During the COVID-19 pandemic, SARS-CoV-2 surveillance efforts integrated genome sequencing of clinical samples to identify emergent viral variants and to support rapid experimental examination of genome-informed vaccine and therapeutic designs. Given the broad range of methods applied to generate new viral genomes, it is critical that consensus and variant calling tools yield consistent results across disparate pipelines. Here we examine the impact of sequencing technologies (Illumina and Oxford Nanopore) and 7 different downstream bioinformatic protocols on SARS-CoV-2 variant calling as part of the NIH Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Tracking Resistance and Coronavirus Evolution (TRACE) initiative, a public-private partnership established to address the COVID-19 outbreak. Our results indicate that bioinformatic workflows can yield consensus genomes with different single nucleotide polymorphisms, insertions, and/or deletions even when using the same raw sequence input datasets. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations. Such consistency among bioinformatic pipelines is fundamental to SARS-CoV-2 and future pathogen surveillance efforts. The application of analysis standards is necessary to more accurately document phylogenomic trends and support data-driven public health responses.

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Purpose: Sub-Saharan Africa bears the highest burden of epilepsy worldwide. A presumed proportion is genetic, but this aetiology is buried under the burden of infections and perinatal insults, in a setting of limited awareness and few options for testing. Children with developmental and epileptic encephalopathies (DEEs), are most severely affected by this diagnostic gap in Africa, as the rate of actionable findings is highest in DEE-associated genes. Methods: We tested 235 genetically naive South African children diagnosed with/possible DEE, using gene panels, exome sequencing and chromosomal microarray. Statistical comparison of electroclinical features in children with and without candidate variants was performed to identify characteristics most likely predictive of a positive genetic finding. Results: Of 41/235 children with likely/pathogenic variants, 26/235 had variants supporting precision therapy. Multivariate regression modelling highlighted neonatal or infantile-onset seizures and movement abnormalities as predictive of a positive genetic finding. We used this, coupled with an emphasis on precision medicine outcomes, to propose the pragmatic Think-Genetics strategy for early recognition of a possible genetic aetiology. Conclusion: Our findings emphasise the importance of an early genetic diagnosis in DEE. We designed the Think-Genetics strategy for early recognition, appropriate interim management and genetic testing for DEE in resource-constrained settings.

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