title: RNA-sequencing of Amomum aculeatum Roxb (stem)
design: RNA-seq library preparation was performed at Genotypic Technologys Genomics facility as per NEBNext UltraTM Directional RNA Library Prep kit. The Illumina paired end raw reads were quality checked using FastQC1. Illumina raw reads were processed by Genotypic ABLT script for adapters and low quality bases trimming towards 3'-end. Reads were assembled using Trinity2 (default k-mers i.e 25. Trinity), represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Briefly, the process works like so: Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts. Chrysalis clusters the Inchworm Transcripts into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs. Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes. Trinity generated assembly, is clustered based on the similarity between the sequences. CD-HIT3 version 4.5.4 4 (Cluster Database at High Identity with Tolerance) reduces the redundancy without exclusion of sequence diversity that is used for further transcript annotation for the differential gene expression. Sample wise clustered transcripts were generated to remove the duplicate transcripts using CD-HIT.