Viromics2024: Glossary

In this course, you will analyze viral sequence data

You need to keep a lab notebook

You need to be able to access the HPC Draco

Viromics is the study of viruses, and in our case bacteriophages, using next-generation sequencing technologies

Bacteriophages are diverse and ubiquitous across all biomes

Bacteriophages have large implications on their environment including the human gut

Sequence data and information about genes and genomes can come in many different formats

The most common file formats are fasta (nucl. and amino acid), fastq, sam and bam, genbank, gff and tsv files

Sequencing quality control is an important first evaluation of our data

NanoPlot can be used to evaluate read quality of long reads

Chopper can be used to filter reads based on quality and/or length

The GC content profile of a metagenome or virome is composed of a mix of bell curves

Sequence assembly can be used to assemble genomes from reads

Metagenome assembly generally yields shorter contigs than genome assembly

Flye can be used to assemble long and noisy nanopore reads from metagenomic samples.

Samples can be assembled individually and combined in a cross-assembly

vClust can be used to assess the diversity of sequences in your assembly

checkV assesses the quality of your contigs with regard to viral completeness and contamination

minimap2 aligns long and noisy nanopore reads efficiently to large (meta)genomes

samtools can be used to read, filter, convert and summarize alignments

Bandage can visualize the de-Bruijn graph

JBrowse2 can visualize genomic data like alignments and coverage

Different approaches can be used in a virus identification tool, such as reference-based and machine learning

Benchmark is a comparison between tools and should offer concrete evidence of performance, such as number of TP, FN, FP and TN

When choosing a tool, you should consider the priorities of the project. E. g. you need to identify as many viruses as possible and FP are not critical, so a tool with high recall is ideal

Filtering contigs by completeness and contamination is crucial to obtain an informative dataset

Tools like Jaeger classify your contigs, enabling you to understand your samples

No wet-lab or dry-lab technique is perfect. Filtering non-viral contigs from your data improves its quality, helping you obtain better results

matplotlib and pyplot provide multiple tools for the visualization of data points

Genome annotation gives meaning to genomic sequences

ORFs can be predicted from start and stop codons in the genomic sequences

Phages have different genomic features than prokaryotes, which influences the design of algorithms

Tools like Phanotate are very useful to process a large amount of contigs. However, no tool is perfect, so a critical interpretation of the results is important

Functional annotation of viral genomes can give clues about viral lifestyle and host interactions

Functional annotation of viral genomes can give clues about viral lifestyle and host interactions

The pyCirclize package provides tools for plotting genomic data, e.g., the set of genes, in a circular layout.

RaFAH uses a random forest model to predict hosts to the genus level for phages

RaFAH returns a probability for each host genus it can predict.

Blastn between the viral and prokaryotic fractions can be used as a quick method for host prediction

Machine learning and classical host prediction methods have a trade-off between recall and sensitivity

Viruses have multiple origins, so there is no universal marker gene

Viral taxonomy is based on many different methods

Gene-sharing networks and marker genes are popular methods for bacteriophage taxonomy

VConTACT3 and geNomad are two programs used to classify viral taxonomy of sequences using differing strategies

VConTACT3 uses gene-sharing networks, while GeNomad uses marker genes

Both methods have strong agreement with the ICTV classifications

The Python package NetworkX can be used to work with networks

The visualization of graphs usually requires the computation of positions for the nodes

Many of our contigs are close to Caudoviricitae, but we also have several connected groups of unclassified sequences