- Methodology article
- Open Access
A consensus-based ensemble approach to improve transcriptome assembly
BMC Bioinformatics volume 22, Article number: 513 (2021)
Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes.
In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble.
Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.
A high quality and comprehensive transcriptome is required in many bioinformatics workflows [1,2,3]. Many methods have been developed for transcriptome assembly (e.g., reviewed in ). However, assembled transcriptomes are often incomplete, especially for non-model organisms or highly divergent strains, and further improvements are needed in assembler performance .
Transcriptome assembly methods can be classified into two general categories: de novo assemblers that generate the assembly based solely on the RNAseq data (read sets) and genome-guided assemblers that use a reference genome or transcriptome. Most of the current de novo assemblers, such as Trinity , rely on kmer decomposition of the reads, where kmers are substrings of length k, and de Bruijn graph construction . However, with this approach, there are tradeoffs with the choice of the kmer length. For example, while shorter kmers are more likely to fully cover the transcript sequences that need to be assembled, they are also more likely to cause ambiguity in assembly graphs when repetitive sequences exist among transcripts. Due to such tradeoffs, each transcript has an optimal kmer length that facilitates the accurate reconstruction of the full-length sequence. Consequently, using different methods or even using the same method with different kmer lengths will generate different sets of contigs. Isoforms, polyploidy, multigene families, and varying levels of gene expression, all contribute to complexity in transcriptome assembly. Therefore, to obtain the complete transcriptome assembly, multiple assemblers often need to be used with a broad parameter space [8,9,10,11,12,13].
Genome-guided assemblers, such as Cufflinks , avoid the ambiguity in kmer assembly graphs by mapping the RNAseq data to the reference genome and clustering the reads based on genomic location . However reads mapping to multiple locations within a genome can cause ambiguity . When the reference genome used has any sequence divergence from the target genome, with more divergence, fewer reads can be mapped to the reference without gaps or mismatches. Each combination of read mapper and assembly algorithm handles these issues differently introducing inconsistent performance among assemblers.
While a core set of transcripts is more likely to be assembled correctly by multiple assemblers, many other transcripts may be missed depending on which specific algorithm and kmer length (for a de novo method) or read mapper (for a genome-guided method) are used. Through combining the results of multiple assemblers, ensemble assemblers such as EvidentialGene  and the method developed by Cerveau and Jackson  (we call their method “Concatenation”) attempt to address the limitations of individual assemblers, retaining contigs that are more likely to be correctly assembled and discarding the rest. Both of these ensemble methods filter the contigs generated by multiple assemblers (usually de novo) by clustering the contigs and determining the representative contig based on both the entire nucleotide and predicted protein sequences. For Concatenation, contigs that contain only portions of longer contigs (without allowing any nucleotide change) are clustered and removed. EvidentialGene clusters contigs by nucleotide similarity (98% identity by default) based on the predicted coding sequences (CDS's) and classifies them into gene loci. From each cluster, it selects contigs including the longest CDS for the main set and those with distinct shorter CDS's for the alternative set. Similar to Concatenation, contigs that are fragments of other contigs are removed (D. Gilbert, personal communication). Both approaches greatly reduce the number of assembled contigs by removing redundant sequences. However, there is no guarantee that the correct sequence is retained as the representative for a given cluster or that each cluster represents a unique gene. In a new genome-guided ensemble assembler, TransBorrow, after extracting reliable subpaths supported by paired-end reads from a splice graph, it merges transcripts assembled by multiple genome-guided methods and extracts reliable assembly subpaths based on the number of assemblers that detect each subpath (transcript) . While the study showed superior performance of TransBorrow compared to individual genome-guided assemblers, how the performance among ensemble assemblers differs has not been examined.
To evaluate the quality of transcriptome assemblies, several methods are available, such as DETONATE , TransRate , and Benchmarking Universal Single-Copy Orthologs (BUSCO) . The former two methods evaluate the assembly quality based on how well the assemblies are explained by the RNAseq data used. BUSCO evaluates the completeness of transcriptome assemblies by focusing on searching the universal single-copy gene sets. These methods, however, cannot measure directly the accuracy of each contig sequence. Moreover, without a benchmark transcriptome, transcripts that are not assembled (missed) cannot be quantified and the impact of false positives tends to be underestimated. In a recent study, a "real time" transcriptome was generated using the PacBio long-read sequencing technology and used as a benchmark to evaluate de novo assemblies generated from short-read RNAseq data obtained from the same biological samples . However, obtaining good quality datasets that have been sequenced by both short- and long-read technologies, especially from many different organisms with various conditions, is not easy nor practical.
An alternative approach is to simulate the short-read sequencing based on a reference genome and generate a benchmark dataset including the reference transcriptome and the RNAseq reads. We previously used a simple simulation protocol and generated a benchmark transcriptome based on a human genome . It allowed us to compare transcriptome assembly performance among different approaches. Our preliminary result showed that de novo transcriptome assembly can be improved by taking the consensus among multiple de novo assemblers.
In our present study, we generated several sets of benchmark transcriptomes with different conditions. Using these simulated benchmark datasets as well as the real RNAseq data, and using also various accuracy metrics, we performed a thorough assessment of currently available methods based on various assembly conditions. We showed that the two currently available ensemble methods that focus on thoroughness of assemblies (higher recall) retain significantly high numbers of incorrectly assembled contigs. Instead, the overlaps between different assemblies can be utilized to decrease false positives in transcriptome assembly. To demonstrate the value of a consensus approach in ensemble transcriptome assembly, we present ConSemble, a new ensemble transcriptome assembler. It combines the results from four transcriptome assemblers including both de novo and genome-guided methods. For de novo assemblers, assemblies generated using multiple kmer lengths are included. ConSemble successfully improved the accuracy of transcriptome assembly over individual tools (both de novo and genome-guided). Compared to other ensemble methods, ConSemble also showed consistent improvements with fewer incorrectly assembled transcripts.
Generation of simulated benchmark RNAseq data
To understand the exact performance of different transcriptome assembly methods, we first generated simulated benchmark transcriptomes and corresponding RNAseq read sets as illustrated in the Additional file 1: Pipeline 1. Three benchmark datasets were generated. "No0-NoAlt" and "Col0-Alt" are based on the Arabidopsis thaliana accessions Nossen (No-0) and Columbia (Col-0), respectively. As the simplest benchmark dataset, no alternatively spliced transcripts (isoforms) were included in "No0-NoAlt". Another benchmark dataset was generated from the human reference genome (HG38). Additional file 2: Table S1 shows the number and distribution of isoforms included in each dataset.
The experimental design we used to compare performance of transcriptome assembly is shown in Additional file 2: Table S2. It allowed us to evaluate whether the transcriptome assembly performance is affected by having isoforms, and for genome-guided methods, how important the choice of the reference genome is. The assembly benchmarking process is summarized in the Additional file 1: Pipeline 2 (see “Materials and Methods” for further details).
Performance comparison among de novo transcriptome assemblers
Performance of individual de novo assembly methods
Using the benchmark datasets, we first compared the following four de novo transcriptome assemblers: Trinity , SOAPdenovo-Trans , IDBA-Tran , and rnaSPAdes [24, 25]. Among the three tests (Additional file 2: Tests 1–3 in Table S2), Test 1 is the simplest where no isoforms produced by alternative splicing are included in the RNAseq dataset (No0-NoAlt). Both Tests 2 and 3 included isoforms.
All four de novo assemblers we tested overestimated the number of assembled contigs regardless of whether isoforms are present ranging from 119% by IDBA-Tran for Test 3 to 203% by rnaSPAdes for Test 2 (Additional file 2: Table S3). The numbers of correctly assembled contigs were low for all four methods. Trinity produced the most accurate assembly for all datasets (e.g., Precision = 0.51, Recall = 0.64, and F = 0.57 for Test 1). All four assemblers performed worse when isoforms were included in the dataset (Tests 2 and 3). The isoform assembly performance was further compared in Additional file 2: Table S4. When more than one isoform existed for a gene (Categories 3–7), often none (Category 3) or only one isoform (Category 4) could be correctly assembled by the de novo assemblers. Trinity was most successful among the four de novo assemblers in identifying multiple isoforms (higher numbers in Categories 5–7). It was the only de novo assembler that identified four or more isoforms (Additional file 3: Fig. S1 right panel).
Contigs assembled by these four individual assemblers are compared in Fig. 1 (left column). The 4-way intersection set included a large portion of correct contigs (40–45%, shown in black letters) for all three datasets. It is also noteworthy that some contigs were correctly assembled only by one assembler. In contrast, while the large portion of the contigs that were assembled only by a single method were incorrect, the 4-way intersection set included only a small number of incorrect contigs (0.6 ~ 2%, shown in red letters). These results illustrate a risk of choosing a single de novo assembler.
Development of a consensus-based de novo ensemble assembler, ConSemble
As described before, different sets of transcripts can be assembled optimally with different kmer lengths. To explore if we can increase the number of correctly assembled contigs (TP) with using multiple de novo assemblers, for each de novo assembler we pooled the assemblies by combining the contigs produced using multiple kmer lengths. The range and number of kmer lengths used for each method is as follows: IDBA-Tran with five kmers (20–60), SOAPdenovo-Trans with 16 kmers (15–75), rnaSPAdes with 14 kmers (19–71), and Trinity with four kmers (19–31).
As expected, this approach increased TP for all assemblers (Additional file 2: Table S5; Recall > 0.67 for Test 1, > 0.60 for Test 2, and > 0.52 for Test 3). However, it also accumulated a disproportionately large number of incorrectly assembled contigs (FP), greatly reducing the accuracy of the assemblies (e.g., Precision = 0.05 for rnaSPAdes in Test 1). When we merged all these assemblies (taking the union set of assemblies generated by all methods with multiple kmer lengths; shown as "Merged" in Additional file 2: Table S5), the number of correctly assembled contigs increased further, although again at the cost of accumulating disproportionately more incorrectly assembled contigs. For example, for the No0-NoAlt dataset (Test 1), merging all the de novo assemblies produced 14,770 correctly assembled contigs (TP) from the 18,875 benchmark transcripts (Recall = 0.78), but also had 549,859 incorrectly assembled contigs (FP) decreasing the Precision significantly (0.03).
When we compared these pooled contig sets assembled by the four assemblers using multiple kmers, we noted that the vast majority of the contigs that were assembled only by one or two methods were incorrect (Fig. 1, right column). However, as the number of de novo assemblers sharing a unique contig sequence increased, the likelihood that the contig was correctly assembled also increased. A large majority (64–89%) of contigs produced by all four of the de novo assemblers were correctly assembled regardless of the test dataset, which is 70–83% of all correctly assembled contigs. Furthermore, for all test datasets, the number of contigs that were incorrectly assembled by all four assemblers was consistently small (0.2–1.1% of all incorrectly assembled contigs).
These observations indicate that by utilizing such consensus information, it is possible to increase the number of correctly assembled contigs and at the same time reduce the number of incorrectly assembled contigs, improving the overall assembly performance. To demonstrate this approach, we present a new consensus-based ensemble transcriptome assembly method, ConSemble, in which contigs shared by at least three of the four assemblers are retained for the final assembly (see Pipeline 3 in Additional file 1 and “Materials and Methods” for further details).
Performance of ConSemble compared against other de novo assembly methods
The transcriptome assembly performance of ConSemble using the four de novo assemblers was compared against the four individual de novo assemblers as well as two other ensemble methods (EvidentialGene and Concatenation) that are also based on de novo assemblers. Specific details for how each of these methods was run is described in “Materials and Methods”. The performance statistics and comparisons are summarized in Fig. 2 and Additional file 2: Table S6.
ConSemble3+d, the default assembly output of ConSemble based on four de novo assemblers, effectively filtered out the incorrectly assembled contigs. When isoforms were not included (the No0-NoAlt dataset), of 20,263 contigs produced by ConSemble3+d (107% of the reference), 13,352 were correctly assembled (Precision = 0.66 and Recall = 0.71), achieving the highest F score (0.68) among any de novo approach including both individual and ensemble methods. EvidentialGene overestimated the number of transcripts significantly (417%). While EvidentialGene recovered more benchmark transcripts (Recall = 0.66) than Trinity (Recall = 0.64; Additional file 2: Table S3), the best of the individual de novo assemblers, many contigs were incorrectly assembled (Precision = 0.16 compared to 0.51 for Trinity) leading to very low overall performance (F = 0.26). While Concatenation produced a lower number of incorrectly assembled contigs (Precision = 0.47) compared to Trinity, it did not achieve the accuracy level shown by Trinity (F = 0.52 compared to 0.57 by Trinity).
When isoforms were included in the dataset (the Col0-Alt and HG38 datasets, Tests 2 and 3), the poor performance of the four individual de novo assemblers (shown in Additional file 2: Table S3) limited the potential for these de novo ensemble assembly methods. ConSemble3+d slightly overestimated the number of transcripts present in the benchmark datasets (109–112%). However, it again successfully reduced the numbers of incorrectly assembled contigs outperforming all other de novo methods (Precision = 0.53 and 0.47, Recall = 0.59 and 0.52, and F = 0.56 and 0.49 for the Col0-Alt and HG38 datasets, respectively). EvidentialGene again had far more contigs than the benchmark transcriptome (283% and 335%, respectively) resulting in very low overall performance (F = 0.24 and 0.16, respectively). While Concatenation showed much higher Precision with lower numbers of incorrectly assembled contigs than EvidentialGene, the overall performance (F = 0.51 and 0.38, respectively) did not exceed those obtained by using only Trinity (F = 0.53 and 0.45, respectively). The limited ability of the most de novo assemblers to reconstruct multiple isoforms further limited ConSemble’s ability to accurately recover all the isoforms present in the merged assembly (Additional file 3: Fig. S2). However, although ConSemble3+d recovered fewer isoforms especially for genes with five or more isoforms than Trinity, ConSemble3+d performed significantly better than other individual de novo assemblers in terms of isoform identification (Additional file 3: Fig. S1 right panel).
Among the three de novo ensemble assemblers, ConSemble3+d and Concatenation performs virtually the same in terms of identifying isoforms up to five (Additional file 3: Fig. S2). For genes containing six or more isoforms, which correspond to only less than 2% of the benchmark genes, Concatenation appears to be able to identify slightly more isoforms. However, with lower Precision, Recall, and F scores with this method compared to ConSemble3+d (Additional file 2: Table S6), it sacrifices the recovery of transcripts from genes with five or fewer isoforms.
As described before, EvidentialGene and Concatenation cluster the contigs and choose the representatives for the final assemblies. These approaches did not seem to significantly improve the overall transcriptome assembly performance compared to the best individual de novo assembler. However, as we observed in Additional file 2; Table S5, the "Merged" de novo assembly correctly assembled up to 78% of the transcripts in the benchmark. By utilizing consensus information, the ConSemble approach successfully recovered many of these transcripts without increasing the number of incorrectly assembled contigs (FP), improving the overall assembly performance.
Performance comparison among genome-guided assemblers
The ConSemble approach can also apply to genome-guided methods (Additional file 1: Pipeline 4). Therefore, we compared the performance of genome-guided transcriptome assemblers including both individual as well as ensemble methods.
Performance of individual genome-guided methods
Using the benchmark datasets, we compared the following four genome-guided transcriptome assemblers: Cufflinks , Bayesembler , Scallop , and StringTie2 [28, 29]. The simplest experiment was to assemble the transcriptome where no alternative splicing happened (the No0-NoAlt dataset) using the same No-0 genome as a reference (Additional file 2: Test 4 in Table S2). The genome-guided assemblers performed surprisingly poorly for this dataset (Additional file 2: Table S7). For all assemblies, only 71–75% of the assembled contigs were correct (shown as Precision). StringTie2 recovered the most benchmark transcripts (Recall = 0.80) and Bayesembler the least (Recall = 0.59).
The complexity of dealing with alternative splice forms is evident from the decreased performance of all four assemblers even when using the same genome as the reference (Additional file 2: Tests 6 and 8 in Table S7). While Bayesembler consistently produced the fewest unique contigs, those assembled contigs were most accurate as shown by the consistently highest values of Precision (> 0.54). Scallop and StringTie2, on the other hand, produced larger numbers of unique contigs recovering more benchmark transcripts as shown by the larger Recall values (> 0.48).
The results were similar when STAR  was used as the aligner (Additional file 2: Table S8). STAR with default parameters was shown to more accurately assign reads to the proper genomic location than Tophat2 . However, we found that the assembly results based on STAR mapping were comparable to those based on Tophat2 showing no clear advantage in using one aligner over the other. Since Tophat2 remains the most widely used read mapper for genome-guided assemblies and is required for Bayesembler, only the results based on Tophat2 alignments are presented for further analyses.
The isoform assembly performance is compared further in Additional file 2: Table S4. When more than one isoform existed for a gene (Categories 3–7), Bayesembler and Scallop recovered more isoforms (higher numbers in Categories 5–7). These two assemblers could identify all isoforms for 50% or more of the genes that have isoforms (Category 6) or for about one third of the genes that have three or more isoforms (Category 7). Among the four genome-guided assemblers, only Bayesembler identified up to seven isoforms correctly, although the success rates were not very high (Additional file 3: Fig. S1 left panel). None of the assemblers could identify all isoforms when there were more than six isoforms.
The performance of all these assemblers was considerably worse when a non-identical reference genome was used as the reference (Additional file 2: Tests 5, 7, and 9 in Table S7). While the total numbers of contigs produced were similar to the corresponding test results with the same reference genomes (Tests 4, 6, and 8), the correctness of the contigs was greatly diminished (e.g., F < 0.34) where, on average, only 32% of the assembled contigs were correct (Precision) and only 33% of the benchmark transcripts were recovered (Recall). Of note is that all the de novo assemblies had higher accuracy compared to the genome-guided assemblies generated with the reference differing from the strain sequenced. Trinity, for example, assembled 41–51% of the contigs correctly (Precision) and recovered 50–64% of benchmark transcripts (Recall) (Additional file 2: Table S3), while all the genome-guided assemblers showed lower than 38% for these statistics. In fact, the de novo assemblers performed at the level similar to those with genome-guided assemblers with the same references (Precision > 0.54 and Recall > 0.51; Additional file 2: Tests 4, 6, and 8 in Table S7).
Contigs assembled by the four genome-guided assemblers are compared in Fig. 3. Similar to what we observed with the de novo methods, while the 4-way intersection set always included the largest numbers of correctly assembled contigs (shown in black letters), which combination of assemblers shared more correctly assembled contigs depended on the dataset and the reference genome used. Some contigs were correctly assembled only by one of the assemblers, while the majority of such contigs were false positives (incorrectly assembled, shown in red letters). Similar to what we observed with de novo assemblers, it illustrates the difficulty in choosing a single genome-guided assembler for any transcriptome assembly problem.
We further compared the contigs generated by the four genome-guided assemblers with those generated by the four de novo assemblers. The results showed that some contigs were assembled correctly only by either genome-guided or de novo assemblers (Fig. 4). For example, for the No0-NoAlt dataset, 396 contigs were produced only by the de novo assemblers. More contigs were correctly identified uniquely by the de novo methods for other datasets (554 for Col0-Alt and 1,836 for Human HG38) despite the reduced performance of the de novo assemblers on these datasets. It shows the advantage of using de novo assemblers especially when a good reference is not available.
Performance of ConSemble compared against other genome-guided methods
Since the individual genome-guided assemblers showed a similar trend in the overlap of correctly assembled contigs as the de novo methods, the consensus-based ConSemble approach with genome-guided methods is expected to improve the accuracy of the assembly. The performance of ConSemble applied to the genome-guided assemblies (ConSemble3+g) is compared with another genome-guided ensemble assembler TransBorrow  as well as individual genome-guided assemblers in Fig. 5 and Additional file 2: Table S9.
Compared to individual genome-guided methods, TransBorrow did not show significantly different assembly performance. Moreover, TransBorrow could assemble a surprisingly small number of contigs (< 12%) from the Col0-Alt dataset (Test 6). The authors mentioned about the possible issues associated to the complexity in building colored graphs depending on the assembly included . The Col0-Alt dataset may have presented one such challenge. In contrast, ConSemble3+g showed significant reduction in miss-assembly (FP) without sacrificing correct assembly (TP). For the No0-NoAlt dataset, it achieved a very high Precision (0.90). For all benchmark datasets, ConSemble3+g assembly showed balanced and high accuracy (F = 0.82, 0.67, and 0.52 for the No0-NoAlt, Col0-Alt, and HG38 datasets, respectively), which was consistently better than TransBorrow as well as individual genome-guided methods. It should be also noted that although TransBorrow recovered more correctly assembled contigs (TP) than ConSemble, showing higher Recall values (except for Col0-Alt), due to a larger number of incorrectly assembled contigs (FP), it showed lower Precision values, and hence lower overall performance (F < 0.76).
For genes with large numbers of isoforms, ConSemble3+g identified more isoforms than other ensemble methods (Additional file 3: Fig. S2). However, because many isoforms were identified in only a limited number of genome-guided assemblies (Additional file 3: Fig. S1 left panel), it also limits the ability of ConSemble3+g to identify multiple isoforms especially for genes with five or more isoforms.
Comparison of nucleotide- and protein-level performance
In addition to the protein-level performance described above, the quality of assembled contigs at the nucleotide sequence level was examined using DETONATE . DETONATE provides a reference-free model-based RSEM-EVAL score as well as reference-based scores (F1 and KC). The RSEM-EVAL metric given by DETONATE evaluates the transcriptome assembly performance by comparing the assembled nucleotide sequences to the original reads . Because genome-guided assembly returns the sequence of the reference genome rather than the RNAseq data, using RSEM-EVAL on assemblies where the RNAseq data did not originate from the reference genome used can lead to poor results. Therefore, for genome-guided assemblies, only those produced using the same reference genome as the simulated RNAseq library (Tests 4, 6, and 8) were examined.
Many of the trends seen in the protein-level performance were also reflected in the nucleotide assembly quality (Additional file 2: Table S10). In general, the assemblies with higher F at the protein level (Additional file 2: Tables S3, S6, S7, and S9) also had relatively high DETONATE scores (RSEM-EVAL and KC). Among the ensemble assemblers, most often TransBorrow (except for Col0-Alt) and ConSemble3+g had the best scores in F and/or DETONATE scores. While EvidentialGene consistently showed the lowest F at the protein level as well as F1 both at the nucleotide and contig levels, it was found to perform somewhat better when the RSEM-EVAL scores were considered. As even a single nucleotide indel can have major impacts on the predicted protein sequences, it is important to evaluate performance statistics at both nucleotide and protein levels .
RSEM-EVAL and KC tend to provide higher scores for assemblies with longer sequences that account for, e.g., more of the kmers in the RNAseq data or the reference over the precision of the contig sequence. To illustrate this issue, using the same ConSemble de novo assembly, we generated two other collections of contig sets. In the ConSemble3+dLong set, instead of the shortest nucleotide sequences for the default ConSemble3+d, the longest sequences were chosen as the representative contigs from those coding identical protein sequences. In the ConSemble3+dHigh set, contig sequences with the highest RSEM-EVAL scores were chosen instead. Although these three ConSemble3+d assemblies have the same protein-level F scores (for the same set of protein sequences), DETONATE scores were significantly different (Additional file 2: Table S10). By choosing the shortest nucleotide sequences, ConSemble assemblies tend to truncate the untranslated regions (UTRs) of the transcripts. ConSemble3+dLong and ConSemble3+dHigh showed similar and much higher DETONATE scores indicating longer contigs are scored higher.
Impact of using lower percent identity thresholds
The results presented so far are based on the numbers of correctly assembled contigs where the assembled sequences need to be fully 100% identical to the reference protein sequences. In practice, this is stricter than necessary for many downstream analyses. We therefore examined the impact of using lower identity thresholds on the assembly metrics.
The number of correctly assembled contigs identified at varied thresholds are shown in Additional file 3: Fig. S3. The increase in the performance metrics was most significant when the threshold was reduced from 100 to 98%. The increase was generally proportional across all methods with the exceptions of the genome-guided assemblies using different reference genomes and EvidentialGene. With the 100% threshold, the performance of the genome-guided methods was significantly lower than de novo methods when different reference genomes were used. However, with less strict thresholds (< 100%), their performance quickly recovered and became better than that of some of the de novo assemblers, although it remained lower than genome-guided assemblers using the same reference genome down to the lowest threshold (90%) used. For the No0-NoAlt dataset, EvidentialGene recovered more correctly assembled contigs with relaxed thresholds and resulted in Recall scores higher than other de novo methods (including both ensemble and individual methods) (Additional file 2: Table S11). However, since EvidentialGene retained a large number of incorrectly assembled contigs (Precision < 0.19 indicating only one in five contigs were correctly assembled), the accuracy level was kept very low (F < 0.31). ConSemble3+d remained the best performing non-genome guided method for all three datasets. ConSemble3+g maintained the highest accuracy among all the methods we tested including individual genome-guided and de novo methods. These trends were consistent in the Col0-Alt and HG38 benchmark datasets as well (Additional file 2: Tables S12 and S13).
Assembling the real plant transcriptomes
Assembly performance was further tested using the real RNAseq data from three plant species. We included a strain of a model dicot A. thaliana where a high-quality genome and transcriptome information is available, another dicot Momordica charantia (bitter melon, a diploid), as well as the hexaploidal cotton (Gossypium hirsutum). This selection represents a range of ploidies, which challenges transcriptome assembly methods. In contrast to testing using simulated benchmark data, when the real RNAseq data are used, we do not know which contigs are assembled correctly or incorrectly and hence, we cannot evaluate the assembly performance using the same set of statistics. We instead used the number of genes identified from the "Eudicotyledons" dataset of BUSCO  to evaluate the thoroughness of each assembly. For A. thaliana Col-0, the number of contigs matching with those in the Araport11 reference transcriptome at the protein level was also counted. For the nucleotide level, the reference-free model-based RSEM-EVAL scores were included. The results are summarized in Table 1.
For A. thaliana Col-0, all ensemble assemblers except TransBorrow produced more contigs that matched Araport sequences and BUSCOs and had slightly better RSEM-EVAL scores than the de novo assemblers. The very low performance by TransBorrow was consistent to what we observed with the Col0-Alt benchmark dataset. All ensemble assemblers had better or comparable performance than the genome-guided assemblers. While ConSemble3+g found slightly fewer BUSCOs compared to SOAPdenovo-Trans, it had the second best RSEM-EVAL following StringTie2. Among the de novo assembly-based ensemble methods, EvidentialGene assembled the most BUSCOs but the fewest Araport-matching contigs and had the lowest RSEM-EVAL score.
For M. charantia, all ensemble methods including TransBorrow recovered more BUSCOs than de novo assemblers. EvidentialGene, Concatenation, and TransBorrow also recovered more BUSCOs than the highest performing genome-guided method, StringTie2. Although EvidentialGene recovered the most BUSCOs, it produced a significantly more contigs (324% of the reference) indicating a high false positive rate. While ConSemble3+d did not find as many BUSCOs as StringTie2 did, it had the best RSEM-EVAL score among all assemblers. Concatenation produced the fewest contigs, which was even fewer than those produced by Scallop, the best genome-guided assembler. Among the genome-guided assemblers, Scallop and StringTie2 had similar numbers of BUSCOs as well as RSEM-EVAL scores, while the number of contigs produced by StringTie2 was close to the number reported for the reference transcriptome.
For the hexaploidal cotton (G. hirsutum), all methods produced significantly more contigs than the 70,478 transcripts reported (except TransBorrow where the assembly largely failed). All genome-guided methods showed high numbers of BUSCOs with StringTie2 producing the most. Among the de novo assemblers, only Trinity produced a comparable number of BUSCOs. The remaining de novo assemblers produced far fewer BUSCOs despite the high numbers of contigs assembled, suggesting high false positives. Both EvidentialGene and Concatenation produced large numbers of BUSCOs. However, EvidentialGene produced more than ten times the number of sequences in the reference transcriptome and had a lower RSEM-EVAL score. ConSemble3+d showed a significantly smaller number of BUSCOs and one of the worst RSEM-EVAL scores. This may be due to the impact of a high ploidy . It is discussed further below.
Despite the number of transcriptome assemblers available, there is still no single assembler or assembly strategy that performs best in all situations. Our simulation study clearly showed that every assembler correctly assembled a set of contigs while missing transcripts correctly assembled by other methods. When the reference genome was available from the same strain, the genome-guided assemblers outperformed de novo and most ensemble assemblers. However, if such reference sequences were not available, the performance of genome-guided assemblers dropped below that of de novo assemblers. Ensemble assembly approaches can overcome some of the limitations of individual assemblers. Our analysis showed that without requiring a reference genome, ensemble de novo methods achieved the assembly performance comparable to or higher than that of individual genome-guided methods.
Both EvidentialGene and Concatenation generally kept more of the correctly assembled contigs produced by the individual de novo assemblers indicated by high Recall and BUSCO scores. However, our simulation study showed that they also had many incorrectly assembled contigs that decreased the overall assembly accuracy. Instead of simply choosing representative contigs, ConSemble used consensus-based information to filter more reliable contigs. By pooling multiple assemblies, especially also pooling those generated using multiple kmer lengths for de novo methods, ConSemble increases the completeness of the assembled transcriptome. By taking the consensus contig set from four contig libraries, both ConSemble3+d and ConSemble3+g reduce the number of incorrectly assembled contigs significantly. This was observed in much lower numbers of incorrectly assembled contigs than those obtained by the other ensemble methods as well as all individual methods. For the three benchmark datasets, ConSemble3+d showed comparable accuracies to the genome-guided assemblers with ideal reference genomes and outperformed all individual de novo assemblers and the individual genome-guided assemblers without good reference genomes. Furthermore, the consensus strategy used with ConSemble allows the users to extract a set of contigs that are more likely to be correctly assembled (ConSemble4 assembly) from the rest of the contigs (ConSemble3 assembly).
Any ensemble method will necessarily be limited by the performance of the individual assemblies it is based on. If no individual method can correctly assemble a contig, no ensemble assembly can recover it. ConSemble uses the overlap of any three of the four individual assemblies (ConSemble3+). Therefore, any contigs that are correctly assembled by only one or two assemblers are omitted from the final contig set. For the three benchmark transcriptome assemblies, 10–20% of correctly assembled contigs (1418 of 14,770, 1988 of 11,179, and 2139 of 11,267 in the No0-NoAlt, Col0-Alt, and HG38 datasets, respectively) were discarded in this manner in ConSemble3+d. In Additional file 3: Fig. S4, we examined how the assembly performance is affected by expression levels of transcripts. It shows that assembly performance is affected by the expression levels regardless of the datasets, and the de novo methods are affected much more significantly than the genome-guided methods. Furthermore, the ConSemble assembly (both ConSemble3+d and ConSemble3+g) performance was affected more for transcripts whose expression levels are low (e.g., Additional file 3: bin # 1–3 in Fig. S4). As we described before, especially when genes have many isoforms (e.g., five or more) both ConSemble3+d and ConSemble3+g missed many of such isoforms (Additional file 3: Fig. S2). However, as shown in "All Assemblies" in Additional file 3: Fig. S1, many of these isoforms in fact existed in at least one of the assemblies that were used as the contig libraries for ConSemble assembly. These isoforms must have been unique to a single contig library or shared only by two contig libraries, and subsequently filtered out during the process of consensus assembly. Since the expression levels of isoforms from a gene are known to vary significantly, it is possible that the isoforms that were missed by ConSemble3+ assembly were those with low expression. This may also explain lower than expected levels of performance observed with all assemblers especially when isoforms were included in the test datasets. To recover such low expression transcripts, more refined assembly strategies beyond the current simple consensus approach need to be considered.
A similar effect is found when performance among the assemblers included in ConSemble varies widely. In the hexaploid cotton assembly, the quality of the ConSemble assembly deteriorated noticeably. This result suggests that the increased ploidy may decrease the likelihood that the true sequences are reconstructed by multiple de novo methods, consistent with the reduced overlap between de novo assemblers in simulated polyploidy benchmarks . Since other ensemble approaches showed much larger BUSCOs for the cotton, it would be interesting to consider an integrated approach. Further benchmarking studies incorporating varied ploidies are necessary to determine the extent and impact of this issue.
ConSemble for de novo methods requires multiple different assemblers to run multiple times over a range of kmer lengths. Therefore, the availability of computational resources could become a limiting factor for deciding the number of assemblies ConSemble can utilize. However, as each assembly process is independent, these jobs can be run in parallel depending on the availability of the resources. This makes the overall assembly time roughly equivalent to the time needed for the longest individual assembly reducing the necessary computational time significantly.
We presented ConSemble, a new consensus-based ensemble transcriptome assembly approach. It can be used with either de novo or genome-guided methods. Using ConSemble with de novo methods, a transcriptome assembly produced showed the quality rivals that of genome-guided assembly without requiring a reference genome and outperforms individual de novo and other ensemble strategies. ConSemble with genome-guided methods further increases the accuracy of the assembly over individual genome-guided methods. Both of de novo and genome-guided ConSemble assembly achieve high performance by retaining the majority of the contigs correctly assembled by individual assemblers and removing a significant number of misassembled contigs. Further work is needed for identifying more correctly assembled contigs especially when multiple lowly expressed isoforms exist or in the case of polyploidies. Improvements are needed to retain more of the contigs that are correctly assembled only by a small number of assemblers without accumulating incorrectly assembled contigs. We also showed the importance of developing realistic simulated RNAseq benchmark datasets that allow evaluating the performance of transcriptome assemblers under various conditions. It provides the only means of assessing the accuracy of the transcripts assembled directly and quantitatively.
Materials and Methods
Genomes and transcriptomes used
RNAseq simulation and genome-guided assemblies of the A. thaliana accession Nossen (No-0) were based on the assembly by Gan et al.  downloaded from the 1001 genomes project [35; accession CS6805]. RNAseq simulations and genome-guided assemblies of the A. thaliana accession Columbia (Col-0) were based on the TAIR reference genome (version 9)  and the atRTD transcriptome dataset (version 3) . The atRTD dataset was chosen over the TAIR transcriptome (version 10) due to the higher prevalence of isoforms in the atRTD dataset, allowing better testing of each assembler’s ability to reconstruct alternative transcripts. The Human RNAseq simulations were based on the HG38 reference genome and transcriptome [39; GCF_000001405.39]. To evaluate the impact of using a different reference for this RNAseq library, the reads were also mapped to the HuaXia1 (HX1) reference genome (available from http://hx1.wglab.org).
Three datasets were used for the assembly of real RNAseq data. The A. thaliana Col-0 dataset consisting of 116 M 76 bp read pairs sequenced using Illumina Genome Analyzer II is from Marquez et al. , which is available at National Center for Biotechnology Information (NCBI) (SRA047499). For M. charantia (bitter melon), a dataset consisting of 228 M 100 bp read pairs was produced by sequencing of RNA samples from leaves, roots, flowers, and seeds using Illumina HiSeq 2500 (NCBI: SRR3535137, SRR3535138, SRR3535144, and SRR3535149). The reference genome was from Urasaki et al.  (NCBI: BDCS01000001–BDCS01001052). For G. hirsutum (upland cotton), an RNAseq dataset consisting of 117 M 200 bp read pairs was produced from RNA samples from leaves, roots, flowers, and seeds using Illumina HiSeq 4000 (NCBI: SRR7689126- SRR7689129). The genome assembly used as the reference is the allotetraploid L. acc. TM-1 [41; http://www.cottongen.org].
Production of simulated benchmark transcriptomes and read sets
Simulated benchmark transcriptomes and corresponding RNAseq read sets were produced from A. thaliana and humans as follows. A modified Flux Simulator v1.2.1 pipeline  illustrated in the Additional file 1: Pipeline 1 was used to produce ~ 250 M 76 bp read pairs for each dataset. The No0-NoAlt transcriptome was generated based on the A. thaliana No-0 genomic sequence and no alternative splice events were included. The Col0-Alt transcriptome was generated based on the A. thaliana Col-0 genomic sequence and the version 3 atRTD transcriptome model. The expressed transcriptome in this dataset contains zero to nine alternative splice events (one to ten isoforms) per gene where only the isoforms unique at the protein level were included. The transcriptome for the Human dataset was based on the HG38 reference genome and transcriptome. It contains zero to 14 alternative splice events (1 to 15 isoforms). Only reference transcripts with full coverage of RNAseq data (all positions are required to be covered by at least one read) were included in the benchmark datasets, as transcripts without full coverage cannot be correctly assembled as a single contig. Additional file 2: Table S1 shows the number and distribution of isoforms per transcript for each dataset. All benchmark datasets used in this study are available from: http://bioinfolab.unl.edu/emlab/consemble/
The read pairs generated by Flux Simulator were quality filtered using Erne-filter 2.0  using the 'ultra-sensitive' flag with a minimum average quality of q20 and in paired-end mode. The remaining reads were normalized using Khmer  with a kmer length of 32, an expected coverage of 50x, and in paired-end mode. As normalization works at the read level, kmer-length selection for this step has minimal impact on the unique kmers kept, and hence on the performance of the assembly.
De novo assembly
De novo assembly was performed using Trinity 2.4.0 , SOAPdenovo-Trans 1.0.3 , IDBA-Tran 1.1.1 , and rnaSPAdes 3.10.0 (using the rnaspades.py script) . Each of the assemblers was run using default settings as well as using multiple kmer lengths. With the default setting, IDBA-Tran assembles across multiple kmer lengths and merges the results for the final assembly. Trinity and SOAPdenovo-Trans use a single default value (25 and 23, respectively). rnaSPAdes chooses the kmer length depending on the dataset. For this study, a kmer length of 55 was chosen for both No0-NoAlt and Col0-Alt read sets and a kmer length of 37 was chosen for the HG38 set. Each method was also run with a range of kmer lengths (k) with increments (i) as follows: IDBA-Tran with k = 20–60 and i = 10, SOAPdenovo-Trans with k = 15–75 and i = 4, rnaSPAdes with k = 19–71 and i = 4, and Trinity with k = 19–31 and i = 4.
The assemblers Velvet 1.2.10  with Oases 0.2.08  and Mira 4.0.2  were excluded from this study after the preliminary analysis due to their high computational requirements (both resources and time), issues with program stability, or both.
The normalized reads were first mapped to the reference genomes using Tophat2 2.0.14 with default settings . Assemblies were performed using Cufflinks 2.2.1 , Bayesembler 1.2.0 , Scallop 0.10.2 , and StringTie2 2.0 . Each assembler was executed on the default settings. Assembled contig sequences were extracted using the gtf_to_fasta module from Tophat2. The two Arabidopsis RNAseq datasets (No0-NoAlt and Col0-Alt) were assembled using each of the two reference genomes. The human RNAseq dataset (HG38) was assembled using each of the two reference genomes (HG38 and HX1). To compare the impact of using different read mappers on assembly quality, additional assemblies were also performed using STAR 2.4.2a  for read mapping.
For EvidentialGene (version 2017.03.09) , we chose the "okay" nucleotide contig set (okay.fa and okalt.fa) produced by the tr2aacds.pl pipeline as the final output for our comparative analysis. The assembly was performed using the same sets of four de novo assemblers and kmer lengths as described above resulting in 39 assemblies in total. Concatenation originally used three assemblers, Trinity, IDBA-Tran, and CLC (https://www.qiagenbioinformatics.com/), with only one kmer length each . For this study, we tested all combinations of three from the four aforementioned de novo assemblers and kmer lengths. CLC was excluded from this study because it is only available under a commercial license. Based on the numbers of correctly and incorrectly assembled contigs, we decided to use the following three assemblies as the best combination for Concatenation: IDBA-Tran with k = 50, Trinity with k = 31, and rnaSPAdes with k = 55. For TransBorrow (version 1.3) , the GTF files for the assemblies with the same reference genome for each of the four genome-guided assemblers (Cufflinks, Bayesembler, Scallop, and StringTie2) were manually merged into a single file. The TransBorrow assembly was performed using default parameters.
ConSemble, a new consensus-based ensemble assembly
The Additional file 1: Pipelines 3 and 4 summarize the process of the ConSemble transcriptome assembly approach. Four transcriptome assembly methods, either de novo or genome-guided, are used to generate four "contig libraries", each containing the unique protein sequences produced by each method. For the de novo based assembly (Pipeline 3), each assembler is used with a given set of multiple kmer lengths (described above), producing a total of 39 assemblies across the four methods. The assembly sets from each method are pooled to produce four contig sets. In these pooled contig sets, unique contigs are identified based on the coded protein sequences, producing four "pooled unique" contig sets. Each of the four pooled unique contig sets is used as the "contig library" from each method. For the genome-guided based assembly (Pipeline 4), the unique contig set (at the protein level) extracted from the assembled contigs using each method is used as the "contig library". Based on the tradeoff among the performance statistics (described below), we decided to take the set of contigs that are found in the intersections of at least three contig libraries as the default output assembly. This assembly is called ConSemble3+ .
The 5’ and 3’ boundaries of the transcripts are handled differently depending on the assembler. Although this does not affect the predicted open reading frame (ORF) and the coded protein sequence, it affects the overall contig length and potentially the sequence of the untranslated regions. Therefore, to minimize the impact of such differing behaviors between assemblers, we concentrated only on the longest ORFs produced by ORFfinder  to compare assembled contigs. When contigs with differing nucleotide sequences code entirely identical protein sequences, the one with the shortest nucleotide sequence is chosen and included in the final assembly. This is done to minimize the risk of including any chimera or over-assembled sequences. Although all redundant contigs and those with no predicted protein product are excluded from the final assembly, these contigs are saved in separate files and available for additional analyses.
ConSemble assembly can be performed based on a smaller or larger number of the assembly overlap. When only two or more assembly overlap was required (ConSemble2+d and ConSemble2+g in Additional file 2: Tables S6 and S9), more correctly assembled contigs (TP) were recovered but at the cost of disproportionally more incorrectly assembled contigs (FP >> TP) leading to higher Recall but lower Precision than ConSemble3+ (e.g., Recall = 0.74 and 0.84 and Precision = 0.27 and 0.79, respectively, for No0-NoAlt). When the 4-way consensus was used (ConSemble4d and ConSemble4g), while fewer correctly assembled contigs were recovered than ConSemble3+ (e.g., Recall = 0.65 and 0.50 for No0-NoAlt, respectively), it reduced the number of incorrectly assembled contigs (FP) significantly only marginally increasing the number of missing benchmark transcripts (FN). This led to significantly higher Precision (e.g., 0.89 and 0.95 for No0-Alt, respectively) compared to ConSemble3+ . Based on these results, we chose ConSemble3+ as the main output of the ConSemble pipeline. Note that, with its very high Precision, the ConSemble4 assembly can be used as part of post-processing of the ConSemble3+ assembly to obtain a contig set with the highest confidence.
ConSemble is implemented in Perl. Source codes and test data are freely available at: http://bioinfolab.unl.edu/emlab/consemble/
The experimental design we used to evaluate the assembly performance is summarized in Additional file 2: Table S2. The assembly benchmarking process is summarized in the Additional file 1: Pipeline 2. Each contig assembly was evaluated based on the accuracy of the coded protein sequences in the ORFs predicted by ORFfinder . This was done to avoid issues with variation in how the assemblers define the 3’ and 5’-ends of the contigs. Only contigs that fully covered the benchmark protein sequences without any mismatches or gaps were considered correctly assembled (true positives, TPs). Contigs with any amino acid mismatches and gaps or those corresponding to benchmark sequences only partially are considered false positives (FPs). Any transcript sequences in the benchmark dataset that were not assembled completely correctly were considered false negatives (FNs).
The overall performance of the assembly is determined as follows:
TP: number of correctly assembled contigs
FP: number of incorrectly assembled contigs
FN: number of benchmark transcripts that are not assembled
Recall (or sensitivity) = TP/(TP + FN)
Precision = TP/(TP + FP)
F-measure (F or F1) = 2(TP)/[2(TP) + FP + FN]
Precision shows the proportion of correctly assembled contigs relative to all assembled contigs. Recall also shows the proportion of correctly assembled contigs but relative to the number of transcripts in the reference (actual positives). F-measure is a combined metric, the harmonic mean of Precision and Recall. It therefore balances the concerns of Recall and Precision. F-measure also does not require the number of true negatives (TN) to be known. This is useful because a true negative is not defined for transcriptome assembly in this benchmarking. The regular accuracy score, which is defined as (TP + TN)/(TP + FP + TN + FN), also requires TN and cannot be calculated.
Isoform detection performance
As described before, only isoforms that produce distinct protein sequences are considered different. This approach does not account for biologically important alternative splice events in the UTRs, which can affect protein trafficking or translation without affecting protein sequences. To simplify the analysis, the performance of the isoform detection was evaluated solely by the number of correctly assembled isoforms without considering the abundance of each isoform. Contigs that did not match completely any of the isoforms included in the benchmark dataset were counted as incorrectly assembled (FP).
Lower identity threshold
To evaluate the effect of using % identity thresholds lower than 100% to identify TPs, identities between each assembled contig and the benchmark transcript at the translated protein level, was also calculated by dividing the edit distance by the length of the global alignment using the python library for Edlib (version 1.3.8post2) .
Venn diagram generation
The contig overlap between assemblies was evaluated based on the coded protein sequences. The Venn diagrams were generated using jvenn (version 1.0) .
Assembly performance at the nucleotide level
The quality of the nucleotide sequences produced by all of the assembly methods were examined using DETONATE (version 1.11) . DETONATE provides a reference-free model-based RSEM-EVAL score as well as reference-based scores including the nucleotide and contig-level F1 and KC (kmer compression). RSEM-EVAL scores how well the contigs are supported by the RNAseq data as well as the compactness of an assembly based on their joint probability. The thresholds used to identify the contig-level TP to calculate F1 score (see the formula above) is 99% identity and having ≤ 1% of insertions and deletions. The KC score is a combination of the weighted kmer recall and inverse compression rate, the latter of which penalizes large assemblies.
The thoroughness of the assemblies generated from the real plant RNAseq libraries was assessed based on the number of complete genes identified from the Eudicotyledons obd10 dataset of BUSCO (version 3.1.0) .
Transcript expression level analysis
To determine the impact of expression level on transcript assembly, the benchmark transcriptome was divided into ten equally sized bins by the expression level. The proportion of transcripts in each bin correctly assembled is determined by the number of benchmark transcripts with at least one exact match (full length with no gaps or mismatches) in an assembly divided by the total number of transcripts in the bin.
Availability of data and materials
The source codes, benchmark datasets, and other datasets used and analyzed in this study are available from: http://bioinfolab.unl.edu/emlab/consemble/.
Huang X, Chen XG, Armbruster PA. Comparative performance of transcriptome assembly methods for non-model organisms. BMC Genom. 2016;17:523.
Wang S, Gribskov M. Comprehensive evaluation of de novo transcriptome assembly programs and their effects on differential gene expression analysis. Bioinformatics. 2017;33(3):327–33.
Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
Voshall A, Moriyama EN. Next-generation transcriptome assembly: strategies and performance analysis. In: Adburakhmonov IY, editor. Bioinformatics in the era of post genomics and big data. Rijeka: IntechOpen; 2018.
Simonis M, Atanur SS, Linsen S, Guryev V, Ruzius FP, Game L, Lansu N, de Bruijn E, van Heesch S, Jones SJ, et al. Genetic basis of transcriptome differences between the founder strains of the rat HXB/BXH recombinant inbred panel. Genome Biol. 2012;13(4):r31.
Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644–52.
Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011;12(10):671–82.
Gruenheit N, Deusch O, Esser C, Becker M, Voelckel C, Lockhart P. Cutoffs and k-mers: implications from a transcriptome study in allopolyploid plants. BMC Genom. 2012;13:92.
Gilbert D. Gene-omes built from mRNA seq not genome DNA. In: 7th Annual Arthropod Genomics Symposium: June 13-June 15 2013; Notre Dame, IN.
Durai DA, Schulz MH. Informed kmer selection for de novo transcriptome assembly. Bioinformatics. 2016;32(11):1670–7.
Rana SB, Zadlock FJ, Zhang Z, Murphy WR, Bentivegna CS. Comparison of de novo transcriptome assemblers and k-mer strategies using the killifish, Fundulus heteroclitus. PLoS ONE. 2016;11(4):e0153104.
Holding ML, Margres MJ, Mason AJ, Parkinson CL, Rokyta DR. Evaluating the performance of de novo assembly methods for venom-gland transcriptomics. Toxins (Basel). 2018;10(6):249.
Holzer M, Marz M. De novo transcriptome assembly: a comprehensive cross-species comparison of short-read RNA-Seq assemblers. Gigascience. 2019;8(5):giz039.
Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562–78.
Smith-Unna R, Boursnell C, Patro R, Hibberd JM, Kelly S. TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 2016;26(8):1134–44.
Gilbert DG. Genes of the pig, Sus scrofa, reconstructed with EvidentialGene. PeerJ. 2019;7:e6374.
Cerveau N, Jackson DJ. Combining independent de novo assemblies optimizes the coding transcriptome for nonconventional model eukaryotic organisms. BMC Bioinform. 2016;17(1):525.
Yu T, Mu Z, Fang Z, Liu X, Gao X, Liu J. TransBorrow: genome-guided transcriptome assembly by borrowing assemblies from different assemblers. Genome Res. 2020;30(8):1181–90.
Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, Dewey CN. Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol. 2014;15(12):553.
Simao FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210–2.
Yang Y, Gribskov M. The evaluation of RNA-Seq de novo assembly by PacBio long read sequencing. bioRxiv 2019:735621.
Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, Huang W, He G, Gu S, Li S, et al. SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads. Bioinformatics. 2014;30(12):1660–6.
Peng Y, Leung HC, Yiu SM, Lv MJ, Zhu XG, Chin FY. IDBA-tran: a more robust de novo de Bruijn graph assembler for transcriptomes with uneven expression levels. Bioinformatics. 2013;29(13):i326-334.
Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. Gigascience. 2019;8(9):giz100.
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77.
Maretty L, Sibbesen JA, Krogh A. Bayesian transcriptome assembly. Genome Biol. 2014;15(10):501.
Shao M, Kingsford C. Accurate assembly of transcripts through phase-preserving graph decomposition. Nat Biotechnol. 2017;35(12):1167–9.
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33(3):290–5.
Kovaka S, Zimin AV, Pertea GM, Razaghi R, Salzberg SL, Pertea M. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 2019;20(1):278.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.
Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR. Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat Methods. 2017;14(2):135–9.
Watson M, Warr A. Errors in long-read assemblies can critically affect protein prediction. Nat Biotechnol. 2019;37(2):124–6.
Voshall A, Moriyama EN. Next-generation transcriptome assembly and analysis: impact of ploidy. Methods. 2020;176:14–24.
Gan X, Stegle O, Behr J, Steffen JG, Drewe P, Hildebrand KL, Lyngsoe R, Schultheiss SJ, Osborne EJ, Sreedharan VT, et al. Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature. 2011;477(7365):419–23.
1001 Genomes Consortium. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell. 2016;166(2):481–91.
Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster H, Li D, Meyer T, Muller R, Ploetz L, et al. The Arabidopsis information resource (TAIR): gene structure and function annotation. Nucleic Acids Res. 2008;36(Database issue):D1009-1014.
Zhang R, Calixto CP, Tzioutziou NA, James AB, Simpson CG, Guo W, Marquez Y, Kalyna M, Patro R, Eyras E, et al. AtRTD—a comprehensive reference transcript dataset resource for accurate quantification of transcript-specific expression in Arabidopsis thaliana. New Phytol. 2015;208(1):96–101.
Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen HC, Kitts PA, Murphy TD, Pruitt KD, Thibaud-Nissen F, Albracht D, et al. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome Res. 2017;27(5):849–64.
Marquez Y, Brown JW, Simpson C, Barta A, Kalyna M. Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res. 2012;22(6):1184–95.
Urasaki N, Takagi H, Natsume S, Uemura A, Taniai N, Miyagi N, Fukushima M, Suzuki S, Tarora K, Tamaki M, et al. Draft genome sequence of bitter gourd (Momordica charantia), a vegetable and medicinal plant in tropical and subtropical regions. DNA Res. 2017;24(1):51–8.
Zhang T, Hu Y, Jiang W, Fang L, Guan X, Chen J, Zhang J, Saski CA, Scheffler BE, Stelly DM, et al. Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement. Nat Biotechnol. 2015;33:531.
Griebel T, Zacher B, Ribeca P, Raineri E, Lacroix V, Guigo R, Sammeth M. Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucleic Acids Res. 2012;40(20):10073–83.
Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM. An extensive evaluation of read trimming effects on Illumina NGS data analysis. PLoS ONE. 2013;8(12):e85024.
Crusoe MR, Alameldin HF, Awad S, Boucher E, Caldwell A, Cartwright R, Charbonneau A, Constantinides B, Edvenson G, Fay S, et al. The khmer software package: enabling efficient nucleotide sequence analysis. F1000Res. 2015;4:900.
Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18(5):821–9.
Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28(8):1086–92.
Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WEG, Wetter T, Suhai S. Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Res. 2004;14(6):1147–59.
Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.
Wheeler DL, Church DM, Federhen S, Lash AE, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, Tatusova TA, et al. Database resources of the National Center for Biotechnology. Nucleic Acids Res. 2003;31(1):28–33.
Sosic M, Sikic M. Edlib: a C/C++ library for fast, exact sequence alignment using edit distance. Bioinformatics. 2017;33(9):1394–5.
Bardou P, Mariette J, Escudie F, Djemiel C, Klopp C. jvenn: an interactive Venn diagram viewer. BMC Bioinform. 2014;15:293.
Cheng CY, Krishnakumar V, Chan AP, Thibaud-Nissen F, Schobel S, Town CD. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 2017;89(4):789–804.
We would like to thank Dr. Don Gilbert (Indiana University) for his discussions and critical feedback regarding EvidentialGene and its performance.
This work has been supported by the National Science Foundation under Grant Nos. 1339385 to JS, EBC, and ENM and 1557417 to EBC and ENM. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of work included in this submission.
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Voshall, A., Behera, S., Li, X. et al. A consensus-based ensemble approach to improve transcriptome assembly. BMC Bioinformatics 22, 513 (2021). https://doi.org/10.1186/s12859-021-04434-8
- Transcriptome assembly
- De novo assembly
- Genome-guided assembly
- Ensemble assembly