Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

By Qiongyi Zhao, Yi Wang, Yimeng Kong, Da Luo, Xuan Li, Pei Hao. BMC Bioinformatics, 2011

476 citations recorded by the source corpus.

BackgroundWith the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data.ResultsTo reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies.ConclusionsOur work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c730e0d39c10509e961c1c58786a60d6c5cc1e4b

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Citations in the corpus

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, background): “Recently, the performances of different transcriptome assemblers have been compared, based on their capability to identify full-length transcripts and on computational demand (28), however, statistical analysis for DGE identification and comparison between the two approaches was not covered.”
Applications of next-generation sequencing to the study of biological invasions (2015, unknown): “…available for de novo transcriptome assembly (Zerbino and Birney, 2008; Simpson et al., 2009; Grabherr et al., 2011; Luo et al., 2012; Schulz et al., 2012), which utilize different approaches to assemble the data, often with large differences between the final assemblies (Zhao et al., 2011).”
Corset: enabling differential gene expression analysis for de novo assembled transcriptomes (2014, background): “Recently, there has been a focus on the potential for using RNA-seq to analyze the transcriptomes of non-model organisms, with a number of studies exploring various steps in the analysis pipeline, such as the effect of cleaning reads [13], digital normalization [35], different assemblers [12] and po”
Sequence assembly demystified (2013, background): “Furthermore, recent comparative studies 73,74 have revealed a trade-off between performance and accuracy.”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, background): “Compared to microarray techniques, RNA-Seq can be performed without prior knowledge of reference sequences and enables a wide range of applications including transcriptome de novo assembly [1-4], abundance estimation [5-9], and detection of alternative splicing events [10-12], all of which have revo”
De novo assembly of bacterial transcriptomes from RNA-seq data (2015, methodology): “A comparison of memory usage by SOAPdenovo2 and Trinity has been performed by others [27] and we did not repeat the analysis here.”