Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
nextclade dataset list --tag latest sars-cov-2 -- list current dataset tags
freyja --version; freyja barcode-build --help (note: HYPHEN, not underscore; some legacy docs show barcode_build)
nextclade run --help -- v3+ syntax replaced v2; old nextclade invocation no longer works
augur --version; augur refine --help for current root-strategy flags
If pangolin --inference usher is rejected, the flag is --analysis-mode usher (no --inference). If nextclade --input-dataset DIR works, the installed version may be v2; v3 accepts both but --dataset NAME is the modern form for built-in datasets. Pangolin and Nextclade output column names differ between major releases -- introspect rather than retry.
Variant Surveillance
"Which lineages are circulating, and how fast are they growing?" -> Assign consensus or wastewater samples to a curated lineage / clade nomenclature, then track frequencies over time with explicit version pinning. The lineage assignment is NOT a stable property of the sequence; it is a property of the sequence interpreted by a specific pangolin-data / Nextclade-dataset version. Two labs running the same Pangolin binary with different pangolin-data versions can produce different calls on the same genome. For published or regulatory output, pin BOTH the executable AND the dataset version (pangolin --all-versions; nextclade dataset list --tag latest), and re-run the whole archive after every dataset update -- comparing today's BA.2.86 call to last month's "Unassigned" call is invalid.
CLI: pangolin sequences.fasta --analysis-mode usher --outfile lineage_report.csv -- UShER mode is the default since v4 (pangoLEARN deprecated mid-2023)
The Single Most Important Modern Insight -- Lineage assignment is dataset-version-dependent
A SARS-CoV-2 sequence called BA.5 today might be called BA.5.2.1 next week and KP.3 a month after that. pangolin-data and nextclade-dataset are updated weekly; lineage definitions evolve through pango-designation GitHub issues, often days-to-weeks before pangolin-data releases include the lineage. During the lag window, the same genome submitted in lab A (older pangolin-data) and lab B (current) gets different calls. The cross-lab "different lineage" result is then misread as biology. For any report, pin BOTH the executable AND the dataset version with pangolin --all-versions and nextclade dataset list --tag latest recorded alongside the call. For longitudinal studies, re-run the WHOLE archive after every dataset update -- comparing today's BA.2.86 call against last month's "Unassigned" call is invalid. Second-order insight: Pangolin's pangoLEARN mode was officially deprecated mid-2023 in favour of UShER mode (Pongmoragot 2024 Virus Evol 10:vead085); cross-study comparison of XBB sub-lineage prevalence from 2022 - mid-2023 is contaminated by the pangoLEARN -> UShER mode switch even when the same pangolin-data version is used.
Variant-specific shedding rate (Omicron BA.1 shed less per case than Delta); pin barcode + report uncertainty
Fixed RNA-to-cases ratio across variants is wrong; variant-specific shedding has been documented in the wastewater literature
Methodology evolves; before any high-stakes lineage report, verify Pangolin's current default analysis-mode and Nextclade's bundled dataset against pango-designation issues for any emerging lineage.
Pangolin Lineage Assignment With Version Pinning
Goal: Assign Pango lineages to SARS-CoV-2 consensus sequences using UShER mode (the default since v4; pangoLEARN deprecated mid-2023), with full pangolin-data version provenance preserved for reproducibility.
Approach: Always pass --analysis-mode usher; record pangolin --all-versions output alongside every lineage call; for published or regulatory output, pin pangolin-data to a specific release tag and re-run the whole archive whenever the version is updated.
pangolin --all-versions prints: pangolin executable version, pangolin-data version (weekly updated; mandatory pin for reproducibility), scorpio version, and constellations version. All four are version-sensitive; in published surveillance reports, pin all four.
Nextclade With Dataset Pinning
Goal: Assign Nextstrain clade, Pango lineage, mutations, and QC flags to SARS-CoV-2 consensus sequences with explicit dataset version provenance.
Approach: Fetch the current dataset with nextclade dataset get --name sars-cov-2 --output-dir nc_dataset/sars-cov-2; record the pathogen.json tag / commit hash; run nextclade run --input-dataset on the pre-downloaded folder so the dataset version is locked in for the analysis.
Different dataset versions assign different mutations as "lineage-defining" because internal-node placement can shift as the tree grows. Cross-version comparison of mutation reports is therefore method-dependent.
Wastewater Lineage Deconvolution With Freyja
Goal: Estimate per-lineage abundance in a wastewater pooled sample with explicit handling of the barcode forward-only date constraint, primer-scheme awareness, and residual mass interpretation.
Approach: Confirm barcode date postdates sample collection; if not, freyja barcode-build from the current UShER tree; variant call with freyja variants then deconvolve with freyja demix; inspect the resid column (residual mass NOT assigned to known lineages; high resid indicates a novel lineage is invisible); apply primer-scheme-aware coverage masking; report variant-specific uncertainty.
The Freyja --barcodes (or default bundled) date MUST postdate the sample collection date. Lineages designated after the barcode date cannot be detected -- the demixing silently fails and presents as elevated abundance of the closest parent lineage. For samples potentially containing emerging lineages, regenerate barcodes:
Subsequent methodological extensions to Karthikeyan have appeared in the wastewater literature, and recent benchmarks comparing the major deconvolution tools (Freyja, COJAC, alcov, lineagespot, LCS) confirm Freyja and COJAC consistently perform well, with performance degrading at low coverage and for divergent lineages.
COJAC for Co-Occurrence Detection
Goal: Detect emerging variants in wastewater earlier than per-site frequency methods by requiring co-occurrence of two signature mutations on the same amplicon (read pair).
Approach: COJAC checks read pairs for joint occurrence of variant-defining mutations; the inferential leap is more robust because a single site can have shared mutations across lineages, but two signature mutations on the same read pair strongly imply a single lineage. Detected Alpha 13 days before clinical samples in Swiss data (Jahn 2022 Nat Microbiol 7:1151).
Goal: Build a curated regional surveillance phylogeny with subsampling, alignment, tree, ancestral-trait inference, and time-scaling -- in Auspice-visualisable format. The Nextstrain platform was introduced by Hadfield 2018 Bioinformatics 34:4121; Augur is the Python CLI (Huddleston 2021 JOSS 6:2906).
Approach: Pull the latest official pathogen build from github.com/nextstrain/; subsample to manageable size (typically 3000-5000 genomes per global build; smaller regional); document subsampling configuration explicitly (it drives the result more than the underlying data per Hodcroft 2021).
Hodcroft 2021 Nature 591:30 documented that Nextstrain subsampling configurations drive lineage-time estimates more than the underlying data. Two researchers using the official pipeline with different subsampling can get different MRCA dates and migration patterns from the same raw genomes.
Per-Method Failure Modes
pangolin-data version skew between labs
Trigger: Two labs submit the same consensus genome to Pangolin with different pangolin-data versions; the lineage call differs.
Mechanism: Lineage designation happens through pango-designation GitHub issues -- days-to-weeks before pangolin-data releases include the lineage. During the lag, the same genome is callable as the parent (older pangolin-data) or the child (current). pangolin-data is updated weekly.
Symptom: Cross-lab lineage prevalence comparisons over time show implausible jumps coinciding with pangolin-data release dates rather than biology.
Fix: Pin pangolin-data version explicitly with pangolin --all-versions recorded alongside every call. For published or regulatory output, re-run the WHOLE archive against a single pangolin-data version before reporting.
Freyja barcode predates the sample collection date
Trigger: Wastewater sample collected after a new lineage was designated; Freyja barcode built before that designation.
Mechanism: Freyja barcodes are built from the UShER tree at a specific date; lineages designated AFTER the barcode date cannot be detected. The demixing silently fails -- the new lineage's signal is misassigned to its closest parent.
Symptom: Wastewater sample shows implausibly high abundance of a single parent lineage; new lineage that should be present is reported as 0%.
Fix: Run freyja update regularly; for samples potentially containing emerging lineages, regenerate barcodes with freyja barcode-build from the current UShER tree. Report resid (residual mass not assigned to known lineages); high resid indicates a novel lineage is being missed.
ARTIC primer dropout misread as deletion
Trigger: SARS-CoV-2 surveillance using ARTIC V4.1 amplicons; new variant has mutation at primer site; amplicons 64 / 76 / 88-90 silently drop out.
Mechanism: When a primer fails to bind, the amplicon doesn't amplify; consensus calling produces N's or reference-derived calls in that region. This LOOKS LIKE a deletion in downstream analysis but is actually missing data. Itokawa 2020 PLoS ONE 15:e0239403 documented primer interactions specifically.
Symptom: "Deletion" calls cluster in known dropout amplicons; Pangolin / Nextclade lineage call shifts when masked positions are filled with reference.
Fix: Inspect per-amplicon coverage with samtools depth -aa; mask consensus positions in dropped amplicons (use Ns -- Pangolin and Nextclade handle Ns gracefully). Document primer scheme version (V3 / V4 / V4.1 / V5.3.2 / Midnight) per isolate.
Recombinant assigned to one parent lineage
Trigger: A SARS-CoV-2 recombinant (e.g., XEC = KS.1.1 x KP.3.3) emerges; pango-designation has not yet issued the X-prefix designation; Pangolin assigns to one of the parents.
Mechanism: Pangolin in either mode assigns a recombinant to one parent lineage if no Pango-X designation exists yet. Identifying recombinants requires breakpoint detection (3SEQ, Bolotie, RDP4) and manual designation through pango-designation; the designation can lag emergence by weeks-to-months for novel recombinants.
Symptom: Outbreak interpretation conflates a recombinant lineage with its parent; transmissibility / immune-escape claims are wrong.
Fix: For any candidate emerging lineage with unusual mutations, run Bolotie or 3SEQ for recombination detection; cross-check Pangolin vs Nextclade lineage call; submit candidate recombinants to cov-lineages issue tracker if novel.
pangoLEARN result reported as authoritative
Trigger: Pangolin run with --analysis-mode pangolearn (or via legacy Docker image that defaults to pangoLEARN); user reports the call.
Mechanism: Pongmoragot 2024 Virus Evol 10:vead085 demonstrated UShER mode is significantly more accurate for recent / divergent lineages. pangoLEARN was officially deprecated mid-2023.
Symptom: Cross-lab comparison reveals one lab using pangoLEARN (legacy) and another using UShER; calls differ at borderline lineages.
Fix: Switch to --analysis-mode usher (default since v4). For longitudinal datasets crossing the mid-2023 mode-switch, re-run the historical archive against UShER mode.
Freyja barcode-build vs barcode_build flag
Trigger: Script written from older Freyja documentation using freyja barcode_build (underscore).
Mechanism: Current Freyja versions use barcode-build (hyphen); the underscore form may not be recognised.
Symptom: Subprocess fails with "unrecognized command".
Fix: Use freyja barcode-build (hyphen). Verify with freyja --help.
Wenseleers / Bedford lineage-growth CI hides covariance
Trigger: Reporting a single lineage's growth advantage 95% CI from a multinomial logistic regression.
Mechanism: The CI for any single lineage is conditional on all other lineages being held at their estimated growth rates; the marginal CI hides covariance among lineages. Early growth-advantage estimates are systematically too large; they shrink as more time passes (alternative explanations become identifiable).
Symptom: Initial published growth advantage > later refined estimate; "outlier-fast" lineages later moderated.
Fix: Report the full multinomial covariance matrix or at minimum the rank-ordered growth advantages with simultaneous CIs. Cite Abousamra 2024 PLoS Comput Biol 20:e1012443.
Nextstrain subsampling drives the result
Trigger: Nextstrain Augur build with default subsampling at 3000-5000 genomes from millions; user interprets the tree topology as authoritative.
Mechanism: Hodcroft 2021 Nature 591:30 commented that subsampling decisions drive lineage-time estimates more than the underlying data. Two researchers using the official Nextstrain pipeline with different subsampling configurations get different MRCA dates and migration patterns from the same raw genomes.
Symptom: Published Nextstrain tree differs from another analysis on the same raw data; conclusions sensitive to subsampling.
Fix: Document subsampling configuration explicitly in any Nextstrain build; run sensitivity analysis with alternative subsampling; treat MRCA dates and migration calls with appropriate uncertainty.
Reconciliation: When Methods Disagree
Pattern
Likely cause
Action
Pangolin "BA.2.86", Nextclade clade "23I"
Equivalent at different resolutions -- BA.2.86 is within 23I
Report both; Pango lineage for sub-clade resolution
Pangolin "BA.5.2", Nextclade "Unassigned"
Nextclade dataset older than pangolin-data; OR Nextclade QC failed
O'Toole Á, Scher E, Underwood A et al (2021) Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evol 7(2):veab064. doi:10.1093/ve/veab064
Aksamentov I, Roemer C, Hodcroft EB, Neher RA (2021) Nextclade: clade assignment, mutation calling and quality control for viral genomes. J Open Source Softw 6(67):3773. doi:10.21105/joss.03773
Karthikeyan S, Levy JI, De Hoff P et al (2022) Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission. Nature 609(7925):101-108. doi:10.1038/s41586-022-05049-6
Hadfield J, Megill C, Bell SM et al (2018) Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34(23):4121-4123. doi:10.1093/bioinformatics/bty407
Huddleston J, Hadfield J, Sibley TR et al (2021) Augur: a bioinformatics toolkit for phylogenetic analyses of human pathogens. J Open Source Softw 6(57):2906. doi:10.21105/joss.02906
Turakhia Y, Thornlow B, Hinrichs AS et al (2021) Ultrafast Sample placement on Existing tRees (UShER) enables real-time phylogenetics for the SARS-CoV-2 pandemic. Nat Genet 53(6):809-816. doi:10.1038/s41588-021-00862-7
Pongmoragot J, Pearson C, Borg ML et al (2024) Comparison of UShER-based and pangoLEARN-based Pangolin lineage assignments for SARS-CoV-2 sequences. Virus Evol 10(1):vead085. doi:10.1093/ve/vead085
Jahn K, Dreifuss D, Topolsky I et al (2022) Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC. Nat Microbiol 7(8):1151-1160. doi:10.1038/s41564-022-01185-x
Pechlivanis N, Tsagiopoulou M, Maniou MC et al (2022) Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater and a use-case in the metropolitan area of Thessaloniki, Greece. Sci Rep 12:2659. doi:10.1038/s41598-022-06625-6
Itokawa K, Sekizuka T, Hashino M, Tanaka R, Kuroda M (2020) Disentangling primer interactions improves SARS-CoV-2 genome sequencing by multiplex tiling PCR. PLoS ONE 15(9):e0239403. doi:10.1371/journal.pone.0239403
Hodcroft EB, De Maio N, Lanfear R et al (2021) Want to track pandemic variants faster? Fix the bioinformatics bottleneck. Nature 591(7848):30-33. doi:10.1038/d41586-021-00525-x
Abousamra E, Figgins M, Bedford T (2024) Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency. PLoS Comput Biol 20(9):e1012443. doi:10.1371/journal.pcbi.1012443
Boni MF, Posada D, Feldman MW (2007) An exact nonparametric method for inferring mosaic structure in sequence triplets. Genetics 176(2):1035-1047. doi:10.1534/genetics.106.068874
Martin DP, Murrell B, Golden M, Khoosal A, Muhire B (2015) RDP4: detection and analysis of recombination patterns in virus genomes. Virus Evol 1(1):vev003. doi:10.1093/ve/vev003
Varabyou A, Pockrandt C, Salzberg SL, Pertea M (2021) Rapid detection of inter-clade recombination in SARS-CoV-2 with Bolotie. Bioinformatics 37(15):2298-2300. doi:10.1093/bioinformatics/btab080
Related Skills
pathogen-typing - Lineage assignment overlaps with typing; this skill owns longitudinal frequency tracking and wastewater deconvolution
