Eidos (Italy)

Five years after the outbreak of the SARS-CoV-2 pandemic in 2020, diagnostic laboratories have moved from massive sequencing of thousands of samples to routine surveillance of SARS-CoV-2 cases, as with all other respiratory viruses. Surveillance remains of paramount importance to prevent a further SARS-CoV-2 surge, as the virus has been shown to mutate rapidly and can render available drugs and vaccines ineffective. During the pandemic, several bioinformatics pipelines and workflows have been developed to streamline analysis, shorten turnaround time and ensure reproducibility. As the number of samples decreases, laboratories are moving towards more flexible sequencing strategies and optimizing the cost per sample. However, workflow redesigns, even if individual steps have proven successful time and time again, can lead to challenges when changes in a bioinformatics pipeline are introduced (e.g. version updates, implementation of new features, etc.), a new combination of viral mutations emerge or a change in wet-lab procedures leads to unpredictable results. Here, we present a report of misidentified frameshift mutations in the consensus sequence of SARS-CoV-2, which led to an incorrect assumption of mutations in the spike and nucleocapsid viral proteins with the potential to affect PCR detection or even antigen testing. This investigation exemplifies the need for better awareness of the challenges that can occur even when using routinely applied protocols and analytical workflows and highlights the need for cooperation between experts of NGS, bioinformaticians and decision-makers towards more harmonized data workflows.

Three-dimensional (3D) rendering of biomedical volumes has become essential for faster comprehension of anatomy, better communication with patients, surgical planning, and training. However, depending on the algorithm, level of detail, volume size, and transfer function, rendering can be quite slow. A multi-target optimization method – voxelization – can be applied to biomedical volume rendering enhancement for empty space skipping, optimized maximum intensity calculation, and advanced Woodcock tracking. Empirical results indicate that the voxelization technique can increase the performance of Direct Volume Rendering (DVR) by up to ten times, Monte Carlo Path Tracing (MCPT) by five times, and Maximum Intensity Projection (MIP) by two times of the original velocity. In this study, we investigate the influence of a 3D fractal dimension of the rendered volumes to the rendering speed and the optimal super voxel size, used in voxelization process, to guarantee the best performance of DVR, MCPT, and MIP, using voxelization. 3D fractal dimensions are calculated for five common transfer functions applied to the Cone-Beam Computed Tomography (CBCT) scans of exotic animals and human extremities (postmortem). Preliminary findings suggest that volumes rendered with similar transfer functions have comparable 3D fractal dimension and, moreover, there is a statistically significant relationship between the DVR and MCPT speed and the 3D fractal dimension. Furthermore, the structures with higher 3D fractal dimension require the smaller super voxel sizes for empty space skipping, meanwhile, optimized maximum intensity calculation and advanced Woodcock tracking are 3D fractal dimension independent. The research encourages the further exploration of the structural complexity to 3D rendering optimization for biomedical volumes.