 282 were above the specified threshold of similarity to known Arachis SSR markers and were eliminated by the BLAST module. The TROLL module detected 213 other chromatograms with microsatellites. Using the Gap4 interface these were assembled into 142 contigs. Although the original dataset is large, the resultant database is small, and is operated efficiently on a standard personal computer. The primer design program produced primer pairs for 75 new microsatellite markers. These primer pairs have not been fully tested yet, but in our previous work (12) only 6 of 81 EST primer pairs (7.4%) failed to amplify an identifiable PCR product.

Text data from GenBank
A total of 563 of the 3890 sequences from GenBank were above the specified threshold of similarity to known Arachis markers and were eliminated by the BLAST module, TROLL detected 482 other sequences with microsatellites and these were assembled into 463 contigs. Although counter-intuitive, it is possible to use quality control even with text data. Staden assigns a nominal quality value of two for bases of text data, but when text data overlaps within a contig, a higher value of confidence is given. This can be used, for instance, to design primers only using consensus sequences supported by at least two reads. However, since the GenBank data we processed has low redundancy, primers were designed without quality control and primer pairs for 352 markers were designed. It is to be expected that data from GenBank may give a lower success rate of primers compared to chromatogram data. Some sequences may have been rejected for marker development by the original authors because the repeat type has relatively low polymorphism, or because of low sequence quality. Nevertheless, in our previous work, data-mining from GenBank was a productive and low cost option for marker development. Of the 69 markers data-mined, only 9 (13%) did not amplify any scorable fragments (12). Example databases and the new primers designed are in Supplementary Data. In total, 427 pairs of primers for new candidate markers for Arachis are presented.
In summary, advances in genetic mapping are reliant upon the development of sufficient numbers of molecular markers, and large scale marker development depends upon efficient processing of the data. The Staden software with the TROLL module and primer design program provides a unified and efficient interface for this.
Availability TROLL, the module and primer design program, and instructions for installation and use are available free and can be downloaded from the Resources section of http://finder. sourceforge.net/. A Web version of the primer design program is also available from the same link.

SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.