Search

An automated tool that uses a hybrid machine-learning and protein-similarity approach to recover and annotate viruses of microbes Deep learning neural network based on protein signatures which also highlights auxiliary metabolic genes and

Pipeline for virus identification by nucleotide and remote amino acid comparisons; multiple k-mer de novo assembly; and phylogeny.

Extension of DeepVirFinder that is trained to identify any virus that may colonise human samples.

A novel k-mer-based tool that identifies viral sequences from assembled metagenomic data; K-mer based machine learning method for identification of viral contigs.

Uses similar oligonucleotide frequency patterns between phages and bacteria to predict host range for phages on a genus level

Uses a combination of nucleotide and protein signals to taxonomically classify viral sequences independently of genome coverage or read overlap; Creates “super-scaffolds” from de novo assembly and a mapping assembly and taxonomically class

Can identify viral genomes from collective raw reads within metagenomes of different environments; Iterative pipeline that relies on the abundance of non-viral sequences in databases to strictly filter out unwanted contigs. Pipeline accept

Web-based pipeline that handles genome assembly, functional annotation using a variety of databases and identification of phage contigs via a random forest algorithm

Deep learning neural network using an attentional neural model trained on nucleotide viral fragments.

Web-based pipeline that classifies viral sequences based on homology to databases and functional annotates them.

Screens metagenomic reads against complete viral genome database before being filtered and then screen against the same database again. Provides viral hit counts and relative abundance.

Can recover novel viruses in metagenomic data using both reference-dependent and reference-independent approaches; Uses referenced-based and reference-free approaches in unison relying on probabilistic similarity models and referenced base

Includes complete genomes of viruses and their hosts gathered from RefSeq, GenBank, UniProt, and ViralZone

An alignment-free approach for distinction between phages and eukaryotic viruses in metagenome data

Made up of two BLAST-based pipelines – Virome and Discovery. Virome aligns reads to a curated database to identify viral sequences and compute their abundance in the sample. Discovery focuses on contig-based analysis to aid novel virus disc

Fast and accurate, making it suitable for predicting phages' host range from metagenomic data

This week, we interviewed Gavin Hands, CEO of Cellexus, about the company's history of helping phage researchers and biotech companies scale up their phage production. This week, we bring you a Q&A with Gavin Hands, CEO of Cellexus. This p

Invitris is a new team out of TU Munich funded to spin off a phage therapy biotech company based on their unique synthetic phage production system. They're looking for an additional team member at the intersection of microbiology and the cl

This issue was sponsored by Cellexus, which pioneers revolutionary, market leading single-use airlift bioreactor systems and technology. Email enquiries@cellexus.com for more info.

Professor Lone Brøndsted (University of Copenhagen, Denmark) has an opening in her lab for a PhD student to work on a phage-host interaction project.