Nextstrain

Nextstrain
Name	Nextstrain
Founded	2015
Founders	Trevor Bedford, Richard Neher
Headquarters	Seattle, Geneva
Products	Real-time phylogenetics, Auspice, Augur

Nextstrain Nextstrain is an open-source project for real-time tracking of pathogen evolution that integrates genomic data, phylogenetics, and epidemiological metadata to visualize transmission patterns. The platform aggregates sequence data from public repositories and surveillance networks to power interactive phylogenies, geographic reconstructions, and temporal analyses for outbreaks such as influenza, Ebola, Zika, and SARS-CoV-2. Its tools have been used by researchers, public health agencies, and consortia to inform responses to pandemics, linking laboratory sequencing efforts with platforms like GISAID, GenBank, and the European Nucleotide Archive.

Overview

Nextstrain provides a pipeline and visualization suite that converts pathogen sequence data into interpretable evolutionary narratives for stakeholders in public health and research. It integrates inputs from GISAID, GenBank, European Nucleotide Archive, Wellcome Sanger Institute, and national sequencing programs like the COVID-19 Genomics UK Consortium and links outputs to institutional actors such as the Centers for Disease Control and Prevention, World Health Organization, Public Health England, Institut Pasteur, and research groups at universities like University of Washington and University of Basel. The project emphasizes open science and collaboration with initiatives including the Global Influenza Surveillance and Response System, the Coalition for Epidemic Preparedness Innovations, and academic publishers.

Data and Methodology

Nextstrain ingests nucleotide sequences, sampling dates, and location metadata to infer time-resolved phylogenies using established algorithms and models from computational biology. The workflow builds on tools and methods such as MAFFT for alignment, IQ-TREE and RAxML for phylogenetic inference, and molecular clock approaches influenced by work from groups like BEAST and researchers including Andrew Rambaut, Emma Hodcroft, and Marc Suchard. Metadata harmonization draws on standards exemplified by the MIxS checklist and links sequence provenance to submitters at institutions such as Broad Institute, Scripps Research, Fred Hutchinson Cancer Research Center, and national public health labs. For phylogeographic reconstruction and ancestral state estimation, Nextstrain implements parsimonious and likelihood-based approaches related to methods developed by teams at University of Oxford, Max Planck Institute, and the Smithsonian Institution.

Visualizations and Tools

The project distributes two principal components: a command-line pipeline and an interactive browser, developed to enable exploration by scientists and decision makers. The processing pipeline, commonly referred to as Augur, orchestrates tasks inspired by software from groups like Biopython, EMBOSS, Galaxy Project, and incorporates models and scripts used by labs at Fred Hutchinson Cancer Research Center, Broad Institute, and Sanger Institute. The interactive web application, Auspice, renders trees, maps, and temporal plots comparable to visualization efforts from Nextbio, Microreact, and platforms used by European Centre for Disease Prevention and Control and National Institutes of Health. Auspice supports layered displays of mutations, clade annotations, and trait mappings referencing nomenclature systems such as those promoted by the PANGO Network, the World Health Organization, and the Global Initiative on Sharing All Influenza Data partners.

Applications and Impact

Nextstrain has been applied to monitor seasonal and pandemic pathogens, informing vaccine strain selection dialogues between stakeholders such as the World Health Organization's Global Influenza Programme and vaccine manufacturers like Sanofi Pasteur and GlaxoSmithKline. During the 2013–2016 West African Ebola epidemic and the 2015–2016 Zika virus epidemic, researchers at institutions including Scripps Research, University of Edinburgh, and University of Cambridge used Nextstrain outputs alongside field studies from organizations such as Médecins Sans Frontières and Centers for Disease Control and Prevention. In the SARS-CoV-2 pandemic, Nextstrain interfaces with data flows from consortia like the COVID-19 Genomics UK Consortium and influenced analyses published in journals associated with groups at Imperial College London, Harvard University, and Johns Hopkins University. Its visualizations have been cited in communications from the World Health Organization, national ministries of health, and academic fora including conferences at Cold Spring Harbor Laboratory.

History and Development

Nextstrain originated in 2015 from collaborations between computational virologists and evolutionary biologists seeking rapid pathogen surveillance solutions, with foundational contributors from labs at Fred Hutchinson Cancer Research Center and University of Basel. Early work built on phylogenetic advances by researchers at Los Alamos National Laboratory, University of Oxford, and Harvard School of Public Health, and benefited from open-data cultures fostered by projects like GISAID and GenBank. The codebase and community expanded through partnerships with public health agencies including Public Health England and the Centers for Disease Control and Prevention, academic centers such as University of Washington and ETH Zurich, and funding or collaboration with organizations like the Chan Zuckerberg Initiative and philanthropic labs.

Limitations and Challenges

Nextstrain's outputs depend on the representativeness and timeliness of input sequences, making analyses sensitive to sampling bias from regions and laboratories, including disparities evident between sequencing capacity at institutions like Wellcome Sanger Institute and under-resourced public health labs in low-income countries. Interpretation requires care given model assumptions derived from methods associated with BEAST and phylogenetic software like RAxML and IQ-TREE, and results can be affected by errors in metadata submitted to databases such as GISAID and GenBank. Legal and ethical constraints around data-sharing negotiated with stakeholders including the Global Initiative on Sharing All Influenza Data and national authorities can limit access, while computational scaling challenges arise when integrating millions of sequences similar to problems confronted by projects like UShER and consortium efforts at the European Molecular Biology Laboratory.

Category:Bioinformatics