Nuvs
Nuvs is a novel workflow used for discovering potential novel viral sequences in a sample library.
Prerequisites
This tutorial assumes you have already done the following:
- created a sample
- created and built a reference
- created a subtraction
- installed HMM profiles and annotations
You use these data sources to run a workflow for discovering potential novel virus sequences in a sample.
How does it work?
Pathoscope detects known viruses by mapping reads to reference sequences. Instead, Nuvs uses profile HMMs to find divergent viral sequences that may not be detected by mapping or the Basic Local Alignment Search Tool (BLAST).
Nuvs relies on profile hidden Markov models (pHMMs) to predict viral domains in sequences assembled from sample libraries.
The first step of the Nuvs workflow is eliminating reads associated with known OTUs or a host genome. First, reads are mapped against a Virtool reference and any matching reads are discarded. The remaining reads are mapped against a host subtraction genome and similarly removed.
The next step is assembly of the remaining, undiscarded reads using SPAdes.
HMMs
Profile hidden Markov models (HMM) are used in Virtool to discover potential novel viruses in sequencing data. The models are used to identify known viral motifs in translated open reading frames derived from sequencing data.
Virtool stores installed profile HMMs in a single profiles.hmm artifact.
HMMER searches these models against translated ORFs. The
models are generated from clustered amino acid sequences sourced from GenBank.
Virtool distributes an official set of models derived from the vFam project. Because these models lack biological annotations, Virtool also provides annotations calculated from metadata associated with the amino acid sequences used to build the models.