MOTIVATION: Long-read sequencing (LRS) is increasingly used for human medical research and clinical diagnostics due to its capacity to generate complete genome information. However, there is a lack of robust and easy-to-use pipelines for comprehensive LRS data analysis. RESULTS: Here we present Nallo, a Nextflow pipeline for analysis of PacBio and Oxford Nanopore data, with additional support for rare disease research projects. The pipeline detects a wide range of genetic variants, performs genome assembly, and reports CpG methylation. It also enables annotation and ranking of variants based on their predicted functional consequences. AVAILABILITY AND IMPLEMENTATION: Nallo is available from GitHub: https://github.com/genomic-medicine-sweden/nallo.

In this paper we present a disruption tolerant routing algorithm based on geographic location information, which improves upon the hop count compared to the current state of the art by up to a factor of three in large scale human networks. Leveraging only the history of geographic movement patterns in the two-hop neighborhood, our algorithm is able to perform well in the absence of knowledge of social interaction between nodes and without detailed future schedule information. Representing previously visited locations as probability distributions encoded in an efficient vector, we formalize a heuristic for efficiently forwarding messages in disruption tolerant networks, implement a framework for comparing our approach with the state of the art, and evaluate key metrics, such as hop count and delivery rate, as well as energy consumption and battery depletion fairness on real world data. We are able to outperform the state of the art in human mobility based networks considerably in terms of energy usage per node, thereby extending data network availability further into areas devoid of otherwise necessary communication infrastructure.