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Abstract Transposable elements (TEs) are important drivers of genome evolution. Nonetheless, TE annotation remains a complex and challenging task. As more genomes from phylogenetically diverse species are published, a comprehensive pipeline for accurate annotation of diverse TEs is increasingly important. Recently, (Ou et al. Genome Biol. 20:275, 2019) developed a new comprehensive pipeline, Extensive De novo Transposable element Annotator (EDTA), and benchmarked its performance on the genomes of three species: maize, wheat, and fruit fly. Because TE landscapes can vary tremendously across species, we tested EDTA’s performance on four additional genomes with different TE landscapes: mouse, zebrafish, zebra finch, and chicken. Our analysis reveals that EDTA faces challenges with repeat classification in these genomes and underperforms overall relative to its benchmark dataset. Notably, EDTA consistently misclassifies nonLTR retrotransposons as DNA transposons, resulting in erroneous TE annotations for species with considerable repertoires of nonLTR retrotransposons. Overall, we set expectations for EDTA’s performance on genomes spanning additional diversity, urge caution when using EDTA on genomes with divergent TE repertoires from the species on which it was initially benchmarked, and hope to motivate the development of methods that are robust to both the diversity of TEs and TE landscapes observed across species.

Abstract Objective The SARS-CoV-2 pandemic has prompted one of the most extensive and expeditious genomic sequencing efforts in history. Each viral genome is accompanied by a set of metadata which supplies important information such as the geographic origin of the sample, age of the host, and the lab at which the sample was sequenced, and is integral to epidemiological efforts and public health direction. Here, we interrogate some shortcomings of metadata within the GISAID database to raise awareness of common errors and inconsistencies that may affect data-driven analyses and provide possible avenues for resolutions. Results Our analysis reveals a startling prevalence of spelling errors and inconsistent naming conventions, which together occur in an estimated ~ 9.8% and ~ 11.6% of “originating lab” and “submitting lab” GISAID metadata entries respectively. We also find numerous ambiguous entries which provide very little information about the actual source of a sample and could easily associate with multiple sources worldwide. Importantly, all of these issues can impair the ability and accuracy of association studies by deceptively causing a group of samples to identify with multiple sources when they truly all identify with one source, or vice versa.

The SARS-CoV-2 pandemic has led to unprecedented, nearly real-time genetic tracing due to the rapid community sequencing response. Researchers immediately leveraged these data to infer the evolutionary relationships among viral samples and to study key biological questions, including whether host viral genome editing and recombination are features of SARS-CoV-2 evolution. This global sequencing effort is inherently decentralized and must rely on data collected by many labs using a wide variety of molecular and bioinformatic techniques. There is thus a strong possibility that systematic errors associated with lab-or protocol-specific practices affect some sequences in the repositories. We find that some recurrent mutations in reported SARS-CoV-2 genome sequences have been observed predominantly or exclusively by single labs, co-localize with commonly used primer binding sites and are more likely to affect the protein-coding sequences than other similarly recurrent mutations. We show that their inclusion can affect phylogenetic inference on scales relevant to local lineage tracing, and make it appear as though there has been an excess of recurrent mutation or recombination among viral lineages. We suggest how samples can be screened and problematic variants removed, and we plan to regularly inform the scientific community with our updated results as more SARS-CoV-2 genome sequences are shared (https://virological.org/t/issues-with-sars-cov-2-sequencing-data/473 and https://virological.org/t/masking-strategies-for-sars-cov-2-alignments/480). We also develop tools for comparing and visualizing differences among very large phylogenies and we show that consistent clade- and tree-based comparisons can be made between phylogenies produced by different groups. These will facilitate evolutionary inferences and comparisons among phylogenies produced for a wide array of purposes. Building on the SARS-CoV-2 Genome Browser at UCSC, we present a toolkit to compare, analyze and combine SARS-CoV-2 phylogenies, find and remove potential sequencing errors and establish a widely shared, stable clade structure for a more accurate scientific inference and discourse.