Annotating genomes with massive-scale RNA sequencing
- France Denoeud†1, 2, 3,
- Jean-Marc Aury†1, 2, 3Email author,
- Corinne Da Silva1, 2, 3,
- Benjamin Noel1, 2, 3,
- Odile Rogier1, 2, 3,
- Massimo Delledonne4,
- Michele Morgante5,
- Giorgio Valle6,
- Patrick Wincker1, 2, 3,
- Claude Scarpelli1, 2, 3,
- Olivier Jaillon1, 2, 3 and
- François Artiguenave1, 2, 3
© Denoeud et al.; licensee BioMed Central Ltd. 2008
Received: 9 September 2008
Accepted: 16 December 2008
Published: 16 December 2008
Next generation technologies enable massive-scale cDNA sequencing (so-called RNA-Seq). Mainly because of the difficulty of aligning short reads on exon-exon junctions, no attempts have been made so far to use RNA-Seq for building gene models de novo, that is, in the absence of a set of known genes and/or splicing events. We present G-Mo.R-Se (Gene Modelling using RNA-Seq), an approach aimed at building gene models directly from RNA-Seq and demonstrate its utility on the grapevine genome.
Next generation sequencing technologies generate many short reads of DNA fragments in a reduced time scale and have lowered the cost per nucleotide [1, 2]. Genomic short reads have been used to investigate genetic variation , genomic rearrangements , DNA methylation , and transcription factor binding sites (Chip-Seq) [6, 7]. New algorithms had to be developed for genome resequencing, in order to map very high numbers of reads efficiently [8–11], as well as for de novo genome assemblies, in order to cope with the short length of reads (usually less than 35 nucleotides) [12–16]. The next-generation sequencing methods have also been applied to sequence cDNAs rather than genomic DNA, in order to catalogue microRNAs [17–19] or analyze the transcriptional landscape of a number of eukaryotic genomes: this technology is called RNA-Seq [20–26].
Before the development of the RNA-Seq technology, large-scale RNA analysis could be performed with two types of approaches. The first, tag-based approaches , such as serial analysis of gene expression (SAGE)  and massively parallel signature sequencing (MPSS) , were based on the sequencing of previously cloned tags located in specific transcript locations (usually 3' or 5' ends). Transcript abundance could be derived from tag counts in already known loci, but no new genes or new alternative splice forms could be discovered. The alternative approach, hybridization-based microarrays, has the potential of monitoring the expression level on the whole transcriptome (not necessarily biased towards known genes, when using whole genome tiling arrays [30–32]) at low cost, but it is biased by the background levels of hybridization and the fact that probes differ in their hybridization properties. Nevertheless, the gold standard method for transcript discovery remains expressed sequence tag (EST) sequencing (by Sanger technology) of cloned cDNAs [33–35]. Its main limitation, in addition to the relatively high cost, is that this method is sensitive to cloning biases. The RNA-Seq technology combines the advantages of the previous large-scale RNA analysis methods by enabling the monitoring of the transcriptional landscape of a whole genome at low cost, without the biases introduced by arrays, and has the additional advantage of providing information on the transcript structures (exon-exon boundaries), as EST Sanger type sequencing does on a longer range, but without cloning biases. Moreover, because a large number of reads can easily be obtained, RNA-Seq is sensitive enough to detect transcription for genes with low expression levels, which are usually missed by EST analysis [21, 23, 25].
In recent studies, RNA-Seq has mainly been used to quantify the expression levels of already annotated loci, identify differentially expressed genes, and measure expression outside of those loci (in intronic or intergenic regions) [21–24, 26]. Additionally, structural information has been used to detect already known alternative splice forms [22, 23], identify new transcriptional events in relation to known loci (alternative splicing, 5' ends) [24, 26], and refine annotated gene structures or propose novel gene models [21, 23]. However, no attempts have been made to take advantage of the connectivity information contained in RNA-Seq data for building gene models de novo, that is, in the absence of a set of known genes and/or splicing events.
Traditionally, EST, cDNA and protein sequences are the most accurate resource for identifying gene loci and annotating the exon/intron structure on genomic sequences . These resources can be mapped on a genomic sequence with a global alignment strategy that allows gap insertions of genomic regions corresponding to potential introns bordered by splice sites [37–41]. The resulting positions of exon and intron boundaries can then be assembled to build complete transcript structures . But the methods used to build spliced alignments of ESTs on genomes are not applicable to short reads, since they require that the sequence blocks surrounding a splice junction are long enough and highly similar to the genomic region in order to build a non-ambiguous alignment covering the exon-exon boundary. New methods are now emerging for building spliced alignments of short sequence reads . However, they still require a priori information about the genome analyzed (splice site characteristics) in order to reduce the number of junctions to test, since testing all possible 'GT/C-AG' pairs in a genome is obviously unfeasible.
In this study, we present a method aimed at using RNA-Seq short reads to build de novo gene models. First, candidate exons are built directly from the positions of the reads mapped on the genome (without ab initio assembly of the reads), and then all possible splice junctions between those exons are tested against unmapped reads: the testing of junctions is directed by the information available in the RNA-Seq dataset rather than by a priori knowledge about the genome. Exons can then be chained into stranded gene models. We demonstrate the feasibility of this method, which we call G-Mo.R-Se (for Gene Modelling using RNA-Seq), on the grapevine genome  using approximately 175 million Solexa/Illumina RNA-Seq reads from four tissues. This allowed the identification of new exons (in known loci) and alternative splice forms, as well as entirely new loci. We show that this approach is an efficient alternative to standard cDNA sequencing: it detects more transcripts at lower cost. It could be particularly helpful in the case of species for which few resources are available (that is, that are very distant from the species currently present in the ESTs/protein databases). G-Mo.R-Se can also be combined with other data into an automatic or manual eukaryotic genome annotation. All the data described in this article are available from the G-Mo.R-Se website .
Results and discussion
Building gene models from RNA-Seq reads
Covtig definition was essential for the subsequent testing of junctions to be efficient, especially with respect to the splits and fusions of exons (see Materials and methods). The splitting of exons into separate covtigs can occur when the read coverage depth goes down (below the depth threshold used for building covtigs), which can be due either to repeated regions (we only retained the reads that mapped at a unique position on the genome), to mismatches/gaps in the genomic sequence (we only kept the reads mapped with at most two mismatches and no indels), or to experimental biases leading to depth variations in the cDNAs sequenced and to non-normalization of the library. Indeed, some biases in the coverage uniformity of reads have been observed in previous RNA-Seq studies .
We aimed at correcting the splits in two ways. First, at the covtig definition step (step 1 in Figure 1), we extended the covtigs using all 16-mers found in the reads, in order to step over mismatches and short repeats. Then, at the model building step (step 4 in Figure 1), we fused together models that were linked by an open reading frame.
The artifactual fusing of exons into one single covtig can occur when the mRNA sample contains immature transcripts with retained introns, providing reads that map into the introns. Since the immature transcripts are expected to be under-represented in the set of mRNAs, the depth in the retained introns is expected to be lower than in the adjacent exons: setting an appropriate depth threshold for the building of covtigs should avoid such fusions.
The depth threshold used for covtig construction was set to balance the number of splits and the number of fusions. Indeed, low thresholds will generate few splits but numerous fusions, and conversely, high thresholds will generate few fusions but numerous splits. In order to correct more fusions, we could extend the testing of junctions inside the covtigs, instead of testing junctions only between covtigs.
Nucleotidic overlap of RNA-Seq reads, G-Mo.R-Se covtigs and G-Mo.R-Se models with different genomic compartments relative to the reference annotation
Genomic compartment relative to the reference annotation (%)
Exonic: 41,603,635 nucleotides
Intronic: 184,047,761 nucleotides
Intergenic: 271,857,375 nucleotides
Reads: 73,580,625 nucleotides
Covtigs: 38,484,212 nucleotides
Models: 22,213,316 nucleotides
We managed to select a satisfying depth threshold with respect to the splits/fusions (Figure S1 in Additional data file 1), as well as the signal/noise ratios. Obviously, the optimal depth threshold will be highly dependent on the characteristics of the dataset analyzed, such as the complexity of the transcriptome, the amount of alternative splicing, the amount of transcription outside of protein-coding genes, and the sequencing depth, and must be carefully selected in order for G-Mo.R-Se to work optimally.
Comparing the G-Mo.R-Sepipeline with direct assembly of reads
Overlap of the 30,434 reference genes with Velvet spliced contigs and G-Mo.R-Se models
Velvet assembly + mapping
Percentage of reference exonic nucleotides covered
Reference genes overlapped on ≥ 1 nucleotide
Reference genes overlapped on ≥ 75% nucleotides
Comparing the G-Mo.R-Seapproach to a classic cDNA sequencing approach
Overlap of cDNA loci (all loci and loci where all 32-mers are unique) with G-Mo.R-Se models
All cDNA clusters (7,895)
cDNAs clusters where all 32-mers are unique (4,822)
Percentage of cDNA (exonic) nucleotides covered by models
cDNA clusters overlapped on ≥ 1 nucleotide by models
cDNA clusters overlapped on ≥ 75% nucleotides by models
Alternative splicing events detected in cDNAs, all G-Mo.R-Se models, and CDS portions of G-Mo.R-Se models
cDNAs: 7,895 loci
Models (all): 19,486 loci
Models (CDS): 12,341 loci
Events common to cDNAs and models
% of cDNA events
2,171 (944 without IR)
Total number of loci with alternative splicing (% of all identified loci)
783 (9.9%) (598 without IR)
Characteristics of known and novel G-Mo.R-Se models (all, and with a plausible CDS)
Known model loci
Novel model loci
Models with a plausible CDS (65%)
Models with a plausible CDS (17%)
Number of loci
Number of models
Average number of models per locus
Average number of exons per model
Number of models with more than two exons
Identifying novel genes and improving gene annotation
Expectedly, since V. vinifera belongs to a phylogenetic branch where a profusion of resources are available, most of the models (95%) that fall outside of cDNAs overlap the reference annotation , or other resources such as GeneWise hits with Uniprot proteins [39, 49], and ESTs from other species (Table S2 in Additional data file 1). However, 675 models are completely novel, or 116 when considering only models with a plausible CDS. We compared the characteristics of the models that are novel and the models that are supported by evidence, which we now call 'known' models (Table 5).
The proportion of models with a plausible CDS drops when considering the novel models compared to the known models (from 65% to 17%), as well as the average number of exons per model (from 8.2 to 2.3 for all models). It is likely that some of the novel models correspond to false predictions: if one junction is validated erroneously, it will create a false two-exon model. Nevertheless, the proportion of models with more than two exons is higher in the subset of novel models having plausible CDSs (53%) compared to all novel models (17%), which suggests that at least some of them are genuine novel coding loci. In addition, the novel loci that are non-coding could either correspond to coding transcripts that were mis-annotated by the pipeline (wrong splice site generating a frameshift, models associating incompatible exons), to coding transcripts where no CDS could be detected because of frameshifts in the genomic sequence, to genuine non-coding transcripts, or to transcriptional/experimental noise (Figure 1). The structure of one of the novel models, spanning eight exons, is shown in Figure S2 in Additional data file 1. A Blast  search against Uniprot  revealed an homology to a transcription regulator from Arabidopsis thaliana. The homology was below the sensitivity threshold required to map proteins to the genome during the annotation process. In addition to the discovery of novel splice forms and novel loci, G-Mo.R-Se models enrich the reference annotation by extending (in 5' or 3') about 40% of the reference genes they hit. G-Mo.R-Se models thus constitute a valuable resource for improving V. vinifera gene annotation.
In this study, we demonstrate the feasibility of building gene models de novo, using only RNA-Seq reads and the corresponding genomic sequence, with a relatively straightforward annotation pipeline that we call G-Mo.R-Se. Using a dataset of approximately 175 million Solexa reads, it could detect more loci than could be identified by cloning and sequencing approximately 120,000 cDNAs, at a cost about 20 times lower (55% of the multi-exonic genes from the annotation are overlapped by models versus only 35% by V. vinifera cDNAs). Especially, G-Mo.R-Se allowed the annotation of loci expressed at very low levels. We show that this approach efficiently deciphers real transcripts from transcriptional/experimental noise since the junction validation step removes false positive covtigs. Additionally, although it was not designed to be exhaustive in the detection of alternative splicing events, G-Mo.R-Se detected more alternative splice forms than the cDNA resource, with no need for a priori knowledge of the exon-exon junctions to test. Finally, we could also identify putative novel genes (that had been missed by the automatic annotation procedure) in a genome that is already very well annotated owing to the plethora of resources available in this phylum. We tested the G-Mo.R-Se pipeline with Solexa/Illumina RNA-Seq reads but it can readily accept any other type of short reads, or combine reads from different technologies.
For future genome projects, it is conceivable to think of performing the annotation using RNA-Seq runs treated with G-Mo.R-Se as the unique resource, provided that the tissues or cell types sampled are representative enough to drive a comprehensive annotation. This approach will be particularly valuable in phyla where few resources are available (that is, that are very distant from the species currently present in the EST/protein databases), where the expensive and time-consuming step of constructing cDNA libraries could be avoided. When other resources are available, the gene models can also be combined with other data into automatic or manual eukaryotic genome annotation pipelines.
Although the G-Mo.R-Se pipeline works satisfactorily on the V. vinifera dataset, it is still fairly simple and we can think of several refinements. First, at the moment, no mono-exonic models are produced (such models represent only 8% of annotated grape genes), but we could easily bring back the covtigs that were not linked to any other covtig by a validated junction, if they contain a CDS that exceeds a certain length. Next, at the covtig building step, instead of using a fixed depth threshold, we could adapt it to the environment: the covtigs would be built to coincide with sharp increases/decreases in depth. Such a strategy should enable the annotation of separate exons in case of IR. In order to correct even more fusions, it would also be straightforward to test candidate junctions inside the covtigs in addition to the junctions tested between covtigs. Since the scope of this study was to annotate as many genes as possible, we chose to pool together the reads from all four tissues before building the covtigs. But we could also consider building covtigs and gene models separately in different samples, in order to investigate differential expression, although to the detriment of sensitivity. A last, more elaborate refinement would be to use the depth information in order to link together only covtigs that are likely to be part of the same transcript, instead of building all models that correspond to the longest possible paths in the graph of covtigs linked by validated junctions. Such an approach would allow speculation on longer range splice contiguity, and to study more exhaustively the alternative splicing landscape.
Materials and methods
RNA-Seq reads were obtained (as described in Del Fabbro et al., unpublished data) by sequencing cDNA obtained from four tissue samples with the Solexa/Illumina technology: leaf (11 lanes), root (9 lanes), callus (9 lanes), and stem (9 lanes). The mRNA molecules were purified from total RNA extractions and fragmented before cDNA synthesis (with random hexamer primers). The protocol was not strand-specific. The single-end reads obtained were 32 nucleotides long, except for 5 lanes in the callus sample, where the reads were 35 nucleotides long. The resulting 172,545,778 usable reads (5.4 Gbases) were mapped to the V. vinifera genome  using SOAP  with a seed length of 12 and default parameters: 138,326,238 reads (4.6 Gbases) were mapped at one unique position with at most two mismatches and no indels. As a consequence, reads that align to exon-exon junctions could not be mapped to the genomic sequence.
Building gene models from short reads
The G-Mo.R-Se method for building gene models from short reads is summarized in Figure 1. The first step is the definition of covtigs (coverage contigs). They are built by contiging the positions where short reads are aligned above a certain coverage depth threshold. This threshold is a parameter that needs to be adjusted in order to balance sensitivity and specificity as well as splits and fusions. In the absence of a training set to quantify the splits and fusions, this parameter can also be optimized by maximizing the number of junctions validated in the next step. Before the subsequent testing of junctions, the covtigs were extended using all 16-mers found in short reads, in order to step over mismatches and short repeats. It is important to note that the read length limits the detection of very short exons (< 35 nucleotides).
In the next step, we searched for donor (GT or GC on the forward strand, and AG or AC on the reverse strand) and acceptor (AG on forward strand and CT on reverse strand) splice sites 100 nucleotides inside and outside each covtig boundary. This enabled us to create a list of oriented candidate exons (with putative alternative donor and/or acceptor splice sites) for each covtig.
The third step was the validation of junctions between candidate exons using unmapped reads, since reads that align to exon-exon junctions were not mapped to the genomic sequence. We tested all candidate exons derived from a given covtig with the candidate exons derived from the 20 next covtigs. All the putative junctions were tested using a word dictionary approach. The dictionary (with a word size of 25) was built using the unmapped reads. Ten words (8 nucleotides on the first exon and 17 nucleotides on the second exon, 9/16, 10/15, 11/14, 12/13, 13/12, 14/11, 15/10, 16/9, 17/8) were derived from each putative junction, and their presence in the dictionary was tested. In order to validate a junction, at least five different words need to be found in the dictionary, and the total number of occurrences of all words derived from each junction needs to be of the same order of magnitude as the average depth of the adjacent covtigs (greater than 1/10 of their average depth).
The efficiency of the junction validation procedure relies on the covtig definition step for the following reasons: only the junctions between each covtig and the 20 next covtigs are tested, meaning that if more than 20 'false' covtigs are defined between 2 'real' covtigs, the junction between the two real covtigs will not be tested; only 100 nucleotides around the covtig boundary are scanned for putative splice sites, meaning that if the covtigs are too short or too long, the correct junction will not be tested; only junctions between covtigs are tested, meaning that if a covtig corresponds to a fusion between two exons, the correct junction will not be tested, and the final model will include a retained intron. On the other hand, if an exon is split between two covtigs, no junction will be valid between those covtigs, leading to the splitting of a gene into separate models. As a consequence, in the absence of a training set (annotated genes, ESTs, and so on) to calibrate the depth threshold used for building covtigs, it is possible to optimize the threshold by maximizing the number of validated junctions. G-Mo.R-Se can thus be used for de novo annotation.
For the last step, the model construction relies on the graph of candidate exons linked by validated junctions on the same strand. The models correspond to all the longest paths linking candidate exons through validated junctions. Candidate exons that are not involved in any validated junction are discarded, implying that no mono-exonic models are produced. In order to correct potential gene splits, we fuse together adjacent models (on the same strand) that are linked by an open reading frame.
Additionally, all models produced by G-Mo.R-Se are searched for CDSs. When the longest CDS (if greater than 50 amino acids) spans at least two-thirds of the nucleotides of a model or the number of non-coding exons is lower than the number of coding exons, the CDS is qualified as plausible. Models with plausible CDSs are likely to correspond to protein coding genes. Plausible CDSs could be detected for about two-thirds of the models. The G-Mo.R-Se models can be downloaded from the G-Mo.R-Se website  and visualized on the V. vinifera genome browser .
G-Mo.R-Semodels and cDNA analysis (clustering, alternative splicing detection)
The same clustering procedure was applied to models and cDNA sequences aligned on the genome. We used a single linkage clustering approach, where a link between two models was created if they had a cumulated exonic overlap (on the same strand) of at least 100 nucleotides (only overlaps of at least 10 nucleotides were considered). A graph-based approach was used to resolve the single linkage clustering. Additionally, the redundancy was removed from the cDNAs by discarding all transcript structures that were fully included in longer structures. We detected all pairwise alternative splicing events between intron pairs, with the same method as described in . All tandemly duplicated genes were discarded from the alternative splicing events detected, since such genes may be artificially linked by cDNA mapping as well as model construction, and would generate false alternative splice forms spanning several loci instead of one. However, it is notable that, since the pipeline builds all possible models, it will always predict the two separate correct models in addition to the incorrect joined model(s).
Additional data files
The following additional data are available with the online version of the paper. Additional data file 1 is a Word file containing Tables S1 and S2 and Figures S1 and S2. Table S1: cDNA transcript structures correctly predicted by G-Mo.R-Se and Velvet. Table S2: support (in public resources) of G-Mo.R-Se models that do not overlap cDNAs. Figure S1: proportions of exon fusions and exon splits obtained with different depth thresholds for the covtig construction step. Figure S2: example of a novel model.
expressed sequence tag
- G-Mo.R-Se :
Gene Modelling using RNA-Seq
Short Oligonucleotide Analysis Package.
This work was financially supported by the Genoscope, Institut de Génomique, CEA and Agence Nationale de la Recherche (ANR). The authors acknowledge Susan Cure for correcting the manuscript and Jean Weissenbach for continuous support.
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