Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER.

The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduces their respective read counts by more than 85%, resulting in an increase in sequencing depth of 47% on average for long non-coding RNAs. We also apply RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as an increase in non-globin reads by 16% on average. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER's modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser .

A Deep Learning-Based Approach for Gait Analysis in Huntington Disease.

Huntington Disease (HD) is a genetic neurodegenerative disease which leads to involuntary movements and impaired balance. These changes have been quantified using footstep pressure sensor mats such as Protokinetics' Zeno Walkway. Drawing from distances between recorded footsteps, patients' disease severity have been measured in terms of high level gait characteristics such as gait width and stride length. However, little attention has been paid to the pressure data collected during formation of individual footsteps. This work investigates the potential of classifying patient disease severity based on individual footstep pressure data using deep learning techniques. Using the Motor Subscale of the Unified HD Rating Scale (UHDRS) as the gold standard, our experiments showed that using VGG16 and similar modules can achieve classification accuracy of 89%. Image pre-processing are key steps for better model performance. This classification accuracy is compared to results based on 3D CNN (82%) and SVM (86.9%).