MicroRNAs (miRNAs) are small regulatory RNAs that derive from distinctive hairpin transcripts. To learn more about the miRNAs of mammals, we sequenced 60 million small RNAs from mouse brain, ovary, testes, embryonic stem cells, three embryonic stages, and whole newborns. Analysis of these sequences confirmed 398 annotated miRNA genes and identified 108 novel miRNA genes. More than 150 previously annotated miRNAs and hundreds of candidates failed to yield sequenced RNAs with miRNA-like features. Ectopically expressing these previously proposed miRNA hairpins also did not yield small RNAs, whereas ectopically expressing the confirmed and newly identified hairpins usually did yield small RNAs with the classical miRNA features, including dependence on the Drosha endonuclease for processing. These experiments, which suggest that previous estimates of conserved mammalian miRNAs were inflated, provide a substantially revised list of confidently identified murine miRNAs from which to infer the general features of mammalian miRNAs. Our analyses also revealed new aspects of miRNA biogenesis and modification, including tissue-specific strand preferences, sequential Dicer cleavage of a metazoan precursor miRNA (pre-miRNA), consequential 59 heterogeneity, newly identified instances of miRNA editing, and evidence for widespread pre-miRNA uridylation reminiscent of miRNA regulation by Lin28.[Keywords: MicroRNA; miRNA biogenesis; noncoding RNA genes; high-throughput sequencing] Supplemental material is available at http://www.genesdev.org.

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.

Summary Core RNA processing reactions in eukaryotic cells occur cotranscriptionally in a chromatin context, but the relationship between chromatin structure and pre-mRNA processing is poorly understood. We observed strong nucleosome depletion around human polyadenylation sites (PAS), and nucleosome enrichment just downstream of PAS. In genes with multiple alternative PAS, higher downstream nucleosome affinity was associated with higher PAS usage, independently of known PAS motifs that function at the RNA level. Conversely, exons were associated with distinct peaks in nucleosome density. Exons flanked by long introns or weak splice sites exhibited stronger nucleosome enrichment, and incorporation of nucleosome density data improved splicing simulation accuracy. Certain histone modifications, including H3K36me3 and H3K27me2, were specifically enriched on exons, suggesting active marking of exon locations at the chromatin level. Together, these findings provide evidence for extensive functional connections between chromatin structure and RNA processing.

any diseases have been linked to SVs, most often defined as genomic changes at least 50 bp in size, but SVs are challenging to detect accurately. Conditions linked to SVs include autism 1 , schizophrenia, cardiovascular disease 2 , Huntington's disease and several other disorders 3. Far fewer SVs exist in germline genomes relative to small variants, but SVs affect more base pairs, and each SV might be more likely to affect phenotype 4-6. Although next-generation sequencing technologies can detect many SVs, each technology and analysis method has different strengths and weaknesses. To enable the community to

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCodeTM WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.

Variation in protein output across the genome is controlled at several levels, but the relative contributions of different regulatory mechanisms remain poorly understood. Here, we obtained global measurements of decay and translation rates for mRNAs with alternative 39 untranslated regions (39 UTRs) in murine 3T3 cells. Distal tandem isoforms had slightly but significantly lower mRNA stability and greater translational efficiency than proximal isoforms on average. The diversity of alternative 39 UTRs also enabled inference and evaluation of both positively and negatively acting cisregulatory elements. The 39 UTR elements with the greatest implied influence were microRNA complementary sites, which were associated with repression of 32% and 4% at the stability and translational levels, respectively. Nonetheless, both the decay and translation rates were highly correlated for proximal and distal 39 UTR isoforms from the same genes, implying that in 3T3 cells, alternative 39 UTR sequences play a surprisingly small regulatory role compared to other mRNA regions.

In the fission yeast Schizosaccharomyces pombe, the RNA interference (RNAi) machinery is required to generate small interfering RNAs (siRNAs) that mediate heterochromatic gene silencing. Efficient silencing also requires the TRAMP complex, which contains the noncanonical Cid14 poly(A) polymerase and targets aberrant RNAs for degradation. Here we use high-throughput sequencing to analyze Argonaute-associated small RNAs (sRNAs) in both the presence and absence of Cid14. Most sRNAs in fission yeast start with a 5′ uracil, and we argue these are loaded most efficiently into Argonaute. In wild-type cells most sRNAs match to repeated regions of the genome, whereas in cid14Δ cells the sRNA profile changes to include major new classes of sRNAs originating from ribosomal RNAs and a tRNA. Thus, Cid14 prevents certain abundant RNAs from becoming substrates for the RNAi machinery, thereby freeing the RNAi machinery to act on its proper targets.

Background: The human genome contains variants ranging in size from small single nucleotide polymorphisms (SNPs) to large structural variants (SVs). High-quality benchmark small variant calls for the pilot National Institute of Standards and Technology (NIST) Reference Material (NA12878) have been developed by the Genome in a Bottle Consortium, but no similar high-quality benchmark SV calls exist for this genome. Since SV callers output highly discordant results, we developed methods to combine multiple forms of evidence from multiple sequencing technologies to classify candidate SVs into likely true or false positives. Our method (svclassify) calculates annotations from one or more aligned bam files from many high-throughput sequencing technologies, and then builds a one-class model using these annotations to classify candidate SVs as likely true or false positives.