Background Peach is being developed as a model organism for Rosaceae, an economically important family that includes fruits and ornamental plants such as apple, pear, strawberry, cherry, almond and rose. The genomics and genetics data of peach can play a significant role in the gene discovery and the genetic understanding of related species. The effective utilization of these peach resources, however, requires the development of an integrated and centralized database with associated analysis tools.
Background
Peach is being developed as a model organism for Rosaceae, an economically important family that includes fruits and ornamental plants such as apple, pear, strawberry, cherry, almond and rose. The genomics and genetics data of peach can play a significant role in the gene discovery and the genetic understanding of related species. The effective utilization of these peach resources, however, requires the development of an integrated and centralized database with associated analysis tools.Description The Genome Database for Rosaceae (GDR) is a curated and integrated web-based relational database. GDR contains comprehensive data of the genetically anchored peach physical map, an annotated peach EST database, Rosaceae maps and markers and all publicly available Rosaceae sequences. Annotations of ESTs include contig assembly, putative function, simple sequence repeats, and anchored position to the peach physical map where applicable. Our integrated map viewer provides graphical interface to the genetic, transcriptome and physical mapping information. ESTs, BACs and markers can be queried by various categories and the search result sites are linked to the integrated map viewer or to the WebFPC physical map sites. In addition to browsing and querying the database, users can compare their sequences with the annotated GDR sequences via a dedicated sequence similarity server running either the BLAST or FASTA algorithm. To demonstrate the utility of the integrated and fully annotated database and analysis tools, we describe a case study where we anchored Rosaceae sequences to the peach physical and genetic map by sequence similarity.
Description
The Genome Database for Rosaceae (GDR) is a curated and integrated web-based relational database. GDR contains comprehensive data of the genetically anchored peach physical map, an annotated peach EST database, Rosaceae maps and markers and all publicly available Rosaceae sequences. Annotations of ESTs include contig assembly, putative function, simple sequence repeats, and anchored position to the peach physical map where applicable. Our integrated map viewer provides graphical interface to the genetic, transcriptome and physical mapping information. ESTs, BACs and markers can be queried by various categories and the search result sites are linked to the integrated map viewer or to the WebFPC physical map sites. In addition to browsing and querying the database, users can compare their sequences with the annotated GDR sequences via a dedicated sequence similarity server running either the BLAST or FASTA algorithm. To demonstrate the utility of the integrated and fully annotated database and analysis tools, we describe a case study where we anchored Rosaceae sequences to the peach physical and genetic map by sequence similarity.Conclusions The GDR has been initiated to meet the major deficiency in Rosaceae genomics and genetics research, namely a centralized web database and bioinformatics tools for data storage, analysis and exchange. GDR can be accessed at  .
Conclusions
The GDR has been initiated to meet the major deficiency in Rosaceae genomics and genetics research, namely a centralized web database and bioinformatics tools for data storage, analysis and exchange. GDR can be accessed at  .

Background In the United States and temperate regions throughout the world, the family Rosaceae ranks third in economic importance. The most important fruit producing crops include apple ( Malus ), pear ( Pyrus ), raspberries/blackberries ( Rubus ), strawberries ( Fragaria ), and stone fruits ( Prunus ) such as peach/nectarine, apricot, plum, cherry and almond [ 1 ]. Additionally, Rosaceae contains a wide variety of ornamental plants including roses, flowering cherry, crabapple, quince and pear. Peach is being developed as a model species for Rosaceae because of its small genome size [haploid size of 300 Mb [ 3 ]], approximately twice that of  Arabidopsis  and other characteristics: a relatively short juvenile period (2–3 yrs) and extensive genetics and genomics resources such as molecular marker maps, interesting mutants and clone library resources [ 1 ]. In addition, it has been demonstrated that molecular marker tools developed in peach are easily applied to other species in the family [ 5 - 8 ]. Developing genetic resources for a model organism can greatly accelerate the genetic understanding of the individual member species in the same family. The utilization of rice physical mapping resources in the study of other crops within Poaceae is an excellent example of the usefulness of a comparative genomics approach [ 2 ]. Two major efforts to develop peach as a model for genomics of Rosaceae have been initiated: (1) Structural genomics – the development of a complete physical map of the peach genome and the anchoring of the genetic markers of many of the economically important Rosaceae species maps on this physical map (2) Functional genomics – the development of an extensive EST database for fruit, shoot and seed tissues and integration of the tentative unigene set onto the physical and genetic maps of peach. The volume and the complexity of the data being produced by these peach genomics projects, in addition to the rapidly accumulating genomics and genetics data for other important rosaceous species, necessitate the development of a properly curated and integrated scientific database. Such a database will help scientists to efficiently access, analyze, integrate and apply the data to their own research in a timely manner. RoseDB, which focused on apple genetics and cherry genomics, has been decommissioned and now only exists as a mirror site at INRA. To meet the major need for a centralized and integrated web-database for genomics and genetics research in Rosaceae, the Genome Database for Rosaceae (GDR;  ) has been initiated. The goals of the GDR are (1) to develop an organized and integrated web resource for peach genomics data to facilitate the gene discovery in other member species by a comparative mapping approach, (2) to collect and integrate all Rosaceae genomics data, (3) to develop online tools and resources for the Rosaceae community. In this paper, we describe the structure and content of the database, we review the database access utility and tools and we report a case study where we mapped publicly available Rosaceae sequences to the peach physical and genetic maps by sequence similarity.
Background
In the United States and temperate regions throughout the world, the family Rosaceae ranks third in economic importance. The most important fruit producing crops include apple ( Malus ), pear ( Pyrus ), raspberries/blackberries ( Rubus ), strawberries ( Fragaria ), and stone fruits ( Prunus ) such as peach/nectarine, apricot, plum, cherry and almond [ 1 ]. Additionally, Rosaceae contains a wide variety of ornamental plants including roses, flowering cherry, crabapple, quince and pear. Peach is being developed as a model species for Rosaceae because of its small genome size [haploid size of 300 Mb [ 3 ]], approximately twice that of  Arabidopsis  and other characteristics: a relatively short juvenile period (2–3 yrs) and extensive genetics and genomics resources such as molecular marker maps, interesting mutants and clone library resources [ 1 ]. In addition, it has been demonstrated that molecular marker tools developed in peach are easily applied to other species in the family [ 5 - 8 ]. Developing genetic resources for a model organism can greatly accelerate the genetic understanding of the individual member species in the same family. The utilization of rice physical mapping resources in the study of other crops within Poaceae is an excellent example of the usefulness of a comparative genomics approach [ 2 ]. Two major efforts to develop peach as a model for genomics of Rosaceae have been initiated: (1) Structural genomics – the development of a complete physical map of the peach genome and the anchoring of the genetic markers of many of the economically important Rosaceae species maps on this physical map (2) Functional genomics – the development of an extensive EST database for fruit, shoot and seed tissues and integration of the tentative unigene set onto the physical and genetic maps of peach.MalusPyrusRubusFragariaPrunusArabidopsisThe volume and the complexity of the data being produced by these peach genomics projects, in addition to the rapidly accumulating genomics and genetics data for other important rosaceous species, necessitate the development of a properly curated and integrated scientific database. Such a database will help scientists to efficiently access, analyze, integrate and apply the data to their own research in a timely manner. RoseDB, which focused on apple genetics and cherry genomics, has been decommissioned and now only exists as a mirror site at INRA. To meet the major need for a centralized and integrated web-database for genomics and genetics research in Rosaceae, the Genome Database for Rosaceae (GDR;  ) has been initiated. The goals of the GDR are (1) to develop an organized and integrated web resource for peach genomics data to facilitate the gene discovery in other member species by a comparative mapping approach, (2) to collect and integrate all Rosaceae genomics data, (3) to develop online tools and resources for the Rosaceae community. In this paper, we describe the structure and content of the database, we review the database access utility and tools and we report a case study where we mapped publicly available Rosaceae sequences to the peach physical and genetic maps by sequence similarity.Construction and content GDR data EST data source and annotation An international cooperative project is in progress to develop an extensive peach EST database from a variety of vegetative and reproductive tissues of peach and almond. Currently, GDR contains 9984 ESTs from developing fruit of peach and 2794 ESTs from developing seed of almond. This translates into 3843 tentative unigenes for peach and 773 for almond. The peach and almond ESTs are being processed at the Clemson University Genomics Institute (CUGI) utilizing publicly available software, integrated in a fully automated in-house developed script (CUGIEST). The processing occurs in three stages: trace file processing to identify a filtered, high quality clone library, assembly of high quality sequences to produce longer transcripts and reduce redundancy, and sequence annotation. Annotation consists of pairwise comparison of both the filtered clone library and the EST contig consensus sequences against the GenBank nr protein database using the fastx3.4 algorithm [ 9 ]. The ten most significant matches with the expectation values (EXP) less than 1 × e -9 , for each contig and individual clone in the library are recorded. The unigene data set is then derived by selecting the clone that best represents each contig (the clone with the most significant EXP for the homology search) and the singletons that have either unique protein matches or no known significant matches. Peach ESTs were further annotated by Gene Ontology (GO) assignment based on the single "best hit" match against the SWISS-PROT database. Of the 1552 sequences from the putative peach unigene set that had matches with the SWISS-PROT database, 1439 could tentatively be assigned GO classifications. Additional sequence annotation includes computational analysis for simple sequence repeats (SSR) and open reading frame (ORF) on both the filtered clone library and the contig library. SSR analysis was preformed using a modified version (CUGISSR) of a Perl script SSRIT [ 10 ] with parameters set to detect di- to pentanucleotide with length greater than 18 bp. To examine the location of SSRs in the EST sequences in relation to the putative coding region, CUGISSR uses the FLIP [ 11 ] program which is available at the OGMP (Organelle Genome Megasequencing Project), Biochemistry department, University of Montreal  . FLIP is a UNIX C program that finds/translates ORFs (open reading frames) in sequences. Using the FLIP output, CUGISSR selects the longest ORF as the putative coding region and reports the location of SSRs in relation to the putative coding region. In addition to the peach and almond ESTs processed by CUGI, all the publicly available Rosaceae EST data are daily downloaded from GenBank dbEST and annotated with the top ten most significant matches (EXP < 1 × e -9 ) following a monthly homology search of GenBank nr protein database using the fastx34 algorithm. Genetically anchored physical map and transcript map data The genetically anchored physical map for peach is under development using peach BAC libraries [ 1 ]. It is being constructed using an approach that employs a combination of hybridization of mapped markers and BAC fingerprinting [ 12 ]. For BAC fingerprinting, FPC [ 13 ] is used for automatic assembly of the bands. Hybridization of mapped markers to BAC clones aids in the physical mapping process and also enables researchers to identify the BACs containing these markers. To date, over 250 genetic markers such as RFLPs, AFLPs and SSRs from several molecular genetic maps have been hybridized to BAC clones. Through this, the peach physical map has been anchored on the general  Prunus  map [ 5 ]. Additional 3,000 peach ESTs from the unigene set are currently being mapped to the peach physical map to develop a transcript map of peach fruit ESTs. The EST hybridization is being done as an international cooperative project to develop a functional genomic database for peach. The EST hybridization results are sent to the peach physical mapping team in Clemson University. The overall BAC fingerprinting results and EST/marker hybridization data, stored in an FPC output file and/or spread sheets, are submitted to GDR by the Clemson peach physical mapping team. Database and software design and implementation The GDR is a relational database implemented using Oracle Database Management System version 9.2.0. Currently, the database is composed of 28 tables which store all the data for EST processing, assembly and annotation, SSR analysis, BAC clones, libraries, genetic markers and maps that are used for BAC hybridization, results of the hybridization of markers and ESTs to BAC clones, contact information, and publications. EST data processing, annotation, and uploading of the database are fully automated using a series of scripts written in Perl version 5.8.2. Daily download, annotation and upload of GenBank Rosaceae ESTs are also automatically performed by a series of Perl scripts. Data for BAC hybridization, genetic maps and markers which are submitted from researchers are examined by a curator for any potential errors and then uploaded using Perl scripts. BAC contig data for the developing peach physical map are uploaded directly from the FPC output file to our oracle database using Perl scripts. Web interfaces for database query and the query result pages are mostly developed using Java Server Page (JSP). JSPs are more efficient, easier to use, more powerful and more portable than traditional CGI and many alternative CGI [ 4 ]. BAC contigs of the developing peach physical maps are displayed using WebFPC and WebChrom which are downloaded from an Arizona Institute of Genomics web site  . We have developed a map viewer to provide users with a convenient access to the integrated genetic, physical and transcript map information. Our map viewer is developed using Scalable Vector Graphics (SVG). The SVG viewer plug-in can be freely downloaded from the Adobe Website   and the system requirements can be found at their website  . Our map viewer program accesses our underlying relational database to dynamically generate an integrated genetic and transcript map with a direct link to the WebFPC physical map from each marker.
Construction and content
GDR data EST data source and annotation An international cooperative project is in progress to develop an extensive peach EST database from a variety of vegetative and reproductive tissues of peach and almond. Currently, GDR contains 9984 ESTs from developing fruit of peach and 2794 ESTs from developing seed of almond. This translates into 3843 tentative unigenes for peach and 773 for almond. The peach and almond ESTs are being processed at the Clemson University Genomics Institute (CUGI) utilizing publicly available software, integrated in a fully automated in-house developed script (CUGIEST). The processing occurs in three stages: trace file processing to identify a filtered, high quality clone library, assembly of high quality sequences to produce longer transcripts and reduce redundancy, and sequence annotation. Annotation consists of pairwise comparison of both the filtered clone library and the EST contig consensus sequences against the GenBank nr protein database using the fastx3.4 algorithm [ 9 ]. The ten most significant matches with the expectation values (EXP) less than 1 × e -9 , for each contig and individual clone in the library are recorded. The unigene data set is then derived by selecting the clone that best represents each contig (the clone with the most significant EXP for the homology search) and the singletons that have either unique protein matches or no known significant matches. Peach ESTs were further annotated by Gene Ontology (GO) assignment based on the single "best hit" match against the SWISS-PROT database. Of the 1552 sequences from the putative peach unigene set that had matches with the SWISS-PROT database, 1439 could tentatively be assigned GO classifications. Additional sequence annotation includes computational analysis for simple sequence repeats (SSR) and open reading frame (ORF) on both the filtered clone library and the contig library. SSR analysis was preformed using a modified version (CUGISSR) of a Perl script SSRIT [ 10 ] with parameters set to detect di- to pentanucleotide with length greater than 18 bp. To examine the location of SSRs in the EST sequences in relation to the putative coding region, CUGISSR uses the FLIP [ 11 ] program which is available at the OGMP (Organelle Genome Megasequencing Project), Biochemistry department, University of Montreal  . FLIP is a UNIX C program that finds/translates ORFs (open reading frames) in sequences. Using the FLIP output, CUGISSR selects the longest ORF as the putative coding region and reports the location of SSRs in relation to the putative coding region. In addition to the peach and almond ESTs processed by CUGI, all the publicly available Rosaceae EST data are daily downloaded from GenBank dbEST and annotated with the top ten most significant matches (EXP < 1 × e -9 ) following a monthly homology search of GenBank nr protein database using the fastx34 algorithm. Genetically anchored physical map and transcript map data The genetically anchored physical map for peach is under development using peach BAC libraries [ 1 ]. It is being constructed using an approach that employs a combination of hybridization of mapped markers and BAC fingerprinting [ 12 ]. For BAC fingerprinting, FPC [ 13 ] is used for automatic assembly of the bands. Hybridization of mapped markers to BAC clones aids in the physical mapping process and also enables researchers to identify the BACs containing these markers. To date, over 250 genetic markers such as RFLPs, AFLPs and SSRs from several molecular genetic maps have been hybridized to BAC clones. Through this, the peach physical map has been anchored on the general  Prunus  map [ 5 ]. Additional 3,000 peach ESTs from the unigene set are currently being mapped to the peach physical map to develop a transcript map of peach fruit ESTs. The EST hybridization is being done as an international cooperative project to develop a functional genomic database for peach. The EST hybridization results are sent to the peach physical mapping team in Clemson University. The overall BAC fingerprinting results and EST/marker hybridization data, stored in an FPC output file and/or spread sheets, are submitted to GDR by the Clemson peach physical mapping team.
GDR data
EST data source and annotation An international cooperative project is in progress to develop an extensive peach EST database from a variety of vegetative and reproductive tissues of peach and almond. Currently, GDR contains 9984 ESTs from developing fruit of peach and 2794 ESTs from developing seed of almond. This translates into 3843 tentative unigenes for peach and 773 for almond. The peach and almond ESTs are being processed at the Clemson University Genomics Institute (CUGI) utilizing publicly available software, integrated in a fully automated in-house developed script (CUGIEST). The processing occurs in three stages: trace file processing to identify a filtered, high quality clone library, assembly of high quality sequences to produce longer transcripts and reduce redundancy, and sequence annotation. Annotation consists of pairwise comparison of both the filtered clone library and the EST contig consensus sequences against the GenBank nr protein database using the fastx3.4 algorithm [ 9 ]. The ten most significant matches with the expectation values (EXP) less than 1 × e -9 , for each contig and individual clone in the library are recorded. The unigene data set is then derived by selecting the clone that best represents each contig (the clone with the most significant EXP for the homology search) and the singletons that have either unique protein matches or no known significant matches. Peach ESTs were further annotated by Gene Ontology (GO) assignment based on the single "best hit" match against the SWISS-PROT database. Of the 1552 sequences from the putative peach unigene set that had matches with the SWISS-PROT database, 1439 could tentatively be assigned GO classifications. Additional sequence annotation includes computational analysis for simple sequence repeats (SSR) and open reading frame (ORF) on both the filtered clone library and the contig library. SSR analysis was preformed using a modified version (CUGISSR) of a Perl script SSRIT [ 10 ] with parameters set to detect di- to pentanucleotide with length greater than 18 bp. To examine the location of SSRs in the EST sequences in relation to the putative coding region, CUGISSR uses the FLIP [ 11 ] program which is available at the OGMP (Organelle Genome Megasequencing Project), Biochemistry department, University of Montreal  . FLIP is a UNIX C program that finds/translates ORFs (open reading frames) in sequences. Using the FLIP output, CUGISSR selects the longest ORF as the putative coding region and reports the location of SSRs in relation to the putative coding region. In addition to the peach and almond ESTs processed by CUGI, all the publicly available Rosaceae EST data are daily downloaded from GenBank dbEST and annotated with the top ten most significant matches (EXP < 1 × e -9 ) following a monthly homology search of GenBank nr protein database using the fastx34 algorithm.
EST data source and annotation
An international cooperative project is in progress to develop an extensive peach EST database from a variety of vegetative and reproductive tissues of peach and almond. Currently, GDR contains 9984 ESTs from developing fruit of peach and 2794 ESTs from developing seed of almond. This translates into 3843 tentative unigenes for peach and 773 for almond.The peach and almond ESTs are being processed at the Clemson University Genomics Institute (CUGI) utilizing publicly available software, integrated in a fully automated in-house developed script (CUGIEST). The processing occurs in three stages: trace file processing to identify a filtered, high quality clone library, assembly of high quality sequences to produce longer transcripts and reduce redundancy, and sequence annotation. Annotation consists of pairwise comparison of both the filtered clone library and the EST contig consensus sequences against the GenBank nr protein database using the fastx3.4 algorithm [ 9 ]. The ten most significant matches with the expectation values (EXP) less than 1 × e -9 , for each contig and individual clone in the library are recorded. The unigene data set is then derived by selecting the clone that best represents each contig (the clone with the most significant EXP for the homology search) and the singletons that have either unique protein matches or no known significant matches. Peach ESTs were further annotated by Gene Ontology (GO) assignment based on the single "best hit" match against the SWISS-PROT database. Of the 1552 sequences from the putative peach unigene set that had matches with the SWISS-PROT database, 1439 could tentatively be assigned GO classifications. Additional sequence annotation includes computational analysis for simple sequence repeats (SSR) and open reading frame (ORF) on both the filtered clone library and the contig library. SSR analysis was preformed using a modified version (CUGISSR) of a Perl script SSRIT [ 10 ] with parameters set to detect di- to pentanucleotide with length greater than 18 bp. To examine the location of SSRs in the EST sequences in relation to the putative coding region, CUGISSR uses the FLIP [ 11 ] program which is available at the OGMP (Organelle Genome Megasequencing Project), Biochemistry department, University of Montreal  . FLIP is a UNIX C program that finds/translates ORFs (open reading frames) in sequences. Using the FLIP output, CUGISSR selects the longest ORF as the putative coding region and reports the location of SSRs in relation to the putative coding region.In addition to the peach and almond ESTs processed by CUGI, all the publicly available Rosaceae EST data are daily downloaded from GenBank dbEST and annotated with the top ten most significant matches (EXP < 1 × e -9 ) following a monthly homology search of GenBank nr protein database using the fastx34 algorithm.Genetically anchored physical map and transcript map data The genetically anchored physical map for peach is under development using peach BAC libraries [ 1 ]. It is being constructed using an approach that employs a combination of hybridization of mapped markers and BAC fingerprinting [ 12 ]. For BAC fingerprinting, FPC [ 13 ] is used for automatic assembly of the bands. Hybridization of mapped markers to BAC clones aids in the physical mapping process and also enables researchers to identify the BACs containing these markers. To date, over 250 genetic markers such as RFLPs, AFLPs and SSRs from several molecular genetic maps have been hybridized to BAC clones. Through this, the peach physical map has been anchored on the general  Prunus  map [ 5 ]. Additional 3,000 peach ESTs from the unigene set are currently being mapped to the peach physical map to develop a transcript map of peach fruit ESTs. The EST hybridization is being done as an international cooperative project to develop a functional genomic database for peach. The EST hybridization results are sent to the peach physical mapping team in Clemson University. The overall BAC fingerprinting results and EST/marker hybridization data, stored in an FPC output file and/or spread sheets, are submitted to GDR by the Clemson peach physical mapping team.
Genetically anchored physical map and transcript map data
The genetically anchored physical map for peach is under development using peach BAC libraries [ 1 ]. It is being constructed using an approach that employs a combination of hybridization of mapped markers and BAC fingerprinting [ 12 ]. For BAC fingerprinting, FPC [ 13 ] is used for automatic assembly of the bands. Hybridization of mapped markers to BAC clones aids in the physical mapping process and also enables researchers to identify the BACs containing these markers. To date, over 250 genetic markers such as RFLPs, AFLPs and SSRs from several molecular genetic maps have been hybridized to BAC clones. Through this, the peach physical map has been anchored on the general  Prunus  map [ 5 ]. Additional 3,000 peach ESTs from the unigene set are currently being mapped to the peach physical map to develop a transcript map of peach fruit ESTs. The EST hybridization is being done as an international cooperative project to develop a functional genomic database for peach. The EST hybridization results are sent to the peach physical mapping team in Clemson University. The overall BAC fingerprinting results and EST/marker hybridization data, stored in an FPC output file and/or spread sheets, are submitted to GDR by the Clemson peach physical mapping team.PrunusDatabase and software design and implementation The GDR is a relational database implemented using Oracle Database Management System version 9.2.0. Currently, the database is composed of 28 tables which store all the data for EST processing, assembly and annotation, SSR analysis, BAC clones, libraries, genetic markers and maps that are used for BAC hybridization, results of the hybridization of markers and ESTs to BAC clones, contact information, and publications. EST data processing, annotation, and uploading of the database are fully automated using a series of scripts written in Perl version 5.8.2. Daily download, annotation and upload of GenBank Rosaceae ESTs are also automatically performed by a series of Perl scripts. Data for BAC hybridization, genetic maps and markers which are submitted from researchers are examined by a curator for any potential errors and then uploaded using Perl scripts. BAC contig data for the developing peach physical map are uploaded directly from the FPC output file to our oracle database using Perl scripts. Web interfaces for database query and the query result pages are mostly developed using Java Server Page (JSP). JSPs are more efficient, easier to use, more powerful and more portable than traditional CGI and many alternative CGI [ 4 ]. BAC contigs of the developing peach physical maps are displayed using WebFPC and WebChrom which are downloaded from an Arizona Institute of Genomics web site  . We have developed a map viewer to provide users with a convenient access to the integrated genetic, physical and transcript map information. Our map viewer is developed using Scalable Vector Graphics (SVG). The SVG viewer plug-in can be freely downloaded from the Adobe Website   and the system requirements can be found at their website  . Our map viewer program accesses our underlying relational database to dynamically generate an integrated genetic and transcript map with a direct link to the WebFPC physical map from each marker.
Database and software design and implementation
The GDR is a relational database implemented using Oracle Database Management System version 9.2.0. Currently, the database is composed of 28 tables which store all the data for EST processing, assembly and annotation, SSR analysis, BAC clones, libraries, genetic markers and maps that are used for BAC hybridization, results of the hybridization of markers and ESTs to BAC clones, contact information, and publications.EST data processing, annotation, and uploading of the database are fully automated using a series of scripts written in Perl version 5.8.2. Daily download, annotation and upload of GenBank Rosaceae ESTs are also automatically performed by a series of Perl scripts. Data for BAC hybridization, genetic maps and markers which are submitted from researchers are examined by a curator for any potential errors and then uploaded using Perl scripts. BAC contig data for the developing peach physical map are uploaded directly from the FPC output file to our oracle database using Perl scripts.Web interfaces for database query and the query result pages are mostly developed using Java Server Page (JSP). JSPs are more efficient, easier to use, more powerful and more portable than traditional CGI and many alternative CGI [ 4 ]. BAC contigs of the developing peach physical maps are displayed using WebFPC and WebChrom which are downloaded from an Arizona Institute of Genomics web site  . We have developed a map viewer to provide users with a convenient access to the integrated genetic, physical and transcript map information. Our map viewer is developed using Scalable Vector Graphics (SVG). The SVG viewer plug-in can be freely downloaded from the Adobe Website   and the system requirements can be found at their website  . Our map viewer program accesses our underlying relational database to dynamically generate an integrated genetic and transcript map with a direct link to the WebFPC physical map from each marker.Utility and discussion Database access and tools The GDR website is composed of general information pages, database query/browse interfaces and other tools such as map viewer and sequence similarity server. The GDR web pages are extensively linked such that users can easily access the data of interest regardless of the navigation starting point. For example, the EST detail pages have links to the BAC detail pages, marker detail pages or map viewer for the ESTs that are anchored to BACs, markers or maps. Similarly, the BAC detail pages have links to EST detail page, marker detail page, WebFPC and map viewer. Users can also access the data detail pages from the map viewer or WebFPC/WebChrom. Instead of displaying the entire EST or BAC data in one page, we used the right hand side navigation bars to help users find specific information easily and quickly. A general GDR navigation tool bar is also included in each page to help provide a more user-friendly interface. Database search interface The generic search site allows users to select data types such as EST, BAC and Marker, and search by name. Users can also follow the link to perform more detailed search for each data type. In the EST search site, users can search either the CUGI peach EST database or Rosaceae EST database downloaded from GenBank. ESTs can be searched by their name(s) and annotation features such as whether the EST belongs to a contig, is a unigene, is used as a probe and has SSRs, or any combination thereof. The EST details page, instead of displaying all the details in one page, initially displays the clone information and the sequence with a side bar containing links to library detail, assembly/unigene information, sequence homology, SSR information, Map position and anchored BACs. Each page linked from this side bar has the same side bar for easy navigation between the features. The Sequence homology page shows the most significant matches (EXP < 1e-9) in the Genbank nr protein database from the fastx sequence similarity search. The SSR information page shows the sequence along with the computationally derived SSRs to help users in the primer design for SSR marker development. The longest putative open reading frame (ORF) is also marked in color in the sequence along with the SSRs. SSRs in the non-coding region tend to be more polymorphic and those in the coding region tend to be more transferable among species, so the information of SSR position in a gene structure will be useful for marker development. The Map position page allows users to view the ESTs' map position using our Map Viewer. Users can retrieve the anchored BAC clones for the EST of interest and all the other ESTs and markers that hybridized to the same BAC through the anchored BACs page. The assembly/unigene information page displays the assembly results which include the contig name and the unigene clone that best represents the contig. The contig name is linked to a contig page that displays the contig sequence with a side bar containing links to the comprising ESTs, sequence homology and SSR information. As ESTs with no match to the GenBank proteins or with no SSRs can still assemble into contigs with a match or SSRs, users may get further annotation results of their ESTs of interest by visiting the contig detail site. In addition, contigs may have longer sequences surrounding the SSRs, allowing more flexibility in the primer design for marker development. In the BAC search site, users can search BACs by name or by probe specifications used for BAC hybridization. The search results site and the linked sites provide users with all the data about the BAC, such as the BAC contigs that the BAC belongs to, other BACs in the contig, probes that hybridized to the BACs, the detailed data about the probes, and link to the WebFPC physical map. Markers can be searched by name or features such as map name, type, and source organism. Similarly with the BAC search result sites, the maker search results sites leads users to pages with the marker information, anchored BACs, other markers and ESTs hybridized to the same BACs and link to the Map Viewer and the WebFPC physical map. Graphical interface to maps GDR hosts peach WebFPC and WebChrom to allow users to view the developing peach FPC contigs. Peach WebChrom displays the eight linkage groups of the general  Prunus  map and each linkage group has a link to a page where the developing contigs are located by the linkage group. Each contig has a direct link to WebFPC in which the individual BAC clones are displayed (Figure  1A ). GDR also provides a graphical tool for users to access the integrated genetic, transcript and physical map information. The map viewer displays the general  Prunus  map [ 5 ] with the number of ESTs anchored to each locus (Figure  1B ). The EST details page is linked from the map so that users can get all the data about the ESTs that are anchored to the loci of interest. When a locus is selected, a box appears to display marker type, the number of anchored BACs and the number of other probes that share the same BACs. Each number entry has links to a page where the detailed information is displayed. Also shown in the box are BAC contig names that are anchored to the loci. The BAC contig names have links to WebFPC so that users can directly access the physical map via this route (Figure  1B ). Sequence similarity server GDR also has a BLAST and FASTA sequence similarity search server that allows users to conduct homology searches between their sequences of interest and the various sequence data sets including annotated sequences in GDR. Users can select the database (e.g. peach ESTs, peach unigenes, mapped peach unigenes, GenBank Rosaceae ESTs, GenBank Rosaceae proteins etc) and various search parameters. Users can upload batch sequence files and the parsed results from the search are formatted in a spread sheet and sent to users by email. When the query sequence has a match to the annotated GDR sequences, users can retrieve all the information such as putative function, SSRs, and the anchored map positions via a hyperlink in the excel spreadsheet. Our sequence similarity server, specifically designed for Rosaceae researchers, will help users utilize the developing peach resources in the studies of other Rosaceae species. For example, as described in the case study below, sequences derived from other Rosaceae species could be immediately anchored to the various Rosaceae maps and the physical map when the query sequences show significant similarity to the mapped peach ESTs. Case study: Mapping of Rosaceae sequences onto Rosaceae maps by sequence similarity We report here a case study illustrating the utility of our sequence similarity server and other integrated GDR web resources. In this study, we performed a sequence similarity search using the FASTA algorithm with the non-peach Rosaceae sequences against the mapped peach ESTs to annotate Rosaceae sequences with map positions. A fasta formatted file with a total of 16258 publicly available non peach Rosaceae sequences was uploaded to the GDR FASTA server. We selected the mapped peach database and used the default parameters. The search results returned from the server are formatted in a spread sheet for easy browsing and the match names are hyperlinked to both GenBank and the GDR web site (Figure  2 ). By following the GDR link, users can get the anchored position in the genetic and physical maps as well as other annotation results such as putative function (Figure  2 ). To summarize our results, we used 259 query/match pairs which have a percent identity over 95 and an align-length greater than 100 nucleotides. The majority of the query sequences that showed high similarity to peach ESTs were  Prunus  sequences such as apricot ( Prunus armeniaca ), almond ( Prunus dulcis ) and sour cherry ( Prunus cerasus ). This was expected as peach also belongs to the genera  Prunus . The 259 query/match pairs consisted of 209 Rosaceae sequences and 61 mapped peach ESTs. The matching of multiple Rosaceae sequences to single peach sequences was expected since the Rosaceae sequences were not assembled and therefore potentially contains multiple sequences representing the same gene. The 209 Rosaceae sequences were anchored to 38 different loci in four different Rosaceae maps. The number of sequences from each Rosaceae species that anchored to each map is shown in Figure  3 . This study demonstrates the usefulness of applying a comparative genomics approach to Rosaceae genomics using the GDR as a data mining tool. The entire data for the mapped Rosaceae sequences with anchored map positions are available at  . Future development We plan to incorporate more Rosaceae genomics and genetics data from researchers worldwide as well as data from the ongoing  Prunus  genomics projects. Data to be added in the near future include apple ESTs, strawberry ESTs, rose ESTs and apricot map/marker data from collaborators. When the genomics projects from  Prunus  are finished, we will host 10–15,000 unique ESTs from a variety of vegetative and reproductive tissues of peach and almond, the complete peach physical map with anchored genetic markers and unique ESTs. In addition to adding new data, future development efforts will focus on improving the tools and functionality of the web interface such as an advanced search site with options for search/display categories, full sequence processing facilities for Rosaceae researchers, a newsgroup for the Rosaceae community, a site for Rosaceae literature, and more analysis tools such as an interactive contig viewer and a comparative map viewer.
Utility and discussion
Database access and tools The GDR website is composed of general information pages, database query/browse interfaces and other tools such as map viewer and sequence similarity server. The GDR web pages are extensively linked such that users can easily access the data of interest regardless of the navigation starting point. For example, the EST detail pages have links to the BAC detail pages, marker detail pages or map viewer for the ESTs that are anchored to BACs, markers or maps. Similarly, the BAC detail pages have links to EST detail page, marker detail page, WebFPC and map viewer. Users can also access the data detail pages from the map viewer or WebFPC/WebChrom. Instead of displaying the entire EST or BAC data in one page, we used the right hand side navigation bars to help users find specific information easily and quickly. A general GDR navigation tool bar is also included in each page to help provide a more user-friendly interface. Database search interface The generic search site allows users to select data types such as EST, BAC and Marker, and search by name. Users can also follow the link to perform more detailed search for each data type. In the EST search site, users can search either the CUGI peach EST database or Rosaceae EST database downloaded from GenBank. ESTs can be searched by their name(s) and annotation features such as whether the EST belongs to a contig, is a unigene, is used as a probe and has SSRs, or any combination thereof. The EST details page, instead of displaying all the details in one page, initially displays the clone information and the sequence with a side bar containing links to library detail, assembly/unigene information, sequence homology, SSR information, Map position and anchored BACs. Each page linked from this side bar has the same side bar for easy navigation between the features. The Sequence homology page shows the most significant matches (EXP < 1e-9) in the Genbank nr protein database from the fastx sequence similarity search. The SSR information page shows the sequence along with the computationally derived SSRs to help users in the primer design for SSR marker development. The longest putative open reading frame (ORF) is also marked in color in the sequence along with the SSRs. SSRs in the non-coding region tend to be more polymorphic and those in the coding region tend to be more transferable among species, so the information of SSR position in a gene structure will be useful for marker development. The Map position page allows users to view the ESTs' map position using our Map Viewer. Users can retrieve the anchored BAC clones for the EST of interest and all the other ESTs and markers that hybridized to the same BAC through the anchored BACs page. The assembly/unigene information page displays the assembly results which include the contig name and the unigene clone that best represents the contig. The contig name is linked to a contig page that displays the contig sequence with a side bar containing links to the comprising ESTs, sequence homology and SSR information. As ESTs with no match to the GenBank proteins or with no SSRs can still assemble into contigs with a match or SSRs, users may get further annotation results of their ESTs of interest by visiting the contig detail site. In addition, contigs may have longer sequences surrounding the SSRs, allowing more flexibility in the primer design for marker development. In the BAC search site, users can search BACs by name or by probe specifications used for BAC hybridization. The search results site and the linked sites provide users with all the data about the BAC, such as the BAC contigs that the BAC belongs to, other BACs in the contig, probes that hybridized to the BACs, the detailed data about the probes, and link to the WebFPC physical map. Markers can be searched by name or features such as map name, type, and source organism. Similarly with the BAC search result sites, the maker search results sites leads users to pages with the marker information, anchored BACs, other markers and ESTs hybridized to the same BACs and link to the Map Viewer and the WebFPC physical map. Graphical interface to maps GDR hosts peach WebFPC and WebChrom to allow users to view the developing peach FPC contigs. Peach WebChrom displays the eight linkage groups of the general  Prunus  map and each linkage group has a link to a page where the developing contigs are located by the linkage group. Each contig has a direct link to WebFPC in which the individual BAC clones are displayed (Figure  1A ). GDR also provides a graphical tool for users to access the integrated genetic, transcript and physical map information. The map viewer displays the general  Prunus  map [ 5 ] with the number of ESTs anchored to each locus (Figure  1B ). The EST details page is linked from the map so that users can get all the data about the ESTs that are anchored to the loci of interest. When a locus is selected, a box appears to display marker type, the number of anchored BACs and the number of other probes that share the same BACs. Each number entry has links to a page where the detailed information is displayed. Also shown in the box are BAC contig names that are anchored to the loci. The BAC contig names have links to WebFPC so that users can directly access the physical map via this route (Figure  1B ). Sequence similarity server GDR also has a BLAST and FASTA sequence similarity search server that allows users to conduct homology searches between their sequences of interest and the various sequence data sets including annotated sequences in GDR. Users can select the database (e.g. peach ESTs, peach unigenes, mapped peach unigenes, GenBank Rosaceae ESTs, GenBank Rosaceae proteins etc) and various search parameters. Users can upload batch sequence files and the parsed results from the search are formatted in a spread sheet and sent to users by email. When the query sequence has a match to the annotated GDR sequences, users can retrieve all the information such as putative function, SSRs, and the anchored map positions via a hyperlink in the excel spreadsheet. Our sequence similarity server, specifically designed for Rosaceae researchers, will help users utilize the developing peach resources in the studies of other Rosaceae species. For example, as described in the case study below, sequences derived from other Rosaceae species could be immediately anchored to the various Rosaceae maps and the physical map when the query sequences show significant similarity to the mapped peach ESTs.
Database access and tools
The GDR website is composed of general information pages, database query/browse interfaces and other tools such as map viewer and sequence similarity server. The GDR web pages are extensively linked such that users can easily access the data of interest regardless of the navigation starting point. For example, the EST detail pages have links to the BAC detail pages, marker detail pages or map viewer for the ESTs that are anchored to BACs, markers or maps. Similarly, the BAC detail pages have links to EST detail page, marker detail page, WebFPC and map viewer. Users can also access the data detail pages from the map viewer or WebFPC/WebChrom. Instead of displaying the entire EST or BAC data in one page, we used the right hand side navigation bars to help users find specific information easily and quickly. A general GDR navigation tool bar is also included in each page to help provide a more user-friendly interface.Database search interface The generic search site allows users to select data types such as EST, BAC and Marker, and search by name. Users can also follow the link to perform more detailed search for each data type. In the EST search site, users can search either the CUGI peach EST database or Rosaceae EST database downloaded from GenBank. ESTs can be searched by their name(s) and annotation features such as whether the EST belongs to a contig, is a unigene, is used as a probe and has SSRs, or any combination thereof. The EST details page, instead of displaying all the details in one page, initially displays the clone information and the sequence with a side bar containing links to library detail, assembly/unigene information, sequence homology, SSR information, Map position and anchored BACs. Each page linked from this side bar has the same side bar for easy navigation between the features. The Sequence homology page shows the most significant matches (EXP < 1e-9) in the Genbank nr protein database from the fastx sequence similarity search. The SSR information page shows the sequence along with the computationally derived SSRs to help users in the primer design for SSR marker development. The longest putative open reading frame (ORF) is also marked in color in the sequence along with the SSRs. SSRs in the non-coding region tend to be more polymorphic and those in the coding region tend to be more transferable among species, so the information of SSR position in a gene structure will be useful for marker development. The Map position page allows users to view the ESTs' map position using our Map Viewer. Users can retrieve the anchored BAC clones for the EST of interest and all the other ESTs and markers that hybridized to the same BAC through the anchored BACs page. The assembly/unigene information page displays the assembly results which include the contig name and the unigene clone that best represents the contig. The contig name is linked to a contig page that displays the contig sequence with a side bar containing links to the comprising ESTs, sequence homology and SSR information. As ESTs with no match to the GenBank proteins or with no SSRs can still assemble into contigs with a match or SSRs, users may get further annotation results of their ESTs of interest by visiting the contig detail site. In addition, contigs may have longer sequences surrounding the SSRs, allowing more flexibility in the primer design for marker development. In the BAC search site, users can search BACs by name or by probe specifications used for BAC hybridization. The search results site and the linked sites provide users with all the data about the BAC, such as the BAC contigs that the BAC belongs to, other BACs in the contig, probes that hybridized to the BACs, the detailed data about the probes, and link to the WebFPC physical map. Markers can be searched by name or features such as map name, type, and source organism. Similarly with the BAC search result sites, the maker search results sites leads users to pages with the marker information, anchored BACs, other markers and ESTs hybridized to the same BACs and link to the Map Viewer and the WebFPC physical map.
Database search interface
The generic search site allows users to select data types such as EST, BAC and Marker, and search by name. Users can also follow the link to perform more detailed search for each data type. In the EST search site, users can search either the CUGI peach EST database or Rosaceae EST database downloaded from GenBank. ESTs can be searched by their name(s) and annotation features such as whether the EST belongs to a contig, is a unigene, is used as a probe and has SSRs, or any combination thereof. The EST details page, instead of displaying all the details in one page, initially displays the clone information and the sequence with a side bar containing links to library detail, assembly/unigene information, sequence homology, SSR information, Map position and anchored BACs. Each page linked from this side bar has the same side bar for easy navigation between the features. The Sequence homology page shows the most significant matches (EXP < 1e-9) in the Genbank nr protein database from the fastx sequence similarity search. The SSR information page shows the sequence along with the computationally derived SSRs to help users in the primer design for SSR marker development. The longest putative open reading frame (ORF) is also marked in color in the sequence along with the SSRs. SSRs in the non-coding region tend to be more polymorphic and those in the coding region tend to be more transferable among species, so the information of SSR position in a gene structure will be useful for marker development. The Map position page allows users to view the ESTs' map position using our Map Viewer. Users can retrieve the anchored BAC clones for the EST of interest and all the other ESTs and markers that hybridized to the same BAC through the anchored BACs page. The assembly/unigene information page displays the assembly results which include the contig name and the unigene clone that best represents the contig. The contig name is linked to a contig page that displays the contig sequence with a side bar containing links to the comprising ESTs, sequence homology and SSR information. As ESTs with no match to the GenBank proteins or with no SSRs can still assemble into contigs with a match or SSRs, users may get further annotation results of their ESTs of interest by visiting the contig detail site. In addition, contigs may have longer sequences surrounding the SSRs, allowing more flexibility in the primer design for marker development.In the BAC search site, users can search BACs by name or by probe specifications used for BAC hybridization. The search results site and the linked sites provide users with all the data about the BAC, such as the BAC contigs that the BAC belongs to, other BACs in the contig, probes that hybridized to the BACs, the detailed data about the probes, and link to the WebFPC physical map. Markers can be searched by name or features such as map name, type, and source organism. Similarly with the BAC search result sites, the maker search results sites leads users to pages with the marker information, anchored BACs, other markers and ESTs hybridized to the same BACs and link to the Map Viewer and the WebFPC physical map.Graphical interface to maps GDR hosts peach WebFPC and WebChrom to allow users to view the developing peach FPC contigs. Peach WebChrom displays the eight linkage groups of the general  Prunus  map and each linkage group has a link to a page where the developing contigs are located by the linkage group. Each contig has a direct link to WebFPC in which the individual BAC clones are displayed (Figure  1A ). GDR also provides a graphical tool for users to access the integrated genetic, transcript and physical map information. The map viewer displays the general  Prunus  map [ 5 ] with the number of ESTs anchored to each locus (Figure  1B ). The EST details page is linked from the map so that users can get all the data about the ESTs that are anchored to the loci of interest. When a locus is selected, a box appears to display marker type, the number of anchored BACs and the number of other probes that share the same BACs. Each number entry has links to a page where the detailed information is displayed. Also shown in the box are BAC contig names that are anchored to the loci. The BAC contig names have links to WebFPC so that users can directly access the physical map via this route (Figure  1B ).
Graphical interface to maps
GDR hosts peach WebFPC and WebChrom to allow users to view the developing peach FPC contigs. Peach WebChrom displays the eight linkage groups of the general  Prunus  map and each linkage group has a link to a page where the developing contigs are located by the linkage group. Each contig has a direct link to WebFPC in which the individual BAC clones are displayed (Figure  1A ). GDR also provides a graphical tool for users to access the integrated genetic, transcript and physical map information. The map viewer displays the general  Prunus  map [ 5 ] with the number of ESTs anchored to each locus (Figure  1B ). The EST details page is linked from the map so that users can get all the data about the ESTs that are anchored to the loci of interest. When a locus is selected, a box appears to display marker type, the number of anchored BACs and the number of other probes that share the same BACs. Each number entry has links to a page where the detailed information is displayed. Also shown in the box are BAC contig names that are anchored to the loci. The BAC contig names have links to WebFPC so that users can directly access the physical map via this route (Figure  1B ).PrunusPrunusSequence similarity server GDR also has a BLAST and FASTA sequence similarity search server that allows users to conduct homology searches between their sequences of interest and the various sequence data sets including annotated sequences in GDR. Users can select the database (e.g. peach ESTs, peach unigenes, mapped peach unigenes, GenBank Rosaceae ESTs, GenBank Rosaceae proteins etc) and various search parameters. Users can upload batch sequence files and the parsed results from the search are formatted in a spread sheet and sent to users by email. When the query sequence has a match to the annotated GDR sequences, users can retrieve all the information such as putative function, SSRs, and the anchored map positions via a hyperlink in the excel spreadsheet. Our sequence similarity server, specifically designed for Rosaceae researchers, will help users utilize the developing peach resources in the studies of other Rosaceae species. For example, as described in the case study below, sequences derived from other Rosaceae species could be immediately anchored to the various Rosaceae maps and the physical map when the query sequences show significant similarity to the mapped peach ESTs.
Sequence similarity server
GDR also has a BLAST and FASTA sequence similarity search server that allows users to conduct homology searches between their sequences of interest and the various sequence data sets including annotated sequences in GDR. Users can select the database (e.g. peach ESTs, peach unigenes, mapped peach unigenes, GenBank Rosaceae ESTs, GenBank Rosaceae proteins etc) and various search parameters. Users can upload batch sequence files and the parsed results from the search are formatted in a spread sheet and sent to users by email. When the query sequence has a match to the annotated GDR sequences, users can retrieve all the information such as putative function, SSRs, and the anchored map positions via a hyperlink in the excel spreadsheet. Our sequence similarity server, specifically designed for Rosaceae researchers, will help users utilize the developing peach resources in the studies of other Rosaceae species. For example, as described in the case study below, sequences derived from other Rosaceae species could be immediately anchored to the various Rosaceae maps and the physical map when the query sequences show significant similarity to the mapped peach ESTs.Case study: Mapping of Rosaceae sequences onto Rosaceae maps by sequence similarity We report here a case study illustrating the utility of our sequence similarity server and other integrated GDR web resources. In this study, we performed a sequence similarity search using the FASTA algorithm with the non-peach Rosaceae sequences against the mapped peach ESTs to annotate Rosaceae sequences with map positions. A fasta formatted file with a total of 16258 publicly available non peach Rosaceae sequences was uploaded to the GDR FASTA server. We selected the mapped peach database and used the default parameters. The search results returned from the server are formatted in a spread sheet for easy browsing and the match names are hyperlinked to both GenBank and the GDR web site (Figure  2 ). By following the GDR link, users can get the anchored position in the genetic and physical maps as well as other annotation results such as putative function (Figure  2 ). To summarize our results, we used 259 query/match pairs which have a percent identity over 95 and an align-length greater than 100 nucleotides. The majority of the query sequences that showed high similarity to peach ESTs were  Prunus  sequences such as apricot ( Prunus armeniaca ), almond ( Prunus dulcis ) and sour cherry ( Prunus cerasus ). This was expected as peach also belongs to the genera  Prunus . The 259 query/match pairs consisted of 209 Rosaceae sequences and 61 mapped peach ESTs. The matching of multiple Rosaceae sequences to single peach sequences was expected since the Rosaceae sequences were not assembled and therefore potentially contains multiple sequences representing the same gene. The 209 Rosaceae sequences were anchored to 38 different loci in four different Rosaceae maps. The number of sequences from each Rosaceae species that anchored to each map is shown in Figure  3 . This study demonstrates the usefulness of applying a comparative genomics approach to Rosaceae genomics using the GDR as a data mining tool. The entire data for the mapped Rosaceae sequences with anchored map positions are available at  .
Case study: Mapping of Rosaceae sequences onto Rosaceae maps by sequence similarity
We report here a case study illustrating the utility of our sequence similarity server and other integrated GDR web resources. In this study, we performed a sequence similarity search using the FASTA algorithm with the non-peach Rosaceae sequences against the mapped peach ESTs to annotate Rosaceae sequences with map positions. A fasta formatted file with a total of 16258 publicly available non peach Rosaceae sequences was uploaded to the GDR FASTA server. We selected the mapped peach database and used the default parameters. The search results returned from the server are formatted in a spread sheet for easy browsing and the match names are hyperlinked to both GenBank and the GDR web site (Figure  2 ). By following the GDR link, users can get the anchored position in the genetic and physical maps as well as other annotation results such as putative function (Figure  2 ). To summarize our results, we used 259 query/match pairs which have a percent identity over 95 and an align-length greater than 100 nucleotides. The majority of the query sequences that showed high similarity to peach ESTs were  Prunus  sequences such as apricot ( Prunus armeniaca ), almond ( Prunus dulcis ) and sour cherry ( Prunus cerasus ). This was expected as peach also belongs to the genera  Prunus . The 259 query/match pairs consisted of 209 Rosaceae sequences and 61 mapped peach ESTs. The matching of multiple Rosaceae sequences to single peach sequences was expected since the Rosaceae sequences were not assembled and therefore potentially contains multiple sequences representing the same gene. The 209 Rosaceae sequences were anchored to 38 different loci in four different Rosaceae maps. The number of sequences from each Rosaceae species that anchored to each map is shown in Figure  3 . This study demonstrates the usefulness of applying a comparative genomics approach to Rosaceae genomics using the GDR as a data mining tool. The entire data for the mapped Rosaceae sequences with anchored map positions are available at  .PrunusPrunus armeniacaPrunus dulcisPrunus cerasusPrunusFuture development We plan to incorporate more Rosaceae genomics and genetics data from researchers worldwide as well as data from the ongoing  Prunus  genomics projects. Data to be added in the near future include apple ESTs, strawberry ESTs, rose ESTs and apricot map/marker data from collaborators. When the genomics projects from  Prunus  are finished, we will host 10–15,000 unique ESTs from a variety of vegetative and reproductive tissues of peach and almond, the complete peach physical map with anchored genetic markers and unique ESTs. In addition to adding new data, future development efforts will focus on improving the tools and functionality of the web interface such as an advanced search site with options for search/display categories, full sequence processing facilities for Rosaceae researchers, a newsgroup for the Rosaceae community, a site for Rosaceae literature, and more analysis tools such as an interactive contig viewer and a comparative map viewer.
Future development
We plan to incorporate more Rosaceae genomics and genetics data from researchers worldwide as well as data from the ongoing  Prunus  genomics projects. Data to be added in the near future include apple ESTs, strawberry ESTs, rose ESTs and apricot map/marker data from collaborators. When the genomics projects from  Prunus  are finished, we will host 10–15,000 unique ESTs from a variety of vegetative and reproductive tissues of peach and almond, the complete peach physical map with anchored genetic markers and unique ESTs. In addition to adding new data, future development efforts will focus on improving the tools and functionality of the web interface such as an advanced search site with options for search/display categories, full sequence processing facilities for Rosaceae researchers, a newsgroup for the Rosaceae community, a site for Rosaceae literature, and more analysis tools such as an interactive contig viewer and a comparative map viewer.PrunusPrunusConclusions The GDR is initiated to support the genomics and genetics research in Rosaceae, which contains numerous economically important fruit trees and horticultural plants. Currently GDR contains all the genomics data for the Rosaceae model peach, maps and markers of Rosaceae species and all the publicly available Rosaceae ESTs. Our integrated database provides users with easy access and retrieval of the annotated data, and the web tools enable them to further analyze their data. With future plans, including more data acquisition and tool developments, GDR will play an important role in the timely and efficient analysis of the data, the exchange of results and ideas among researchers worldwide, the support of Rosaceae labs worldwide with Bioinformatics tools and the utilization of the data from the model species in the study of other Rosaceae species. The methodology and tools applied to develop GDR should be easily applied to develop other comparative genomic databases for different families.
Conclusions
The GDR is initiated to support the genomics and genetics research in Rosaceae, which contains numerous economically important fruit trees and horticultural plants. Currently GDR contains all the genomics data for the Rosaceae model peach, maps and markers of Rosaceae species and all the publicly available Rosaceae ESTs. Our integrated database provides users with easy access and retrieval of the annotated data, and the web tools enable them to further analyze their data. With future plans, including more data acquisition and tool developments, GDR will play an important role in the timely and efficient analysis of the data, the exchange of results and ideas among researchers worldwide, the support of Rosaceae labs worldwide with Bioinformatics tools and the utilization of the data from the model species in the study of other Rosaceae species. The methodology and tools applied to develop GDR should be easily applied to develop other comparative genomic databases for different families.Availability The GDR is publicly available and can be accessed at  .
Availability
The GDR is publicly available and can be accessed at  .List of abbreviations used AFLP: Amplified Fragment Length Polymorphism BAC: Bacterial Artificial Chromosome CGI: Common Gateway Interface EST: Expressed Sequence Tag EXP: EXpectation Value HTML: HyperText Markup Language HTTP: HyperText Transfer Protocol INRA: Institut National de la Recherche Agronomique RFLP: Restriction Fragment Length Polymorphism SSR: Simple Sequence Repeat XML: eXtensible Markup Language
List of abbreviations used
AFLP: Amplified Fragment Length PolymorphismBAC: Bacterial Artificial ChromosomeCGI: Common Gateway InterfaceEST: Expressed Sequence TagEXP: EXpectation ValueHTML: HyperText Markup LanguageHTTP: HyperText Transfer ProtocolINRA: Institut National de la Recherche AgronomiqueRFLP: Restriction Fragment Length PolymorphismSSR: Simple Sequence RepeatXML: eXtensible Markup LanguageAuthors' contributions SJ performed web interface design and programming for html pages and JSP search pages, participated in the database construction, carried out database curation, designed the Map Viewer and performed the case study. CJ wrote scripts for database upload and sequence processing, programmed Map Viewer and implemented WebFPC and WebChrom. MS participated in the database construction and wrote scripts for data upload and search pages for Genbank Rosaceae sequences. SF performs database administration. IC developed the application prototype for servlet-database connection and participated in the database construction. AA conceived of the project and participated in its design and coordination. ZD developed the sequence similarity server application. JT participated in the coordination of the project. DM conceived and supervised the project, participated in the database construction, carried out EST analysis, and provided input in the web interface design. All authors read and approved the final manuscript.
Authors' contributions
SJ performed web interface design and programming for html pages and JSP search pages, participated in the database construction, carried out database curation, designed the Map Viewer and performed the case study. CJ wrote scripts for database upload and sequence processing, programmed Map Viewer and implemented WebFPC and WebChrom. MS participated in the database construction and wrote scripts for data upload and search pages for Genbank Rosaceae sequences. SF performs database administration. IC developed the application prototype for servlet-database connection and participated in the database construction. AA conceived of the project and participated in its design and coordination. ZD developed the sequence similarity server application. JT participated in the coordination of the project. DM conceived and supervised the project, participated in the database construction, carried out EST analysis, and provided input in the web interface design. All authors read and approved the final manuscript.


In  PLoS Biology , volume 2, issue 12.PLoS Biology10.1371/journal.pbio.0020380In Materials and Methods, the sentence “The magnetic beads were added to the extract to a ratio of about 8 ×10 9  beads per g of cells” contains an error. The correct amount of beads is 8 × 10 8 , i.e., ten times less.Published February 15, 2005


The ongoing battle between hosts and pathogens has long been of interest to evolutionary biologists.

The ongoing battle between hosts and pathogens has long been of interest to evolutionary biologists. Because hosts and pathogens act as environments for each other, their intertwined struggle for existence is both continual and rapid. At the molecular level, this cycle of environmental change and evolutionary response means that mutations are continually being tried out by natural selection. It is therefore little wonder that the host and pathogen genes that control infection and immunity frequently show high levels of genetic diversity and present some of the best examples of positive selection (adaptive evolution) reported to date ( Yang and Bielawski 2000 ). In particular, rates of nonsynonymous substitution per site (resulting in an amino acid change; d N ) often greatly exceed those of synonymous substitution per site (silent change; d S ), as expected if most mutations are fixed because they increase fitness ( Figure 1 ).At the host level, most studies of the selection pressures acting on immune system genes have concentrated on genes implicated in the adaptive immune response against microbial pathogens, particularly those producing antibodies ( Sitnikova and Nei 1998 ;  Sumiyama et al. 2002 ), or genes encoding reconnaissance molecules known as the major histocompatibility complex (MHC), which control the action of T-cells ( Hughes and Nei 1988 ;  Yeager and Hughes 1999 ) ( Box 1 ). As its name suggests, the role of the adaptive immune response is to stimulate and ‘memorise’ immunity to specific pathogens. As microbial pathogens such as viruses are both abundant and rapidly evolving, positive selection on components of the adaptive immune response is often very strong ( Yeager and Hughes 1999 ). Far less attention has been directed toward the less specific innate (‘nonadaptive’) immune response, even though this response requires a wide array of genes and acts as the front line of immune defence ( Box 1 ). Would we expect the same strength of positive selection on a generalized pathogen control system? This is a question of fundamental importance because the luxury of adaptive immunity is not available to most organisms, having probably evolved along with the vertebrates ( Bartl et al. 1994 ), whereas the more widespread innate immune system is often depicted as a primitive characteristic.Molecular Evolution of the Innate Immune System The genes involved in innate immunity have recently come under the molecular evolutionists' gaze. One important group are the defensins, a large class of short antimicrobial peptides that constitute an effective immune response team in organisms as diverse as plants and primates ( Boman 1995 ). Because defensins are cationic (positively charged), they are able to interact with negatively charged molecules on the surface of microbes and permeate their membranes. Sequence analyses of defensins and similar antimicrobial peptides have revealed the telltale signatures of positive selection, with d N  greater than d S  in many comparisons ( Hughes 1999 ;  Duda et al. 2002 ;  Maxwell et al. 2003 ). Other genes of the innate immune system also seem to be subject to powerful positive selection. One dramatic example described in this issue of  PLoS Biology  is the APOBEC3G gene of primates ( Sawyer et al. 2004 ). This case is especially striking because rather than killing pathogens through protein or cellular interactions, like most immune genes, APOBEC3G works by manipulating the genome sequence of the invading microbe. The genomes of primates contain a family of nine APOBEC genes that encode enzymes involved in the editing of RNA and/or DNA through the deamination of cytosine (C), so that this nucleotide mutates to uracil (U). This is essential for various aspects of cellular function. APOBEC1, for example, is involved in the C→U editing of apolipoprotein B mRNA (therein christening the family), while another family member, the activation-induced deaminase, has a vital role in adaptive immunity in that it assists in the diversification of antibodies. Two more enzymes, APOBEC3G and APOBEC3F, form part of the innate immune system; they function as antiviral agents and are being intensively studied in the context of infection with the human immunodeficiency virus (HIV), the cause of AIDS. In particular, APOBEC3G targets the reverse transcription step of the HIV life cycle, in which the viral genomic RNA is converted into proviral DNA, which is then integrated into the host genome ( Mangeat et al. 2003 ). APOBEC3G-induced deamination at this stage results in monotonous guanine-to-adenine (G→A) nucleotide changes, a phenomenon called G→A hypermutation that had long been noted by HIV researchers without a clear understanding of its cause. We now know that G→A hypermutation is part of the innate immune response to retroviral infections. Although there is still some debate over exactly how APOBEC3G leads to viral eradication, the most likely scenario is that G3A hypermutation results in the generation and incorporation of a multitude of deleterious mutations that fatally disrupt viral functions. This strategy is likely to work well for retroviruses like HIV because their genomes are so compact that individual sequence regions often perform multiple functions. Under these cramped conditions, most mutations are likely to severely disrupt some aspect of viral function and thereby reduce fitness ( Holmes 2003 ). Indeed, it has been estimated that the deleterious mutation rate in viruses that replicate using RNA polymerases (either reverse transcriptase in the case of retroviruses or RNA-dependent RNA polymerase for other RNA viruses) is on the order of one error per replication cycle, so that many of the viral progeny produced by replication are defective ( Elena and Moya 1999 ). HIV is normally able to overcome this burden of deleterious mutation because of its remarkable reproductive power; each day, on the order of 10 10  virions are produced in a single infected individual ( Perelson et al. 1996 ), so that enough fit and able recruits will make it through to the next generation.
Molecular Evolution of the Innate Immune System
The genes involved in innate immunity have recently come under the molecular evolutionists' gaze. One important group are the defensins, a large class of short antimicrobial peptides that constitute an effective immune response team in organisms as diverse as plants and primates ( Boman 1995 ). Because defensins are cationic (positively charged), they are able to interact with negatively charged molecules on the surface of microbes and permeate their membranes. Sequence analyses of defensins and similar antimicrobial peptides have revealed the telltale signatures of positive selection, with d N  greater than d S  in many comparisons ( Hughes 1999 ;  Duda et al. 2002 ;  Maxwell et al. 2003 ). Other genes of the innate immune system also seem to be subject to powerful positive selection. One dramatic example described in this issue of  PLoS Biology  is the APOBEC3G gene of primates ( Sawyer et al. 2004 ). This case is especially striking because rather than killing pathogens through protein or cellular interactions, like most immune genes, APOBEC3G works by manipulating the genome sequence of the invading microbe.PLoS BiologyThe genomes of primates contain a family of nine APOBEC genes that encode enzymes involved in the editing of RNA and/or DNA through the deamination of cytosine (C), so that this nucleotide mutates to uracil (U). This is essential for various aspects of cellular function. APOBEC1, for example, is involved in the C→U editing of apolipoprotein B mRNA (therein christening the family), while another family member, the activation-induced deaminase, has a vital role in adaptive immunity in that it assists in the diversification of antibodies. Two more enzymes, APOBEC3G and APOBEC3F, form part of the innate immune system; they function as antiviral agents and are being intensively studied in the context of infection with the human immunodeficiency virus (HIV), the cause of AIDS. In particular, APOBEC3G targets the reverse transcription step of the HIV life cycle, in which the viral genomic RNA is converted into proviral DNA, which is then integrated into the host genome ( Mangeat et al. 2003 ). APOBEC3G-induced deamination at this stage results in monotonous guanine-to-adenine (G→A) nucleotide changes, a phenomenon called G→A hypermutation that had long been noted by HIV researchers without a clear understanding of its cause. We now know that G→A hypermutation is part of the innate immune response to retroviral infections.Although there is still some debate over exactly how APOBEC3G leads to viral eradication, the most likely scenario is that G3A hypermutation results in the generation and incorporation of a multitude of deleterious mutations that fatally disrupt viral functions. This strategy is likely to work well for retroviruses like HIV because their genomes are so compact that individual sequence regions often perform multiple functions. Under these cramped conditions, most mutations are likely to severely disrupt some aspect of viral function and thereby reduce fitness ( Holmes 2003 ). Indeed, it has been estimated that the deleterious mutation rate in viruses that replicate using RNA polymerases (either reverse transcriptase in the case of retroviruses or RNA-dependent RNA polymerase for other RNA viruses) is on the order of one error per replication cycle, so that many of the viral progeny produced by replication are defective ( Elena and Moya 1999 ). HIV is normally able to overcome this burden of deleterious mutation because of its remarkable reproductive power; each day, on the order of 10 10  virions are produced in a single infected individual ( Perelson et al. 1996 ), so that enough fit and able recruits will make it through to the next generation.Treating RNA Virus Infections Through Lethal Mutagenesis The high mutation rates of RNA viruses mean that adaptively useful genetic variation is produced frequently. The rub, however, is that fitness-enhancing mutations are a small minority, and the preponderance of deleterious mutations means that RNA viruses live on the edge of survival ( Domingo 2000 ). By increasing the rate at which deleterious mutations appear, APOBEC3G pushes viruses over this edge, causing a form of ‘lethal mutagenesis’ that results in their destruction; the rate of mutation becomes so high that no genome can reproduce itself faithfully, and the population crashes. Intriguingly, researchers designing new antiviral drugs have also begun to realise that forcing viruses into this sort of ‘error catastrophe’ might be an effective way to treat them ( Figure 2 ). There are a growing number of studies in which mutagens, such as ribavirin and 5-fluorouracil, are applied to viral infections in vitro and in vivo, including HIV, in the hope that these will induce so many deleterious mutations that the virus suffers an error catastrophe and is cleared ( Loeb et al. 1999 ;  Sierra et al. 2000 ;  Crotty et al. 2001 ;  Ruiz-Jarabo et al. 2003 ). The results produced to date are highly encouraging, particularly when these error-inducing drugs are combined with more conventional treatment strategies that aim to reduce the rate of viral replication ( Pariente et al. 2001 ). The discovery that a natural antiviral agent, APOBEC3G, probably works in much the same way should provide even more encouragement. Figure 2 Lethal Mutagenesis As a Means of Controlling RNA Virus Infections (A) In a viral population prior to the application of mutagens, the mean error rate (white) is on the order of one per genome per replication (mutations marked by asterisks). (B) If a mutagen such as ribavirin is then applied to an infected patient, the mean error rate of the virus (black) is increased so that the population crosses a threshold of ‘error catastrophe’; after this point fitness declines dramatically and the population crashes. This drug-induced lethal mutagenesis seems to work more efficiently when it is used in combination with drugs that reduce the rate of viral replication. Sadly, however, the pathogens have fought back. The anti-HIV properties of APOBEC3G were discovered because most viral strains escape its neutralising properties. Lentiviruses like HIV possess a gene that encodes an protein called Vif (‘viral infectivity factor’) that counters APOBEC3G ( Sheehy et al. 2002 ). Hence, it is probably only in naturally occurring Vif-defective mutants that APOBEC3G is effective against HIV. Furthermore, because positive selection on APOBEC3G has operated for at least 30 million years and lentiviruses in general, and HIV in particular, are likely to be more recently evolved than this, it is clear that a broad range of retroviral pathogens have been responsible for the adaptive evolution of this particular immune gene ( Sawyer et al. 2004 ). Given the frequency with which the remnants of past retroviral infections are found in the mammalian genome ( Smit 1999 ), in the form of usually defunct endogenous retroviruses ( Box 1 ), it is likely that our genomes are continually bombarded with retroviruses like HIV but that the majority are cleared by innate immune mechanisms like APOBEC3G. It is possible that the retroviruses that successfully infect us are those, like HIV, that have managed to evolve strategies to avoid the destructive capacities of APOBEC3G. The intense selective pressure on the defensins and APOBEC3G illustrates that although the innate immune response is generalist in its action, it is as highly and intricately evolved as its better-studied ally, the adaptive immune system. Rather than being an evolutionary remnant, it is a dynamic and continually adapting system. Less clear is whether other host proteins act in the same manner as APOBEC3G. In particular, the most common and destructive pathogens faced by humans and other mammals are RNA viruses, such as influenza A, yellow fever, and hepatitis C. In most cases, our ability to survive these viral infections is simply a combination of good luck and good breeding; with the right combination of MHC alleles, itself a function of population history and what we by chance inherit from our parents, some individuals may be more able to fight off viral infections than others. The ubiquity of RNA viruses hints that our genomes might also contain an innate, yet highly adapted, defence system that targets this abundant class of pathogens by manipulating their mutation rate. Although utilizing lethal mutagenesis might one day be an important way to design new drugs against a variety of viral pathogens, it would come as no surprise if nature got there first. Box 1. Glossary
 Adaptive immune system.  The pathogen-specific part of the vertebrate immune system. It is comprised of two major arms, antibodies (the humoral response) and T-cells (the cellular response), both of which lay down an immunological memory for future defence.
 Innate immune system.  The nonspecific part of the vertebrate immune system. It has a wide variety of components, ranging from lysozymes in saliva to cytokines, defensins, interferons, and natural killer cells in a variety of tissues.
 Endogenous retroviruses.  The (usually) dead remnants of functional retroviruses that are now passed on through the germ line like normal genes. It has been estimated that approximately 5% of the human genome is composed of endogenous retroviruses.
Treating RNA Virus Infections Through Lethal Mutagenesis
The high mutation rates of RNA viruses mean that adaptively useful genetic variation is produced frequently. The rub, however, is that fitness-enhancing mutations are a small minority, and the preponderance of deleterious mutations means that RNA viruses live on the edge of survival ( Domingo 2000 ). By increasing the rate at which deleterious mutations appear, APOBEC3G pushes viruses over this edge, causing a form of ‘lethal mutagenesis’ that results in their destruction; the rate of mutation becomes so high that no genome can reproduce itself faithfully, and the population crashes. Intriguingly, researchers designing new antiviral drugs have also begun to realise that forcing viruses into this sort of ‘error catastrophe’ might be an effective way to treat them ( Figure 2 ). There are a growing number of studies in which mutagens, such as ribavirin and 5-fluorouracil, are applied to viral infections in vitro and in vivo, including HIV, in the hope that these will induce so many deleterious mutations that the virus suffers an error catastrophe and is cleared ( Loeb et al. 1999 ;  Sierra et al. 2000 ;  Crotty et al. 2001 ;  Ruiz-Jarabo et al. 2003 ). The results produced to date are highly encouraging, particularly when these error-inducing drugs are combined with more conventional treatment strategies that aim to reduce the rate of viral replication ( Pariente et al. 2001 ). The discovery that a natural antiviral agent, APOBEC3G, probably works in much the same way should provide even more encouragement.Sadly, however, the pathogens have fought back. The anti-HIV properties of APOBEC3G were discovered because most viral strains escape its neutralising properties. Lentiviruses like HIV possess a gene that encodes an protein called Vif (‘viral infectivity factor’) that counters APOBEC3G ( Sheehy et al. 2002 ). Hence, it is probably only in naturally occurring Vif-defective mutants that APOBEC3G is effective against HIV. Furthermore, because positive selection on APOBEC3G has operated for at least 30 million years and lentiviruses in general, and HIV in particular, are likely to be more recently evolved than this, it is clear that a broad range of retroviral pathogens have been responsible for the adaptive evolution of this particular immune gene ( Sawyer et al. 2004 ). Given the frequency with which the remnants of past retroviral infections are found in the mammalian genome ( Smit 1999 ), in the form of usually defunct endogenous retroviruses ( Box 1 ), it is likely that our genomes are continually bombarded with retroviruses like HIV but that the majority are cleared by innate immune mechanisms like APOBEC3G. It is possible that the retroviruses that successfully infect us are those, like HIV, that have managed to evolve strategies to avoid the destructive capacities of APOBEC3G.The intense selective pressure on the defensins and APOBEC3G illustrates that although the innate immune response is generalist in its action, it is as highly and intricately evolved as its better-studied ally, the adaptive immune system. Rather than being an evolutionary remnant, it is a dynamic and continually adapting system. Less clear is whether other host proteins act in the same manner as APOBEC3G. In particular, the most common and destructive pathogens faced by humans and other mammals are RNA viruses, such as influenza A, yellow fever, and hepatitis C. In most cases, our ability to survive these viral infections is simply a combination of good luck and good breeding; with the right combination of MHC alleles, itself a function of population history and what we by chance inherit from our parents, some individuals may be more able to fight off viral infections than others. The ubiquity of RNA viruses hints that our genomes might also contain an innate, yet highly adapted, defence system that targets this abundant class of pathogens by manipulating their mutation rate. Although utilizing lethal mutagenesis might one day be an important way to design new drugs against a variety of viral pathogens, it would come as no surprise if nature got there first.Box 1. Glossary
Adaptive immune system.  The pathogen-specific part of the vertebrate immune system. It is comprised of two major arms, antibodies (the humoral response) and T-cells (the cellular response), both of which lay down an immunological memory for future defence.Adaptive immune system.Innate immune system.  The nonspecific part of the vertebrate immune system. It has a wide variety of components, ranging from lysozymes in saliva to cytokines, defensins, interferons, and natural killer cells in a variety of tissues.Innate immune system.Endogenous retroviruses.  The (usually) dead remnants of functional retroviruses that are now passed on through the germ line like normal genes. It has been estimated that approximately 5% of the human genome is composed of endogenous retroviruses.Endogenous retroviruses.


In February 2003, the first (and so far only) epidemic of severe acute respiratory syndrome (SARS) started in Guangdong Province, China. A respiratory illness that begins with a high temperature and can develop into life-threatening pneumonia, SARS is spread by close person-to-person contact. Before the end of the month, a Guangdong doctor had inadvertently taken the infection to Hong Kong. A woman staying in the same Hong Kong hotel as the doctor then carried the disease to Toronto. In March, the World Health Organization issued a global alert and warned against unnecessary travel to affected areas. Because of these and other containment efforts, 8,098 people became ill with SARS, rather than the predicted millions; 774 people died. The last case of the epidemic was reported in Taiwan in June 2003, and since then there have been only two cases in Singapore and nine in China.By May 2003, a coronavirus had been identified as the cause of SARS, and the full genome sequence of this new human pathogen, which may have jumped from civet cats to people, had been published. From the viral genome, researchers have deduced the sequences and structures of the viral proteins, hoping to use this information to develop treatments and vaccines for SARS. But could the structure of the RNA genome itself also be a target for antiviral drugs?The genome of the SARS virus is a single strand of RNA that folds into regular repeating patterns to form secondary structures such as helices. These then fold and bend in three dimensions to form complex tertiary structures. William Scott and colleagues have used X-ray crystallography to measure the exact positions of individual ribonucleotides and the interactions between them in a small segment of the SARS virus genome called the s2m element. This element sits at one end of the viral genome, and, as the researchers show, its sequence is highly conserved in related coronaviruses. Furthermore, unlike the rest of the SARS genome, which changes rapidly, the s2m element is absolutely conserved in SARS variants obtained from patients during the SARS epidemic. This strong sequence conservation indicates that the tertiary structure of s2m could be important for viral function, and when the researchers solved the three-dimensional crystal structure of the element, they found that it had a unique tertiary structure. In particular, there was a right-angle kink in its helical axis and a tunnel with a net negative charge.The biological role of a new protein can often be deduced by comparing its shape with that of proteins with known functions. Scott and colleagues used this approach to hypothesize that the function of the s2m element involves interaction with a conserved host factor during the SARS life cycle. Finding a similar 90° kink in a region of ribosomal RNA that binds factors necessary for the initiation of protein synthesis, the researchers speculate that the SARS virus may use the s2m element to hijack its host cell's protein synthesis machinery. This and other putative roles need to be tested experimentally, but given that the s2m element is absent in the human genome, its unusual structural features could be an attractive target for the design of antiviral therapeutic agents.