title: Comparative analysis of de novo transcriptome assembly.
abstract: The fast development of next-generation sequencing technology presents a major computational challenge for data processing and analysis. A fast algorithm, de Bruijn graph has been successfully used for genome DNA de novo assembly; nevertheless, its performance for transcriptome assembly is unclear. In this study, we used both simulated and real RNA-Seq data, from either artificial RNA templates or human transcripts, to evaluate five de novo assemblers, ABySS, Mira, Trinity, Velvet and Oases. Of these assemblers, ABySS, Trinity, Velvet and Oases are all based on de Bruijn graph, and Mira uses an overlap graph algorithm. Various numbers of RNA short reads were selected from the External RNA Control Consortium (ERCC) data and human chromosome 22. A number of statistics were then calculated for the resulting contigs from each assembler. Each experiment was repeated multiple times to obtain the mean statistics and standard error estimate. Trinity had relative good performance for both ERCC and human data, but it may not consistently generate full length transcripts. ABySS was the fastest method but its assembly quality was low. Mira gave a good rate for mapping its contigs onto human chromosome 22, but its computational speed is not satisfactory. Our results suggest that transcript assembly remains a challenge problem for bioinformatics society. Therefore, a novel assembler is in need for assembling transcriptome data generated by next generation sequencing technique.
authors: Bioinformatics Core, Department of Pathology, University of North Dakota, Grand Forks, ND 58202, USA.
authors: Kaitlin
authors: K
authors: Clarke
authors: Yi
authors: Y
authors: Yang
authors: Ronald
authors: R
authors: Marsh
authors: Linglin
authors: L
authors: Xie
authors: Ke K
authors: KK
authors: Zhang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism; D002892:Chromosomes, Human, Pair 22 / Q000235:genetics; D019295:Computational Biology; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000706:statistics & numerical data
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