Drosophila melanogaster is one of the most well studied genetic model organisms, nonetheless its genome still contains unannotated coding and non-coding genes, transcripts, exons, and RNA editing sites. Full discovery and annotation are prerequisites for understanding how the regulation of transcription, splicing, and RNA editing directs development of this complex organism. We used RNA-Seq, tiling microarrays, and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages. We identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. Together, these data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout development.

High-throughput sequencing of cDNA (RNA-seq) is a widely deployed transcriptome profiling and annotation technique, but questions about the performance of different protocols and platforms remain. We used a newly developed pool of 96 synthetic RNAs with various lengths, and GC content covering a 2 20 concentration range as spike-in controls to measure sensitivity, accuracy, and biases in RNA-seq experiments as well as to derive standard curves for quantifying the abundance of transcripts. We observed linearity between read density and RNA input over the entire detection range and excellent agreement between replicates, but we observed significantly larger imprecision than expected under pure Poisson sampling errors. We use the control RNAs to directly measure reproducible protocol-dependent biases due to GC content and transcript length as well as stereotypic heterogeneity in coverage across transcripts correlated with position relative to RNA termini and priming sequence bias. These effects lead to biased quantification for short transcripts and individual exons, which is a serious problem for measurements of isoform abundances, but that can partially be corrected using appropriate models of bias. By using the control RNAs, we derive limits for the discovery and detection of rare transcripts in RNA-seq experiments. By using data collected as part of the model organism and human Encyclopedia of DNA Elements projects (ENCODE and modENCODE), we demonstrate that external RNA controls are a useful resource for evaluating sensitivity and accuracy of RNA-seq experiments for transcriptome discovery and quantification. These quality metrics facilitate comparable analysis across different samples, protocols, and platforms.[Supplemental material is available for this article.]High-throughput sequencing applications are revolutionizing genome-wide analysis (Mardis 2008;Mortazavi et al. 2008;Celniker et al. 2009;Morozova et al. 2009;Gerstein et al. 2010;Metzker 2010;Roy et al. 2010). RNA-seq offers single-nucleotide resolution, strand specificity, and short-range connectivity through pairedend sequencing. Because of these strengths, there has been great interest in using RNA-seq to distinguish isoforms, calculate expression levels for transcripts, and uncover low abundance RNAs (He et al. 2008;Mortazavi et al. 2008;Nagalakshmi et al. 2008;Sultan et al. 2008;Wang et al. 2008Wang et al. , 2010Passalacqua et al. 2009;Gerstein et al. 2010;Roy et al. 2010;Trapnell et al. 2010;Berezikov et al. 2011;Graveley et al. 2011).While there are clear advantages to RNA-seq, it is less clear how well the procedure performs, as several studies have reported conflicting RNA-seq accuracy results. RNA-seq-determined concentrations of six in vitro synthetic transcripts show good linearity (Mortazavi et al. 2008), and in a study using quantitative PCR as the benchmark, RNA-seq showed better performance for genes with high expression, while two-channel microarrays were more sensitive in identifying differential expression between genes with low ex...

Decidualization is a complex process involving cellular proliferation and differentiation of the endometrial stroma that is required to establish and support pregnancy. Progesterone acting via its nuclear receptor, the progesterone receptor (PGR), is a critical regulator of decidualization and is known to interact with certain members of the activator protein-1 (AP-1) family in the regulation of transcription. In this study, we identified the cistrome and transcriptome of PGR and identified the AP-1 factors FOSL2 and JUN to be regulated by PGR and important in the decidualization process. Direct targets of PGR were identified by integrating gene expression data from RNA sequencing with the whole-genome binding profile of PGR determined by chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) in primary human endometrial stromal cells exposed to 17β-estradiol, medroxyprogesterone acetate, and cAMP to promote in vitro decidualization. Ablation of FOSL2 and JUN attenuates the induction of 2 decidual marker genes, IGFBP1 and PRL. ChIP-seq analysis of genomic binding revealed that FOSL2 is bound in proximity to 8586 distinct genes, including nearly 80% of genes bound by PGR. A comprehensive assessment of the PGR-dependent decidual transcriptome integrated with the genomic binding of PGR identified FOSL2 as a potentially important transcriptional coregulator of PGR via direct interaction with regulatory regions of genes actively regulated during decidualization.

Summary Osteogenic sarcoma (OS) is a deadly skeletal malignancy whose cause is unknown. We report here a mouse model of OS based on conditional expression of the intracellular domain of Notch1 (NICD). Expression of the NICD in immature osteoblasts was sufficient to drive the formation of bone tumors, including OS, with complete penetrance. These tumors display features of human OS, namely histopathology, cytogenetic complexity, and metastatic potential. We show that Notch activation combined with loss of p53 synergistically accelerates OS development in mice although p53-driven OS is not Rbpj-dependent, which demonstrates a dual dominance of the Notch oncogene and p53 mutation in the development of OS. Using this model, we also reveal the osteoblasts as the potential sources of OS.