Comparative Evaluation of Bioinformatic Pipelines for Full-Length Viral Genome Assembly

Abstract

The increasingly widespread application of next-generation sequencing (NGS) in clinical diagnostics and epidemiological research has generated a demand for robust, fast, automated, and user-friendly bioinformatic workflows. To guide the choice of tools for the assembly of full-length viral genomes from NGS datasets, we assessed the performance and applicability of four widely adopted bioinformatic pipelines (shiver - for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign, viral-ngs, and V-pipe) using both simulated datasets and real-world HIV-1 paired-end short-read sequences and default settings. All four pipelines produced high-quality consensus genome assemblies and minority variant calls when the reference sequence used for assembly had high similarity to the analyzed sample. However, while shiver and SmaltAlign showed robust performance also with more divergent samples (non-matching subtypes), viral-ngs and V-Pipe proved to be sensitive to genetic distance from the reference sequence. With empirical datasets, SmaltAlign and viral-ngs exhibited substantially shorter runtime compared to V-Pipe and shiver. In terms of applicability, V-Pipe provides the broadest functionalities; SmaltAlign and dshiver combine user-friendliness with robustness; while the use of viral-ngs requires a less computational resources compared to other tools. To conclude, all four pipelines can perform well in terms of quality metrics; however, the reference sequence needs to be adjusted to closely match the sample data for viral-ngs and V-Pipe. Differences in user-friendliness and runtime may guide the choice of the pipeline in a particular setting. The new Dockerized version of shiver offers ease of use in addition to the accuracy and robustness of the original pipeline.