Showing papers in "Genome Research in 2012"

The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression.

Abstract: 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 tissue-specific 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.

GENCODE: The reference human genome annotation for The ENCODE Project

Abstract: The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.

VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing

Abstract: Exome sequencing of tumor samples and matched normal controls has the potential to rapidly identify protein-altering mutations across hundreds of patients, potentially enabling the discovery of recurrent events driving tumor development and growth (International Cancer Genome Consortium 2010; Stratton 2011). Yet the analysis of such data presents significant challenges. Sequencing coverage is nonuniform across targeted regions and from one sample to the next (Ng et al. 2009; Bainbridge et al. 2010; Teer et al. 2010). Many regions achieve high read depth (more than 100×), which can confound variant callers and depth-based filters if not properly addressed (Ku et al. 2011). Repetitive and paralogous sequences can give rise to numerous false positives. The detection of somatic mutations in tumor genomes is even more challenging. The genomes of primary tumors are genetically heterogeneous (Ding et al. 2010), with frequent rearrangements (Campbell et al. 2008) and copy number alterations (CNAs) (Beroukhim et al. 2010). Further, somatic mutations are relatively rare compared with germline variation, often representing <0.1% of variants in a tumor genome (Ley et al. 2008; Mardis et al. 2009). Simply subtracting variants in the matched normal from variants in the tumor (Wei et al. 2011) is poorly suited for the analysis of exome sequence data, because it fails to account for regions that were undersampled in the normal. Accurate mutation detection requires a direct, simultaneous comparison of tumor–normal pairs at every position in the exome, but few algorithms to do so have been described. Numerous algorithms have been developed to assess genome-wide copy number using whole-genome sequencing (WGS) data. Most of these approaches (Campbell et al. 2008; Alkan et al. 2009; Chiang et al. 2009; Yoon et al. 2009; Abyzov et al. 2011) would be confounded by exome data sets, because of the biases introduced by hybridization and the sparse and uneven coverages throughout the genome. However, when both DNA samples in a tumor–normal pair were captured and sequenced under identical hybridization conditions, we reasoned that it might be possible to detect somatic CNAs (SCNAs) as deviations from the log-ratio of sequence coverage depth within a tumor–normal pair, and then quantify the deviations statistically. Such an approach would provide a gene-centric view of copy number in a tumor sample, though it would be limited to the ∼1% of the genome captured by current exome platforms. Previously, we published VarScan (Koboldt et al. 2009), an algorithm for variant detection in next-generation sequencing data. We have since released a new tool, VarScan 2 (http://varscan.sourceforge.net), with several improvements, including the ability to identify somatic mutation, loss of heterozygosity (LOH), and CNA events in tumor–normal pairs. VarScan 2 analyzes sequence data from a tumor sample and its corresponding normal sample simultaneously, applying heuristic methods and a statistical test to detect variants—single nucleotide variants (SNVs) and insertions/deletions (indels)—and classify them by somatic status. By direct comparison of normalized sequence depth, our method also detects SCNAs in the tumor genome. Here, we utilize VarScan 2 for the analysis of exome sequence data from 151 patients with high-grade serous ovarian adenocarcinoma (HGS-OVCa) that were initially characterized within the Cancer Genome Atlas (TCGA) project (Cancer Genome Atlas Research Network 2011). We present a robust pipeline for the detection of both germline (inherited) and somatic (acquired) mutations by exome sequencing and describe filtering approaches for detecting variants with high sensitivity and specificity. To evaluate the performance of our SCNA detection algorithm, we compare our results to copy number data from high-density SNP array and WGS approaches. Our results demonstrate the accuracy of VarScan 2 for somatic mutation and CNA detection and enable a new survey of the genetic landscape in ovarian carcinoma.

Annotation of functional variation in personal genomes using RegulomeDB

Abstract: As the sequencing of healthy and disease genomes becomes more commonplace, detailed annotation provides interpretation for individual variation responsible for normal and disease phenotypes. Current approaches focus on direct changes in protein coding genes, particularly nonsynonymous mutations that directly affect the gene product. However, most individual variation occurs outside of genes and, indeed, most markers generated from genome-wide association studies (GWAS) identify variants outside of coding segments. Identification of potential regulatory changes that perturb these sites will lead to a better localization of truly functional variants and interpretation of their effects. We have developed a novel approach and database, RegulomeDB, which guides interpretation of regulatory variants in the human genome. RegulomeDB includes high-throughput, experimental data sets from ENCODE and other sources, as well as computational predictions and manual annotations to identify putative regulatory potential and identify functional variants. These data sources are combined into a powerful tool that scores variants to help separate functional variants from a large pool and provides a small set of putative sites with testable hypotheses as to their function. We demonstrate the applicability of this tool to the annotation of noncoding variants from 69 full sequenced genomes as well as that of a personal genome, where thousands of functionally associated variants were identified. Moreover, we demonstrate a GWAS where the database is able to quickly identify the known associated functional variant and provide a hypothesis as to its function. Overall, we expect this approach and resource to be valuable for the annotation of human genome sequences.

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

Abstract: Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals.

Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma

Abstract: An estimated 15% or more of the cancer burden worldwide is attributable to known infectious agents. We screened colorectal carcinoma and matched normal tissue specimens using RNA-seq followed by host sequence subtraction and found marked over-representation of Fusobacterium nucleatum sequences in tumors relative to control specimens. F. nucleatum is an invasive anaerobe that has been linked previously to periodontitis and appendicitis, but not to cancer. Fusobacteria are rare constituents of the fecal microbiota, but have been cultured previously from biopsies of inflamed gut mucosa. We obtained a Fusobacterium isolate from a frozen tumor specimen; this showed highest sequence similarity to a known gut mucosa isolate and was confirmed to be invasive. We verified overabundance of Fusobacterium sequences in tumor versus matched normal control tissue by quantitative PCR analysis from a total of 99 subjects (p = 2.5 × 10(-6)), and we observed a positive association with lymph node metastasis.

Genomic analysis identifies association of Fusobacterium with colorectal carcinoma

Abstract: The tumor microenvironment of colorectal carcinoma is a complex community of genomically altered cancer cells, nonneoplastic cells, and a diverse collection of microorganisms. Each of these components may contribute to carcinogenesis; however, the role of the microbiota is the least well understood. We have characterized the composition of the microbiota in colorectal carcinoma using whole genome sequences from nine tumor/normal pairs. Fusobacterium sequences were enriched in carcinomas, confirmed by quantitative PCR and 16S rDNA sequence analysis of 95 carcinoma/normal DNA pairs, while the Bacteroidetes and Firmicutes phyla were depleted in tumors. Fusobacteria were also visualized within colorectal tumors using FISH. These findings reveal alterations in the colorectal cancer microbiota; however, the precise role of Fusobacteria in colorectal carcinoma pathogenesis requires further investigation.

Detecting differential usage of exons from RNA-seq data.

Abstract: RNA-seq is a powerful tool for the study of alternative splicing and other forms of alternative isoform expression. Understanding the regulation of these processes requires sensitive and specific detection of differential isoform abundance in comparisons between conditions, cell types, or tissues. We present DEXSeq, a statistical method to test for differential exon usage in RNA-seq data. DEXSeq uses generalized linear models and offers reliable control of false discoveries by taking biological variation into account. DEXSeq detects with high sensitivity genes, and in many cases exons, that are subject to differential exon usage. We demonstrate the versatility of DEXSeq by applying it to several data sets. The method facilitates the study of regulation and function of alternative exon usage on a genome-wide scale. An implementation of DEXSeq is available as an R/Bioconductor package.

Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis

Abstract: Atopic dermatitis (AD) has long been associated with Staphylococcus aureus skin colonization or infection and is typically managed with regimens that include antimicrobial therapies. However, the role of microbial communities in the pathogenesis of AD is incompletely characterized. To assess the relationship between skin microbiota and disease progression, 16S ribosomal RNA bacterial gene sequencing was performed on DNA obtained directly from serial skin sampling of children with AD. The composition of bacterial communities was analyzed during AD disease states to identify characteristics associated with AD flares and improvement post-treatment. We found that microbial community structures at sites of disease predilection were dramatically different in AD patients compared with controls. Microbial diversity during AD flares was dependent on the presence or absence of recent AD treatments, with even intermittent treatment linked to greater bacterial diversity than no recent treatment. Treatment-associated changes in skin bacterial diversity suggest that AD treatments diversify skin bacteria preceding improvements in disease activity. In AD, the proportion of Staphylococcus sequences, particularly S. aureus, was greater during disease flares than at baseline or post-treatment, and correlated with worsened disease severity. Representation of the skin commensal S. epidermidis also significantly increased during flares. Increases in Streptococcus, Propionibacterium, and Corynebacterium species were observed following therapy. These findings reveal linkages between microbial communities and inflammatory diseases such as AD, and demonstrate that as compared with culture-based studies, higher resolution examination of microbiota associated with human disease provides novel insights into global shifts of bacteria relevant to disease progression and treatment.

Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture

Abstract: Improvements in technology have reduced the cost of DNA sequencing to the point that the limiting factor for many experiments is the time and reagent cost of sample preparation. We present an approach in which 192 sequencing libraries can be produced in a single day of technician time at a cost of about $15 per sample. These libraries are effective not only for low-pass whole-genome sequencing, but also for simultaneously enriching them in pools of approximately 100 individually barcoded samples for a subset of the genome without substantial loss in efficiency of target capture. We illustrate the power and effectiveness of this approach on about 2000 samples from a prostate cancer study.

Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors.

Abstract: Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) has become the dominant technique for mapping transcription factor (TF) binding regions genome-wide. We performed an integrative analysis centered around 457 ChIP-seq data sets on 119 human TFs generated by the ENCODE Consortium. We identified highly enriched sequence motifs in most data sets, revealing new motifs and validating known ones. The motif sites (TF binding sites) are highly conserved evolutionarily and show distinct footprints upon DNase I digestion. We frequently detected secondary motifs in addition to the canonical motifs of the TFs, indicating tethered binding and cobinding between multiple TFs. We observed significant position and orientation preferences between many cobinding TFs. Genes specifically expressed in a cell line are often associated with a greater occurrence of nearby TF binding in that cell line. We observed cell-line-specific secondary motifs that mediate the binding of the histone deacetylase HDAC2 and the enhancer-binding protein EP300. TF binding sites are located in GC-rich, nucleosome-depleted, and DNase I sensitive regions, flanked by well-positioned nucleosomes, and many of these features show cell type specificity. The GC-richness may be beneficial for regulating TF binding because, when unoccupied by a TF, these regions are occupied by nucleosomes in vivo. We present the results of our analysis in a TF-centric web repository Factorbook (http://factorbook.org) and will continually update this repository as more ENCODE data are generated.

Efficient de novo assembly of large genomes using compressed data structures

Abstract: De novo genome sequence assembly is important both to generate new sequence assemblies for previously uncharacterized genomes and to identify the genome sequence of individuals in a reference-unbiased way. We present memory efficient data structures and algorithms for assembly using the FM-index derived from the compressed Burrows-Wheeler transform, and a new assembler based on these called SGA (String Graph Assembler). We describe algorithms to error-correct, assemble, and scaffold large sets of sequence data. SGA uses the overlap-based string graph model of assembly, unlike most de novo assemblers that rely on de Bruijn graphs, and is simply parallelizable. We demonstrate the error correction and assembly performance of SGA on 1.2 billion sequence reads from a human genome, which we are able to assemble using 54 GB of memory. The resulting contigs are highly accurate and contiguous, while covering 95% of the reference genome (excluding contigs <200 bp in length). Because of the low memory requirements and parallelization without requiring inter-process communication, SGA provides the first practical assembler to our knowledge for a mammalian-sized genome on a low-end computing cluster.

GAGE: A critical evaluation of genome assemblies and assembly algorithms

Abstract: New sequencing technology has dramatically altered the landscape of whole-genome sequencing, allowing scientists to initiate numerous projects to decode the genomes of previously unsequenced organisms. The lowest-cost technology can generate deep coverage of most species, including mammals, in just a few days. The sequence data generated by one of these projects consist of millions or billions of short DNA sequences (reads) that range from 50 to 150 nt in length. These sequences must then be assembled de novo before most genome analyses can begin. Unfortunately, genome assembly remains a very difficult problem, made more difficult by shorter reads and unreliable long-range linking information. In this study, we evaluated several of the leading de novo assembly algorithms on four different short-read data sets, all generated by Illumina sequencers. Our results describe the relative performance of the different assemblers as well as other significant differences in assembly difficulty that appear to be inherent in the genomes themselves. Three overarching conclusions are apparent: first, that data quality, rather than the assembler itself, has a dramatic effect on the quality of an assembled genome; second, that the degree of contiguity of an assembly varies enormously among different assemblers and different genomes; and third, that the correctness of an assembly also varies widely and is not well correlated with statistics on contiguity. To enable others to replicate our results, all of our data and methods are freely available, as are all assemblers used in this study.

Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis

Abstract: Long noncoding RNAs (lncRNAs) comprise a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins. Recent genome-wide studies in humans and the mouse have annotated lncRNAs expressed in cell lines and adult tissues, but a systematic analysis of lncRNAs expressed during vertebrate embryogenesis has been elusive. To identify lncRNAs with potential functions in vertebrate embryogenesis, we performed a time-series of RNA-seq experiments at eight stages during early zebrafish development. We reconstructed 56,535 high-confidence transcripts in 28,912 loci, recovering the vast majority of expressed RefSeq transcripts while identifying thousands of novel isoforms and expressed loci. We defined a stringent set of 1133 noncoding multi-exonic transcripts expressed during embryogenesis. These include long intergenic ncRNAs (lincRNAs), intronic overlapping lncRNAs, exonic antisense overlapping lncRNAs, and precursors for small RNAs (sRNAs). Zebrafish lncRNAs share many of the characteristics of their mammalian counterparts: relatively short length, low exon number, low expression, and conservation levels comparable to that of introns. Subsets of lncRNAs carry chromatin signatures characteristic of genes with developmental functions. The temporal expression profile of lncRNAs revealed two novel properties: lncRNAs are expressed in narrower time windows than are protein-coding genes and are specifically enriched in early-stage embryos. In addition, several lncRNAs show tissue-specific expression and distinct subcellular localization patterns. Integrative computational analyses associated individual lncRNAs with specific pathways and functions, ranging from cell cycle regulation to morphogenesis. Our study provides the first systematic identification of lncRNAs in a vertebrate embryo and forms the foundation for future genetic, genomic, and evolutionary studies.

Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis

Abstract: Alternative splicing (AS) is a widespread mechanism which increases transcriptome and proteome complexity and controls developmental programs and responses to the environment in higher eukaryotes. The splicing process, removal of introns and ligation of exons, is performed by a large RNA-protein complex, the spliceosome, consisting of five small nuclear RNAs (snRNAs) and about 180 proteins with different functions (Wahl et al. 2009). Assembly of the spliceosome on introns in a precursor messenger RNA (pre-mRNA) is directed by cis elements and trans-acting factors (Black 2003; Stamm et al. 2005). The cis sequences include the splice sites, branchpoint, and polypyrimidine tract which have degenerate consensus sequences in higher eukaryotes. While many splice sites are selected in all transcripts (constitutive splicing), others are used to various levels, resulting in alternative transcripts. Selection of such alternative splice sites is affected by auxiliary cis elements located within exonic and intronic sequences, termed splicing enhancers and silencers. These elements are binding sites for trans-acting splicing factors, for example, hnRNP and SR proteins. These proteins, in addition to their functions in constitutive splicing, play a key role in AS by inhibition or promotion of selection of particular splice sites. The presence and abundance of different splicing factors in different cell types, tissues, developmental stages, and environmental conditions determines the AS profiles of expressed genes and ultimately shapes the transcriptome. In addition, alternative transcripts can code for protein isoforms with altered amino acid and domain composition affecting their activity, interaction capacity, localization, and stability, thus affecting the proteome (Stamm et al. 2005). Alternative splicing was first described in 1977 as peculiar rearrangements in the adenovirus type 2 mRNA (Berget et al. 1977; Chow et al. 1977). Since the discovery of the first example of AS in an endogenous mammalian gene coding for calcitonin (Rosenfeld et al. 1981), the alignment of expressed sequence tag (EST) contigs to genomic DNA allowed the identification of a large number (∼35%) of alternatively spliced genes in humans (Mironov et al. 1999). Estimates of AS in many different organisms have been made using EST/cDNA libraries (Okazaki et al. 2002; Zavolan et al. 2003; Iida et al. 2004; Cusack and Wolfe 2005; Wakamatsu et al. 2009). With the advent of tiling arrays and high-throughput sequencing, the number of genes which undergo AS has continued to increase (Jones-Rhoades et al. 2007; Weber et al. 2007; Kwan et al. 2008; Mortazavi et al. 2008; Pan et al. 2008). In particular, the application of high-throughput sequencing to transcriptomes (RNA-seq) has now demonstrated that AS occurs in ∼95% of intron-containing genes in human (Pan et al. 2008). In plants, estimates of the occurrence of AS have been hampered by a low number of ESTs (Brett et al. 2002). However, the levels of AS have continued to increase with greater EST/cDNA coverage: 1.2% (Zhu et al. 2003), 5% (Zhu et al. 2003), 11.6% (Iida et al. 2004), 21.8% (Wang and Brendel 2006), 29% (Xiao et al. 2005), and >30% (Campbell et al. 2006). Many transcriptome studies using high-throughput sequencing have been performed in plants, but few have been used to examine AS (Weber et al. 2007; Filichkin et al. 2010; Lu et al. 2010; Zhang et al. 2010). The most recent estimate based on RNA-seq is that ∼42% of Arabidopsis intron-containing genes undergo AS (Filichkin et al. 2010). In terms of identifying AS in plants, the expression profile itself influences the representation of many transcripts in databases. For example, an Arabidopsis transcriptome study using 454 Life Sciences (Roche) sequencing (Weber et al. 2007) showed that the top 10 most highly expressed genes represent 25% of the total mapped reads, thus tremendously compromising the representation of less abundant transcripts. To improve gene representation and discovery of AS events in Arabidopsis, we have used RNA-seq of a normalized cDNA library made from Arabidopsis seedlings and flowers. We have shown that normalization significantly increases the coverage of reads across the genes, and we have identified a large number (∼47 k) of new splice junctions. Taking advantage of a high-resolution RT-PCR panel (Simpson et al. 2008a,b), we were able to validate many novel AS events. Altogether, our results show that at least 61% of intron-containing genes are alternatively spliced under normal growth conditions, which indicates a high complexity of the Arabidopsis transcriptome.

Mutual exclusivity analysis identifies oncogenic network modules

Abstract: Although individual tumors of the same clinical type have surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modules is incomplete. To systematically identify such modules, we have developed a novel method, Mutual Exclusivity Modules in cancer (MEMo). The method uses correlation analysis and statistical tests to identify network modules by three criteria: (1) Member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. Applied to data from the Cancer Genome Atlas (TCGA), the method identifies the principal known altered modules in glioblastoma (GBM) and highlights the striking mutual exclusivity of genomic alterations in the PI(3)K, p53, and Rb pathways. In serous ovarian cancer, we make the novel observation that inactivation of BRCA1 and BRCA2 is mutually exclusive of amplification of CCNE1 and inactivation of RB1, suggesting distinct alternative causes of genomic instability in this cancer type; and, we identify RBBP8 as a candidate oncogene involved in Rb-mediated cell cycle control. When applied to any cancer genomics data set, the algorithm can nominate oncogenic alterations that have a particularly strong selective effect and may also be useful in the design of therapeutic combinations in cases where mutual exclusivity reflects synthetic lethality.

MuSiC: Identifying mutational significance in cancer genomes

Abstract: Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery.

Copy number variation detection and genotyping from exome sequence data

Abstract: While exome sequencing is readily amenable to single-nucleotide variant discovery, the sparse and nonuniform nature of the exome capture reaction has hindered exome-based detection and characterization of genic copy number variation. We developed a novel method using singular value decomposition (SVD) normalization to discover rare genic copy number variants (CNVs) as well as genotype copy number polymorphic (CNP) loci with high sensitivity and specificity from exome sequencing data. We estimate the precision of our algorithm using 122 trios (366 exomes) and show that this method can be used to reliably predict (94% overall precision) both de novo and inherited rare CNVs involving three or more consecutive exons. We demonstrate that exome-based genotyping of CNPs strongly correlates with whole-genome data (median r(2) = 0.91), especially for loci with fewer than eight copies, and can estimate the absolute copy number of multi-allelic genes with high accuracy (78% call level). The resulting user-friendly computational pipeline, CoNIFER (copy number inference from exome reads), can reliably be used to discover disruptive genic CNVs missed by standard approaches and should have broad application in human genetic studies of disease.

A quantitative atlas of polyadenylation in five mammals

Abstract: We developed PolyA-seq, a strand-specific and quantitative method for high-throughput sequencing of 3′ ends of polyadenylated transcripts, and used it to globally map polyadenylation (polyA) sites in 24 matched tissues in human, rhesus, dog, mouse, and rat. We show that PolyA-seq is as accurate as existing RNA sequencing (RNA-seq) approaches for digital gene expression (DGE), enabling simultaneous mapping of polyA sites and quantitative measurement of their usage. In human, we confirmed 158,533 known sites and discovered 280,857 novel sites (FDR < 2.5%). On average 10% of novel human sites were also detected in matched tissues in other species. Most novel sites represent uncharacterized alternative polyA events and extensions of known transcripts in human and mouse, but primarily delineate novel transcripts in the other three species. A total of 69.1% of known human genes that we detected have multiple polyA sites in their 3′UTRs, with 49.3% having three or more. We also detected polyadenylation of noncoding and antisense transcripts, including constitutive and tissue-specific primary microRNAs. The canonical polyA signal was strongly enriched and positionally conserved in all species. In general, usage of polyA sites is more similar within the same tissues across different species than within a species. These quantitative maps of polyA usage in evolutionarily and functionally related samples constitute a resource for understanding the regulatory mechanisms underlying alternative polyadenylation.

The transcriptional landscape and mutational profile of lung adenocarcinoma

Abstract: All cancers harbor molecular alterations in their genomes. The transcriptional consequences of these somatic mutations have not yet been comprehensively explored in lung cancer. Here we present the first large scale RNA sequencing study of lung adenocarcinoma, demonstrating its power to identify somatic point mutations as well as transcriptional variants such as gene fusions, alternative splicing events, and expression outliers. Our results reveal the genetic basis of 200 lung adenocarcinomas in Koreans including deep characterization of 87 surgical specimens by transcriptome sequencing. We identified driver somatic mutations in cancer genes including EGFR, KRAS, NRAS, BRAF, PIK3CA, MET, and CTNNB1. Candidates for novel driver mutations were also identified in genes newly implicated in lung adenocarcinoma such as LMTK2, ARID1A, NOTCH2, and SMARCA4. We found 45 fusion genes, eight of which were chimeric tyrosine kinases involving ALK, RET, ROS1, FGFR2, AXL, and PDGFRA. Among 17 recurrent alternative splicing events, we identified exon 14 skipping in the proto-oncogene MET as highly likely to be a cancer driver. The number of somatic mutations and expression outliers varied markedly between individual cancers and was strongly correlated with smoking history of patients. We identified genomic blocks within which gene expression levels were consistently increased or decreased that could be explained by copy number alterations in samples. We also found an association between lymph node metastasis and somatic mutations in TP53. These findings broaden our understanding of lung adenocarcinoma and may also lead to new diagnostic and therapeutic approaches.

Widespread plasticity in CTCF occupancy linked to DNA methylation

Abstract: CTCF is a ubiquitously expressed regulator of fundamental genomic processes including transcription, intra- and interchromosomal interactions, and chromatin structure. Because of its critical role in genome function, CTCF binding patterns have long been assumed to be largely invariant across different cellular environments. Here we analyze genome-wide occupancy patterns of CTCF by ChIP-seq in 19 diverse human cell types, including normal primary cells and immortal lines. We observed highly reproducible yet surprisingly plastic genomic binding landscapes, indicative of strong cell-selective regulation of CTCF occupancy. Comparison with massively parallel bisulfite sequencing data indicates that 41% of variable CTCF binding is linked to differential DNA methylation, concentrated at two critical positions within the CTCF recognition sequence. Unexpectedly, CTCF binding patterns were markedly different in normal versus immortal cells, with the latter showing widespread disruption of CTCF binding associated with increased methylation. Strikingly, this disruption is accompanied by up-regulation of CTCF expression, with the result that both normal and immortal cells maintain the same average number of CTCF occupancy sites genome-wide. These results reveal a tight linkage between DNA methylation and the global occupancy patterns of a major sequence-specific regulatory factor.

Genome-scale analysis of aberrant dna methylation in colorectal cancer

Abstract: Particular aspects provide methods and compositions (e.g., gene marker panels) having substantial utility for at least one of diagnosis, identification and classification of colorectal cancer (CRC) (e.g., tumors) relating to distinctive DNA methylation-based subgroups of CRC including CpG island methylator phenotype (CIMP) groups (e.g., CIMP-H and CIMP-L) and non-CIMP groups. Exemplary marker panels include: B3GAT2, FOXL2, KCNK13, RAB31 and SLIT1 (CIMP marker panel); and FAM78A, FSTL1, KCNC1, MYOCD, and SLC6A4 (CIMP-H marker panel). Further aspects relate to genetic mutations, and other epigenetic markers relating to said CRC subgroups that can be used in combination with the gene marker panels for at least one of diagnosis, identification and classification of colorectal cancer (CRC) (e.g., tumors) relating to distinctive CIMP and non-CIMP groups.

Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer

Abstract: While genetic mutation is a hallmark of cancer, many cancers also acquire epigenetic alterations during tumorigenesis including aberrant DNA hypermethylation of tumor suppressors, as well as changes in chromatin modifications as caused by genetic mutations of the chromatin-modifying machinery. However, the extent of epigenetic alterations in cancer cells has not been fully characterized. Here, we describe complete methylome maps at single nucleotide resolution of a low-passage breast cancer cell line and primary human mammary epithelial cells. We find widespread DNA hypomethylation in the cancer cell, primarily at partially methylated domains (PMDs) in normal breast cells. Unexpectedly, genes within these regions are largely silenced in cancer cells. The loss of DNA methylation in these regions is accompanied by formation of repressive chromatin, with a significant fraction displaying allelic DNA methylation where one allele is DNA methylated while the other allele is occupied by histone modifications H3K9me3 or H3K27me3. Our results show a mutually exclusive relationship between DNA methylation and H3K9me3 or H3K27me3. These results suggest that global DNA hypomethylation in breast cancer is tightly linked to the formation of repressive chromatin domains and gene silencing, thus identifying a potential epigenetic pathway for gene regulation in cancer cells.

Genome-wide analysis of long noncoding RNA stability

Abstract: Transcriptomic analyses have identified tens of thousands of intergenic, intronic, and cis-antisense long noncoding RNAs (lncRNAs) that are expressed from mammalian genomes. Despite progress in functional characterization, little is known about the post-transcriptional regulation of lncRNAs and their half-lives. Although many are easily detectable by a variety of techniques, it has been assumed that lncRNAs are generally unstable, but this has not been examined genome-wide. Utilizing a custom noncoding RNA array, we determined the half-lives of ∼800 lncRNAs and ∼12,000 mRNAs in the mouse Neuro-2a cell line. We find only a minority of lncRNAs are unstable. LncRNA half-lives vary over a wide range, comparable to, although on average less than, that of mRNAs, suggestive of complex metabolism and widespread functionality. Combining half-lives with comprehensive lncRNA annotations identified hundreds of unstable (half-life 16 h). Analysis of lncRNA features revealed that intergenic and cis-antisense RNAs are more stable than those derived from introns, as are spliced lncRNAs compared to unspliced (single exon) transcripts. Subcellular localization of lncRNAs indicated widespread trafficking to different cellular locations, with nuclear-localized lncRNAs more likely to be unstable. Surprisingly, one of the least stable lncRNAs is the well-characterized paraspeckle RNA Neat1, suggesting Neat1 instability contributes to the dynamic nature of this subnuclear domain. We have created an online interactive resource (http://stability.matticklab.com) that allows easy navigation of lncRNA and mRNA stability profiles and provides a comprehensive annotation of ~7200 mouse lncRNAs.

A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing

Abstract: The identification of the molecular events that drive cancer transformation is essential to the development of targeted agents that improve the clinical outcome of lung cancer. Many studies have reported genomic driver mutations in non-small-cell lung cancers (NSCLCs) over the past decade; however, the molecular pathogenesis of >40% of NSCLCs is still unknown. To identify new molecular targets in NSCLCs, we performed the combined analysis of massively parallel whole-genome and transcriptome sequencing for cancer and paired normal tissue of a 33-yr-old lung adenocarcinoma patient, who is a never-smoker and has no familial cancer history. The cancer showed no known driver mutation in EGFR or KRAS and no EML4-ALK fusion. Here we report a novel fusion gene between KIF5B and the RET proto-oncogene caused by a pericentric inversion of 10p11.22-q11.21. This fusion gene overexpresses chimeric RET receptor tyrosine kinase, which could spontaneously induce cellular transformation. We identified the KIF5B-RET fusion in two more cases out of 20 primary lung adenocarcinomas in the replication study. Our data demonstrate that a subset of NSCLCs could be caused by a fusion of KIF5B and RET, and suggest the chimeric oncogene as a promising molecular target for the personalized diagnosis and treatment of lung cancer.

Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression

Abstract: Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma is its most common histological subtype. Clinical and molecular evidence indicates that lung adenocarcinoma is a heterogeneous disease, which has important implications for treatment. Here we performed genome-scale DNA methylation profiling using the Illumina Infinium HumanMethylation27 platform on 59 matched lung adenocarcinoma/non-tumor lung pairs, with genome-scale verification on an independent set of tissues. We identified 766 genes showing altered DNA methylation between tumors and non-tumor lung. By integrating DNA methylation and mRNA expression data, we identified 164 hypermethylated genes showing concurrent down-regulation, and 57 hypomethylated genes showing increased expression. Integrated pathways analysis indicates that these genes are involved in cell differentiation, epithelial to mesenchymal transition, RAS and WNT signaling pathways, and cell cycle regulation, among others. Comparison of DNA methylation profiles between lung adenocarcinomas of current and never-smokers showed modest differences, identifying only LGALS4 as significantly hypermethylated and down-regulated in smokers. LGALS4, encoding a galactoside-binding protein involved in cell-cell and cell-matrix interactions, was recently shown to be a tumor suppressor in colorectal cancer. Unsupervised analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation and was significantly associated with KRAS mutation and to a lesser extent, with smoking. Our analysis lays the groundwork for further molecular studies of lung adenocarcinoma by identifying novel epigenetically deregulated genes potentially involved in lung adenocarcinoma development/progression, and by describing an epigenetic subgroup of lung adenocarcinoma associated with characteristic molecular alterations.

Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs

Abstract: 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 chromatin-associated 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.

De novo discovery of mutated driver pathways in cancer

Abstract: Next-generation DNA sequencing technologies are enabling genome-wide measurements of somatic mutations in large numbers of cancer patients. A major challenge in the interpretation of these data is to distinguish functional “driver mutations” important for cancer development from random “passenger mutations.” A common approach for identifying driver mutations is to find genes that are mutated at significant frequency in a large cohort of cancer genomes. This approach is confounded by the observation that driver mutations target multiple cellular signaling and regulatory pathways. Thus, each cancer patient may exhibit a different combination of mutations that are sufficient to perturb these pathways. This mutational heterogeneity presents a problem for predicting driver mutations solely from their frequency of occurrence. We introduce two combinatorial properties, coverage and exclusivity, that distinguish driver pathways, or groups of genes containing driver mutations, from groups of genes with passenger mutations. We derive two algorithms, called Dendrix, to find driver pathways de novo from somatic mutation data. We apply Dendrix to analyze somatic mutation data from 623 genes in 188 lung adenocarcinoma patients, 601 genes in 84 glioblastoma patients, and 238 known mutations in 1000 patients with various cancers. In all data sets, we find groups of genes that are mutated in large subsets of patients and whose mutations are approximately exclusive. Our Dendrix algorithms scale to whole-genome analysis of thousands of patients and thus will prove useful for larger data sets to come from The Cancer Genome Atlas (TCGA) and other large-scale cancer genome sequencing projects.

Genome-wide determination of RNA stability reveals hundreds of short-lived noncoding transcripts in mammals

Abstract: Whole transcriptome analyses using tiling microarrays (Bertone et al 2004) and deep sequencing (Nagalakshmi et al 2008) have revealed huge numbers of novel transcripts, including long and short noncoding RNAs (ncRNAs) The ratio of noncoding to protein-coding genomic regions increases as a function of developmental complexity (Mattick 2004), suggesting that revealing the functions of ncRNAs transcribed from noncoding genomic regions is important for understanding genome function in higher organisms The ncRNAs can be roughly classified into two groups: small transcripts, such as microRNAs and piwi-interacting RNAs (piRNAs), and long transcripts (Prasanth and Spector 2007) Although the biological importance of small ncRNAs has been documented in recent years, the physiological functions of long ncRNAs (lncRNAs) are poorly understood Recently, significant efforts have been applied to reveal the function of lncRNAs Several approaches have succeeded in identifying dozens of functional lncRNAs (Guttman et al 2009) However, the biological functions of the vast majority of lncRNAs remain unclear Thus, novel properties that can distinguish functional ncRNAs from transcriptional noise are required Numerous studies of mRNAs have revealed that changing the abundance of transcripts by regulated RNA degradation is a critical step in the control of various biological pathways (Keene 2010) It has been estimated that the mRNA abundance of 5%–10% of human genes is controlled through the regulation of RNA stability (Bolognani and Perrone-Bizzozero 2008) It has been proposed that the specific half-life of each mRNA is closely related to its physiological function (Lam et al 2001; Yang et al 2003; Raghavan and Bohjanen 2004; Sharova et al 2009; Rabani et al 2011; Schwanhausser et al 2011) Although mRNAs of most housekeeping genes have long half-lives, mRNAs of many regulatory genes, which encode proteins that are required for only a limited time in the cell—such as cell cycle regulators, factors responsible for responses to external stimuli, and regulators of growth or differentiation—often have short half-lives Moreover, most transcriptionally inducible genes are disproportionately classified into the group of genes with rapid mRNA turnover It is possible, therefore, that the RNA stability of noncoding transcripts also reflects their functions Traditionally, RNA decay has been assessed by blocking global transcription with transcriptional inhibitors, eg, actinomycin D (ActD), and subsequently monitoring ongoing RNA decay over time However, inhibitor-mediated global transcriptional arrest has a profoundly disruptive impact on cellular physiology and interferes with the precise determination of the RNA degradation rate (Blattner et al 2000; Friedel et al 2009) Here, we present a novel inhibitor-free method (5′-bromo-uridine immunoprecipitation chase, BRIC) that enables measurement of RNA decay under nondisruptive conditions Determination of the half-lives of whole transcripts by BRIC, combined with multifaceted deep sequencing (BRIC-seq), suggest that there is a relationship between the stability of ncRNAs, as well as mRNAs, and their physiological functions

Long noncoding RNAs are rarely translated in two human cell lines

Abstract: In addition to over 20,000 protein-coding genes and known small-RNA, including microRNA host genes, the human genome includes at least 9640 loci transcribed solely into long, non-protein-coding RNAs (long noncoding RNAs; lncRNAs), often with multiple transcript isoforms (Derrien et al. 2012). Of these, only a minority (under 100) have been functionally characterized at an individual level by forward and reverse genetic approaches in organismal and cell culture models. The remainder are known purely via high-throughput discovery and expression analysis. Well-known examples of lncRNAs that have been functionally characterized in-depth include the imprinted Myc target H19 (Gabory et al. 2009), the epigenetic homeobox gene regulator HOTAIR, which promotes cancer metastasis (Gupta et al. 2010), and Xist, the lncRNA that is responsible for inactivation of the mammalian X-chromosome (Jeon and Lee 2011). While these few examples already attest to the diversity of lncRNA functions in chromatin remodeling and imprinting, the diversity of heretofore-uncharacterized lncRNAs hints at numerous additional lncRNA-dependent regulatory mechanisms in mammalian systems. Miat is another example of a recently discovered lncRNA that takes part in a direct network feedback loop with the Pou5f1 pluripotency factor in stem cells (Pou5f1 is also known as Oct4); Miat is both a direct target of and a direct regulator of Pou5f1 (Lipovich et al. 2010; Sheik Mohamed et al. 2010). Hence, lncRNAs can be both regulated by and regulators of key transcription factors. LncRNA genes are transcribed in a diverse range of human tissues and cell lines, and show highly specific spatial and temporal expression profiles, which, in conjunction with detailed molecular characterization of the lncRNAs, attest to numerous distinct functions. These functions include, but are not limited to, epigenetic and post-transcriptional gene expression regulation, sense-antisense interactions with known protein-coding genes, direct binding and regulation of transcription factor proteins, nuclear pore gatekeeping, and enhancer function by transcriptional initiation of lncRNAs that cause chromatin remodeling (Lipovich et al. 2010). Mammalian lncRNAs have epigenetic signatures comparable to those of protein-coding genes, frequently associate with the polycomb repressor complex PRC2 which renders them capable of regulating numerous target genes through histone modifications suppressing gene expression, and mediate global transcriptional programs of cancer transcription factors (Guttman et al. 2009; Khalil et al. 2009; Huarte et al. 2010; Derrien et al. 2012). A particularly intriguing property of mammalian lncRNAs is their lack of evolutionary conservation, relative to protein-coding genes. Primate-specific lncRNAs in the human genome are increasingly well-documented in the literature (for a review citing multiple pertinent recent reports, see Lipovich et al. 2010). Previously, Tay et al. (2009) screened the human genome for primate-specific single-copy genomic sequences, uncovering 131 primate-specific transcriptional units supported by transcriptome data. The brain-derived neurotrophic factor (BDNF) gene, a key contributor to synaptic plasticity, learning, memory, and multiple neurological diseases, is overlapped by a cis-encoded primate-specific lncRNA (Pruunsild et al. 2007). Most recently, Derrien et al. (2012) found that ∼30% of human lncRNA transcripts in GENCODE, many of which are expressed in the brain, are primate specific. The resulting relevance of lncRNAs to species-specific phenotypes, including primate and human uniqueness, highlights the importance of using empirical methodologies to document whether lncRNAs are actually non-protein-coding. The majority of definitively known lncRNAs have been annotated using empirical evidence such as cDNA and EST alignments to genome assemblies (Carninci et al. 2005; Katayama et al. 2005; Affymetrix/Cold Spring Harbor Laboratory ENCODE Transcriptome Project 2009). Yet, despite the attention that they have received, the noncoding status of most lncRNA genes and transcripts has been established mostly through computational means including: examining the size of open reading frames (ORFs), assessing conservation of ORFs that are shorter than known proteins, and looking for conserved translation initiation and termination codons. However, a recent flurry of literature suggests that there may exist a class of bifunctional RNAs encoding both mRNAs and functional noncoding transcripts: Indeed, there is direct evidence for rare members of this transcript class in human, mouse, and fly (Hube et al. 2006; Kondo et al. 2010; Dinger et al. 2011; Ingolia et al. 2011; Ulveling et al. 2011). Hence, identifying the fraction of ostensibly noncoding RNAs that may encode polypeptides is a compelling and open question. In this report, we utilize empirical evidence to estimate, in two ENCODE cell lines, the fraction of annotated lncRNAs that may encode, and therefore possibly function through, polypeptides. As part of the Encyclopedia of DNA Elements (ENCODE) project, matched-sample long polyA+ and polyA− RNA-seq data were produced, along with tandem mass spectrometry (MS/MS) data for cellular proteins, for the Tier-1 “ENCODE-prioritized” human cell lines K562 and GM12878. The RNA-seq data provides measures of relative gene expression in various cellular compartments (Djebali et al. 2012); for both GM12878 and K562, nucleus, cytosol, and whole-cell samples were used to sequence both polyA+ and polyA− RNA populations. These data have been used to obtain measures of transcript abundance for all genes in GENCODE v7 annotation (the annotation generated for the ENCODE Consortium), based on ENCODE and other data (Harrow et al. 2012). The mass spec data were produced via a “shotgun” approach, wherein cells were cultured, subcellular fractionation performed, followed by protein separations, tryptic digestion, and MS/MS analysis. The resulting spectra were mapped directly to a 6-frame translation of the entire hg19 assembly to produce a “proteogenomic track” within the UCSC Genome Browser (Kent 2002; Karolchik et al. 2009), and were also mapped against the GENCODE gene annotation set (J Khatun, Y Yu, J Wrobel, BA Risk, HP Gunawardena, A Secrest, WJ Spitzer, L Xie, L Wang, X Chen, et al., in prep.). Integrative analysis of RNA and proteomics data has been explored in the literature and is examined in another ENCODE paper, highlighting translation of novel splice variants and expressed pseudogenes (Tian et al. 2004, Djebali et al. 2012). However, these data have not yet been applied to examine the empirical evidence for or against translation of computationally classified human long noncoding RNAs. A recent joint study of RNA and proteomic data in mouse revealed that protein levels and mRNA levels correlate such that RNA concentration is predictive of at least 40% of the variation in protein levels (Schwanhausser et al. 2011). Since lncRNA genes are expressed, on average, at 4% of the level of protein-coding genes in the ENCODE cell lines (Derrien et al. 2012), we expect a similarly low level of expression for any putative protein(s) translated from lncRNAs. Therefore, to interrogate the translational competence of lncRNAs, we must account for the relative expression levels of these transcripts. It has been shown that the quantity of detectable matches between MS/MS spectra and their corresponding peptides in a transcript correlate to protein abundance levels (Lu et al. 2007). This means that the number of detected peptide matches is an approximate surrogate for protein abundance (Liu et al. 2004; Vogel and Marcotte 2008). We used this characteristic to determine a calibration function that links mRNA expression abundance and protein expression abundance for the ENCODE data from K562 and GM12878. In our analysis, 21% of GENCODE v7 protein-coding genes are represented by at least one uniquely mapping peptide in any MS/MS sample, and the majority of those genes detected are expressed above 5 RPKMs in the whole-cell RNA-seq data (Harrow et al. 2012). We used these data, applying state-of-the-art machine-learning models to estimate the translational competence of transcripts as a function of RNA expression levels in various cellular compartments and RNA fractions. Using these models, we “regressed out” the expression-level effects to compare the translation competency of ostensibly noncoding transcripts to that of known mRNAs. We then manually examined each lncRNA for which we obtained empirical evidence of coding capacity. From these data, we determined the proportion of lncRNAs that appear to be truly “noncoding” in ENCODE Tier 1 cell lines, and we examined the exceptional cases where there was strong evidence of protein translation to determine whether these are indeed translated lncRNAs or simply misannotated mRNAs.