Evaluating assembly and variant calling software for strain-resolved analysis of large DNA viruses. Issue 3 (7th July 2020)

• Record Type: Journal Article
• Authors: Deng, Zhi-Luo; Dhingra, Akshay; Fritz, Adrian; Götting, Jasper; Münch, Philipp C; Steinbrück, Lars; Schulz, Thomas F; Ganzenmüller, Tina; McHardy, Alice C
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 3(2021)
• Publication Date: 2020-07-07
• Subjects: HCMV -- strain mixtures -- virus -- benchmark -- genome assembly -- variant calling; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa123
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and 6 variant callers on 10 lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive variant calls, which were strongly enriched in T to G changes in a 'G.G' context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo ), to enable full reproducibility and further benchmarking on these and other data.