A review published in the journal Discover Viruses examines how bioinformatics tools, sequencing platforms, and analytical pipelines support genomic surveillance and evolutionary tracking of emerging viral pathogens. Written by Taru Singh, Gorangi Mudgal, and Chandra Shekhar, the article takes a deliberately comparative approach: rather than surveying software in the abstract, it evaluates the same categories of tools against five biologically distinct case studies—SARS-CoV-2, Ebola virus, monkeypox virus, avian influenza, and honeybee viruses—to identify which methodological choices generalize across surveillance contexts and which depend on a pathogen’s biology or the resources of a given setting.
The review follows a narrative, non-systematic methodology, drawing on literature identified through PubMed, Scopus, and Google Scholar searches covering 2015 to 2025, with preference given to primary studies and papers describing bioinformatic tools. Its central conclusion is that no single pipeline performs best universally. Tool and platform selection must instead be matched to each pathogen’s evolutionary behavior and to the surveillance infrastructure available locally, from high-throughput national consortia to field laboratories with minimal equipment.
The technical foundation of the review rests on the properties of viral genomes themselves. RNA viruses mutate rapidly because their RNA-dependent RNA polymerases lack proofreading activity, producing roughly 10⁻⁴ to 10⁻⁶ substitutions per nucleotide per replication cycle. Coronaviruses are a notable exception: they encode an nsp14 exoribonuclease that confers proofreading, which is a major reason SARS-CoV-2 mutates substantially more slowly than most other RNA viruses. Segmented viruses such as influenza can additionally undergo reassortment, exchanging genome segments during co-infection to generate novel strains with pandemic potential, while recombination further diversifies coronavirus and other viral genomes.
On the sequencing side, the review contrasts the trade-offs among platforms. Illumina short-read sequencing offers high throughput, low per-base error rates, and cost efficiency for population-level surveillance, but its 150 to 300 base pair reads limit resolution of long-range haplotypes and structural variants. Oxford Nanopore platforms generate long reads with near-real-time output and field deployability, as demonstrated during the 2014–2016 Ebola outbreak, but carry higher per-base error rates, typically 5 to 15 percent, requiring high read depth for accurate consensus calling. Amplicon-based protocols such as those from the ARTIC network reduce cost and increase sensitivity but can introduce primer-binding biases that mask mutations at primer sites. Hybrid strategies combining accurate short reads with long reads are increasingly used to resolve difficult regions such as homopolymer runs and short tandem repeats.
The downstream analysis workflow runs from raw reads through quality control with tools like FastQC and fastp, host sequence removal with Bowtie2, genome assembly, variant calling, annotation, phylogenetics, and lineage classification. Assembly can be reference-based, using tools such as BWA-MEM, which is fast and accurate when a close reference exists, or de novo, using SPAdes or MEGAHIT, which is essential for discovering novel or highly divergent viruses where no reference genome applies. Variant calling choices matter biologically: LoFreq’s sensitivity to low-frequency variants suits intra-host viral quasispecies analysis, whereas GATK, originally developed for diploid human genomes, can under-call minority variants in viral populations and is better reserved for consensus-level, inter-host calling. Bayesian tools such as BEAST co-estimate phylogenies and evolutionary rates through Markov chain Monte Carlo sampling, enabling molecular clock reconstruction, while maximum-likelihood packages IQ-TREE and RAxML provide the speed needed for real-time outbreak tracking. Pangolin classifies SARS-CoV-2 genomes into Pango lineages using a trained machine learning classifier, and Nextstrain integrates genomic, epidemiological, and geographic data into interactive real-time visualizations.
The SARS-CoV-2 case study illustrates surveillance at unprecedented scale. The COVID-19 Genomics UK Consortium alone sequenced and shared more than three million SARS-CoV-2 genomes by 2024. Early metagenomic sequencing of patient samples identified a novel betacoronavirus closely related to bat coronaviruses, with the closest known relative being the bat virus RaTG13, sharing approximately 96 percent genome identity. Genomic surveillance enabled rapid detection and characterization of variants of concern, including Alpha, Beta, Gamma, Delta, and Omicron, each defined by mutations affecting transmissibility, immune escape, or diagnostic performance. The review notes these approaches contributed to tangible outcomes such as mRNA vaccine development, but also exposed a global capacity gap: the high-throughput pipelines that worked in well-resourced settings were far less accessible elsewhere.
The Ebola case study demonstrates the opposite extreme. During the 2014–2016 West African epidemic, which caused over 28,000 cases and 11,000 deaths across Guinea, Liberia, and Sierra Leone, researchers deployed portable Oxford Nanopore MinION sequencers in the field, establishing sequencing facilities that produced genomes within days of sample collection. Phylogenetic analysis showed the outbreak stemmed from a single introduction of a Zaire ebolavirus strain related to Central African viruses, followed by sustained human-to-human transmission, with complex transmission chains, superspreading events, and cross-border spread. One glycoprotein mutation, A82V, was associated with increased infectivity in human cells; the review cautions this association, based on in vitro assays and the mutation’s rising frequency, indicates a plausible fitness advantage rather than direct proof of increased human-to-human transmission.
The mpox case study highlights how surveillance can reveal unexpected evolutionary patterns. The 2022 global outbreak, with over 87,000 confirmed cases across more than 100 countries by year’s end, predominantly affecting men who have sex with men, involved a West African clade virus assigned to lineage B.1 and closely related to viruses circulating in Nigeria since 2017. A striking finding was APOBEC3-mediated hypermutation: host antiviral enzymes inducing TC→TT transitions in the viral genome, a signature detectable through standard variant-calling pipelines. The high frequency of these mutations suggested sustained human-to-human transmission rather than repeated zoonotic introductions, challenging prior assumptions about mpox transmission dynamics. Phylodynamic analyses further suggested the outbreak began months before the first reported cases.
The remaining case studies extend the framework beyond human pathogens. For avian influenza, surveillance involves sequencing all eight genome segments and detecting reassortment through incongruent phylogenetic trees among segments, with tools such as FluSurver, GiRaF, and SCUEAL automating detection; coordinated monitoring relies on GISAID’s EpiFlu database, the WHO GISRS system, and the OFFLU network. H5N1 has acquired mutations increasing binding affinity to human-type receptors, raising concerns that surveillance is designed to catch early. Honeybee viruses, including Deformed Wing Virus with its DWV-A, DWV-B, and DWV-C genotypes, illustrate a One Health application: metatranscriptomic sequencing of total RNA from bees, using de novo assembly and machine-learning tools like DeepVirFinder, reveals viral diversity in a non-human host system with direct implications for pollination and food security, with Varroa mite transmission associated with increased viral diversity and more virulent strains.
The review closes with persistent challenges and priorities. Sequencing capacity remains unevenly distributed, with high-income countries producing most sequences, and many public health laboratories lack personnel with combined biological and programming expertise. Data sharing is hindered by concerns over ownership, intellectual property, and misuse, and metadata remain unstandardized. Machine learning holds promise for variant detection and risk assessment, but the review stresses that validated, deployed uses are currently concentrated in lineage classification and protein-structure prediction, while broader outbreak-forecasting claims remain exploratory and lack prospective validation, with interpretability of deep-learning models a practical concern. The authors propose five priorities: adopting modular, reusable pipelines; investing in local sequencing and bioinformatics capacity in under-represented regions; standardizing metadata with tiered data-sharing agreements; matching phylogenetic and molecular-clock methods to each pathogen’s evolutionary rate and recombination profile; and integrating machine-learning tools only where validated performance gains over established methods have been demonstrated.
Subject of Research: A narrative review of bioinformatics tools and pipelines for genomic surveillance and evolutionary tracking of emerging viral pathogens across five case studies.
Article Title: Bioinformatics-driven genomic surveillance and evolutionary tracking of emerging viral pathogens
Article References: Singh, T., Mudgal, G., & Shekhar, C. (2026). Bioinformatics-driven genomic surveillance and evolutionary tracking of emerging viral pathogens. Discover Viruses, 3(1), Article 25. https://doi.org/10.1007/s44370-026-00061-0
Image Credits: AI Generated
DOI: 10.1007/s44370-026-00061-0
Keywords: genomic surveillance, bioinformatics, SARS-CoV-2, Ebola virus, mpox, avian influenza, honeybee viruses, phylogenetics, next-generation sequencing, Nextstrain, BEAST, machine learning
News Source: Kristina Jarvis. (November 5, 2026). Mapping the bioinformatics toolbox that tracks emerging viral outbreaks. Scienmag.
