Optimizing an efficient ensemble approach for high-quality de novo transcriptome assembly of Thymus daenensis

Abstract

Non-erroneous and well-optimized transcriptome assembly is a crucial prerequisite for authentic downstream analyses. Each de novo assembler has its own algorithm-dependent pros and cons to handle the assembly issues and should be specifically tested for each dataset. Here, we examined efficiency of seven state-of-art assemblers on ~ 30 Gb data obtained from mRNA-sequencing of Thymus daenensis. In an ensemble workflow, combining the outputs of different assemblers associated with an additional redundancy-reducing step could generate an optimized outcome in terms of completeness, annotatability, and ORF richness. Based on the normalized scores of 16 benchmarking metrics, EvidentialGene, BinPacker, Trinity, rnaSPAdes, CAP3, IDBA-trans, and Velvet-Oases performed better, respectively. EvidentialGene, as the best assembler, totally produced 316,786 transcripts, of which 235,730 (74%) were predicted to have a unique protein hit (on uniref100), and also half of its transcripts contained an ORF. The total number of unique BLAST hits for EvidentialGene was approximately three times greater than that of the worst assembler (Velvet-Oases). EvidentialGene could even capture 17% and 7% more average BLAST hits than BinPacker and Trinity. Although BinPacker and CAP3 produced longer transcripts, the EvidentialGene showed a higher collinearity between transcript size and ORF length. Compared with the other programs, EvidentialGene yielded a higher number of optimal transcript sets, further full-length transcripts, and lower possible misassemblies. Our finding corroborates that in non-model species, relying on a single assembler may not give an entirely satisfactory result. Therefore, this study proposes an ensemble approach of accompanying EvidentialGene pipelines to acquire a superior assembly for T. daenensis.

RNA-seq analysis reveals narrow differential gene expression in MEP and MVA pathways responsible for phytochemical divergence in extreme genotypes of Thymus daenensis Celak

“…This superiority also appeared in the annotation results, in which over 74% of unigenes were functionally identified, surpassing the reported ratios from other studies (Fig. 2 ) [ 31 ]. Based on the reference KEGG maps, a large portion of the unigenes (15,234) were attributed to secondary metabolic processes.…”

“…In our recently published paper, we developed an efficient workflow for de novo assembly of transcriptomic data, which is also used here for DE analysis. The nucleotide data were assembled by utilizing five the state-of-art assembly tools, outputs combined, clustered through CD-HIT, and finalized by EvidentialGene pipelines [ 31 ]. In this ensemble workflow, EvidentialGene won the competition among seven assemblers based on the normalized scores of 16 evaluation metrics [ 31 ].…”

“…The nucleotide data were assembled by utilizing five the state-of-art assembly tools, outputs combined, clustered through CD-HIT, and finalized by EvidentialGene pipelines [ 31 ]. In this ensemble workflow, EvidentialGene won the competition among seven assemblers based on the normalized scores of 16 evaluation metrics [ 31 ]. The superiority of EvidentialGene is due to the fact that this software monitored the deduced amino-acid sequences, concurrently examined the quality of coding regions, UTRs, gaps, and also checked for the presence of start-stop codons [ 31 ].…”

“…In this ensemble workflow, EvidentialGene won the competition among seven assemblers based on the normalized scores of 16 evaluation metrics [ 31 ]. The superiority of EvidentialGene is due to the fact that this software monitored the deduced amino-acid sequences, concurrently examined the quality of coding regions, UTRs, gaps, and also checked for the presence of start-stop codons [ 31 ]. This superiority also appeared in the annotation results, in which over 74% of unigenes were functionally identified, surpassing the reported ratios from other studies (Fig.…”

“…Tracking the biosynthetic trails of bioactive compounds, which may be complex and engaged with several transcriptional regulators and crosstalks, has become a research spotlight in bioprospecting of natural constituents [ 35 ]. Development of high-throughput sequencing platforms has provided unprecedented opportunities to explore the reduced representation of genomes and to profile global gene expression patterns in non-model species [ 31 ]. In various medicinal plant species, systematic analysis of transcriptomes is being extensively used to comprehend the metabolic pathways, gene functions and their participation in the synthesis of natural products [ 36 ].…”