MOTIVATION: Oxford Nanopore Technologies recently adopted the POD5 file format for storing raw nanopore sequencing data. The information stored in these files provides detailed insights into the sequencing features and enhances the understanding of raw nanopore data. However, the process of visualizing the data can be cumbersome, especially for users without programming skills. To address this issue, we developed the pod5Viewer, a GUI application for inspecting POD5 files. RESULTS: The pod5Viewer offers straightforward access to raw sequencing data and associated metadata in POD5 files. It includes functionalities for viewing, plotting, and exporting individual reads. Designed with user-friendliness in mind, the pod5Viewer is easy to install and use, making it suitable for users with all technical backgrounds. AVAILABILITY AND IMPLEMENTATION: The pod5Viewer is available as open source from the pod5Viewer Github repository (https://github.com/dietvin/pod5Viewer).

SUMMARY: Oxford Nanopore Technologies' (ONT) sequencing platform offers an excellent opportunity to perform real-time analysis during sequencing. This feature allows for early insights into experimental data and accelerates a potential decision-making process for further analysis, which can be particularly relevant in the clinical context. Although some tools for the real-time analysis of DNA-sequencing data already exist, there is currently no application available for differential transcriptome data analysis designed for scientists or physicians with limited bioinformatics knowledge. Here, we introduce NanopoReaTA, a user-friendly real-time analysis toolbox for RNA-sequencing data from ONT. Sequencing results from a running or finished experiment are processed through an R Shiny-based graphical user interface with an integrated Nextflow pipeline for whole transcriptome or gene-specific analyses. NanopoReaTA provides visual snapshots of a sequencing run in progress, thus enabling interactive sequencing and rapid decision making that could also be applied to clinical cases. AVAILABILITY AND IMPLEMENTATION: Github https://github.com/AnWiercze/NanopoReaTA; Zenodo https://doi.org/10.5281/zenodo.8099825.

This paper proposes an energy-efficient deep in-memory architecture for NAND flash (DIMA-F) to perform machine learning and inference algorithms on NAND flash memory. Algorithms for data analytics, inference, and decision-making require processing of large data volumes and are hence limited by data access costs. DIMA-F achieves energy savings and throughput improvement for such algorithms by reading and processing data in the analog domain at the periphery of NAND flash memory. This paper also provides behavioral models of DIMA-F that can be used for analysis and large scale system simulations in presence of circuit non-idealities and variations. DIMA-F is studied in the context of linear support vector machines and k-nearest neighbor for face detection and recognition, respectively. An estimated 8×-to-23× reduction in energy and 9×-to-15× improvement in throughput resulting in EDP gains up to 345× over the conventional NAND flash architecture incorporating an external digital ASIC for computation.