Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.
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We thank B. Palen and J. Hallum for technical assistance with the high-performance computing cluster, S. Roychowdhury for reviewing the manuscript, the University of Michigan DNA Sequencing Core for Sanger sequencing and K. Giles for critically reading the manuscript and for the submission of documents. This work was supported in part by US National Institutes of Health Prostate Specialized Program of Research Excellence grant P50 CA69568, Early Detection Research Network grant UO1 CA111275, US National Institutes of Health grants R01 CA132874 and RO1 CA154365 (D.G.B. and A.M.C.), and US Department of Defense grant PC100171 (A.M.C.). A.M.C. is supported by the Prostate Cancer Foundation and the Howard Hughes Medical Institute. A.M.C. is an American Cancer Society Research Professor and a Taubman Scholar of the University of Michigan. R.M. was supported by a Prostate Cancer Foundation Young Investigator Award and by US Department of Defense Post-Doctoral Fellowship W81XWH-13-1-0284. Y.S.N. is supported by a University of Michigan Cellular and Molecular Biology National Research Service Award Institutional Predoctoral Training Grant.
Oncomine is supported by ThermoFisher, Inc. (previously Life Technologies and Compendia Biosciences). A.M.C. was a co-founder of Compendia Biosciences and served on the scientific advisory board of Life Technologies before it was acquired. The University of Michigan has filed a patent application for the use of a subset of the lncRNAs described in this study as biomarkers of cancer.
Integrated supplementary information
(a) Pie chart showing the number of studies curated from TCGA, ENCODE, MCTP and other publicly available datasets. (b) Workflow for bioinformatics processing of individual RNA-seq libraries. Data sets downloaded as BAM files were first converted to FASTQ format. Quality assessment of FASTQ files was performed using FASTQC. Reads mapping to mitochondria, ribosomal RNA, poly-A sequence, poly-C sequence or phiX virus (a spiked-in control) were filtered out. Fragment length distribution and orientation were determined by mapping a subset of the input reads to a set of large human exons (>500 bp). Reads were aligned using TopHat (v2.0.6) with Bowtie2 (v2.1.0). Gene fusion calling was performed using TopHat-Fusion (v2.0.6) with Bowtie1 (v0.12.9). Read alignment metrics were computed using Picard Tools, and genome track information was generated using BEDTools and UCSC binary utilities. Finally, ab initio transcriptome assembly was performed using Cufflinks version 2.0.2. (c) Scatter plot showing the total fragments (x axis) and the fraction of aligned fragments (y axis) for each RNA-seq library. Coarse quality control filters were used to remove libraries with fewer than 20 million total fragments or 20 million alignments (red point). (d) Dot plot showing for each library the fraction of aligned bases corresponding to RefSeq mRNAs (black points), intronic regions (green points) or intergenic regions (blue points) on the y axis. Libraries with fewer than 50% of aligned bases corresponding to RefSeq mRNA were filtered out (dotted line). (e) Pie chart showing the numbers of primary tumors (red), metastatic tumors (yellow), benign adjacent tissues or tissues from healthy individuals (blue), or cell lines (green) for 6,503 RNA-seq libraries that passed coarse quality control filters.
(a) The dot plot shows the numbers of short transfrags (red), short clipped exons (blue) and long transfrags (black) for each library. (b) The dot plot shows the numbers of unannotated intergenic or antisense transfrags (blue), sense intronic transfrags (green) and annotated transfrags (black) for each library. (c) Example transcript models illustrating categories of ab initio transcripts and sources of background noise. Annotated transfrags (black) overlap reference transcripts on the same strand. Unannotated antisense intronic or intergenic transfrags (blue) may be confounded by genomic DNA contamination. Unannotated sense intronic transfrags (green) may be confounded by contamination from both genomic DNA and incompletely processed RNA. (d) Decision tree depicting the transfrag filtering steps for a single library. First, transfrags were labeled ‘annotated’ or ‘unannotated’ on the basis of overlap with a reference transcriptome catalog. Annotated transfrags and unannotated multiexonic transfrags were considered expressed. Unannotated monoexonic transfrags within introns in the sense orientation of an overlapping transcript were discarded as incompletely processed RNA artifacts. Unannotated antisense or intergenic monoexonic transfrags were subjected to a bivariate kernel density classification method to discriminate recurrent, reliable transcription from genomic DNA contamination artifacts. Transfrags predicted as ‘expressed’ were incorporated into meta-assemblies. (e) Scatter plot comparing the sensitivity of the monoexonic transfrag classifier for correctly detecting annotated transcripts (y axis) and the fraction of unannotated transfrags predicted to be expressed (x axis). (f) Histogram demonstrating the sensitivity for correctly detecting annotated test transcripts held out of the classifier training process.
(a) Schematic of the transcriptome meta-assembly algorithm using a simplified example with three transfrags transcribed from left to right. The input to the meta-assembly is a list of weighted transfrags (in this case, the weights correspond to FPKM expression values). First, a splice graph is constructed using the transfrag exon boundaries. The splice graph is a directed acyclic graph (DAG) with nodes (rounded rectangular boxes) representing contiguously transcribed genomic bases and edges (arrows) corresponding to possible alternative splicing and promoter usage. The splice graph is then trimmed to remove poorly expressed starting/ending nodes, and adjacent nodes with a degree of one are collapsed. (b) The pruned splice graph from a is subjected to meta-assembly. To encapsulate the splicing pattern information present in the original transfrags, the pruned splice graph is converted into a splicing pattern graph. A splicing pattern graph is a de Bruijn graph where each node represents a group of k consecutive connected nodes from the splice graph (in this example, k = 3), and edges connect adjacent node groups. In real cases, k is automatically chosen to optimize the number of nodes in the splicing pattern graph. Finally, the splicing pattern graph is repeatedly traversed using a greedy dynamic programming algorithm to determine the set of most highly abundant isoforms from the graph. In this example, isoforms ACDE and ABCE recapitulate input transfrags with nearly identical FPKM values, and the invalid isoform combinations ACE and ABCDE are discarded. (c) Genome view showing an example of the meta-assembly procedure for breast cohort transfrags in a chromosome 12q13.3 locus containing the lncRNA HOTAIR and the protein-coding gene HOXC11 on opposite strands (chr. 12: 54,349,995–54,377,376, hg19). In total, 883 transfrags were considered background noise and not used for meta-assembly. A dense cluster of 7,471 expressed transfrags from 1,076 breast RNA-seq libraries was used as input. The aggregated transfrag signal on the positive (+) and negative (–) strands is shown below. Meta-assembly produced 17 transcripts from the transfrags, including transcripts that matched GENCODE HOTAIR and HOXC11 splicing patterns as well as HOTAIR transcripts with unannotated splice sites.
(a) Dot plots depicting the comparison of the MiTranscriptome with reference transcripts from RefSeq, UCSC or GENCODE. Precision (blue), precision for the subset of transcripts overlapping annotated transcripts (light blue) and sensitivity (orange) are plotted for each comparison. (b) Dot plots comparing the base-wise, splice-site and splicing pattern precision and sensitivity of MiTranscriptome and GENCODE using lncRNAs from RefSeq (left) or Cabili et al. (right). (c) Bar plots comparing the numbers of unannotated transcripts versus different classes of annotated transcripts for each of the 18 cohorts. Top, stacked bar plot showing annotated ncRNAs (red), pseudogenes (cyan), read-throughs (purple) and protein-coding genes (blue). Bottom, bar plot showing unannotated transcripts (pink).
(a) Density histogram depicting the confidence scores for annotated and unannotated lncRNAs. (b) Comparison of the relationship of the maximum number of exons per gene to the number of isoforms per gene. LncRNAs tend to have fewer exons than protein-coding genes, but they have complex splicing patterns that yield multiple transcript isoforms. (c) Cumulative distribution plot for the base-wise conservation fraction of proteins (blue), read-throughs (purple), pseudogenes (cyan), TUCPs (green) and lncRNAs (red). Random intergenic (black) and intronic (gray) regions are plotted as controls. The inset plot highlights the top 5th percentile of the distribution. (d) Bar plot showing KS test statistics for classes of transcripts versus random intergenic controls. (e) ROC curve for predicting the conservation of protein-coding genes versus random intergenic controls. The cutoff (pink point) chosen for calling highly conserved transcripts is plotted. (f) Cumulative distribution plot for promoter conservation (legend shared with c). The inset plot highlights the top 5th percentile of the distribution. (g) Bar plot showing KS tests for promoter conservation versus random intergenic regions. (h) ROC curve for predicting ultraconserved noncoding elements versus random intergenic regions. The cutoff (pink point) chosen for nominating ultraconserved lncRNAs is plotted.
One hundred lncRNA transcripts were validated by qRT-PCR across the A549, LNCaP and MCF-7 cell lines using an approach with or without revers transcriptase. Ct values were first normalized to housekeeping genes (CHMP2A, EMC7, GPI, PSMB2, PSMB4, RAB7A, REEP5, SNRPD3) and then to the median value of all samples using the DDCt method. Here data are plotted as a logirithmic of fold change over the median with s.e.m. Validation was performed on (a) 38 monoexonic transcripts and (b) 62 multiexonic transcripts. The boxed transcripts are two representative examples of lncRNAs with lineage/cancer specificity in breast or prostate according to SSEA analysis (Supplementary Table 10) whose cell line expression profile (by qRT-PCR) reflects what is expected from tissue analysis.
(a) Heat-map representation of the correlation between qPCR (fold change over the median) with RNA-seq (FPKM) of 100 selected transcripts in the A549, LNCaP and MCF-7 cell lines. (b,c) Representative example of 2 of 20 previously unannotated lncRNA transcripts that were analyzed by Sanger sequencing to ensure primer specificity with their associated chromatograms. As seen in the UCSC Genome Browser View, a (b) multiexonic lncRNA (Gene ID: G021137) and (c) monoexonic lncRNA (Gene ID: G030545).
(a) Decision tree showing the categorization of ab initio transcripts. Unannotated transcripts and annotated noncoding RNAs were classified as either lncRNA or TUCP. Transcript categories for protein-coding genes, pseudogenes and read-throughs were imputed from overlapping reference annotations. (b) ROC curve comparing the false positive rate (x axis) with the true positive rate (y axis) for CPAT coding potential predictions of noncoding RNAs versus protein-coding genes. (c) Curve comparing the probability cutoff (x axis) with balanced accuracy (y axis). The dotted line shows the cutoff used to call TUCP transcripts. (d) Scatter plot comparing the frequency of Pfam domain occurrences in non-transcribed intergenic space versus transcribed regions. Points in red were considered valid Pfam domain hits, and points in black were considered artifacts. (e) Three-dimensional scatter plot comparing Fickett score (x axis), ORF size (y axis) and Hexamer score (z axis) for all transcripts. Transcripts represented by red points contain valid Pfam domains, while blue do not. (f–h) Box plots comparing ORF size (f), Hexamer score (g) and Fickett score (h) for lncRNAs (red), TUCPs predicted by Pfam only (yellow), TUCPs predicted by CPAT (green) and TUCPs predicted by both Pfam and CPAT (blue).
(a) Venn diagram comparing the coverage of disease- or trait-associated genomic regions (i.e., GWAS SNPs) for the MiTranscriptome assembly (yellow) in comparison to reference catalogs (blue). (b) Pie charts comparing the distributions of intronic and exonic GWAS SNP coverage of the MiTranscriptome assembly (left) and reference catalogs (right). (c) Dot plot displaying the enrichment of GWAS SNPs versus random SNPs for different transcript categories. Enrichment odds ratios (transcript-SNP overlaps versus shuffled transcript-SNP overlaps) are plotted on the y axis. Points indicate the mean of 100 permutations for tests of enrichment with GWAS SNPs (circle) or random SNPs (diamond), and error bars depict ±2 s.d. of the distribution of odds ratios. Both exonic and whole-transcript enrichment is reported. (d) Dot plot showing the enrichment of GWAS SNPs (circle) versus random SNPs (diamond) for novel intergenic lncRNAs and TUCPs. Enrichment odds ratios (transcript-SNP overlaps versus shuffled transcript-SNP overlaps) are plotted on the y axis. Points indicate the mean of 100 shuffles for comparisons with GWAS SNPs (circle) or random SNPs (diamond), and error bars depict ±2 s.d. of the distribution of odds ratios. Both exonic and whole-transcript enrichment is reported.
(a) Heat map of lineage-specific transcripts nominated by SSEA. Each column represents a sample set from 1 of 25 cancer (dark gray) and 13 normal (light gray) lineages, and each row represents an individual transcript. Colored labels above columns reflect the organ system cohorts used in assembly. Row side colors correspond to lncRNAs (red), TUCPs (green), pseudogenes (cyan), read-throughs (purple) and protein-coding transcripts (blue). All transcripts were statistically significant (FDR < 1 × 10−7) and ranked in the top 1% of the most positively or negatively enriched transcripts within at least one sample set. The heat-map color spectrum corresponds to percentile ranks, with underexpressed transcripts colored blue and overexpressed transcripts colored red. The column dendrogram shows unsupervised hierarchical clustering of the sample sets. (b) Heat map of cancer-specific transcripts (CATs) nominated by SSEA. Columns represent 12 cancer types, and colored column labels reflect the organ system cohorts used in assembly. All transcripts were statistically significant (FDR < 1 × 10−3) and ranked in the top 1% of the most positively or negatively enriched transcripts within at least one sample set. The column dendrogram shows unsupervised clustering results. The row side color and heat-map color schemes are identical to those in a.
(a) Scatter plot grid showing lineage-specific and cancer-specific transcripts nominated by SSEA. A row of scatter plots for each transcript category is plotted across 12 cancer types. Each plot shows the cancer versus normal enrichment score (x axis) and the cancer lineage enrichment score (y axis). Red points indicate cancer and lineage associated transcripts within the respective cancer types, and gray points indicate all other cancer and lineage associated transcripts. (b,c) Box plots comparing the performance of (b) positively enriched cancer and lineage associated transcripts and (c) negatively enriched transcripts for each category across 12 cancer types. The average of the lineage and cancer versus normal ES is plotted on the y axis.
(a) Genomic view of the chromosome 6q26-q27 locus. The protein-coding genes QKI and PDE10A flank an intergenic region with two annotated lncRNAs, AK093114 and AK090788. MiTranscriptome transcripts are shown in a dense view populating this intergenic space. The most zoomed view (bottom) depicts MEAT6, a melanoma-associated lncRNA. AK090788 overlaps a portion of MEAT6, but the full MEAT6 transcript uses an alternate start site (black arrow). (b) Expression data for MEAT6 (demarcated by an asterisk in a). This isoform variant does not use the alternate start site used by MEAT6 and closely resembles AK090788. (c,d) Expression profiles for cancer- and lineage-associated transcripts across all MiTranscriptome tissue cohorts are shown for (c) lung adenocarcinoma and (d) thyroid cancer.
Supplementary Figures 1-12 and Supplementary Note. (PDF 8387 kb)
Supplementary Tables 1-9, 11, 12, 14 and 15. (XLSX 4529 kb)
Specific details for lncRNA discoveries. (XLSX 22135 kb)
GSEA results. (XLSX 19085 kb)
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Iyer, M., Niknafs, Y., Malik, R. et al. The landscape of long noncoding RNAs in the human transcriptome. Nat Genet 47, 199–208 (2015). https://doi.org/10.1038/ng.3192
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