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Since the initial annotation of miRNAs from cloned short RNAs by the Ambros, Tuschl, and Bartel groups in 2001, more than a hundred studies have sought to identify additional miRNAs in various species. We report here a meta-analysis of short RNA data from Drosophila melanogaster, aggregating published libraries with 76 data sets that we generated for the modENCODE project. In total, we began with more than 1 billion raw reads from 187 libraries comprising diverse developmental stages, specific tissue- and cell-types, mutant conditions, and/or Argonaute immunoprecipitations. We elucidated several features of known miRNA loci, including multiple phased byproducts of cropping and dicing, abundant alternative 5′ termini of certain miRNAs, frequent 3′ untemplated additions, and potential editing events. We also identified 49 novel genomic locations of miRNA production, and 61 additional candidate loci with limited evidence for miRNA biogenesis. Although these loci broaden the Drosophila miRNA catalog, this work supports the notion that a restricted set of cellular transcripts is competent to be specifically processed by the Drosha/Dicer-1 pathway. Unexpectedly, we detected miRNA production from coding and untranslated regions of mRNAs and found the phenomenon of miRNA production from the antisense strand of known loci to be common. Altogether, this study lays a comprehensive foundation for the study of miRNA diversity and evolution in a complex animal model.

The liver and pancreas share a common origin and coexpress several transcription factors. To gain insight into the transcriptional networks regulating the function of these tissues, we globally identify binding sites for FOXA2 in adult mouse islets and liver, PDX1 in islets, and HNF4A in liver. Because most eukaryotic transcription factors bind thousands of loci, many of which are thought to be inactive, methods that can discriminate functionally active binding events are essential for the interpretation of genome-wide transcription factor binding data. To develop such a method, we also generated genome-wide H3K4me1 and H3K4me3 localization data in these tissues. By analyzing our binding and histone methylation data in combination with comprehensive gene expression data, we show that H3K4me1 enrichment profiles discriminate transcription factor occupied loci into three classes: those that are functionally active, those that are poised for activation, and those that reflect pioneer-like transcription factor activity. Furthermore, we demonstrate that the regulated presence of H3K4me1-marked nucleosomes at transcription factor occupied promoters and enhancers controls their activity, implicating both tissue-specific transcription factor binding and nucleosome remodeling complex recruitment in determining tissue-specific gene expression. Finally, we apply these approaches to generate novel insights into how FOXA2, PDX1, and HNF4A cooperate to drive islet- and liver-specific gene expression.

The spontaneously hypertensive rat (SHR) is the most widely studied animal model of hypertension. Scores of SHR quantitative loci (QTLs) have been mapped for hypertension and other phenotypes. We have sequenced the SHR/OlaIpcv genome at 10.7-fold coverage by paired-end sequencing on the Illumina platform. We identified 3.6 million high-quality single nucleotide polymorphisms (SNPs) between the SHR/OlaIpcv and Brown Norway (BN) reference genome, with a high rate of validation (sensitivity 96.3%–98.0% and specificity 99%–100%). We also identified 343,243 short indels between the SHR/OlaIpcv and reference genomes. These SNPs and indels resulted in 161 gain or loss of stop codons and 629 frameshifts compared with the BN reference sequence. We also identified 13,438 larger deletions that result in complete or partial absence of 107 genes in the SHR/OlaIpcv genome compared with the BN reference and 588 copy number variants (CNVs) that overlap with the gene regions of 688 genes. Genomic regions containing genes whose expression had been previously mapped as cis-regulated expression quantitative trait loci (eQTLs) were significantly enriched with SNPs, short indels, and larger deletions, suggesting that some of these variants have functional effects on gene expression. Genes that were affected by major alterations in their coding sequence were highly enriched for genes related to ion transport, transport, and plasma membrane localization, providing insights into the likely molecular and cellular basis of hypertension and other phenotypes specific to the SHR strain. This near complete catalog of genomic differences between two extensively studied rat strains provides the starting point for complete elucidation, at the molecular level, of the physiological and pathophysiological phenotypic differences between individuals from these strains.

We describe a new method, Tag-seq, which employs ultra high-throughput sequencing of 21 base pair cDNA tags for sensitive and cost-effective gene expression profiling. We compared Tag-seq data to LongSAGE data and observed improved representation of several classes of rare transcripts, including transcription factors, antisense transcripts, and intronic sequences, the latter possibly representing novel exons or genes. We observed increases in the diversity, abundance, and dynamic range of such rare transcripts and took advantage of the greater dynamic range of expression to identify, in cancers and normal libraries, altered expression ratios of alternative transcript isoforms. The strand-specific information of Tag-seq reads further allowed us to detect altered expression ratios of sense and antisense (S-AS) transcripts between cancer and normal libraries. S-AS transcripts were enriched in known cancer genes, while transcript isoforms were enriched in miRNA targeting sites. We found that transcript abundance had a stronger GC-bias in LongSAGE than Tag-seq, such that AT-rich tags were less abundant than GC-rich tags in LongSAGE. Tag-seq also performed better in gene discovery, identifying >98% of genes detected by LongSAGE and profiling a distinct subset of the transcriptome characterized by AT-rich genes, which was expressed at levels below those detectable by LongSAGE. Overall, Tag-seq is sensitive to rare transcripts, has less sequence composition bias relative to LongSAGE, and allows differential expression analysis for a greater range of transcripts, including transcripts encoding important regulatory molecules.

We characterized the relationship of H3K4me1 and H3K4me3 at distal and proximal regulatory elements by comparing ChIP-seq profiles for these histone modifications and for two functionally different transcription factors: STAT1 in the immortalized HeLa S3 cell line, with and without interferon-gamma (IFNG) stimulation; and FOXA2 in mouse adult liver tissue. In unstimulated and stimulated HeLa cells, respectively, we determined approximately 270,000 and approximately 301,000 H3K4me1-enriched regions, and approximately 54,500 and approximately 76,100 H3K4me3-enriched regions. In mouse adult liver, we determined approximately 227,000 and approximately 34,800 H3K4me1 and H3K4me3 regions. Seventy-five percent of the approximately 70,300 STAT1 binding sites in stimulated HeLa cells and 87% of the approximately 11,000 FOXA2 sites in mouse liver were distal to known gene TSS; in both cell types, approximately 83% of these distal sites were associated with at least one of the two histone modifications, and H3K4me1 was associated with over 96% of marked distal sites. After filtering against predicted transcription start sites, 50% of approximately 26,800 marked distal IFNG-stimulated STAT1 binding sites, but 95% of approximately 5800 marked distal FOXA2 sites, were associated with H3K4me1 only. Results for HeLa cells generated additional insights into transcriptional regulation involving STAT1. STAT1 binding was associated with 25% of all H3K4me1 regions in stimulated HeLa cells, suggesting that a single transcription factor can interact with an unexpectedly large fraction of regulatory regions. Strikingly, for a large majority of the locations of stimulated STAT1 binding, the dominant H3K4me1/me3 combinations were established before activation, suggesting mechanisms independent of IFNG stimulation and high-affinity STAT1 binding.