MOTIVATION: RNA-Seq (also called whole-transcriptome sequencing) is an emerging technology that uses the capabilities of next-generation sequencing to detect and quantify entire transcripts. One of its important applications is the improvement of existing genome annotations. RNA-Seq provides rapid, comprehensive and cost-effective tools for the discovery of novel genes and transcripts compared with expressed sequence tag (EST), which is instrumental in gene discovery and gene sequence determination. The rat is widely used as a laboratory disease model, but has a less well-annotated genome as compared with humans and mice. In this study, we incorporated deep RNA-Seq data from three rat tissues-bone marrow, brain and kidney-with EST data to improve the annotation of the rat genome. RESULTS: Our analysis identified 32 197 transcripts, including 13 461 known transcripts, 13 934 novel isoforms and 4802 new genes, which almost doubled the numbers of transcripts in the current public rat genome database (rn5). Comparisons of our predicted protein-coding gene sets with those in public datasets suggest that RNA-Seq significantly improves genome annotation and identifies novel genes and isoforms in the rat. Importantly, the large majority of novel genes and isoforms are supported by direct evidence of RNA-Seq experiments. These predicted genes were integrated into the Rat Genome Database (RGD) and can serve as an important resource for functional studies in the research community. AVAILABILITY AND IMPLEMENTATION: The predicted genes are available at http://rgd.mcw.edu.

The Rat Genome Database (RGD) was started >10 years ago to provide a core genomic resource for rat researchers. Currently, RGD combines genetic, genomic, pathway, phenotype and strain information with a focus on disease. RGD users are provided with access to structured and curated data from the molecular level through the organismal level. Those users access RGD from all over the world. End users are not only rat researchers but also researchers working with mouse and human data. Translational research is supported by RGD's comparative genetics/genomics data in disease portals, in GBrowse, in VCMap and on gene report pages. The impact of RGD also goes beyond the traditional biomedical researcher, as the influence of RGD reaches bioinformaticians, tool developers and curators. Import of RGD data into other publicly available databases expands the influence of RGD to a larger set of end users than those who avail themselves of the RGD website. The value of RGD continues to grow as more types of data and more tools are added, while reaching more types of end users.

abstracts. As can be seen by the amount oftime spent in curation, the time savings forresearchers are substantial. Education and Outreach An essential part of the curator’s job is toprovide education and training for RGDusers and potential users. This is accom-plished in several ways. The RGD Web sitecontains a ‘‘Help’’ section developed by thecurators and which is accessible from allpages. This component contains a Glossaryof Terms, general information on how touse the searches and tools, a FrequentlyAsked Questions (FAQ) section, and acomponent that walks users through typicaluse case scenarios. Curators also handleindividual questions through the userrequest system accessible via the ContactUs button on each page, and throughtelephone calls and the Rat CommunityForum. RGD has published seven tutorialvideos at SciVee (http://www.scivee.tv/), aWeb site for video publications for re-search, and videos are available at RGD aswell. Curators present tutorials at majorconferences such as Experimental Biology,Society of Toxicology, Neuroscience andRat Genomics and Models, as well asindividual rat research laboratories. Inaddition, RGD is well represented at majorconferences such as Biology of Genomes,Genome Informatics, Intelligent Systemsfor Molecular Biology, and the Interna-tional Mammalian Genome Conferencewith presentations and posters highlightingnew tools and datasets. Other outreachactivities involve contact with individualresearchers for nomenclature assignment togenes, QTLs, and strains, as well asconstruction of customized datasets.

BACKGROUND: Gene expression profiling using microarrays has become an important genetic tool. Spotted arrays prepared in academic labs have the advantage of low cost and high design and content flexibility, but are often limited by their susceptibility to quality control (QC) issues. Previously, we have reported a novel 3-color microarray technology that enabled array fabrication QC. In this report we further investigated its advantage in spot-level data QC. RESULTS: We found that inadequate amount of bound probes available for hybridization led to significant, gene-specific compression in ratio measurements, increased data variability, and printing pin dependent heterogeneities. The impact of such problems can be captured through the definition of quality scores, and efficiently controlled through quality-dependent filtering and normalization. We compared gene expression measurements derived using our data processing pipeline with the known input ratios of spiked in control clones, and with the measurements by quantitative real time RT-PCR. In each case, highly linear relationships (R2 > 0.94) were observed, with modest compression in the microarray measurements (correction factor < 1.17). CONCLUSION: Our microarray analytical and technical advancements enabled a better dissection of the sources of data variability and hence a more efficient QC. With that highly accurate gene expression measurements can be achieved using the cDNA microarray technology.