Speaker
Description
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 bioinformatics 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 open-source bioinformatics pipelines (shiver—for which we created a user-friendly Dockerized version, referred to as dshiver; SmaltAlign; viral-ngs; and V-pipe) using both simulated and real-world HIV-1 paired-end short-read datasets and default settings. All four pipelines produced consensus genome assemblies with high quality metrics (genome fraction recovery, mismatch and indel rates, variant calling F1 scores) when the reference sequence used for assembly had high similarity to the analyzed sample. The shiver and SmaltAlign pipelines (but not viral-ngs and V-Pipe) also showed robust performance with more divergent samples (non-matching subtypes). With empirical datasets, SmaltAlign and viral-ngs exhibited an order of magnitude 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 less computational resources compared to other pipelines. In conclusion, if a closely matched reference sequence is available, all pipelines can reliably reconstruct viral consensus genomes; therefore, differences in user-friendliness and runtime may guide the choice of the pipeline in a particular setting. If a matched reference sequence cannot be selected, we recommend shiver or SmaltAlign for robust performance. The new Dockerized version of shiver offers ease of use in addition to the accuracy and robustness of the original pipeline.
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