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An alignment-free tool that identifies viral sequences in metagenomes using machine learning

A tool that uses machine learning for prediction of dsDNA phages in metagenomes; Machine learning tool for predicting phage sequences in metagenomic bins.

A web-based tool for identifying and annotating prophage sequences within bacterial genomes; Web server for identification and annotation of prophages in bacterial genomes and metagenomes

A 3-class classifier that allows identification of phages from metagenomic assemblies with enhanced performance for short fragments Deep learning CNN approach to identify both phages and plasmids

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

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 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

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