Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq

Abstract

Transcriptome reconstruction is an important application of RNA-Seq, providing critical information for further analysis of transcriptome. Although RNA-Seq offers the potential to identify the whole picture of transcriptome, it still presents special challenges. To handle these difficulties and reconstruct transcriptome as completely as possible, current computational approaches mainly employ two strategies: de novo assembly and genome-guided assembly. In order to find the similarities and differences between them, we firstly chose five representative assemblers belonging to the two classes respectively, and then investigated and compared their algorithm features in theory and real performances in practice. We found that all the methods can be reduced to graph reduction problems, yet they have different conceptual and practical implementations, thus each assembly method has its specific advantages and disadvantages, performing worse than others in certain aspects while outperforming others in anther aspects at the same time. Finally we merged assemblies of the five assemblers and obtained a much better assembly. Additionally we evaluated an assembler using genome-guided de novo assembly approach, and achieved good performance. Based on these results, we suggest that to obtain a comprehensive set of recovered transcripts, it is better to use a combination of de novo assembly and genome-guided assembly.transcriptome reconstruction, RNA-Seq, de novo assembly, genome-guided assembly Citation:Lu B X, Zeng Z B, Shi T L. Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based

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“…1A]. Specifically, we were looking for high contiguity (a high rate of annotated transcripts being spanned by one assembled transcript over at least 90 % of the annotated transcript exonic length), low chimerism (a low rate of assembled transcripts spanning more than one annotated transcript), and for the final assembly to be manageable and high-confidence [77, 78]. Importantly, our calculations conservatively assumed that all of the chimeric predictions represent assembly artifacts.…”

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“…To date, most published differential gene expression analyses have relied solely on data derived from either reference-based or de novo analyses (Lu et al 2013), despite the potential drawbacks of either methods. If used complementarily, both methods can significantly improve the robustness of differential expression analysis.…”

“…3a). Despite differences in the algorithms implemented in Cuffdiff and edgeR (Lu et al 2013), this discrepancy in DEG numbers is most likely due to the higher number of transcripts assembled de novo (as Pair-wise comparisons Pair-wise comparisons compared to reference-based assemblies, see "Methods" section). Given that multiple individuals were pooled to assemble the transcriptomes, it is likely that Trinity may have inflated the real number of transcripts due to the presence of among-individual polymorphisms in the data.…”

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