Main

RNA-seq yields quantitative information about gene expression, alternative splicing, RNA editing, polyadenylation sites and other phenomena1,2,3. The prospect of using RNA-seq data to assemble reads into models of genes and transcripts has motivated the development of algorithms and software4,5,6,7,8. De novo assembly methods, such as Trinity5, Oases7 and Trans-Abyss8, assemble reads to construct transcript sequences, which are then mapped to a reference genome. Genome-guided approaches, such as Cufflinks4 and Scripture6, use reads that are aligned to a reference genome to identify transcript models.

The impact of RNA-seq data on genome annotation has been most substantial for organisms with minimal cDNA resources. For instance, RNA-seq data and Cufflinks were used to produce a de novo annotation for the sea urchin (Strongylocentrotus purpuratus), but, to prevent the inclusion of transcript fragments, this study incorporated a stringent filtering system that removed transcript models that lacked ORFs longer than 500 aa or that did not encode a known protein9. In contrast, in organisms where substantial annotation efforts are ongoing (for example, human10, fruit fly11, zebrafish12 and worms13), the impact of RNA-seq data has largely been by the manual incorporation of elements including new transcription start sites, splice junctions and polyadenylation sites. GRIT is designed to assemble a high-quality, experimentally driven genome annotation using a reference genome and short-read sequencing data, allowing it to be useful for the study both of model and nonmodel organisms.

It is not surprising that genome annotation has primarily remained in the domain of manual annotation and full-insert cDNA sequencing, because RNA-seq reads are too short to cover full transcripts, typically providing information only about three or four exons at a time14. This means that it is not always possible to positively identify alternative transcript isoforms, even as the read depth approaches infinity. Furthermore, biases in the RNA-seq assay make positive identification of novel transcript boundaries difficult1,15,16,17. Other genome annotation tools attempt to circumvent these problems by restricting the space of discoverable transcripts. For instance, Cufflinks only permits the minimal set of transcripts needed to explain the splice junctions, oversimplifying complex loci such as Down syndrome cell adhesion molecule 1 (Dscam1) of D. melanogaster. Trinity always extends transcript contigs to the last base, disallowing nested promoters and nested poly(A) sites. As we show, these restrictions can produce annotation sets that are in direct contradiction to observed data from complementary assays.

We use a sparse statistical model amenable to modern optimization techniques, combined with the integration of gene boundary data, to analyze RNA-seq data. In principle, our approach allows the construction of any transcript models that can be built by Cufflinks, Trinity, Scripture, Oases or Trans-Abyss, although our requirement that every transcript model be supported by experimental evidence can make GRIT more restrictive in practice. For the purposes of benchmarking, we have used a subset of the modENCODE data set (1.67 billion bp; Supplementary Table 1) to compare the performance of GRIT to the most widely used transcript-level RNA-seq analysis tools. We also applied GRIT to the full set of modENCODE RNA data (over 1 terabase of sequence data from CAGE, rapid amplification of cDNA ends (RACE), expressed sequence tags (EST), cDNA, 454, stranded paired-end RNA-seq and poly(A)-site-seq experiments) to generate a data-driven annotation with unprecedented detail of gene and transcript models for the fruit fly. The full-length transcript models have yielded a number of discoveries, including the findings that >20% of D. melanogaster protein-coding genes encode multiple localization signals and that alternative polyadenylation is more common than alternative splicing in neuronal tissue. These and related insights reported here and in a companion manuscript18 were not obtainable with other analysis tools, and they underscore the importance of integrating multiple types of assays when interpreting RNA sequencing data.

Results

GRIT

A brief overview of the GRIT method is outlined below; for details, see the Online Methods. GRIT makes few assumptions about the structure of transcripts, defining them as sets of genomic regions that begin at a transcription start site (TSS), optionally extend through one or more exons connected by splice junctions, and end with a transcription end site (TES). This implies four distinct element types: TSS exons, internal exons, TES exons and single-exon transcripts. TSS exons begin with an experimentally detected promoter (for example, via the CAGE or RACE assays or 5′ EST sequencing19) and end with a splice donor site. Similarly, TES exons begin with a splice acceptor site and end with an observed TES (for example, a poly(A) site). Internal exons begin and end with a verified splice site, and single exon transcripts begin with a TSS and end with a TES. GRIT uses both canonical and noncanonical splice sites.

We identify elements by segmenting the genome into non-overlapping segments with attached labels that describe their segment boundary (Fig. 1a). After removing low-coverage segments, groups of adjacent segments are combined and labeled on the basis of their segment boundaries. For instance, an internal exon's 5′ boundary is a splice acceptor, and its 3′ boundary is a splice donor. TSS exons' 5′ boundaries are TSS sites, and their 3′ boundaries are splice donors. Similarly, the 3′ end of a TES exon is a TES, and the 5′ end is a splice acceptor.

Figure 1: Element discovery overview.
figure 1

(a) Exon discovery. For each gene segment, we identify CAGE peaks; segment the gene region using the CAGE peaks, splice boundaries and poly(A) sites; label the segments on the basis of their boundaries; filter intron segments with low RNA-seq coverage; and build labeled exons from adjacent segments. (b) Transcript discovery. For each gene, we construct a graph where each node is an exon discovered in a, and each edge is a junction. Then each candidate transcript is identified with a single path through this directed graph that begins with a TSS node and ends with a TES node.

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After the exons are identified, we assemble transcript models. We define the set of candidate transcripts as the union of single-exon transcripts and transcripts that begin with a TSS exon, optionally contain splice junction–connected internal exons, and end with a TES exon (Fig. 1b). GRIT differs from other methods, such as Cufflinks, in that it considers all possible paths subject to this restriction, rather than some minimal set of covering paths, allowing GRIT to correctly build transcripts in very complex loci such as Dscam1 (Supplementary Fig. 1a).

After identifying the possible set of candidate transcripts, we estimate their relative concentrations in the sample of interest; that is, their expression. This is challenging because reads cannot necessarily be assigned to a single transcript. Thus, we first identify transcripts by the non-overlapping exon segments. Then we group reads into the set of non-overlapping exon segments that they overlap, which we refer to as a bin (Supplementary Fig. 2). We define Yi as the number of reads of bin i.

The number of reads that are of a particular bin, when combined with information about the expected distribution of fragments from a particular transcript, provides information about transcript frequencies. Formally, we define an entry Xij in the design matrix X to be the probability of sampling bin j given that the read originated from transcript j. The maximum likelihood estimate of the transcript frequencies, , is then the vector that maximizes the log-likelihood, , subject to the constraints tj ≥ 0 and ∑jtj = 1. Whenever no row of Xij can be constructed from a positively weighted sum of other rows, the statistical model is said to be identifiable given the data, and we can use a convex optimization algorithm to estimate (Supplementary Note 1). When the model is not identifiable, we use a penalized likelihood that produces a sparse estimate of ; we choose the sparsity parameter so that the sparse estimate achieves approximately the same maximum as the unpenalized likelihood (Supplementary Note 1).

Forming confidence bounds on a particular transcript's frequency estimate, , requires finding the minimum and maximum values that tj can take while still being reasonably likely to produce the observed data set. Given some desired marginal significance level, α, we estimate our lower confidence bound for transcript j by the minimum value that tj can take over all possible values for such that . When the model is identifiable, simulations show that this approach produces confidence bounds with the correct rejection rates for realistic sample sizes (Supplementary Fig. 1e). When the model is not identifiable, the confidence bounds are conservative; that is, the lower confidence bound is zero.

The fact that GRIT produces conservative confidence bounds is a major advantage over other methods. GRIT allows the user to be confident that transcripts with lower confidence bounds greater than zero were likely present in the sample of interest, while unidentifiable regions can be easily detected and targeted for further experimentation. In contrast, the credible bounds that Cufflinks and Rsem20 produce are strongly dependent on a prior distribution, which can lead to dramatically anti-conservative confidence bounds even in moderately complex genes (Supplementary Fig. 1f).

Comparison to other tools

Current transcript discovery tools make assumptions about the structure of the underlying transcripts, usually restricting them to some identifiable subset. For instance, Cufflinks assumes that the set of possible transcripts is the minimal set of covering paths in the graphical model described in Online Methods, “Transcript Construction.” Trinity requires that transcript models extend to the furthest base of an assemblable contig, which disallows transcript models with nested transcription start and termination sites. The GRIT model allows both of these but requires gene boundary information. We benchmarked GRIT against Cufflinks, Scripture and Trinity+Rsem, using stranded RNA-seq, CAGE and poly(A)-site-seq data produced from dissected heads of 20-d-old adult flies (Ad20dHeads) (Supplementary Table 1).

We analyzed the recall and precision of the transcriptomes generated by GRIT and the three other annotation tools by comparing the transcripts predicted by each tool to 13,141 FlyBase 5.45 (ref. 21) transcripts corresponding to 7,079 genes expressed in Ad20dHeads. We considered transcripts equivalent when they had the same internal splicing structure and had gene boundaries within 50 bp of each other. Under this measure, GRIT recovered 44.2% of transcripts with 17.8% precision; Cufflinks recovered 13.4% of transcripts with 8.8% precision; Trinity+Rsem recovered 8.6% or transcripts with 3.0% precision; Scripture recovered 0.9% of transcripts with 1.4% precision (Fig. 2a). When we filtered predicted transcripts with expression score lower bounds less than 1 × 10−6 estimated fragments per kilobase per million reads (FPKM) at a marginal 99% significance level, then GRIT recovered 39.8% of FlyBase transcripts with 41.3% precision. The Cufflinks, Trinity and Scripture numbers were essentially unchanged.

Figure 2: Comparison with existing tools.
figure 2

(a) Recall and precision analysis. We compared the set of transcript isoforms discovered by GRIT, Cufflinks, Scripture and Trinity to the FlyBase annotation. A transcript was identified as a match if the internal structure was the same and the distal boundaries were within 50 bp (top) or 200 bp (bottom) of one another. (b) FPKM versus CAGE and poly(A)-site-seq counts. For each sample, we calculated the Spearman rank correlation between estimated transcript FPKM values and raw CAGE and poly(A)-site-seq read counts within 50 bp of each annotated promoter or poly(A) site. (c) Motif analysis. For each sample, we considered the sequence within 50 bp of annotated promoters. A position was considered a TATA motif hit if it matched the sequence T-A-T-A-A-A and an Inr motif match if it matched the sequence C/T-C/T-A-N-A/T-C/T-C/T. The plots are aligned with respect to the first base in the annotated promoter; they plot the fraction of promoters that contain a motif match at each position, averaged over replicates.

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This substantial rise in GRIT's precision when low-expression transcripts are filtered is largely due to eliminating complex genes. The GRIT annotation is heavily penalized in complex loci—for example, Dscam1 or Myosin heavy chain (Mhc)—because FlyBase includes new transcript models only when they contribute a new exon, intron or gene boundary (http://flybase.org/reports/FBgn0033159.html under “Comments for Gene Model”). The superior performance of GRIT is not purely a result of its increased ability to precisely predict transcript boundaries; when we relax the transcript boundary match distance to 200 bp or even 1,000 bp, GRIT still outperforms competing methods (Fig. 2a and Supplementary Fig. 3).

We studied the consistency of tools' estimated transcript expression scores by calculating the correlation between estimated FPKM values and both CAGE and poly(A)-site-seq tag counts. GRIT annotated transcripts achieved Spearman rank correlations between 0.71 and 0.80 across replicates, whereas Cufflinks, Trinity and Scripture correlations were all below 0.5 (Fig. 2b).

To study the precision of TSSs, we analyzed the motif enrichment of the two most spatially localized core promoter motifs, TATA22 and Inr23, in regions within 50 bp of annotated TSSs (Fig. 2c). The genome sequence surrounding TSSs identified by GRIT and Scripture were significantly enriched (P < 0.01; see Online Methods) for the TATA motif 24–32 and 30–35 bp upstream of the TSS, respectively. These correspond to 3.2% and 1.1% of distinct annotated TSSs. Regions identified by Cufflinks and Trinity were not significantly enriched for the TATA motif at any position. Similarly, regions identified by GRIT were significantly enriched (P < 0.01; Online Methods) for the Inr motif enrichment at ±1 bp of the TSS, which corresponds to 12% of annotated TSSs. Neither Cufflinks, Trinity nor Scripture identified regions significantly enriched (P < 0.01; Online Methods) at any bases for the Inr motif. This is expected because identifying transcript boundaries from RNA-seq data alone is very difficult (Supplementary Result 1 and Supplementary Fig. 4)

We also analyzed the regions within 50 bp of TSSs annotated in FlyBase 5.45 and found TATA enrichment at 27–34 bp, corresponding to 2.9% of distinct TSSs, and Inr enrichment 2 or 3 bp upstream of annotated TSSs, corresponding to 1.5% of distinct annotated TSSs. Although GRIT and FlyBase TSS regions showed similar TATA enrichment, GRIT more precisely identified the 26–31 bp upstream positioning23. The GRIT enrichment results are consistent with previous studies19, which report TATA and Inr motifs in 2.1% and 13.8% of peaked promoters identified by RACE24.

Alternative transcript boundaries are common and functional

Alternative promoters have long been known to serve a regulatory role. Some 5′ UTRs have sequence motifs that modulate translational efficiency25,26,27 and subcellular localization of the mRNA28. Alternative N-terminal protein sequence is known to control the localization of many proteins29.

Genes encoding alternative N-terminal domains, either by alternative promoter usage or splicing, include well-studied examples such as the Prothoracicotropic hormone (Ptth) gene, critical for metamorphosis in insects30,31. Ptth encodes three neurally secreted hormone protein isoforms. The canonical form contains a signal peptide sequence for exportation from the cell. A second isoform with a 25-amino-acid N-terminal extension contains a mitochondrial targeting peptide. The third form, which to our knowledge has not been reported previously, is shorter than the canonical isoform by 9 amino acids (Fig. 3a) and is predicted to localize to the cytoplasm or nucleus.

Figure 3: GRIT annotation of D. melanogaster.
figure 3

(a) Ptth. The Ptth gene encodes multiple proteins due to alternative N-terminal splicing as well as promoter usage. The sample labeled “imaginal disc” corresponds to mass-isolated tissues enriched more than 50% for imaginal discs. Transcript validation is provided by the cDNA IPO7958. The 3′ terminus of the distal gene coiled (cold) is shown. (b) Gene complexity. Although most genes have less than five isoforms, nearly half of transcript isoforms originate in genes that encode 100 or more distinct transcripts. (c) Sources of gene complexity. The Venn diagram represents the 59.6% of genes that encode multiple transcript isoforms.

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The potential of Ptth to encode multiple localization signals appears to be an example of a general phenomenon. Our improved annotation of the Drosophila transcriptome suggests that 19.6% of all protein-coding genes encode multiple localization signals, versus 5.7% for FlyBase 5.45 (Supplementary Result 2). We also found substantial complexity at the 3′ ends of transcripts, including neuron-specific 3′ UTR extensions32,33. In addition, for 77 genes, we detected polyadenylation sites in canonical coding DNA sequence exons that result in truncated transcript variants, some of which have been shown to be functional34.

Current tools underestimate splicing diversity

We identified 47 genes that each has capacity to encode >1,000 transcript isoforms18, 13 of which are only expressed in samples enriched for neuronal tissue. Together, these 13 genes account for nearly 13.5% of the predicted expressed transcript isoforms. In Ad20dHeads, 59.6% of genes expressed encode multiple transcript isoforms (Fig. 3b). Of these, 29.8% have multiple promoters, 48.1% show multiple poly(A) events and 40.1% show alternative splicing (Fig. 3c).

Dscam1 has the potential to encode 38,016 distinct protein isoforms35, 3,000 of which bind preferentially to themselves; that is, specific homophilic binding has been observed36. DSCAM1 is known to be crucial for axonal tract formation in the developing fly nervous system and is expressed in neurons throughout the lifecycle. We observed the highest expression in the central nervous system of 2-day-old white pre-pupae (WPP + 2 d CNS), where we are able to identify a 3′ extension and two new cassette exons, allowing Dscam1 to produce as many as 228,096 distinct transcripts. In the data collected from Ad20dHeads, GRIT recovered 720 DSCAM isoforms annotated in previous studies, whereas Cufflinks and Trinity were unable to recover a single full-length transcript.

We used simulated data to study the ability of GRIT, Cufflinks and Trinity to recapitulate known Dscam1 transcripts (Supplementary Fig. 1a). When GRIT analyzed 10,000 RNA-seq reads simulated uniformly from the canonical 38,016 isoforms, it recovered every exon, and was thus able to predict every transcript isoforms with perfect precision, in 19 of 20 simulations. Trinity was never able to build a full-length transcript, and Cufflinks recovered one transcript in 1 of 20 simulations, demonstrating the inability of these methods to model complex genes. Running simulations using the 228,096 isoforms identified in WPP + 2 d CNS produced similar results.

Discussion

The development of tools that enable the accurate interpretation of RNA sequence data is an important challenge. Our tool, GRIT, leverages multiple RNA sequence data types, including CAGE, mRNA-seq, polyA+site-seq, ESTs and cDNAs to discover transcript models. The use of gene boundary data prevents fragmentary transcript models and models that erroneously merge distinct genes.

Transcript models assembled by GRIT begin with a transcript start site, are connected by intervening mRNA-seq signal, and end in a polyadenylation site. We benchmarked GRIT and three other annotation tools using a subset of the modENCODE Drosophila RNA data sets18 and found that GRIT performed substantially better than competing methods, both at identifying previously annotated transcript models and at discovering new genes and transcripts. We devised a transcript quantification procedure that correctly accounts for model unidentifiability when estimating the confidence bounds, permitting conservative confidence bounds even in gene loci with the potential to produce thousands of transcript isoforms.

In cases where the extant set of transcripts cannot be confidently identified, GRIT could be coupled with other classes of genomic information, including conservation, protein functional data and RNA structure, to produce a sparse subset of transcripts that preserve known function. This may aid in generating high-quality transcript annotations. As long-read sequencing technologies mature, it may become possible to observe full-length transcripts directly37. GRIT incorporates cDNA sequences into transcript models, providing valuable connectivity information, and will make use of single-molecule data as they become available.

Among the most remarkable findings of our work on the modENCODE Drosophila RNA data sets is the fact that >20% of genes encode proteins with alternative localization signals. Although previous studies have identified individual genes encoding proteins with different subcellular localizations and distinct functional roles38, our data indicate that this is a ubiquitous function of alternative splicing and promoter usage throughout the genome. This suggests that molecular pleiotropy may be more common than previously thought.

The gene Ptth has been characterized for over a decade, yet GRIT discovered a previously unreported start codon modulated by an alternative promoter. In addition to emphasizing the importance of accurate gene-boundary information, our studies make evident the need for well-resolved tissue and cell-type transcript maps: the isoform in question is expressed in only 2 of the 108 modENCODE samples, where it is the dominant form. Future functional studies are needed to determine the biological role of this protein and, indeed, of the thousands of newly predicted protein isoforms with previously undetected protein localization signals.

GRIT generates full-length transcript models with sample-by-sample expression scores. The accuracy of these automated, purely empirical annotations yields a view of animal transcriptomes of unprecedented depth and complexity, which has not been previously obtained through manual annotation or the application of tools that model only a single data type (for example, RNA-seq without gene boundary information). GRIT alleviates an analytical bottleneck and will enhance the accessibility and usefulness of RNA sequencing data.

Methods

GRIT method.

Below we describe the GRIT methodology including the tuning parameters used for this study; all numerical constants can be changed at the command line. See Supplementary Note 2 for details about the tuning parameters.

GRIT uses reads aligned to a reference genome to build transcript models. We make few assumptions about the structure of a transcript, as follows, and require that every element (for example, promoter or splice junction) be supported experimentally. We define a transcript as a set of genomic regions that begin at a transcription start site (TSS), optionally extend through one or more exons connected by splice junctions, and end with a transcription end site (TES).

We define four distinct element types: TSS exons, internal exons, TES exons and single-exon transcripts. TSS exons begin with an experimentally detected promoter (for example, via the CAGE or RACE assays or 5′ EST sequencing19) and end with a splice donor site. Similarly, TES exons begin with a splice acceptor site and end with an observed TES (for example, a poly(A) site). Internal exons begin and end with a verified splice site, and single-exon transcripts begin with a TSS and end with a TES. Our transcript models can use both canonical and noncanonical splice sites. The set of candidate transcripts includes both single-exon transcripts and transcripts that begin with a TSS exon, contain splice junction–connected exons and end with a TES exon (Fig. 1b).

The GRIT annotation pipeline consists of four parts: gene region identification, element discovery, transcript construction and transcript expression estimation.

Gene region identification.

Segmenting the genome into gene regions involves three distinct steps: identifying exonic regions, identifying intronic regions and merging exonic and intronic regions into gene regions. To build a set of exon regions, we identify all 100-bp regions without any RNA-seq, CAGE or poly(A)-site-seq reads. These empty regions form boundaries between the different exonic regions. To identify introns, we collect reads that map in a noncontiguous fashion to the reference genome, typically known as junction reads. To avoid junction reads that may be experimental or mapping artifacts, we filter the set of identified junctions using the filtering criterion described as follows. We require that junctions have an entropy score, defined as

of at least 2.0 in one biological sample. To remove incorrectly stranded reads, we remove junctions on the strand opposite canonical acceptor/donor sequences if their frequency is less than 10% of the junction frequency on the canonical strand, and all junctions with a count less than 1% of the count of junctions at the same position but opposite strand. The junction reads that pass this filter are then aggregated into a set of discovered introns.

Finally, we construct gene regions by collecting exon regions that share one or more discovered introns. Note that although 100 bp is too large to properly separate many gene pairs, in practice it provides a good first approximation. During the element discovery stage, we use the identified CAGE and poly(A)-site-seq peaks in combination with the read coverage to further segment when necessary, as described below.

Element discovery.

Element discovery proceeds independently in each gene region and is parallelized for multithreaded processing in GRIT. We split each gene region into non-overlapping segments with attached labels. Segment labels describe the segment boundary (Fig. 1a). For instance, a segment where the 5′ boundary is a splice donor and 3′ boundary is a splice acceptor is a canonical intron; a segment where the 5′ boundary is a splice acceptor and 3′ boundary is a splice donor is a canonical exon. There are four boundary labels: splice acceptor, splice donor, TSS and TES. Splice donors and acceptors are identified directly from junction reads, as above; TSS and TES are, respectively, identified from CAGE and poly(A)-site-seq data, as follows.

Identifying peaks from transcript boundary data (for example, CAGE and poly(A)-site-seq) involves both filtering noisy reads and identifying peaks from the filtered data. We use essentially the approach described in Hoskins et al.19. Briefly, we model the data as a mixture of reads sampled from actual gene bounds and from a noise component. Because the dominant source of noise is the selection of RNA fragments that did not originate from true gene bounds (for example, RNA fragmentation before selection or nonspecifically bound DNA), we use the RNA-seq data as an estimate of the density of the noise component. For each base i, we estimate the read background density pi as the fraction of RNA-seq reads that start at base i. Then we model the distribution of the transcript boundary data read counts under the null as Bin(N,p), where N is the total number of mapped gene boundary reads. If we cannot, at significance level 0.01, reject the null hypothesis that the observed transcript boundary data read count originated wholly from the noise component, we zero the count at base i. To identify peaks, we greedily find the set of regions with the smallest combined length that are at least 5 bp long and cover 99% of the gene region's filtered transcript boundary signal. In the absence of poly(A)-site-seq data, we have successfully applied a machine-learning approach to the identification of TESs (Brown et al.18, Supplementary Section 12) and expect that a similar approach would work for the identification of TSSs.

There are 16 possible pairwise combinations of the four segment boundary labels, which we group into seven segment labels: TSS segments, canonical introns, canonical exons, exon extensions, TES segments, single-exon transcripts and intergenic segments (Fig. 1a). TSS segments are any segments where the 5′ boundary has a TSS label; similarly, a TES segment's 3′ boundary has a TES label. Canonical introns have a 5′ splice donor label and a 3′ splice acceptor label. Canonical exons have a 5′ splice acceptor label and a 3′ splice donor label. Exon extensions either have two splice donor labels or two splice acceptor labels. Single-exon transcripts have a 5′ TSS label and a 3′ TES label. Regions that begin with a 5′ TES label and end with a 3′ TSS label are intergenic segments. If intergenic segments are discovered and the average base coverage is sufficiently low, then the gene region is split and the element discovery process is restarted recursively. At this stage, poorly supported segments, meaning those with low read coverage, are removed.

Within a gene region, a low coverage region is defined as a segment where the average read coverage is lower than 10−2 with high probability or the ratio of a segment's average read coverage to the highest read coverage segment in the same gene region is less than 1% at a 0.01 significance level. GRIT is relatively robust to changes in these parameters for a given data type, but they may have to be changed when using, for instance, total versus poly(A)+ RNA-seq data (Supplementary Note 2).

The set of candidate exons is all combinations of adjacent segments that start with TSS or splice acceptor, and end with a TES or splice donor. Regions that begin with a TSS label and end with a donor junction are TSS exons; regions that begin with an acceptor junction and end with a TES label are TES exons; regions that begin with a acceptor junction and end with an donor junction are internal exons; regions that start with a TSS label and end with a TES label are single-exon transcripts (Fig. 1a).

Transcript construction.

For the purposes of candidate transcript construction, we model a gene as a directed graph in which each exon is a node and splice junctions are edges (Fig. 1b). Then the set of candidate transcripts is composed of the single-exon transcripts and all possible paths through this graph that begin with a TSS exon and end with a TES exon (Fig. 1b). This differs from other methods—for example, Cufflinks—in that we consider all possible paths subject to this restriction, rather than some minimal set of covering paths.

Transcript expression estimation.

The primary challenge in estimating transcript expression for a given gene is identifying a vector, , that corresponds to the relative concentrations of all transcripts in the sample of interest. This is difficult because reads cannot necessarily be unambiguously assigned to one transcript. Therefore, the first step in estimating transcript expression is further segmenting the transcripts into non-overlapping exon segments, or pseudo-exons. It is then possible to unambiguously group reads by the set of pseudo-exons that they overlap, which we refer to as a “bin” (Supplementary Fig. 2). Hence, the bins that can be observed unambiguously are a function of gene structure, sequenced read length and fragment length distribution.

We estimate the fragment length distribution by the 5-base uniform kernel–smoothed middle 99% of the empirical distribution of read fragments in the 100 unspliced gene regions with the highest average base coverage that are at least 2,000 bp long. If there are less than 5,000 total fragments that satisfy these criteria, we estimate the fragment distribution by a normal distribution truncated at ±2 s.d., with mean and s.d. estimated from unspliced fragments.

We encode gene structure, read length and type, and fragment length distribution in a design matrix, X, which connects the probability of observing reads of a particular bin to the presence of a particular transcript. Each entry Xij is a conditional probability that applies to individual reads. Formally, Xij is the probability of sampling a read of bin i given that the read originated from transcript j. In practice, we estimate Xij by

where fl is the estimated fraction of fragments of length l, Ci,j,l is the count of distinct fragments of length l in transcript j that produce fragments of type i, and Nj is the total number of bins in transcript j. This estimate formalizes the assumption that, within a transcript, all fragments with the same length are equally likely to be observed.

Given a vector of observed bin counts, , the maximum-likelihood estimate of the transcript frequencies, , is the vector that maximizes the log-likelihood, , subject to tj ≥ 0 and ∑jtj = 1. This is the multinomial log likelihood where the event probabilities, , are the bin proportions weighted by the transcript frequencies. The maximum-likelihood estimate is unique whenever no row of Xij can be constructed from a positively weighted sum of other rows. In such unique cases, the statistical model is said to be identifiable given the data, and we can use a convex optimization algorithm to estimate (Supplementary Note 1).

To form confidence bounds on a particular transcript's frequency estimate, , our goal is to find the minimum and maximum values that tj can take while still being 'reasonably likely' to result in the observed data. We identify a subset ΔR of the probability simplex such that is sufficiently high for every . Convexity of the likelihood function guarantees that this region is simple and convex, which allows us to form our confidence bound for transcript i as the interval conservative estimate for individual coverage rates.

This interval can be estimated directly by finding the on the probability simplex that minimizes tj such that the log-likelihood ratio is above some critical value (Supplementary Note 1). Since the asymptotic distribution of , a log likelihood ratio statistic39 with one degree of freedom, is , we set the critical value to for some desired marginal significance level α. When the model is identifiable, simulations show that this approach produces confidence bounds with the correct rejection rates for realistic sample sizes (Supplementary Fig. 1e).

If the statistical model is not identifiable, then the likelihood solution has no unique maximum even as the read depth approaches infinity. However, for the purposes of visualization or comparative analysis, it may still be useful to quantify a representative set of transcripts, in which case we must make further assumptions. A natural assumption is that the set of transcripts present in solution for a given gene is small. Optimally, we would identify the smallest such subset of transcripts that achieves near the maximum likelihood, but this is not computationally feasible. Instead, we maximize the augmented objective,

subject to tj ≥ 0 and ∑jtj = 1, where λ is a tuning parameter that determines the sparsity of the resulting solution. Although this optimization problem is not convex, it can be solved by solving Nt convex problems40. We set λ to , which guarantees that the estimate for lies within the confidence region ΔR (Supplementary Note 1).

For unidentifiable models, our method produces a lower confidence bound of zero for every transcript in the gene. This allows the user to easily identify regions in which RNA-seq data alone is not sufficient to identify the set of transcripts present. In contrast, Cufflinks and Rsem 20 both use a Bayesian approach, sampling from a posterior distribution to estimate confidence bounds. In complex genes, such as Dscam1 or Mhc, the resulting confidence bounds are strongly dependent on the prior distribution, which typically leads to dramatically anti-conservative confidence bounds (Supplementary Fig. 1f).

Ptth analysis.

Protein subcellular localization signals were predicted using the WoLFPSORT41 Command Line Package Version 0.2 with the setting “animal.”

Identifying FlyBase transcripts expressed in Ad20dHeads.

A FlyBase transcript was considered expressed in Ad20dHeads if either it was unspliced or, if it was spliced, every splice junction was present in at least one RNA-seq sample.

TSS motif enrichment.

To identify motif enrichment in the genome sequence surrounding annotated TSSs, for each tool, we first identified the unique set of transcript start sites. Then, for each TSS, we scanned the genome sequence taken from the BDGP5 genome for the TATA motif (TATAAA) and the Inr motif ([CT][CT]A[ACGT][AT][CT][CT]). A base position was considered a hit if the motif match was exact. Finally, we summed the number of hits at each position and then divided by the total number of sequences to produce enrichment numbers.

To identify significantly enriched regions, we used a nonparametric approach, performing the above analysis on 100,000 randomly sampled sequences. We sampled randomly from FlyBase 5.45 identified 3′ UTRs, which provides a background with similar sequence composition to that of 5′ UTRs. A particular position was considered enriched if its value was greater than 99,990 of the bootstrapped samples, so that the type I error rate is expected to be 1%, after correcting for multiple testing (Bonferroni).

Mapping.

We used the RNA-seq and CAGE mappings provided by the modENCODE Consortium and distributed by the SRA (Supplementary Table 1). The poly(A)-site-seq data were mapped with Statmap 19 using the polya assay annotation option, which discards mappings that map to the reference genome before the poly(A) tail is trimmed.

Running Cufflinks.

We ran Cufflinks version 2.1.1 on the merged male and female RNA-seq data using the default configuration options to build the initial transcript sets. Then we ran Cufflinks in quantification mode (-G option) to provide replicate-level quantifications. We did analyze the quantifications produced by running Cufflinks on the replicates independently, but we found them to be of much lower quality than the requantified versions (data not shown).

Running Scripture.

Owing to technical issues, we were not able to run the Scripture version available at http://www.broadinstitute.org/software/scripture/ in house. The authors (S. Kadri, Broad Institute) generously ran alpha version 3.1 on the merged female 20-d adult dissected head data and provided us the resulting annotation. The parameters used were as follows: premature assembly filter, 0.2; minimum number of spliced reads, 3.0; percentage of total spliced reads, 0.05; alpha for single exon assemblies, 0.01.

Running Trinity.

We used Trinity version 2013-08-14 with the max_number_of_paths_per_node set to 1,000 to identify transcript models. We used Rsem to estimate expression (described below) and gmap version 2013-09-11 to map the quantified models to the BDGP5 reference genome. All other command line options were the package defaults.

Running Rsem.

We used Rsem version 1.2.7 with the –stranded option to quantify transcripts. We used gmap version 2013-09-11 to map the quantified models to the BDGP5 reference genome. All other command line options were the package defaults.

Simulations.

We used the simulation script distributed with GRIT to simulate mapped read data for all simulations. The tool works by first sampling a random transcript from the provided frequency distribution, then sampling a random fragment length from the provided fragment length distribution, and finally choosing a fragment uniformly from the chosen transcript with the chosen fragment length until the desired number of samples is achieved. It does not introduce any sequencing or mapping artifacts into the simulated reads. We note that this simulation is consistent with the GRIT, Cufflinks and Rsem transcript expression models.

We only compared the performance of GRIT, Cufflinks and Trinity+Rsem in simulations because they were the tools that performed best on real data.

For the synthetic gene simulations (Supplementary Fig. 2 b–f), we sampled from the transcripts uniformly, with a Normal(150, 25) fragment length distribution truncated at ±2 s.d. We ran GRIT, Trinity and Cufflinks in quantification mode. We used GRIT's compare_annotations.py with a boundary match of ±20 bp to calculate recall and precision numbers. We ran 100 simulations total: 20 simulations with each of 100, 1,000, 10,000 and 100,000 simulated reads.

For the Dscam1 simulations (Supplementary Fig. 2a), we used the set of Dscam1 exons from FlyBase 5.45 to enumerate all possible 38,016 Dscam1 transcript models. We used a Normal(300,25) fragment length distribution truncated at ±2 s.d. We ran GRIT, Trinity and Cufflinks in quantification mode. We used GRIT's compare_annotations.py with a boundary match of ±20 bp to calculate recall and precision numbers.

Accession codes.

All software associated with this project and the pipelines run to generate these annotations are available for download at http://grit-bio.org/ and as Supplementary Data Set 1. All annotation data are available at http://grit-bio.org/nature-biotech-submission.html. SRA: SRR488279, SRR488280, SRR070420, SRR111882, SRR070421, SRR070424, SRR1151373, SRR1151374 (Supplementary Table 1).