Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes
De novo transcriptomes generated using both Trinity and velvet-oases were merged with CD-HIT-EST to reduce the transcript redundancy to 90% similarity and generate unique genes. Functional annotation
Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes