TRAPID 2.0: a web application for taxonomic and functional analysis of de novo transcriptomes

Advances in high-throughput sequencing have resulted in a massive increase of RNA-Seq transcriptome data. However, the promise of rapid gene expression profiling in a specific tissue, condition, unicellular organism or microbial community comes with new computational challenges. Owing to the limited availability of well-resolved reference genomes, de novo assembled (meta)transcriptomes have emerged as popular tools for investigating the gene repertoire of previously uncharacterized organisms. Yet, despite their potential, these datasets often contain fragmented or contaminant sequences, and their analysis remains difficult. To alleviate some of these challenges, we developed TRAPID 2.0, a web application for the fast and efficient processing of assembled transcriptome data. The initial processing phase performs a global characterization of the input data, providing each transcript with several layers of annotation, comprising structural, functional, and taxonomic information. The exploratory phase enables downstream analyses from the web application. Available analyses include the assessment of gene space completeness, the functional analysis and comparison of transcript subsets, and the study of transcripts in an evolutionary context. A comparison with similar tools highlights TRAPID's unique features. Finally, analyses performed within TRAPID 2.0 are complemented by interactive data visualizations, facilitating the extraction of new biological insights, as demonstrated with diatom community metatranscriptomes.

Automated summary

Advances in high-throughput sequencing have led to a massive increase in RNA-Seq transcriptome data, with de novo assembled (meta)transcriptomes becoming popular tools for investigating gene repertoires. However, these datasets often contain fragmented or contaminant sequences, making analysis difficult. To address these challenges, TRAPID 2.0 was developed as a web application for efficient processing of assembled transcriptome data, offering global characterization, functional analysis, and interactive data visualizations for extracting biological insights.