D1200 Nucleic Acids Research, 2012, Vol. 40, Database issue elements onto model genomes. This tool allows a user to enter a list of genes derived from any expression experi- ment (20,21) (A) and immediately visualize their positions on the rice genome (for monocots) or Arabidopsis (for dicots) (B). Using the table that is generated below the map, the user can then get details on position and align- ment of orthologous ESTs by a direct link to Gramene or annotation in terms of predicted function. DEVELOPMENT AND CHALLENGES FOR EXPRESSION DATABASES There are many challenges faced by expression databases, including new and increasingly prevalent data types, such as RNA-seq, tiling arrays and whole-genome array plat- forms. Part of the challenge will be ﬁnding core identiﬁers that reach across assemblies and technologies to unify transcriptome data. In addition, probe sets for early array releases were designed several years ago from the available EST assemblies. As a consequence, the current probe conﬁguration of a signiﬁcant number of probe sets in microarray platforms is no longer congruent with the updated versions of the corresponding gene models. This means that probe sets must be remapped and annotated. In addition to unifying data across a dizzying array of platforms, the data need to be integrated with databases that provide focused resources for a species or clade to help put transcriptomics data into the proper perspective. Interactive connections with sequence centric, community genome databases provide easy access to physical align- ments, genetic map positions and known phenotypes for all genes. The challenge is to make the expression and community data easily accessible for analysis. For integration of RNA-seq data, new statistical methods to detect how diverse treatments affect alterna- tive promoter use, splicing and other aspects of RNA pro- cessing and metabolism will need to be established, as well as meta-analysis methods to facilitate comparison of results across data sets from different experiments, differ- ent species and different technologies. This will be espe- cially important for users investigating ‘orphan’ genomes (i.e. no reference transcriptome or genome). For this to be possible, the data models from both DNA-array and RNA-seq resources must converge. Microarray data has been represented as raw values per probe per biological sample and normalized values (quantile normalization at PLEXdb) after summarization takes the form, per probeset (gene) per sample. For example, a popular representation of RNA-seq data, the Reads Per Kilobase of exon model per Million mapped reads (RPKM) (22), derived from the raw read counts, can be treated as analogous to the raw data from CEL or pair ﬁles from Affymetrix or NimbleGen. Quantile normaliza- tion can be performed on this data to eventually represent it as an expression value per gene per sample. The best method of normalizing RNA-seq data is still being debated. The availability of new reference genomes will require updated assemblies of archived short-read data, and hence the need for resources to attend to these reitera- tive efforts. CONCLUSIONS Ultimately, omics databases need to ﬁll a community need by providing the biologist user with easy access to analysis capabilities for diverse plant and pathogen transcriptome data sets. PLEXdb seeks to provide a consistent web inter- face for plant transcriptome data, so that the user can access diverse types of data from multiple starting points, e.g. a particular gene, an experimental factor, physical or genetic map position (i.e. location within a QTL) or gene expression data. ACKNOWLEDGEMENTS The PLEXdb team would like to thank the following undergraduate students who have helped develop tools and import experimental data: Jodan Goldie, Royce Blackledge, Jack (Pu) Hou, Joseph Grgic, Emmanuel Owusu, Akul Singhania, Ajani Thomas and Andrew Couch. The PLEXdb team also thanks Ethalinda Cannon of MaizeGDB for her work mapping expression probes to the maize v2 (5b.60) assembly. FUNDING National Science Foundation (DBI-0543441; IOS-0922746); United States Department of Agriculture– Agricultural Research Service Project (3625-21000-049-00D to R.W.). Funding for open access charge: grant funding from NSF. Conﬂict of interest statement. None declared. REFERENCES 1. Shen,L., Gong,J., Caldo,R.A., Nettleton,D., Cook,D., Wise,R.P. and Dickerson,J.A. (2005) BarleyBase–an expression proﬁling database for plant genomics. Nucleic Acids Res., 33, D614–D618. 2. Wise,R.P., Caldo,R.A., Hong,L., Shen,L., Cannon,E. and Dickerson,J.A. (2007) BarleyBase/PLEXdb: a uniﬁed expression proﬁling database for plants and plant pathogens. Methods Mol. Biol., 406, 347–363. 3. Barrett,T., Troup,D.B., Wilhite,S.E., Ledoux,P., Evangelista,C., Kim,I.F., Tomashevsky,M., Marshall,K.A., Phillippy,K.H., Sherman,P.M. et al. (2011) NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res., 39, D1005–D1010. 4. Parkinson,H., Sarkans,U., Kolesnikov,N., Abeygunawardena,N., Burdett,T., Dylag,M., Emam,I., Farne,A., Hastings,E., Holloway,E. et al. (2011) ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res., 39, D1002–D1004. 5. Hruz,T., Laule,O., Szabo,G., Wessendorp,F., Bleuler,S., Oertle,L., Widmayer,P., Gruissem,W. and Zimmermann,P. (2008) Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes. Adv Bioinformatics, 2008, 420747. 6. Bombarely,A., Menda,N., Tecle,I.Y., Buels,R.M., Strickler,S., Fischer-York,T., Pujar,A., Leto,J., Gosselin,J. and Mueller,L.A. (2011) The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl. Nucleic Acids Res., 39, D1149–D1155. 7. Zimmermann,P., Schildknecht,B., Craigon,D., Garcia-Hernandez,M., Gruissem,W., May,S., Mukherjee,G., Parkinson,H., Rhee,S., Wagner,U. et al. (2006) MIAME/Plant - adding value to plant microarrray experiments. Plant Methods, 2, 1. 8. Duvick,J., Fu,A., Muppirala,U., Sabharwal,M., Wilkerson,M.D., Lawrence,C.J., Lushbough,C. and Brendel,V. (2008) PlantGDB: a