Abstract
BackgroundNext-generation sequencing (NGS) technologies are arguably the most revolutionary technical development to join the list of tools available to molecular biologists since PCR. For researchers working with nonconventional model organisms one major problem with the currently dominant NGS platform (Illumina) stems from the obligatory fragmentation of nucleic acid material that occurs prior to sequencing during library preparation. This step creates a significant bioinformatic challenge for accurate de novo assembly of novel transcriptome data. This challenge becomes apparent when a variety of modern assembly tools (of which there is no shortage) are applied to the same raw NGS dataset. With the same assembly parameters these tools can generate markedly different assembly outputs.ResultsIn this study we present an approach that generates an optimized consensus de novo assembly of eukaryotic coding transcriptomes. This approach does not represent a new assembler, rather it combines the outputs of a variety of established assembly packages, and removes redundancy via a series of clustering steps. We test and validate our approach using Illumina datasets from six phylogenetically diverse eukaryotes (three metazoans, two plants and a yeast) and two simulated datasets derived from metazoan reference genome annotations. All of these datasets were assembled using three currently popular assembly packages (CLC, Trinity and IDBA-tran). In addition, we experimentally demonstrate that transcripts unique to one particular assembly package are likely to be bioinformatic artefacts. For all eight datasets our pipeline generates more concise transcriptomes that in fact possess more unique annotatable protein domains than any of the three individual assemblers we employed. Another measure of assembly completeness (using the purpose built BUSCO databases) also confirmed that our approach yields more information.ConclusionsOur approach yields coding transcriptome assemblies that are more likely to be closer to biological reality than any of the three individual assembly packages we investigated. This approach (freely available as a simple perl script) will be of use to researchers working with species for which there is little or no reference data against which the assembly of a transcriptome can be performed.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1406-x) contains supplementary material, which is available to authorized users.
Highlights
Next-generation sequencing (NGS) technologies are arguably the most revolutionary technical development to join the list of tools available to molecular biologists since PCR
These features result in both genome guided and de novo transcriptome assembly approaches displaying a large number of bioinformatically derived artefacts, a phemonenon that is well known [3]
Individual transcriptome assemblies In order to broadly compare the outputs of the individual assemblers (CLC, Trinity and IDBA_tran) with our concatenated assemblies, we calculated some standard assembly metrics that are commonly used to characterize these kinds of datasets [13]
Summary
Next-generation sequencing (NGS) technologies are arguably the most revolutionary technical development to join the list of tools available to molecular biologists since PCR. For researchers working with nonconventional model organisms one major problem with the currently dominant NGS platform (Illumina) stems from the obligatory fragmentation of nucleic acid material that occurs prior to sequencing during library preparation This step creates a significant bioinformatic challenge for accurate de novo assembly of novel transcriptome data. For datasets with high proportions of “novel” genes (often the case for nonconventional model organisms), this problem has few solutions that can be generally applied to all datasets Statistics such as the N50, average transcript size or coverage are not usually informative nor relevant when assessing the quality of an RNA-Seq assembly [13]. In combination with standard sequence similarity searches against public databases, the recently released BUSCO (Benchmarking Universal Single-Copy Orthologs) package falls under this umbrella, and can be used to assess the completeness of a given transcriptome or genome assembly [14]
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