 Research
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DTASiST: de novo transcriptome assembly by using simplified suffix trees
BMC Bioinformatics volume 20, Article number: 698 (2019)
Abstract
Background
Alternative splicing allows the premRNAs of a gene to be spliced into various mRNAs, which greatly increases the diversity of proteins. Highthroughput sequencing of mRNAs has revolutionized our ability for transcripts reconstruction. However, the massive size of short reads makes de novo transcripts assembly an algorithmic challenge.
Results
We develop a novel radical framework, called DTASiST, for de novo transcriptome assembly based on suffix trees. DTASiST first extends contigs by reads that have the longest overlaps with the contigs’ terminuses. These reads can be found in linear time of the lengths of the reads through a welldesigned suffix tree structure. Then, DTASiST constructs splicing graphs based on contigs for each gene locus. Finally, DTASiST proposes two strategies to extract transcriptrepresenting paths: a depthfirst enumeration strategy and a hybrid strategy based on length and coverage. We implemented the above two strategies and compared them with the stateoftheart de novo assemblers on both simulated and real datasets. Experimental results showed that the depthfirst enumeration strategy performs always better with recall and also better with precision for smaller datasets while the hybrid strategy leads with precision for big datasets.
Conclusions
DTASiST performs more competitive than the other compared de novo assemblers especially with precision measure, due to the readbased contig extension strategy and the elegant transcripts extraction rules.
Background
Alternative splicing plays an important role in regulating gene expression and producing diversity of proteins. Through alternative splicing, the premRNAs of a gene can be spliced into various mRNAs, which results in the large difference between the number of genes and that of proteins [1, 2]. A transcript is defined as a mature mRNA that encodes protein. We call the set of all the transcripts the transcriptome. The transcriptome can be seen as a precursor of the proteome, i.e., the entire set of proteins expressed by a genome. Transcriptome reconstruction is an important mean for studying cell phenotype and function [3]. However, this task is quite nontrivial. For the time being, we only know a small part of the landscape of alternative splicing of some species.
The highthroughput sequencing has revolutionized our ability to study many challenging issues such as motif finding, DNA/RNAprotein interaction, ribosome profiling, small RNA expression profiling, transcripts assembly, and disease diagnosis [4–9]. The RNA sequencing (RNAseq) offers a great opportunity to identify the expressed transcripts.
There are generally two alternative computational strategies for transcriptome assembly problems: genomeguided approaches such as Scallop [10], Cufflinks [11], StringTie [12], CIDANE [13], Scripture [14], IsoLasso [15], and TransComb [16], and de novo transcripts assembly approaches such as Trinity [17], Oases [18], SOAPdenovoTrans [19], IDBATran [20], BinPacker [21], Bridger [22], ABySS [23], and IsoTree [24]. Genomeguided assemblers are generally more accurate than de novo assemblers when the highquality reference genome is available. However, the highquality reference genome is not always available. In this situation, de novo transcriptome assembly is required. In this paper, we mainly consider de novo transcriptome assembly methods.
Existing strategies for transcriptome assembly usually adopt the following scheme: first constructing graphs based on the RNAseq reads, and then extracting paths from the graphs to represent plausible transcripts. Various algorithms are employed to recover transcriptrepresenting paths. For example, Cufflinks [11] and Bridger [22] employ the minimum path cover algorithm to extract the minimum number of paths that cover all the vertices. StringTie [12] applies the network flow algorithm on the splicing graphs to recover all the possible transcripts. Trinity [17] extracts the sufficiently covered paths from the compact graphs based on a bruteforce enumeration strategy. BinPacker [21] models the paths in the splicing graphs as a set of trajectories of items by solving a series of binpacking problems. Note that Cufflinks and StringTie belong to genomeguided approaches. They usually start by aligning reads to a reference genome, and then construct graphs according to alignment results. Trinity, Bridger, and BinPacker are de novo transcriptome assembly approaches, and they build graphs solely based on the overlaps of kmers (kcharacter substrings of read sequences). To investigate the influence of path extracting strategies on the assemblers’ performances, DTASiST developed two strategies: a depthfirst enumeration strategy and a hybrid strategy based on length and coverage. The first strategy aims to distinguish as many as possible transcripts while the second tries to target candidates more accurately. DTASiST implemented these two strategies and compared them with the stateoftheart assemblers.
Historically, the de novo assembly approaches mostly rely on the pioneering works on de Bruijn graphs [25], including Trinity [17], SOAPdenovoTrans [19], Oases [18], IDBATran [20], and TransAByss [23]. Recently, Bridger [22], BinPacker [21], and IsoTree [24] applied splicing graphs [26] to represent alternative splicing. Both de Bruijn graphs and splicing graphs are usually constructed by extending contigs with kmers. Each node in a de Bruijn graph represents a kmer while a node in a splicing graph usualls to an exon. Hence, the number of nodes in a splicing graph is far less than that of the nodes in a de Bruijn graph, which makes the models based on splicing graphs more tractable.
The kmerbased extension strategies extend a contig through a kmer whose first (or last) k−1character substring is exactly the same as the last (or first) k−1character substring of the contig. For convenience, we say that the kmer has a k−1character overlap with the contig’s ternimus. For example, suppose the contig sequence is ACATCG, and the kmer set contains the kmers of TACA, ACAT, CATC, ATCG, and TCGG. Since the last 3character substring of TACA is exactly the same as the first 3character substring of the contig, i.e., the kmer of TACA has a 3character overlap with the contig’s terminus, the contig can be extended by TACA to TACATCG. Similarly, the first 3character substring of TCGG is exactly the same as the last 3character substring of the contig. Through TCGG, the contig of TACATCG can be extended to TACATCGG. By applying a hash table to hold the kmers with their original reads’ IDs as their corresponding values, the kmerbased strategies can find the candidate kmers quickly. However, since a kmer may originate from quite a few different reads which may easily lead to a wrong extension, these strategies deny making full use of the information of the whole nucleotides arrangement in each read. Although the kmerbased contig extension strategy has been widely used in de novo assemblers, both the accuracy and sensitivity are still far from meeting the requirement.
Some multiplek strategies have been developed such as OasesM [18], IDBATran [20], and BridgerM [22], but the problem is not solved basically since these works just intuitively tried several k values one after another without fully considering the variances of the lengths of the actual overlaps between reads. In our last work IsoTree [24, 27], we proposed a method to find the candidate reads that have the longest overlap to the contig’s terminus. But the method needs to try all the candidate reads that may have overlaps of lengths between L−1 and x with the contig’s terminus, which is time consuming and a little tedious. Here, L is the length of reads, and x is the length of the longest overlap between the contig’s terminus and the available reads. In this work, we introduce a more straightforward contig extension strategy that extends a contig by the read that has the longest overlap with the contig’s terminus. Through the suffix trees of reads, we can find the candidate reads that have the longest overlap with a contig’s terminus in O(L) time by just scanning the first (for left extension) or the last (for right extension) L−1 characters of the contig once.
In the rest of this work, the methods of constructing suffix trees and splicing graphs as well as the algorithms of extracting transcriptrepresenting paths from splicing graphs are first introduced. Then, the experimental results and discussions are presented. The final conclusions are given in the end.
Methods
Suffix tree construction
Traditional kmerbased extension strategies build overlapping kmers from reads and extend contigs by kmers, which can only guarantee k−1character overlaps between contigs and kmers. The whole nucleotides arrangements of reads are usually ignored in kmerbased extension strategies. Obviously, extending contigs by reads is more credible than extending contigs by kmers, especially for long reads. However, it is much more complicated to find a read that holds the longest overlap to the current contig’s terminus than to find a kmer that overlaps the current contig’s terminus by exactly k−1 characters. The latter can be conveniently realized by using hash tables. Some multiplek strategies have been developed to overcome the shortage of singlek strategies by trying several k values. But these intuitive designs were far from the real sense that reads may overlap with each other by different numbers of characters. In order to make full use of nucleotide arrangements of reads, DTASiST presents a readbased contig extension strategy. The main point is that DTASiST applies a suffix tree structure to quickly find the candidate reads.
To facilitate the contig extension from 5’ to 3’, DTASiST builds a suffix tree, called right extension suffix tree (REST). Although the beginning of the maximum overlap between the current contig’s 3’ terminus and the candidate reads is unknown, the ending of the maximum overlap is known. The maximum overlap is actually a prefix of the candidate reads. Consequently, DTASiST reverses all the read sequences and constructs a suffix tree for the l∼L−1 character suffixes of all the reverse reads, where l denotes the predefined minimum overlap length and L represents the read length. In the right extension suffix tree, the path from the root node to each node represents a substring of some reverse reads. If the xlength (l≤x≤L−1) path from the root node to the node v represents a suffix of the reverse read of r, DTASiST stores the read id r in the node v (as shown in Fig. 1).
Similarly, DTASiST constructs a left extension suffix tree (LEST) to facilitate the contig extension from 3’ to 5’. In this case, the maximum overlap is actually a suffix of candidate reads and the start point of the overlap is known. Hence, the left extension suffix tree consists exactly of the l∼L−1 character suffixes of all the reads. An example for adding a read to the left extension suffix tree is shown in Fig. 2.
Through the suffix trees constructed with the above method, DTASiST gets the candidate reads that hold the longest overlap with current contig’s terminus in O(L) time by scanning prefix of the contig (for left extension, right extension is processed similarly) along the edges in the left extension tree as far as possible and then moving back to find the reads’ IDs stored in the nearest node. Suppose there are totally N reads. In theory, the suffix trees will take up O(M+N(L−l)) space, where M is the size of all the suffixes. Notice that M can be up to O(N(L+l)(L−l)) supposing that all the suffixes are different. The space of the suffix trees can be divided into two parts, one is to store the reads’ IDs, and the other is to store the tree structure. In order to reduce the space consumed in storing reads’ IDs, we compress the reads with the same sequence into one read and assign them the same ID. After numerous experiments, we found that many vertices and reads’ IDs stored in the suffix trees are useless. The reason is that not all the suffixes (or prefixes) of reads can match contigs’ 5’ (or 3’) terminuses, and thus they will never be used in the left (or right) extension. If the suffix (or prefix) of a read can be used in the left (or right) extension, this suffix (or prefix) must be a prefix (or suffix) of another read. Based on this observation, we construct the simplified suffix trees with the following steps. The idea is that we first extract all the L−1character prefixes of all the reads. Then we check and store the suffixes with lengths of l,l+1,...,L−1 that overlap with these prefixes and thus get the left extension suffix tree LEST. Finally, we check and store the prefixes with lengths of l,l+1,...,L−1 that overlap with the suffixes in the LEST and thus get the right extension suffix tree REST. As defined before, the prefixes stored in the REST are actually their reversals.
step 1: Initialize the left extension suffix tree LEST structure by the L−1character prefixes of all the reads. If a suffix is usable in the left extension, it must overlap with a L−1character prefix of reads. Hence, the tree constructed with the L−1character prefixes covers all the usable suffixes.
Figure 3 gives an example to construct the simplified suffix trees. As shown in this figure, there are total of four reads, i.e., Read1 as sequence CATTC, Read2 as sequence ATTCT, Read3 as sequence AGCTC, and Read4 as sequence TCCAT. The length of these reads is 5bp (L=5), and the minimum overlap length is 2bp (l=2). The 4character prefixes of these reads are used to construct the tree structure.
step 2: Add reads’ IDs to the left extension suffix tree. If one suffix (whose length is between l and L−1) of read r follows a path from the root node to node v in the LEST, DTASiST stores the read id r in the node v.
In the above example (Fig. 3), the suffixes with lengths 4,3,2 of Read1 are ATTC, TTC, and TC, respectively. DTASiST first scans the 4character suffix (ATTC) in the left extension suffix tree. DTASiST finds that there is a path from the root node in the tree that exactly corresponds to ATTC, and DTASiST adds the ID of Read1 into the left extension suffix tree. Then, DTASiST scans the 3character suffix (TTC) and the 2character suffix (TC) in the left extension suffix tree successively. Since there is no path from the root node in the left extension suffix tree marked as TTC while there is one marked as TC, DTASiST adds the ID of Read1 only in the node corresponding to path TC. Similarly, DTASiST adds the IDs of the other reads into the left extension suffix tree.
step 3: Trim the left extension suffix tree. DTASiST iteratively deletes the leaf nodes without reads’ IDs.
As shown in Fig. 3, the leaf nodes without reads’ IDs are iteratively removed. Through the trimming operations, the number of nodes in the left extension suffix tree is reduced from 16 to 10.
step 4: Construct the right extension suffix tree REST based on the left extension suffix tree. DTASiST scans each xcharacter (l≤x≤L−1) prefix of all the reads to check whether it matches a path from the root to a node with some stored read IDs in the left extension suffix tree. If yes, DTASiST will reverse the prefix, add the reversal sequence into the right extension suffix tree, and store the corresponding read’s ID in the corresponding node.
In the above example (Fig. 3), the 2,3,4character prefixes of Read1 are CA, CAT, and CATT, respectively. DTASiST first scans the 2character prefix (CA) in the left extension suffix tree. Although there is a path from the root node marked CA, but the ending node of the path contains no IDs which means that CA is only part of a usable suffix. In this case, DTASiST will not add the ID of Read1 but continue to scan the 3character prefix (CAT). Since CAT matches a path from the root node in the left extension suffix tree and the ending node of the path contains reads’ IDs, DTASiST reverses CAT to TAC and adds TAC as well as the ID of Read1 to the right extension suffix tree. Then, DTASiST scans the 4character prefix (CATT) in the left extension suffix tree similarly. The prefixes of the other reads are processed in the same way.
step 5: Compress the left extension suffix tree and the right extension suffix tree. In order to save the space and improve the speed, each lcharacter path from the root node is compressed into one node and stored as a 64bit unsigned integer each.
As shown in Fig. 3, through the compressing operation, the number of nodes in the left extension suffix tree is reduced from 10 to 7.
With these improvements on the suffix trees, the memory cost by DTASiST is comparable to that cost by the hash table data structure as shown in Table 1. Note that in theory, the hash table for single kmer representation takes up a memory of O(N(L−k+1)), and the simplified suffix tree takes up a memory of O(N^{′}(L−l)), where N^{′}(N^{′}≤N) is the number of reads with usable suffixes or prefixes. As mentioned above, the memory of the trees is spent in storing the tree structures and reads’ IDs. Since the initial left extension suffix tree structure is constructed from the L−1character prefixes of all the reads and that the lcharacter paths from the root node are compressed into one node each, there are at most O(N^{′}(L−l)) nodes in the tree. The memory that is used to store the reads’ IDs can be up to O(N^{′}(L−l)) supposing that all the suffixes of these N^{′} reads are usable. The same results can be drawn for the right extension suffix tree. Note that the hash table for multiple kmer representation takes up a memory of O(M^{′}+N(L−l)^{2}), where M^{′} is the size of all the l+1,l+2,...,Lmers if we use hash table of all these substrings to find the kmers that have the longest overlaps with the contigs’ terminuses. Notice that M^{′} is quite larger than M, the size of all the l,l+1,...,L−1suffixes. We compared the memories occupied by the suffix trees, the hash table of kmers with single length of l+1, and hash table of kmers with multiple lengths of l+1,l+2,...,L.
Table 1 shows that the simplified suffix tree strategy occupies comparable memory as that used by the singlek strategy while the multiplek strategy costs much more. Notice that multiplek strategy is impractical in finding the best candidate that has the longest overlap with the contig’s terminus due to its memory usage for real data (notice that multiplek strategies such as OasesM [18], IDBATran [20], and BridgerM [22] basically just repeated the singlek strategy for several different k values, which are excluded from discussion here). As shown in Table 1, the memory occupied by these three strategies is significantly correlated with the read length and the data size. These three strategies cost the most memory on the sample that contains 0.1 million reads with length of 100bp, which indicates that the read length greatly affects the memory usage. Table 1 also shows an interesting phenomenon that the simplified suffix tree strategy occupies less memory than the singlek strategy and the multiplek strategy on the sample that contains 1 million reads with length of 50bp. We attribute the surprising performance of the simplified suffix tree strategy on this sample to the operation of compressing the reads with the same sequence into one read. With the increase of data size, the number of reads with the same sequence increases. The read compression step significantly reduced the data size of this sample.
Splicing graph construction
The splicing graph used by DTASiST is similar to that defined in Bridger [22], BinPacker [21], and IsoTree [24]. A splicing graph is a directed acyclic graph, in which the vertex represents a part of an exon or an exon. An edge exists between two vertices only if these two vertices come from a same transcript. Simple paths in such graphs usually represent plausible transcripts or parts of them.
Briefly, DTASiST constructs splicing graphs as follows: DTASiST first sets the reads whose coverage exceeds the average as seeds, and then selects an unused seed as the main contig and extends the contig with help of the suffix trees. DTASiST explores the suffix trees along the edges marked with the characters scanned from the endpoints of the contig successively to get the candidate read that holds the longest overlap with the current contig’s terminus. When there are multiple candidate reads, DTASiST selects the candidate read whose coverage is closest to that of the contig. DTASiST repeats the above candidates selection and extension steps until there are no candidate reads that hold at least lcharacter overlaps with the contig’s terminus. When the contig cannot be extended in either direction, DTASiST makes the branch extensions to construct splicing variants. DTASiST applies a similar strategy described in our previous work [24] to construct splicing variants and trim the splicing graph.
Figure 4 gives an example for extending contigs by reads under the help of right extension suffix tree and left extension suffix tree. For the left extension (from 3’ to 5’): DTASiST searches the left extension suffix tree with characters from 5’ to 3’ successively, i.e., 8 (simplified code for GA), G, T, T, C, G, ⋯. There is a path of 8(GA)GTTC in the left extension suffix tree but no path of 8(GA)GTTCG. Hence, 8(GA)GTTC is the maximum overlap of reads with the contig’s 5’ terminus. DTASiST takes out the read id (a) stored in the last node in the path of 8(GA)GTTC and extends the contig by the read a. Similarly, DTASiST can find the candidate reads that hold the maximum overlap with the contig’s 3’ terminus through the right extension suffix tree. The contig is then extended in right direction.
The main difference between DTASiST and IsoTree for contig extension is the method of finding the candidate reads that hold the longest overlaps with the contig’s terminus. IsoTree extracts the candidates by trying the overlap lengths from L−1 to l. To get the reads that hold xcharacter overlaps with contig’s terminus, IsoTree first sets the first and the last kmers of the xcharacter terminus as tags. Then, IsoTree searches the hash table to find the reads that contain these two tags in O(a_{x}+b_{x}) time, where a_{x} and b_{x} are the numbers of reads that contain the two tags, respectively. If an obtained read exactly has a xcharacter overlap with the contig’ terminus, it is identified as a candidate read. Once a candidate read is obtained, no smaller overlap length will be further tried. Thus, if the maximum overlap length is x, IsoTree will cost \(O(N\sum \limits _{i = L1}^{x}{(a_{i}+b_{i})i})\) time to find the candidate reads. Notice that DTASiST can find the candidate that has the longest overlap with the contig’s terminus in O(L) time for each extension and thus O(NL) in total. The suffix tree strategy is more timesaving considering that the time for building the suffix trees is O(N(L+l−1)(L−l)), which is usually far smaller than \(O(N\sum \limits _{i = L1}^{x}{(a_{i}+b_{i})i})\). Besides, although DTASiST needs more memory to hold the suffix trees, its memory consumption is endurable as described in “Results” section.
Transcripts assembly
In the transcriptrepresenting path detection step, most of researchers formulated it as an optimization problem with objective of either extracting the minimum number of paths to cover the whole graph or ensuring the minimum gaps between the coverage of the edges entering and leaving the nodes. Wondering whether the real world follows these optimization principles, we developed two new strategies for transcriptrepresenting path extraction.
In order to detect as many transcripts as possible, DTASiST develops a depthfirst enumeration strategy to recover all the possible transcripts that can be represented by the splicing graph. Besides, DTASiST develops a strict criterion to exclude the fake transcripts (these rules are generally adopted in most transcriptassembly methods): (i) the transcript sequence should be longer than 200bp (by default); (ii) the coverage of each transcript must be larger than 2 (by default); (iii) at least 20 reads (by default) can be mapped to the transcript; (iv) the whole transcript sequence must be covered by pairedend reads when pairedend reads are available.
Additionally, considering that thicker paths (paths with higher coverage) are more likely to be transcripts, DTASiST proposes a hybrid strategy to extract the longest path among the thickest paths. The hybrid strategy iteratively extracts paths with the compromise of length and thickness by a dynamic programming method until all the edges are covered by these paths or a predefined condition is met. The hybrid strategy based on length and coverage is also a good juxtapose to evaluate the depthfirst enumeration strategy.
In the following discussion, let G(V,E) denote the splicing graph. DTASiST adds a source vertex s and a sink vertex t into the graph, and connects s (or t) with the vertices without incoming edges (or outgoing edges). DTASiST applies the function w(node) (or w(edge)) to weigh the vertex sequences (or the edge sequences) by the number of reads per base. Specifically, DTASiST assigns the weight of edge (s,v) as the weight of node v, assigns the weight of edge (u,t) as the weight of node u, assigns the weight of node s as the sum of the weights of edges leaving node s, and assigns the weight of node t as the sum of the weights of edges entering node t.
Depthfirst enumeration strategy. DTASiST enumerates the paths from the source vertex s to the sink vertex t by a depthfirst search strategy (Algorithm 1). The algorithm starts from the source vertex s, and iteratively traverses an unexplored edge leading from a vertex already reached. Once it encounters the sink vertex t, the algorithm will output the path. The algorithm uses a stack to store the nodes on the current depthfirst search path, so that the path can be extracted by scanning the stack. The algorithm has a time bound of O((V+E)p), where p is the number of paths starting from s.
The hybrid strategy based on length and coverage. DTASiST iteratively calls Algorithm 2 to find the longest path from the paths that are extracted with the maximum coverage strategy until the path meets a pregiven empirical condition. Once a path is derived, it will be deleted from the splicing graph, i.e., the coverage of all the edges and vertices along the path will be reduced by the minimum coverage of the edges and vertices along the path. The time consumed by this strategy is O((V+E)E).
As shown in Algorithm 2, it introduces π[v] to denote the predecessor of vertex v. By using backtracking, a path with the compromise of length and thickness can be extracted in one iteration according to π[v]. For each node v in topological order, capacity(v) denotes the maximum coverage among all the paths that start from vertex s and end at vertex v, where the coverage of a path is defined as the minimum weight among all the vertices and edges in the path. Algorithm 2 computes the capacity(v) for each vertex v in topological order in a dynamic way until it has computed capacity(t). Besides, the variable l[v] denotes the length of the path from vertex s to vertex v.
Results
We realized DTASiST in two versions: DTASiSTE and DTASiSTH corresponding to the depthfirst enumeration strategy and the hybrid strategy based on length and coverage, respectively. We compared them with six stateoftheart de novo assemblers including IsoTree (version 1.0), Trinity (version 2.3.2), BinPacker (version 1.0), SOAPdenovoTrans (version 1.03), IDBATran (version 1.1.1), and Oases (version 0.2.8) on both simulated and real datasets. We carried out the experiments on a server with 256GB of RAM and E52620V3*2 CPU processor.
Datasets
In order to explore the sensitivity of assemblers on the length of reads, we used FluxSimulator [28] to simulate 11 samples with read lengths of 50bp,60bp,70bp,80bp,90bp,100bp,110bp,120bp,130bp,140bp, and 150bp, respectively. The only difference between these simulated samples is the length of reads. Each sample contains 0.1 million pairedend reads that are generated from 100 isoform transcripts originated from 41 different genes in chromosome 1 (CRCh38.83, NCBI).
We retrieved a dog dataset and a human dataset from NCBI SRA database, with Accession Code SRX295047 and SRR3692633, respectively. In the dog dataset, there are totally 30968059 pairedend reads with length of 50bp. The human dataset contains total of 43675886 pairedend reads with length of 75bp. The reads both in the dog dataset and the human dataset are single strandspecific. Besides, we obtained total of 62516 and 46993 annotated transcripts from UCSC for dogs and humans, respectively.
Evaluation criteria
In this paper, we aligned transcripts predicted by assemblers to annotated transcripts by blast+ [29]. We define the fulllength reconstructed transcript as an assembled transcript that holds at least 95% sequence identity to an annotated transcript. The fulllength identified transcript represents an annotated transcript with at least 95% sequence covered by an assembled transcript.
On the simulated datasets, we applied recall and precision to measure the performances of the de novo assemblers. The recall is defined as the ratio between the number of fulllength identified transcripts and the number of annotated transcripts, while the precision is defined as the ratio between the number of fulllength reconstructed transcripts and the number of assembled transcripts.
As the annotated transcripts of the real datasets are usually not the ground truth expressed transcripts, the recall and precision defined above are not suitable to the real datasets [10, 24]. For the real datasets, we use the number of fulllength identified transcripts to represent the recall, and we measure the precision by comparing the number of fulllength reconstructed transcripts and the number of assembled transcripts (i.e., candidate transcripts) [21, 22].
Simulated data
On the simulated datasets, besides the recall and precision, we also evaluated the sensitivity on read length of our method and the other leading approaches. We implemented all assemblers on the simulated datasets whose read lengths fall in the scope of 50bp∼150bp (Fig. 5). We ran all the compared assemblers with the kmer length of 25bp and other parameters as default.
Figure 5 shows that DTASiSTE performed more competitive than the other compared de novo assemblers especially with precision measure. The average precision obtained by DTASiSTE on these 11 simulated samples was 0.68, which had 36.0, 25.9, 11.5, 74.4, 106.1, 78.9, and 4.6% increase over that achieved by Trinity (0.50), BinPacker (0.54), IsoTree (0.61), SOAPdenovoTrans (0.39), Oases (0.33), IDBATran (0.38), and DTASiSTH (0.65), respectively. DTASiSTE’s outstanding performance with precision benefits from the transcript filtering criterion. For the recall measure, DTASiSTE performed better than the other de novo assemblers except that IsoTree led a little with read lengths of 120bp,130bp, and 150bp. We attribute the outstanding performances of DTASiST and IsoTree to their readbased contig extension strategies. They extend contigs by reads while most other de novo assemblers extend contigs by kmers.
From Fig. 5, we observed that the performances of the depthfirst enumeration strategy DTASiSTE are better than those of the hybrid strategy DTASiSTH with both recall and precision measures, which is interesting since there is usually a tradeoff between these two measures.
Real data
On the real datasets, we evaluated the de novo assemblers by counting the number of fulllength identified transcripts and the number of fulllength reconstructed transcripts. In terms of resource requirements, we assessed the de novo assemblers with regard to the running times and memory usages.
The numbers of fulllength identified transcripts, fulllength reconstructed transcripts, and candidate transcripts collected by the de novo assemblers are shown in Table 2.
From Table 2, we observed that DTASiST always performed more competitive than the other de novo assemblers on recovering the fulllength identified transcripts (estimate of recall). On the dog dataset, DTASiSTE recovered total of 1504 fulllength identified transcripts and improved IsoTree (1354), Trinity (1017), BinPacker (1149), SOAPdenovoTrans (1005), IDBATran (598), and Oases (530) with 11.1, 47.9, 30.9, 49.7, 151.5, and 183.8%, respectively. Except DTASiSTE, DTASiSTH and IsoTree outperformed all the other de novo assemblers in terms of recovering fulllength identified transcripts. The outstanding performances of DTASiST and IsoTree with recall measurement benefit from their readbased extension strategies. They extend the contig by reads while the other de novo assemblers extend the contig by kmers. The kmerbased extension strategies can only guarantee k−1length overlaps while the readbased extension strategies ensure at least llength overlaps. Both the theoretical conclusion and the experimental results show that the readbased extension strategies perform better than the kmerbased extension strategies.
On the dog dataset, DTASiSTE, DTASiSTH, IsoTree, Trinity, BinPacker, SOAPdenovoTrans, IDBATran, and Oases obtained total of 3916, 2514, 2974, 1663, 2601, 1006, 1011, and 957 fulllength reconstructed transcripts out of 103461, 71356, 81597, 96018, 73419, 85028, 69757, and 113361 candidates, respectively, which suggests that DTASiSTE also outperformed its competitors with precision.
On the human dataset, DTASiST maintained its superior performance on recovering the fulllength identified transcripts. DTASiSTE obtained the most number of fulllength identified transcripts (2175), followed by IsoTree (2015), DTASiSTH (1950), Trinity (1913), Oases (1762), BinPacker (1491), and IDBATran (1376). However, DTASiSTE guessed more candidates than IsoTree and DTASiSTH. We attribute the large number of candidates collected by DTASiSTE to its enumeration strategy. Although DTASiSTH, IsoTree, and Trinity recovered almost the same number of fulllength identified transcripts (1950, 2015, and 1913, respectively), DTASiSTH guessed the least number of candidate transcripts (199642 vs 218269 and 437730, respectively). Besides, Trinity collected more fulllength identified transcripts than BinPacker on the human dataset while BinPacker detected a larger number of fulllength identified transcripts than Trinity on the dog dataset.
Additionally, DTASiSTE, DTASiSTH, IsoTree, Trinity, BinPacker, IDBATran, and Oases collected total of 4930, 3959, 3821, 3039, 3449, 2196, and 3126 fulllength reconstructed transcripts out of 278255, 199642, 218269, 437730, 192674, 182651, and 439865 candidates on the human dataset. From these numbers we can figure out that DTASiSTE not only holds a superior performance with recall measurement, its precision is also higher than the other assemblers except DTASiSTH. We conclude that DTASiSTH is applicable to the high precision demand while DTASiSTE is suitable to the general demand.
Throughout the experiments on both simulated and real datasets, we found that most assemblers’ performances decreased as the magnitude of the data increased. The magnitudes of the simulated samples are far less than those of the real datasets, and all the assemblers performed better on the simulated datasets than on the real datasets. In addition, the human dataset is larger than the dog dataset, and almost all the assemblers performed better on the dog dataset than on the human dataset. The outstanding performance of DTASiSTH with precision becomes obvious when the data size increased. When the data size is big and the users focus more on precision, DTASiSTH is the best choice. Otherwise, DTASiSTE is applicable to most cases.
We examined the assemblers’ computational demands of running time and peak memory. From Fig. 6, we can see that DTASiST (notice that since each splicing graph has only very limited number of s−t paths, the difference between the time cost by DTASiSTE and DTASiSTH can be ignored) cost much less time (about half on the dog dataset and 0.11 on the human dataset) than IsoTree while DTASiST performed more competitive than IsoTree in most cases. Although DTASiST (the difference between the memory cost by DTASiSTE and that by DTASiSTH can be ignored) consumed more memory than IsoTree, its memory consumption is endurable on both dog dataset (peak memory: 80G) and human dataset (peak memory: 160G which is even smaller than that 164.7G of the kmerbased strategy Oases).
Discussion
Experimental results showed that DTASiST performs more competitive than the other compared de novo assemblers especially with precision measure. We owe this improvement to the readbased contig extension strategy and the transcript extraction methods. However, there are still quite a few issues need to be studied further. First, despite the outstanding performance of readbased contig extension strategy on precision and speed, it needs more memory to keep the suffix trees. More elegant implementations of the suffix trees or new data structures for assisting realizing readbased extension strategy are expected. Second, due to sequencing error and the giant gap in isoform expression level of the same gene, the number of erroneous reads originating from highexpressed transcripts may be larger than that of the correct reads originating from lowexpressed transcripts [30]. These erroneous reads may introduce artificial branches in splicing graphs and thus introduce chimeric transcripts while lowexpressed transcripts may have little support from reads and thus are missed from the splicing graphs. High quality sequencing data and more exquisite algorithms are needed. Third, some transcripts that originate from different genes may share a long subsequence, which results in a large mixed graph (a mixture of splicing graphs that represent different genes). Once the common subsequence is longer than a read, it is hard to split the mixed graph into splicing graphs for different gene loci. In this case, a mixture graph will be seen as a splicing graph, and it will lead to a higher possibility to obtain pseudo transcripts and a lower opportunity to extract all truth expressed transcripts. To overcome this problem we need longer high quality reads and fine tuned transcript extraction rules. In short, there are still a lot of works need to do on de novo transcriptome assembly.
Conclusion
De novo transcriptome assembly is a challenging problem that arises with the development of RNAseq. In this article, we proposed a new approach for contig extension. We applied suffix trees of reads to quickly find the candidate reads that have the longest overlaps with contigs’ terminuses, and extended the contigs by these reads directly. We also developed two strategies to extract the transcriptrepresenting paths in the splicing graphs: a depthfirst enumeration strategy and a hybrid strategy based on length and coverage. We ran and compared these two strategies with other leading de novo transcriptome assemblers on both simulated and real datasets. The experimental results provide a whole picture of the superior performance of DTASiST with the cost of acceptable more memory. Future work includes developing smallersized data structure while keeping the searching speed and transcript extracting algorithms with higher recall and precision.
Availability of data and materials
DTASiST with choice for either strategy is available at https://github.com/Jane110111107/DTASiST. The real datasets were downloaded from https://www.ncbi.nlm.nih.gov. The annotation transcripts were downloaded from https://genome.ucsc.edu.
Abbreviations
 LEST:

Left extension suffix tree
 NCBI:

National Center for Biotechnology Information
 REST:

Right extension suffix tree
 RNAseq:

RNA sequencing
 SRA:

Sequence read archive
 UCSC:

University of California SANTA CRUZ
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Acknowledgements
The authors would like to thank the editors and reviewers for their constructive comments and suggestions on this manuscript.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 20 Supplement 25, 2019: Proceedings of the 2018 International Conference on Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical Informatics (ICBI) 2018 conference: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume20supplement25.
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Publication costs are funded by National Natural Science Foundation of China under No. 61672325, No. 61472222, and No. 61732009.
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HF, DZ, and YX contributed to the design of the study. JZ implemented DTASiST and performed experiments. JZ, HF, and CZ wrote the manuscript. All authors read and approved the final manuscript.
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Zhao, J., Feng, H., Zhu, D. et al. DTASiST: de novo transcriptome assembly by using simplified suffix trees. BMC Bioinformatics 20, 698 (2019). https://doi.org/10.1186/s1285901932729
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DOI: https://doi.org/10.1186/s1285901932729
Keywords
 Alternative splicing
 RNAseq
 De novo assembly