Comparing and optimizing transcriptome assembly pipeline for diploid wheat

Gene expression and transcriptome analysis are currently one of the main focuses of research for a great number of scientists. However, the assembly of raw sequence data to obtain a draft transcriptome of an organism is a complex multi-stage process usually composed of preprocessing, assembling, and postprocessing. Each of these stages includes multiple steps such as data cleaning, contaminant removal, error correction and assembly validation. In order to implement all these steps, a great knowledge of different algorithms, various bioinformatics tools and software is required. In this paper, we generate multiple transcriptome assembly pipelines by using different tools and approaches in the process. Analyzing these pipelines, we can observe that using the error correction method with Velvet Oases and merging the individual k-mer assemblies with highest N50 produce the most stable base for further transcriptome biological analysis.