Due to rapid advances in sequencing technology, increasing amounts of genomic and transcriptomic data are available for plant species, presenting enormous challenges for biocomputing analysis. A crucial first step for a successful transcriptomics-based study is the building of a high-quality assembly. Here, we utilized three different de novo assemblers (Trinity, Velvet, and CLC) and the EvidentialGene pipeline tr2aacds to assemble two optimized transcript sets for the notorious weed species, Eleusine indica. Two RNA sequencing (RNA-seq) datasets from leaf and aboveground seedlings were processed using three assemblers, which resulted in 20 assemblies for each dataset. The contig numbers and N50 values of each assembly were compared to study the effect of read number, k-mer size, and in silico normalization on assembly output. The 20 assemblies were then processed through the tr2aacds pipeline to remove redundant transcripts and to select the transcript set with the best coding potential. Each assembly contributed a considerable proportion to the final transcript combination with the exception of the CLC-k14. Thus each assembler and parameter set did assemble better contigs for certain transcripts. The redundancy, total contig number, N50, fully assembled contig number, and transcripts related to target-site herbicide resistance were evaluated for the EvidentialGene and Trinity assemblies. Comparing the EvidentialGene set with the Trinity assembly revealed improved quality and reduced redundancy in both leaf and seedling EvidentialGene sets. The optimized transcriptome references will be useful for studying herbicide resistance in E. indica and the evolutionary process in the three allotetraploid E. indica offspring.
© 2015 The Authors.