Clustering of Short Read Sequences for de novo Transcriptome Assembly

Given the importance of transcriptome analysis in various biological studies and considering the vast amount of whole transcriptome sequencing data, it seems necessary to develop an algorithm to assemble transcriptome data. In this study we propose an algorithm fortranscriptome assembly in the absence of a reference genome. First, the contiguous sequences are generated using de Bruijn graph with different k-mer lengths. Then, the eclectic mixtures of sequences are gathered in order to form the final sequences. Lastly, the contiguous sequences are clustered and the isoform groups are provided. This proposed algorithm is capable of generating long contiguous sequences and accurately clustering them into isoform groups.To evaluate our algorithm, we applied it to a simulated RNA-seq dataset of rat transcriptome and a real RNA-seq experiment of the loricaria gr. cataphracta transcriptome. The correctness of the assembled contigs was more than 95%, and our algorithm was able to reconstruct over 70% of the transcripts at more than 80% of the transcripts’ lengths. This study demonstrates thatapplying a sophisticated merging method improves transcriptome assembly. The source code isavailable upon request by contacting the corresponding author by email.