Abstract
BackgroundThe technological revolution in next-generation sequencing has brought unprecedented opportunities to study any organism of interest at the genomic or transcriptomic level. Transcriptome assembly is a crucial first step for studying the molecular basis of phenotypes of interest using RNA-Sequencing (RNA-Seq). However, the optimal strategy for assembling vast amounts of short RNA-Seq reads remains unresolved, especially for organisms without a sequenced genome. This study compared four transcriptome assembly methods, including a widely used de novo assembler (Trinity), two transcriptome re-assembly strategies utilizing proteomic and genomic resources from closely related species (reference-based re-assembly and TransPS) and a genome-guided assembler (Cufflinks).ResultsThese four assembly strategies were compared using a comprehensive transcriptomic database of Aedes albopictus, for which a genome sequence has recently been completed. The quality of the various assemblies was assessed by the number of contigs generated, contig length distribution, percent paired-end read mapping, and gene model representation via BLASTX. Our results reveal that de novo assembly generates a similar number of gene models relative to genome-guided assembly with a fragmented reference, but produces the highest level of redundancy and requires the most computational power. Using a closely related reference genome to guide transcriptome assembly can generate biased contig sequences. Increasing the number of reads used in the transcriptome assembly tends to increase the redundancy within the assembly and decrease both median contig length and percent identity between contigs and reference protein sequences.ConclusionsThis study provides general guidance for transcriptome assembly of RNA-Seq data from organisms with or without a sequenced genome. The optimal transcriptome assembly strategy will depend upon the subsequent downstream analyses. However, our results emphasize the efficacy of de novo assembly, which can be as effective as genome-guided assembly when the reference genome assembly is fragmented. If a genome assembly and sufficient computational resources are available, it can be beneficial to combine de novo and genome-guided assemblies. Caution should be taken when using a closely related reference genome to guide transcriptome assembly. The quantity of read pairs used in the transcriptome assembly does not necessarily correlate with the quality of the assembly.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2923-8) contains supplementary material, which is available to authorized users.
Highlights
The technological revolution in next-generation sequencing has brought unprecedented opportunities to study any organism of interest at the genomic or transcriptomic level
Genome-guided assembly using the Ae. albopictus reference genome produced less than half the number of contigs produced by de novo assembly
Across all assembly strategies performed using three datasets, increasing the amount of input read pairs used in the transcriptome assembly led to an almost two-fold increase in the number of contigs generated, except for Transcriptome Post-Scaffolding (TransPS), where the increase was minimal (Table 1)
Summary
The technological revolution in next-generation sequencing has brought unprecedented opportunities to study any organism of interest at the genomic or transcriptomic level. Due to the technological revolution in NGS, vast amounts of both transcriptome and genome sequences across a wide range of species are accumulating, especially from large-scale projects including Genome 10 K [2] and Insect 5 K [3]. In the current “-omics” era, a much greater variety of organisms can be studied at the genomic and transcriptomic level This revolution in DNA sequencing technologies has far-reaching applications for the field of biology, dramatically increasing opportunities to elucidate gene regulatory networks [4] and the genetic basis of complex traits [5, 6]. Combining genomic data from 41 arthropod species with transcriptomic data from 103 species, the analysis was able to resolve with high confidence the timing of the origin and diversification (topology) of insects [7], addressing a fundamental question regarding the history of life on Earth
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