Dataset Information

Evaluating de Bruijn graph assemblers on 454 transcriptomic data.

ABSTRACT: Next generation sequencing (NGS) technologies have greatly changed the landscape of transcriptomic studies of non-model organisms. Since there is no reference genome available, de novo assembly methods play key roles in the analysis of these data sets. Because of the huge amount of data generated by NGS technologies for each run, many assemblers, e.g., ABySS, Velvet and Trinity, are developed based on a de Bruijn graph due to its time- and space-efficiency. However, most of these assemblers were developed initially for the Illumina/Solexa platform. The performance of these assemblers on 454 transcriptomic data is unknown. In this study, we evaluated and compared the relative performance of these de Bruijn graph based assemblers on both simulated and real 454 transcriptomic data. The results suggest that Trinity, the Illumina/Solexa-specialized transcriptomic assembler, performs the best among the multiple de Bruijn graph assemblers, comparable to or even outperforming the standard 454 assembler Newbler which is based on the overlap-layout-consensus algorithm. Our evaluation is expected to provide helpful guidance for researchers to choose assemblers when analyzing 454 transcriptomic data.

SUBMITTER: Ren X

PROVIDER: S-EPMC3517413 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Evaluating de Bruijn graph assemblers on 454 transcriptomic data.

Ren Xianwen X Liu Tao T Dong Jie J Sun Lilian L Yang Jian J Zhu Yafang Y Jin Qi Q

PloS one 20121207 12

Next generation sequencing (NGS) technologies have greatly changed the landscape of transcriptomic studies of non-model organisms. Since there is no reference genome available, de novo assembly methods play key roles in the analysis of these data sets. Because of the huge amount of data generated by NGS technologies for each run, many assemblers, e.g., ABySS, Velvet and Trinity, are developed based on a de Bruijn graph due to its time- and space-efficiency. However, most of these assemblers were ...[more]

PMID: 23236450

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Project description:Roche 454 pyrosequencing has become a method of choice for generating transcriptome data from non-model organisms. Once the tens to hundreds of thousands of short (250-450 base) reads have been produced, it is important to correctly assemble these to estimate the sequence of all the transcripts. Most transcriptome assembly projects use only one program for assembling 454 pyrosequencing reads, but there is no evidence that the programs used to date are optimal. We have carried out a systematic comparison of five assemblers (CAP3, MIRA, Newbler, SeqMan and CLC) to establish best practices for transcriptome assemblies, using a new dataset from the parasitic nematode Litomosoides sigmodontis.Although no single assembler performed best on all our criteria, Newbler 2.5 gave longer contigs, better alignments to some reference sequences, and was fast and easy to use. SeqMan assemblies performed best on the criterion of recapitulating known transcripts, and had more novel sequence than the other assemblers, but generated an excess of small, redundant contigs. The remaining assemblers all performed almost as well, with the exception of Newbler 2.3 (the version currently used by most assembly projects), which generated assemblies that had significantly lower total length. As different assemblers use different underlying algorithms to generate contigs, we also explored merging of assemblies and found that the merged datasets not only aligned better to reference sequences than individual assemblies, but were also more consistent in the number and size of contigs.Transcriptome assemblies are smaller than genome assemblies and thus should be more computationally tractable, but are often harder because individual contigs can have highly variable read coverage. Comparing single assemblers, Newbler 2.5 performed best on our trial data set, but other assemblers were closely comparable. Combining differently optimal assemblies from different programs however gave a more credible final product, and this strategy is recommended.

Dataset Information

Evaluating de Bruijn graph assemblers on 454 transcriptomic data.

Publications

Evaluating de Bruijn graph assemblers on 454 transcriptomic data.

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