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Ribosome profiling reveals resemblance between long non-coding RNAs and 5′ leaders of coding RNAs
Guo-Liang Chew, Andrea Pauli, John L. Rinn, Aviv Regev, Alexander F. Schier, Eivind Valen


Large-scale genomics and computational approaches have identified thousands of putative long non-coding RNAs (lncRNAs). It has been controversial, however, as to what fraction of these RNAs is truly non-coding. Here, we combine ribosome profiling with a machine-learning approach to validate lncRNAs during zebrafish development in a high throughput manner. We find that dozens of proposed lncRNAs are protein-coding contaminants and that many lncRNAs have ribosome profiles that resemble the 5′ leaders of coding RNAs. Analysis of ribosome profiling data from embryonic stem cells reveals similar properties for mammalian lncRNAs. These results clarify the annotation of developmental lncRNAs and suggest a potential role for translation in lncRNA regulation. In addition, our computational pipeline and ribosome profiling data provide a powerful resource for the identification of translated open reading frames during zebrafish development.


Long non-coding RNAs (lncRNAs) have emerged as important regulators of gene expression during development (Pauli et al., 2011; Rinn and Chang, 2012). lncRNAs were initially discovered for their essential roles in imprinting (Barlow et al., 1991; Bartolomei et al., 1991; Jinno et al., 1995; Sleutels et al., 2002) and mammalian X chromosome inactivation (Borsani et al., 1991; Brockdorff et al., 1992; Brown et al., 1992). Studies of Hox gene regulation in mammals and of flowering control in plants have identified additional lncRNAs, such as HOTTIP (Wang et al., 2011) and COOLAIR (Ietswaart et al., 2012; Swiezewski et al., 2009). The past decade has seen an explosion of genome-wide studies that have identified thousands of putative lncRNAs in a range of organisms (Bertone et al., 2004; Cabili et al., 2011; Carninci et al., 2005; Collins et al., 2012; Derrien et al., 2012; Djebali et al., 2012; Birney et al., 2007; Fejes-Toth et al., 2009; Guttman et al., 2009; Guttman et al., 2010; Kapranov et al., 2002; Kapranov et al., 2007; Okazaki et al., 2002; Pauli et al., 2012; Ravasi et al., 2006; Tilgner et al., 2012). Although the developmental roles of the vast majority of these novel transcripts are unknown, recent studies in zebrafish and embryonic stem cells (ESCs) have indicated roles for lncRNAs during embryogenesis, pluripotency and differentiation (Guttman et al., 2011; Ulitsky et al., 2011).

A prerequisite for the functional analysis of lncRNAs is the high-confidence annotation of this class of genes as truly non-coding. The distinction of lncRNAs from coding mRNAs has often relied on the computational classification of expressed transcripts (Dinger et al., 2008; Guttman and Rinn, 2012). These classifiers evaluate transcript features, such as open reading frame (ORF) lengths, coding potential, and protein sequence conservation. Such computational approaches can distinguish between coding RNAs and lncRNAs (Cabili et al., 2011; Carninci et al., 2005; Guttman et al., 2009; Pauli et al., 2012; Ulitsky et al., 2011), but may also give rise to misclassifications: lncRNAs containing short conserved regions might be misclassified as protein-coding (false negatives), whereas protein-coding transcripts containing short or weakly conserved ORFs might be misclassified as non-coding (false positives). For example, two recent zebrafish lncRNA catalogs (Pauli et al., 2012; Ulitsky et al., 2011) share little overlap, suggesting that novel approaches are needed to distinguish coding from non-coding RNAs.

One approach to detect potential coding sequences is ribosome profiling (Ingolia et al., 2009; Ingolia et al., 2012). In this method, mRNA fragments protected from RNaseI digestion by cycloheximide (CHX)-stalled 80S ribosomes are isolated and sequenced. The resultant ribosome-protected fragments (RPFs) correspond to the sites where translating ribosomes resided on mRNA transcripts at the time of isolation, yielding a quantitative, genome-wide snapshot of translation at nucleotide (nt) resolution. Application of this method to mouse embryonic stem cells (mESCs) detected RPFs associated with many previously annotated lncRNAs (Ingolia et al., 2011). This study suggested that the majority of annotated lncRNAs contain highly translated regions comparable to protein-coding genes and might encode proteins. However, translation of a transcript was inferred by measuring localized densities of ribosome profiling reads relative to expression (translational efficiency; TE). As shown below, we find that this approach does not reliably distinguish the main ORFs (coding sequences; CDSs) from upstream ORFs (uORFs). This distinction is important because the vast majority of uORFs are unlikely to code for functional peptide products because their peptide sequences are not conserved, even though their presence in the 5′ leader may be (Hood et al., 2009). Indeed, a recent peptidomics study suggested that most annotated lncRNAs do not generate stable protein products (Bánfai et al., 2012). It has therefore remained unclear what fraction of currently annotated putative lncRNAs are truly non-coding.

Here, we address the issue of lncRNA annotation by combining ribosome profiling during early zebrafish development with a new machine-learning approach. Our study suggests that dozens of previously annotated lncRNAs are protein-coding contaminants. In addition, we find that many lncRNAs in zebrafish and ESCs resemble the 5′ leaders of coding mRNAs, raising the possibility that translation is involved in lncRNA regulation. The methods and datasets provided in this study provide a broad resource for the identification of translated ORFs during zebrafish development.


Ribosome profiling

Ribosome profiling was adapted from Ingolia et al. (Ingolia et al., 2011) and applied to a zebrafish developmental time course. For each stage [2-4 cells, 256 cells, 1000 cells, dome, shield, bud, 28 hours post fertilization and 5 days post fertilization (Kimmel et al., 1995)], 400-600 embryos were washed with cold PBS, flash-frozen and stored at -80°C. Embryos were lysed by repeated micropipetting in 1.5 ml of cold polysome buffer (20 mM Tris-HCl pH 7.4, 250 mM NaCl, 15 mM MgCl2, 1 mM dithiothreitol, 100 μg/ml CHX) with added 0.5% Triton X-100, 500 μg/ml guanosine 5′-[β,γ-imido]triphosphate (GMP-PNP), 24 U/ml TurboDNase (Ambion AM2238), then incubated with agitation for 10 minutes at 4°C, and clarified by centrifugation at 1300 g for 10 minutes at 4°C. For ribosome footprinting, 20 μl RNaseI (Ambion AM2294) was added to the 1.5 ml of supernatant and incubated for 30 minutes at 37°C, then stopped by chilling on ice and addition of 40 μl of SuperaseIn (Ambion AM2694). Footprinted samples were pelleted through a sucrose cushion (1 M sucrose in polysome buffer with added 100 U/ml SuperaseIn) by centrifugation at 260,000 g for 4.5 hours at 4°C, and re-suspended in 800 μl 10 mM Tris pH 7.4 with 1% SDS. RNA was purified by hot acid phenol/chloroform extraction and precipitated by standard ethanol precipitation. From this point, ribosome profiling Illumina-compatible sequencing libraries were prepared as previously described (Ingolia et al., 2011). Supplementary material Table S1 lists the primers and subtractive hybridization oligonucleotides corresponding to the most abundant rRNA contaminants that were determined in a pilot ribosome profiling experiment.

Sequencing and mapping of RPFs

Ribosome profiling libraries were sequenced on an Illumina HiSeq 2000 (one stage per lane, 44 bp reads), resulting in a total of 880 million reads (for an overview, see supplementary material Fig. S1). Following adapter sequence trimming, RPFs were compared with zebrafish rRNAs from the SILVA rRNA database (Quast et al., 2013) using Bowtie2 (Langmead and Salzberg, 2012) (parameters: -N 1; -L 20; -k 20). Reads matching rRNA (∼50%) were discarded. The remaining RPFs were mapped by Tophat2 (Trapnell et al., 2009) (parameters: no indels; no novel junctions; -M; -g 10) to a zebrafish developmental transcriptome (Pauli et al., 2012) and the Zv9 genome assembly, resulting in 317 million mapped reads. To obtain near-nucleotide resolution from ribosome profiling (supplementary material Fig. S2), RPFs aligning at annotated start and stop codons of RefSeq genes were subdivided by read length (supplementary material Fig. S2B). Approximate P-site position for each read-length was determined by inspection of coverage and phasing of the read’s left-most position relative to annotated start and stop codons. Offsets were determined to be +12 for 27-28 nt RPFs, +13 for 29-31 nt RPFs, and +14 for 32 nt RPFs (supplementary material Fig. S2A). Based on observable phasing over the coding sequences, RPFs between 27 and 32 nts (totaling 220 million) were deemed to be high quality and were used in subsequent analysis. The remaining RPFs were likely to be over- or under-digested, and were discarded. Library sizes between stages were normalized by the number of RPFs in each stage that mapped to annotated coding regions of RefSeq genes. mESC ribosome profiling data was obtained from Ingolia et al. (Ingolia et al., 2011).

Construction of training and lncRNA data sets

The zebrafish training set was constructed from RefSeq genes in the Zv9/danRer7 zebrafish genome assembly. Only genes expressed at fragments per kilobase of exon per million fragments mapped (FPKM) >1 (summed over the developmental transcriptome) (Pauli et al., 2012) were used. Similarly, the mouse training set was based on RefSeq genes in the mm9 mouse genome assembly expressed at FPKM >1 in mESCs (Guttman et al., 2010). ORFs were defined as regions starting with either an ATG or CTG and ending with an in-frame stop codon. Three classes of ORFs were defined: (1) the CDSs in the context of their respective transcripts, (2) all RPF-containing ORFs in transcript leaders in the context of the detached 5′ leaders and (3) all RPF-containing ORFs in the transcript trailers in the context of the detached 3′ trailers (Fig. 1). CDSs with trailers shorter than 100 nt were not included. Owing to the high number of truncated transcripts annotated in zebrafish, all ORFs in the zebrafish set were required to be at least 20 nts from the transcript edge. ORFs in leaders and trailers were filtered to ensure lack of any overlap with annotated RefSeq, Ensembl or XenoRefSeq coding regions.

For classification, lncRNAs were required to be expressed at >1 FPKM over the developmental time course (for zebrafish) and in ESCs (for mouse). As a few transcripts had a clear RPF-covered coding ORF, but lacked start/stop codons (probably owing to truncations in transcript assembly), ORFs were allowed to extend beyond the ends of transcripts. To account for possible transcript truncations, it was assumed that the start/stop of the ORF was at the edge and a pseudo-trailer of 10 nt was added to all transcripts when calculating IO scores (see below).


For each ORF, we used four metrics designed to distinguish between the three classes and capture the features of protein coding genes:

Translational efficiency (TE) is defined as (density of RPFs within ORF)/(RNA expression). Density is the average sum of normalized RPFs over the embryonic time course within the ORF divided by the length of the ORF. RNA expression is the average FPKM of the locus over the embryonic time course containing this transcript.

Inside versus outside (IO) is defined as (coverage inside ORF)/(coverage outside ORF). Coverage refers to the number of nt positions having any RPF divided by the total number of nts inside or outside the ORF. A pseudo-count of 1 is added to both the inside and outside sums.

Fraction length (FL) is defined as (length of ORF)/(length of transcript), i.e. the fraction of the transcript covered by the ORF.

Disengagement score (DS) is defined as (RPFs over ORF)/(RPFs downstream), i.e. the number of RPFs inside the ORF divided by the number of RPFs downstream. A pseudo-count of one was added to both the ORF and downstream sums.

A random forest classifier (Breiman, 2001) (implemented in the R package randomForest) was trained using these four metrics on the respective training sets. The three classes were weighted according to size, and standard options were used (500 trees, two variables per split). Classes were assigned to loci in order to minimize cross-mapping between coding and non-coding isoforms. If any ORF was classified as coding, the locus was considered to be coding. If not, the locus was considered to be leader-like if at least one ORF was classified as leader-like. Finally, if all ORFs were classified as trailer-like or if no ORF had RPFs, the locus was classified as trailer-like.

Public database accession numbers

The ribosome profiling data are accessible at Gene Expression Omnibus (GEO) with accession number GSE46512. The RNA-seq data was published previously (Pauli et al., 2012) and is available at GEO (accession number GSE32900, subseries GSE32898).


Ribosome profiling outlines translated regions of zebrafish transcripts

To identify ribosome-associated regions in the zebrafish transcriptome, we generated high-depth ribosome profiles over a time course of eight early developmental stages (Fig. 1; supplementary material Fig. S1; for details see Materials and methods). Of 220 million high-quality RPFs, 84.5 million RPFs mapped to RefSeq genes (see Fig. 2A for examples of ribosome profiles). Approximately 81% of RefSeq genes that expressed >1 FPKM (12,228 genes) had at least ten normalized RPFs (supplementary material Fig. S3A), and about 68% of genes had reads over at least 10% of their annotated coding sequence (CDS; supplementary material Fig. S3B). Within exons of RefSeq transcripts, 95.7% of RPFs mapped to CDSs (mean density of 3.64 RPFs per nt), 0.54% of RPFs mapped to 3′ transcript trailers (mean density of 0.054 RPFs per nt), and the rest (3.71%) mapped to 5′ transcript leaders (mean density of 1.46 RPFs per nt). This distribution corresponds to a >65-fold enrichment of RPFs associated with CDSs compared with 3′ trailers, and a >25-fold enrichment of RPFs associated with 5′ leaders compared with 3′ trailers, consistent with ribosome profiling data in other systems (Brar et al., 2012; Ingolia et al., 2011). As observed in previous studies, we found triplet phasing of ribosome profiles in the CDSs of coding genes, corresponding to the translocation of translating 80S ribosomes in steps of 3 nts (Fig. 2B).

Fig. 1.

Overview of lncRNA classification pipeline. (A,B) High-throughput sequencing data (ribosome profiling and RNA-seq) from eight early developmental stages (A) is used to train a classifier with RefSeq coding sequences (CDSs), 5′ leaders and 3′ trailers (B). (C) The translated ORF classifier (TOC) uses ribosome profiles and gene expression levels to classify putative lncRNAs as protein-coding (blue), leader-like (green) or trailer-like (red).

Fig. 2.

Ribosome profiles outline translated ORFs of coding genes. (A) Representative examples of ribosome-protected fragment (RPF) densities associated with protein-coding genes. Gene structures are depicted as thick bars for the coding sequence (CDS), thin bars for 5′ leaders and 3′ trailers, and dashed lines for introns. Note that the majority of RPFs map within the CDSs and are flanked by the annotated initiation (START, green) and termination codon (STOP, red). The bottom three panels show examples of uORF-containing genes. For these genes, RPF reads map to the CDSs and to short ORFs within the 5′ leaders. (B) RefSeq metagene analysis of relative phasing of ribosome P-sites (see Materials and methods). Relative phasing is defined as the number of RPFs at a given position divided by the mean of the number of RPFs at the four adjacent positions. i.e. relative phasing at position i=RPFs at position i/mean (RPFs at positions i-2, i-1, i+1 and i+2). As in previous studies (Ingolia et al., 2011), triplet phasing of ribosome profiles was observed.

Consistent with the release of 80S ribosomes at in-frame stop codons, RPFs over 3′ trailers tend to be sparse and randomly distributed (Fig. 2A), and may represent background experimental noise inherent to the ribosome profiling method. As observed in ribosome profiling data in other systems (Brar et al., 2012; Fritsch et al., 2012; Ingolia et al., 2011; Lee et al., 2012), 5′ leaders of coding transcripts are widely associated with ribosomes, showing relatively high densities of RPFs at locations often corresponding, but not limited, to uORFs. The stop codons of annotated ORFs are significantly enriched for RPFs (supplementary material Fig. S2C). We find widespread occurrence of uORFs (49.5% of RefSeq genes have RPF-containing uORFs), as well as many instances of translated, extremely short ORFs that are as small as an AUG followed by a stop (minimal ORFs or minORFs) (supplementary material Fig. S4). These results highlight the power of this approach in identifying translated regions of zebrafish transcripts.

Translated ORF classifier (TOC) distinguishes ORFs in annotated 5′ leaders, CDSs and 3′ trailers

To use the ribosome profiling dataset for the classification of ORFs, we developed a random forest classifier (Breiman, 2001). We tested whether ribosome profiles over RNA subregions might reliably distinguish CDSs from ORFs in 5′ leaders and from ORFs in 3′ trailers. To train the classifier, we used the RefSeq gene sets in zebrafish and mouse (see Materials and methods for details). Our classifier, called TOC (translated ORF classifier), employs four features (Fig. 3A): (1) Translational efficiency (TE) - the density of ribosome profiling reads over an ORF relative to its expression level; (2) inside versus outside (IO) - the ratio of bases covered within an ORF versus outside (upstream and downstream), capturing a distinct feature of coding transcripts for which read coverage tends to be predominantly over a single ORF; (3) fraction length (FL) - the fraction of the transcript covered by the ORF, accounting for the observation that annotated CDSs tend to span a significant portion of the transcript; and (4) disengagement score (DS) - the degree to which RPFs are absent downstream of the ORF, building on prior knowledge that re-initiation after extended translation and stop-codon read-through are rare events (Jackson et al., 2007). These features effectively integrate intrinsic transcript information, such as sequence and location of ORFs, with external data, such as ribosome profiling and expression levels derived from RNA-seq.

Fig. 3.

TOC distinguishes ORFs in 5′ leaders, CDSs and 3′ trailers. (A) A training set is constructed from RefSeq genes using (1) annotated CDSs (coding ORFs, blue) in the context of the whole transcript, (2) RPF-containing ORFs in the 5′ leader sequence (green) in the context of the 5′ leader, and (3) RPF-containing ORFs in the 3′ trailer (red) in the context of the 3′ trailer (see Materials and methods). The four metrics used to train the classifier are displayed in the gray box (TE, translational efficiency; IO, inside versus outside; FL, fragment length; DS, disengagement score). After training, TOC uses RPF-covered ORFs to classify transcripts. (B) The combination of the four metrics separates coding ORFs, leaders and trailers of the training set. Transcripts lacking a protein-coding ORF cluster with trailers and leaders of the training set, as shown for three validated zebrafish lncRNAs (black). The density of each measure is shown along the axes.

Although individual features were able to separate one class of RefSeq ORFs from the other two, the combination of all four was necessary to distinguish reliably ORFs within annotated 5′ leaders, CDSs and 3′ trailers (Fig. 3; supplementary material Fig. S5). Notably, TE distinguished 3′ trailers from 5′ leaders and CDSs, whereas DS helped separate uORFs in 5′ leaders from CDSs (Fig. 3B for zebrafish; supplementary material Fig. S5 for mouse). The combination of IO and FL differentiated CDSs from ORFs in 5′ leaders and 3′ trailers (Fig. 3B; supplementary material Fig. S5). The use of all four features in the TOC classifier was highly accurate in distinguishing CDSs from 5′ leader-like ORFs and 3′ trailer-like ORFs even at low RNA expression levels (supplementary material Fig. S6; overall out-of-bag error for zebrafish: 3.25%). These results establish TOC as a powerful classifier to distinguish ORFs in annotated 5′ leaders, CDSs and 3′ trailers.

TOC refines classification of lncRNAs

To refine the classification of putative lncRNAs, we applied TOC to the catalogs recently published for zebrafish embryos (Pauli et al., 2012; Ulitsky et al., 2011) and mESCs (Guttman et al., 2011). The application of TOC to these datasets is justified by the biochemical similarity between coding mRNAs and recently annotated lncRNAs (e.g. both are 5′ capped and 3′ poly-adenylated). Notably, TOC analysis revealed that dozens of putative lncRNAs have the same characteristics as protein-coding mRNAs: a main CDS engaged by ribosomes and few (if any) RPFs downstream (Fig. 4; supplementary material Fig. S7 and Tables S2-S4). Depending on the dataset, we find that 8-45% of previously proposed lncRNAs are likely to be bona fide protein-coding mRNAs (Fig. 4; supplementary material Tables S2-S4). These transcripts will be an interesting resource for identifying previously uncharacterized proteins. By contrast, 18-44% of putative lncRNAs showed little or no association with ribosomes, akin to 3′ trailers of coding transcripts (Fig. 4; supplementary material Tables S2-S4). These transcripts are bona fide lncRNAs and warrant functional characterization.

Fig. 4.

TOC refines classification of lncRNAs. (A) TOC-based classification improves previous lncRNA predictions. Shown are RNA-seq and ribosome profiling read densities associated with three putative lncRNAs (Ulitsky et al., 2011), which had conflicting annotations in published zebrafish lncRNA sets (Pauli et al., 2012; Ulitsky et al., 2011). Transcript structures are shown in black. Introns are indicated as dashed lines. The region scoring highest in PhyloCSF (Lin et al., 2011) is indicated in orange. Whereas TOC reveals the protein-coding nature of linc-ca2, it confirms the non-coding nature of the two conserved lncRNAs megamind and cyrano. These two lncRNAs had been filtered out in the Pauli et al. lncRNA set owing to their relatively high phylogenetic codon substitution frequency scores (PhyloCSF >20). (B) Fraction of loci that are classified by TOC as coding (blue), leader-like (green) and trailer-like (red) in three collections of lncRNAs: ZF1 (Pauli et al., 2012), ZF2 (Ulitsky et al., 2011) and mESCs (Guttman et al., 2011).

Strikingly, we found that the ribosome profiles over more than 40% of putative zebrafish and mouse lncRNAs resemble 5′ leaders rather than 3′ trailers (Fig. 4; supplementary material Tables S2-S4). These lncRNAs contain ORFs with a higher TE than 3′ trailer-like lncRNAs, but have shorter and less conserved ORFs than do the CDSs of protein-coding genes (supplementary material Fig. S8). Similar to leaders, RPFs are often distributed over multiple ORFs, none of which stand out as a main CDS of a protein-coding gene. The leader-like class of lncRNAs represents a distinct subset of the previously described short, polycistronic ribosome-associated coding RNAs (sprcRNAs) (Ingolia et al., 2011). Unlike sprcRNAs, which are identified solely by TE, leader-like lncRNAs exclude misannotated protein-coding mRNAs and transcripts with spuriously associated ribosomes.

The association of ribosomes with leader-like lncRNAs raises two important questions: Do the associated ribosomes generate proteins? Are these proteins functional? Several observations suggest that leader-associated ribosomes might generate proteins that are likely to be non-functional. Recent studies have shown that the CHX used in ribosome profiling protocols acts through the E-site of the 60S ribosomal subunit (Schneider-Poetsch et al., 2010), and should only stabilize the translating 80S ribosome during the ribosomal footprinting step. Moreover, the sizes of ribosome footprints isolated in ribosome profiling protocols (∼30 nts) correspond to RNA fragments protected by 80S ribosomes (Wolin and Walter, 1988). The translation of ORFs within 5′ leaders is further supported by mass spectrometry data (Slavoff et al., 2013) and by observed enrichment of RPFs over sites of translation initiation in ribosome profiling data from harringtonine-treated (Ingolia et al., 2011), lactimidomycin-treated (Lee et al., 2012) and puromycin-treated (Fritsch et al., 2012) samples. Thus, leader-associated ribosome profiles are likely to represent actual translation of ORFs rather than ribosomal subunits scanning the transcript.

The lack of conservation of most uORFs suggests that the protein product might not be functional (Calvo et al., 2009; Hood et al., 2009; Neafsey and Galagan, 2007). Instead, ribosomal engagement with leader-like lncRNAs might be regulatory. Given the regulatory role of uORFs in some coding transcripts (Arribere and Gilbert, 2013; Calvo et al., 2009; Hinnebusch, 2005; Hood et al., 2009; Johansson and Jacobson, 2010), 5′ leader-like translation might affect lncRNA stability and/or subcellular localization. Translating ORFs within lncRNAs might target the transcript for nonsense-mediated decay (Tani et al., 2013), degrading it in the cytoplasm and/or retaining it in the nucleus (de Turris et al., 2011), resulting in the predominantly nuclear localization of most lncRNAs (Derrien et al., 2012). Prime candidates for such regulation are the minORF-containing lncRNAs for which the single amino acid product of their translation could not conceivably be functional. Alternatively, association of ribosomes with leader-like lncRNAs might be translational noise caused by the cytoplasmic location of 5′-capped and poly-adenylated transcripts. Such spurious translation may only be functional on evolutionary time scales as the source of novel coding genes (Carvunis et al., 2012).

In summary, our ribosome profiling data and translated ORF classifier allow the high-confidence annotation of coding and non-coding RNAs, complementing and extending previous computational approaches such as PhyloCSF. As demonstrated by our previously published pipeline (Pauli et al., 2012), these more traditional computational approaches can exclude the large majority of potential false-positives but misannotate some conserved lncRNAs as coding RNAs (e.g. cyrano and megamind) (Ulitsky et al., 2011) (Fig. 4). The use of additional approaches such as mass spectrometry will further improve the annotation of coding and non-coding RNAs in zebrafish (Slavoff et al., 2013).

Although our study has focused on the classification of lncRNAs, the accompanying ribosome profiling data will be a rich resource for the discovery of novel protein-coding genes that act during development. Our dataset increases the depth of previous ribosome profiling datasets in zebrafish by an order of magnitude (Bazzini et al., 2012) and expands the temporal coverage to five days of development. The nucleotide resolution of the data allows annotation of translated subregions of transcripts and the identification of potential protein isoforms, furthering ongoing efforts to refine zebrafish genome annotation (Kettleborough et al., 2013). Finally, the quantitative nature of ribosome profiling combined with existing RNA-seq data will enable studies of post-transcriptional and translational regulation during zebrafish development.


We thank Jonathan Weissman and Nick Ingolia for helpful advice on ribosome profiling and for sharing mouse ESC ribosome profiling data; Mitchell Guttman for sharing results before publication; and Rahul Satija and Schraga Schwartz for discussions and comments on the manuscript.


  • Funding

    G.-L.C. is supported by a Howard Hughes Medical Institute International Student Fellowship. A.P. and E.V. are supported by Human Frontier Science Program (HFSP) postdoctoral fellowships. This work was funded by the National Institutes of Health [grant numbers R01HG005111, R01GM056211 and R01HL109525 to J.L.R., A.R. and A.F.S.]. Deposited in PMC for release after 12 months.

  • Competing interests statement

    The authors declare no competing financial interests.

  • Author contributions

    A.P., A.F.S. and E.V. conceived the study. G.L.C. adapted and applied ribosome profiling to zebrafish, and analyzed resultant sequencing data, with support from A.P., A.F.S. and E.V. E.V. designed and implemented the classifier, with input from G.L.C. and A.P., and discussions with J.L.R., A.R. and A.F.S. G.L.C., A.P., E.V. and A.F.S. wrote the manuscript, with contributions from J.L.R. and A.R.

  • Supplementary material

    Supplementary material available online at http://dev.biologists.org/lookup/suppl/doi:10.1242/dev.098343/-/DC1

  • Accepted May 7, 2013.


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