The human genome contains many thousands of long noncoding RNAs (lncRNAs). While several studies have demonstrated compelling biological and disease roles for individual examples, analytical and experimental approaches to investigate these genes have been hampered by the lack of comprehensive lncRNA annotation. Here, we present and analyze the most complete human lncRNA annotation to date, produced by the GENCODE consortium within the framework of the ENCODE project and comprising 9277 manually annotated genes producing 14,880 transcripts. Our analyses indicate that lncRNAs are generated through pathways similar to that of protein-coding genes, with similar histone-modification profiles, splicing signals, and exon/intron lengths. In contrast to protein-coding genes, however, lncRNAs display a striking bias toward two-exon transcripts, they are predominantly localized in the chromatin and nucleus, and a fraction appear to be preferentially processed into small RNAs. They are under stronger selective pressure than neutrally evolving sequences-particularly in their promoter regions, which display levels of selection comparable to protein-coding genes. Importantly, about one-third seem to have arisen within the primate lineage. Comprehensive analysis of their expression in multiple human organs and brain regions shows that lncRNAs are generally lower expressed than protein-coding genes, and display more tissue-specific expression patterns, with a large fraction of tissuespecific lncRNAs expressed in the brain. Expression correlation analysis indicates that lncRNAs show particularly striking positive correlation with the expression of antisense coding genes. This GENCODE annotation represents a valuable resource for future studies of lncRNAs.

Summary Eukaryotic cells make many types of primary and processed RNAs that are found either in specific sub-cellular compartments or throughout the cells. A complete catalogue of these RNAs is not yet available and their characteristic sub-cellular localizations are also poorly understood. Since RNA represents the direct output of the genetic information encoded by genomes and a significant proportion of a cell’s regulatory capabilities are focused on its synthesis, processing, transport, modifications and translation, the generation of such a catalogue is crucial for understanding genome function. Here we report evidence that three quarters of the human genome is capable of being transcribed, as well as observations about the range and levels of expression, localization, processing fates, regulatory regions and modifications of almost all currently annotated and thousands of previously unannotated RNAs. These observations taken together prompt to a redefinition of the concept of a gene.

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.

Graphical Abstract Highlights d SynGO is a public knowledge base and online analysis platform for synapse research d SynGO has annotated 1,112 genes with synaptic localization and/or function d SynGO genes are exceptionally large, well conserved, and intolerant to mutations d SynGO genes are strongly enriched among genes associated with brain disorders Correspondence guus.smit@cncr.vu.nl (A.B.S.), matthijs@cncr.vu.nl (M.V.) In BriefThe SynGO consortium presents a framework to annotate synaptic protein locations and functions and annotations for 1,112 synaptic genes based on published experimental evidence. SynGO reports exceptional features and disease associations for synaptic genes and provides an online data analysis platform. SUMMARYSynapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders (''synaptopathies''). However, systematic annotation of synaptic genes and ontology of synaptic processes are currently lacking. We established SynGO, an interactive knowledge base that accumulates available research about synapse biology using Gene Ontology (GO) annotations to novel ontology terms: 87 synaptic locations and 179 synaptic processes. SynGO annotations are exclusively based on published, expert-curated evidence. Using 2,922 annotations for 1,112 genes, we show that synaptic genes are exceptionally well conserved and less tolerant to mutations than other genes. Many SynGO terms are significantly overrepresented among gene variations associated with intelligence, educational attainment, ADHD, autism, and bipolar disorder and among de novo variants associated with neurodevelopmental disorders, including schizophrenia. SynGO is a public, universal reference for synapse research and an online analysis platform for interpretation of large-scale -omics data (https://syngoportal.org and

Global RNA studies have become central to understanding biological processes, but methods such as microarrays and short-read sequencing are unable to describe an entire RNA molecule from 5′ to 3′ end. Here we use single-molecule long-read sequencing technology from Pacific Biosciences to sequence the polyadenylated RNA complement of a pooled set of 20 human organs and tissues without the need for fragmentation or amplification. We show that full-length RNA molecules of up to 1.5 kb can readily be monitored with little sequence loss at the 5′ ends. For longer RNA molecules more 5′ nucleotides are missing, but complete intron structures are often preserved. In total, we identify ~14,000 spliced GENCODE genes. High-confidence mappings are consistent with GENCODE annotations, but >10% of the alignments represent intron structures that were not previously annotated. As a group, transcripts mapping to unannotated regions have features of long, noncoding RNAs. Our results show the feasibility of deep sequencing full-length RNA from complex eukaryotic transcriptomes on a single-molecule level.

Chromatin structure influences transcription, but its role in subsequent RNA processing is unclear. Here we present analyses of high-throughput data that imply a relationship between nucleosome positioning and exon definition. First, we have found stable nucleosome occupancy within human and Caenorhabditis elegans exons that is stronger in exons with weak splice sites. Conversely, we have found that pseudoexons--intronic sequences that are not included in mRNAs but are flanked by strong splice sites--show nucleosome depletion. Second, the ratio between nucleosome occupancy within and upstream from the exons correlates with exon-inclusion levels. Third, nucleosomes are positioned central to exons rather than proximal to splice sites. These exonic nucleosomal patterns are also observed in non-expressed genes, suggesting that nucleosome marking of exons exists in the absence of transcription. Our analysis provides a framework that contributes to the understanding of splicing on the basis of chromatin architecture.

Splicing remains an incompletely understood process. Recent findings suggest that chromatin structure participates in its regulation. Here, we analyze the RNA from subcellular fractions obtained through RNA-seq in the cell line K562. We show that in the human genome, splicing occurs predominantly during transcription. We introduce the coSI measure, based on RNA-seq reads mapping to exon junctions and borders, to assess the degree of splicing completion around internal exons. We show that, as expected, splicing is almost fully completed in cytosolic polyA+ RNA. In chromatinassociated RNA (which includes the RNA that is being transcribed), for 5.6% of exons, the removal of the surrounding introns is fully completed, compared with 0.3% of exons for which no intron-removal has occurred. The remaining exons exist as a mixture of spliced and fewer unspliced molecules, with a median coSI of 0.75. Thus, most RNAs undergo splicing while being transcribed: ''co-transcriptional splicing.'' Consistent with co-transcriptional spliceosome assembly and splicing, we have found significant enrichment of spliceosomal snRNAs in chromatin-associated RNA compared with other cellular RNA fractions and other nonspliceosomal snRNAs. CoSI scores decrease along the gene, pointing to a ''first transcribed, first spliced'' rule, yet more downstream exons carry other characteristics, favoring rapid, co-transcriptional intron removal. Exons with low coSI values, that is, in the process of being spliced, are enriched with chromatin marks, consistent with a role for chromatin in splicing during transcription. For alternative exons and long noncoding RNAs, splicing tends to occur later, and the latter might remain unspliced in some cases.[Supplemental material is available for this article.]Central in the pathway leading from primary transcripts to mature functional RNAs is splicing, the process by which intervening sequences in the primary transcript (introns) are excised and the remaining sequences (exons) are concatenated together to form the mature eukaryotic RNAs. Conserved sequence motifs, the splice sites, mark exon-intron boundaries and are recognized by elements of the splicing machinery. Splice site sequences, however, do not carry enough information to unequivocally specify exon-intron boundaries, and a plethora of other sequence motifs, recognized by a variety of RNA binding proteins, contribute to define and regulate splice site selection (Graveley 2000;Smith and Valcárcel 2000;Wang and Burge 2008). While there have been considerable advances in modeling splicing from features in the primary transcript sequence (Wang et al. 2004;Barash et al. 2010), it is currently close to impossible to predict from the analysis of mammalian primary RNA sequence alone neither the entire exonintron structure of transcripts nor their tissue specific expression pattern (i.e., the abundance of given transcript in a given cell type).It appears thus that other factors, not necessarily encoded in the sequence of the primary transcript, may play a role in...

Full-length RNA sequencing (RNA-Seq) has been applied to bulk tissue, cell lines and sorted cells to characterize transcriptomes 1-11 , but applying this technology to single cells has proven to be difficult, with less than ten single-cell transcriptomes having been analyzed thus far 12,13. Although single splicing events have been described for ≤200 single cells with statistical confidence 14,15 , full-length mRNA analyses for hundreds of cells have not been reported. Singlecell short-read 3′ sequencing enables the identification of cellular subtypes 16-21 , but full-length mRNA isoforms for these cell types cannot be profiled. We developed a method that starts with bulk tissue and identifies single-cell types and their full-length RNA isoforms without fluorescence-activated cell sorting. Using single-cell isoform RNA-Seq (ScISOr-Seq), we identified RNA isoforms in neurons, astrocytes, microglia, and cell subtypes such as Purkinje and Granule cells, and cell-typespecific combination patterns of distant splice sites 6-9,22,23. We used ScISOr-Seq to improve genome annotation in mouse Gencode version 10 by determining the cell-type-specific expression of 18,173 known and 16,872 novel isoforms. Unlike sorting-based methods (Supplementary Fig. 1a), ScISOr-Seq identifies isoforms in >1,000 single cells from bulk tissue without cell sorting by combining two technologies (Fig. 1a). We used microfluidics to amplify full-length cDNA from single cells in a sample. cDNA produced from each single cell was barcoded to enable cell-of-origin identification and then split into two pools, with one pool being used for short-read Illumina 3′ sequencing to measure gene expression and the other pool being used for long-read sequencing and isoform identification. Short-read 3′ sequencing provided molecular counts for each gene and cell, which enabled clustering of cells and cell type assignment using cell-type-specific markers. Long-read sequencing with Pacific Biosciences (PacBio) 1,2,4,5 or Oxford Nanopore 3 was used to identify full-length RNA isoforms. Single-cell barcodes were also present in long reads and could be used to determine the individual