MOTIVATION: Metagenomics is a powerful tool for assaying the DNA from every genome present in an environment. Recent advances in bioinformatics have enabled the rapid assembly of near-complete metagenome-assembled genomes (MAGs), and there is a need for reproducible pipelines that can annotate and characterize thousands of genomes simultaneously, to enable identification and functional characterization. RESULTS: Here we present MAGpy, a scalable and reproducible pipeline that takes multiple genome assemblies as FASTA and compares them to several public databases, checks quality, suggests a taxonomy and draws a phylogenetic tree. AVAILABILITY AND IMPLEMENTATION: MAGpy is available on github: https://github.com/WatsonLab/MAGpy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Oxford Nanopore's MinION device has matured rapidly and is now capable of producing over one million reads and several gigabases of sequence data per run. The nature of the MinION output requires new tools that are easy to use by scientists with a range of computational skills and which enable quick and simple QC and data extraction from MinION runs. RESULTS: We have developed two GUIs for the R package poRe that allow parallel and real-time processing of MinION datasets. Both GUIs are capable of extracting sequence- and meta- data from large MinION datasets via a friendly point-and-click interface using commodity hardware. AVAILABILITY AND IMPLEMENTATION: The GUIs are packaged within poRe which is available on SourceForge: https://sourceforge.net/projects/rpore/files/ . CONTACT: [email protected].

An asynchronous communication library for accessing and managing dynamic hash tables over a network of Symmetric Multiprocessors (SMP) is presented. A blocking factor is shown experimentally to reduce the variance of the wall clock time. It is also shown that remote accesses to a distributed hash table can be as effective and scalable as the one-sided operations of the low-level communication middleware on an IBM SP.

The seeded region growing (SRG) algorithm is a fast robust parameter-free method for segmenting intensity images given initial seed locations for each region. The requirement of predetermined seeds means that the model cannot operate fully autonomously. In this paper, we demonstrate a novel region growing variant of the pulse-coupled neural network (PCNN), which offers comparable performance to the SRG and is able to generate seed locations internally, opening the way to fully autonomous operation.