---
title: "Pathotypr: Alignment-Free Lineage and Resistance Typing for Tuberculosis Genomes"
id: "cdc-emerging-infectious-diseases-journal-0-alignment-free-tool-for-assemblies-and-reads-in-genomic-surveillance-of"
canonical_url: "https://medichelpline.com/clinical-feed/cdc-emerging-infectious-diseases-journal-0-alignment-free-tool-for-assemblies-and-reads-in-genomic-surveillance-of"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "CDC Emerging Infectious Diseases Journal"
source_url: "https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article"
published_at: "2026-10-18T04:00:00.000Z"
evidence_level: "Agency Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Pathotypr: Alignment-Free Lineage and Resistance Typing for Tuberculosis Genomes
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/cdc-emerging-infectious-diseases-journal-0-alignment-free-tool-for-assemblies-and-reads-in-genomic-surveillance-of
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** CDC Emerging Infectious Diseases Journal
- **Source URL:** [Original Journal Publication](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article)
- **Published At:** 2026-10-18T04:00:00.000Z
- **Evidence Rating:** Agency Feed
## Executive GIST (TL;DR)
- The authors developed **Pathotypr**, an open-source, alignment-free tool that accepts assembled genomes (FASTA) or raw short reads (FASTQ) and returns lineage assignments, WHO-cataloged resistance-associated variants, and a closest-reference genome for downstream analysis. - Investigators built a lineage marker framework from a maximum-likelihood phylogeny of 26,813 Mycobacterium tuberculosis complex (MTBC) genomes containing 609,003 polymorphic sites, then mapped substitutions to branches to select lineage-defining SNPs. - After excluding 3,039 missense and 454 homoplastic nonmissense variants, 2,145 markers remained from 5,638 candidate variants; these markers comprised 33%–46% of SNPs across lineages. - Pathotypr has five modules grouped into machine-learning, genotyping, and utilities: Train/Predict (k-mer Random Forest), Classify (SNP-based genotyping on assemblies), Split-FASTQ (SNP-based genotyping on reads), and Match Ref (lineage-aware reference selection). - In validation vs phylogenetic placements: Predict matched all 498 complete RefSeq genomes; Classify matched 497. Both Predict and Classify agreed on all 254 chromosome-level assemblies not used in training. Split-FASTQ assigned all 162 clinical read datasets to expected lineages. - Match Ref reduced nonfixed SNPs (median 89% reduction versus ancestral reference) and increased median coverage breadth by 0.84 percentage points across 162 clinical read sets. - A surveillance-scale test typed 88,071 UShER-TB samples with Split-FASTQ (≈24 hours on 4 threads). After excluding mixed-lineage calls, Pathotypr and TB-Profiler assigned the same major lineage for directly comparable samples; Pathotypr additionally identified isolates from L10, A1, and A2 that TB-Profiler missed or left unassigned. - Pathotypr uniquely combined rapid k-mer/Random Forest classification (~1 s per call), support for FASTA and FASTQ inputs, drug-resistance prediction, both GUI and CLI interfaces, and lineage coverage across L1–L10 and A1–A4 while preserving sublineage resolution. - Resistance-informed surveillance: Pathotypr analyzed 7,148 CRyPTIC genomes and reported performance for WHO confidence group 1–2 resistance markers compared with phenotypic susceptibility; the source reported these analyses but did not provide additional unreported methodological or numerical details in the abstract. - Authors noted underrepresentation of some groups (L7–L10, A1–A3) and that small sample counts might miss unsampled diversity; the article is an early-release version and the Appendix contains expanded tables and figures.
## Clinical Analysis & Structured Key Points
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[EID Journal](https://wwwnc.cdc.gov/eid/) 2. [Volume 32](https://wwwnc.cdc.gov/eid/early-release) 3. [Early Release](https://wwwnc.cdc.gov/eid/early-release#issue-1347) 4. [Main Article](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article) * [Facebook](https://www.facebook.com/sharer/sharer.php?u=%2Feid%2Farticle%2F32%2F11%2F26-1074_article "Share to Facebook") * [Twitter](http://twitter.com/share?url=%2Feid%2Farticle%2F32%2F11%2F26-1074_article&text= "Share to Twitter") * [LinkedIn](https://www.linkedin.com/shareArticle?url=%2Feid%2Farticle%2F32%2F11%2F26-1074_article&title= "Share to LinkedIn") * [Syndicate](https://tools.cdc.gov/medialibrary/index.aspx#/sharecontent//eid/article/32/11/26-1074_article "Embed this Page") [ Emerging Infectious Disease journal ISSN: 1080-6059 ](https://wwwnc.cdc.gov/eid/) _Disclaimer: Early release articles are not considered as final versions. Any changes will be reflected in the online version in the month the article is officially released._ #### Volume 32, Number 11—November 2026 ##### _Dispatch_ ### Alignment-Free Tool for Assemblies and Reads in Genomic Surveillance of Tuberculosis On This Page [The Study](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article) * * * [Conclusions](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article) * * * [Suggested Citation](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article) Figures [Figure 1](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f1) * * * [Figure 2](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f2) * * * [Figure 3](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f3) Tables [Table](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-t1) Downloads [Appendix ](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-app1.pdf) * * * [RIS [TXT - 2 KB] ](https://wwwnc.cdc.gov/eid/article/32/11/26-1074.ris) Article Metrics [Metric Details](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article) Related Articles [Pediatric Tuberculosis Care Outcomes in a Tertiary Care Center, France, 2014–2024](https://wwwnc.cdc.gov/eid/article/32/9/26-0600_article) * * * [Recent _Mycobacterium tuberculosis_ Transmission, United States, 2018–2022](https://wwwnc.cdc.gov/eid/article/32/9/26-1176_article) * * * [Post-TB Consequences on Prevention Effectiveness](https://wwwnc.cdc.gov/eid/article/32/9/26-0473_article) * * * [More articles on Tuberculosis](https://wwwnc.cdc.gov/eid/spotlight/tuberculosis) Paula Ruiz-Rodriguez and Mireia Coscollá[![Comments to Author](https://wwwnc.cdc.gov/eid/content/images/icon/email.gif)](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#comment) Author affiliation: Institute for Integrative Systems Biology, Spanish National Research Council–University of Valencia, FISABIO Joint Research Unit “Infection and Public Health,” Paterna, Spain [Suggested citation for this article](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#suggestedcitation) ### Abstract Whole-genome sequencing strengthens tuberculosis surveillance, but incomplete lineage schemes can misclassify isolates. We developed Pathotypr, an alignment-free tool for assemblies and reads, using 2,145 markers derived from 26,813 genomes. Across 88,071 samples, Pathotypr improved concordance, expanded uncommon lineage coverage, and detected resistance markers, supporting molecular epidemiology and public health surveillance. According to the World Health Organization, tuberculosis caused an estimated 1.23 million deaths in 2024 ( [External Link](https://www.who.int/teams/global-programme-on-tuberculosis-and-lung-health/tb-reports/global-tuberculosis-report-2025)). Whole-genome sequencing can strengthen disease control by linking cases, detecting resistance, and distinguishing local transmission from repeated introduction; however, those benefits depend on results that are both rapid and comparable. The _Mycobacterium tuberculosis_ complex (MTBC) currently includes 10 human-adapted lineages (L1–L10) and 4 animal-adapted clades (A1–A4), and several uncommon or recently described groups remain sparsely sampled or absent from some typing schemes ([_2_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r2 "2")–[ _6_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r6 "6")). When a scheme does not represent that diversity, isolates may remain unclassified or be assigned to the nearest available label. Differences in sequencing access and reporting remain broader challenges across programs in Europe, but a shared and current lineage framework is still essential wherever genomic surveillance is used ([_7_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r7 "7"),[_8_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r8 "8")). We developed Pathotypr as an open-source, alignment-free software that accepts assembled genomes or raw short reads and returns lineage assignments, World Health Organization–cataloged resistance-associated variants, and a closely related reference genome for downstream analysis. Assemblies can be classified from phylogenetic markers or an independent _k_ -mer model, whereas reads are screened directly for lineage and resistance markers and can return >1 supported lineage when mixed signals are present. We evaluated those functions in independent datasets, an 88,071-sample benchmark, and 7,148 genomes from the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) dataset ([Appendix](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-app1.pdf)). ### The Study Figure 1 ![Development and implementation of the lineage framework for Pathotypr, an alignment-free tool for assemblies and reads in genomic surveillance of tuberculosis. A\) Maximum-likelihood phylogeny of 26,813 Mycobacterium tuberculosis complex genomes, showing L1–L10 and A1–A4. B\) Selection of 2,145 lineage-defining markers from 5,638 candidate variants after excluding missense and homoplastic variants. SNP, single-nucleotide polymorphism.](https://wwwnc.cdc.gov/eid/images/26-1074-F1-tn.jpg) [Figure 1](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f1 "Figure 1"). Development and implementation of the lineage framework for Pathotypr, an alignment-free tool for assemblies and reads in genomic surveillance of tuberculosis. A) Maximum-likelihood phylogeny of 26,813 _Mycobacterium tuberculosis_ complex... To extract lineage phylomarkers, we obtained a maximum-likelihood phylogeny of 26,813 MTBC genomes and 609,003 polymorphic sites, which placed L1–L10 and A1–A4 within 1 evolutionary backbone and exposed the marked underrepresentation of certain lineages ([Figure 1](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f1), panel A). We then mapped substitutions to branches and selected single-nucleotide polymorphisms (SNPs) on the stem branch leading to each monophyletic lineage or clade. After removing 3,039 missense variants and 454 homoplastic nonmissense variants, 2,145 of 5,638 candidates remained as lineage-defining markers. A variable overlap of lineage markers occurred with other tools, ranging from no overlap to 698 (62.8%) with TB-Profiler ( [External Link](https://tbdr.lshtm.ac.uk)) and 1,750 (12.9%) with SNP-IT ( [External Link](https://github.com/samlipworth/snpit)) ([Appendix](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-app1.pdf) Table 1). Pathotypr lineage-defining markers comprised 33%–46% of total SNPs across lineages, supporting the filtering approach ([Figure 1](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f1), panel B). However, the small number of genomes for L7–L10 and A1–A3 might be missing diversity not sampled to date. Figure 2 ![Schematic overview of the 5 modules grouped into 3 functional categories—machine learning, genotyping, and utilities—in developing Pathotypr, an alignment-free tool for assemblies and reads in genomic surveillance of tuberculosis. A\) Workflow and data flow across the 5 modules. The Train and Predict modules perform machine-learning-based lineage classification; Split-FASTQ and Classify provide alignment-free genotyping from sequencing reads or genome assemblies; and Match identifies the closest reference genome using k-mer containment. B\) Summary of the input data, analytical method, and primary output of each module. SNP, single-nucleotide polymorphism.](https://wwwnc.cdc.gov/eid/images/26-1074-F2-tn.jpg) [Figure 2](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f2 "Figure 2"). Schematic overview of the 5 modules grouped into 3 functional categories—machine learning, genotyping, and utilities—in developing Pathotypr, an alignment-free tool for assemblies and reads in genomic surveillance of tuberculosis. A)... Pathotypr uses 3 modules for lineage assignment ([Figure 2](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-f2)). The Predict module assigns lineages to genome assemblies using the _k_ -mer–based Random Forest algorithm generated by the Train module, and the Classify and Split-FASTQ modules assign lineages on the basis of SNP markers to assemblies (Classify) and raw sequencing reads (Split-FASTQ). First, we compared lineage assignments by Predict and Classify results with the phylogenetic placement of 498 complete RefSeq genomes: Predict matched the phylogenetic lineage in all 498 genomes, and Classify assigned 497. The unassigned genome did not contain the complete marker path required for a call, which could reflect assembly gaps or errors at diagnostic marker positions. Next, Predict and Classify successfully returned the same major-lineage assignment for all 254 chromosome-level assemblies that were not used to train the Random Forest. Finally, Split-FASTQ assigned all 162 clinical read datasets to the expected lineage. Those results show the specific role of each module: Classify provides traceable SNP-based assignments, Predict offers an alternative when the marker path is incomplete, and Split-FASTQ extends the lineage framework directly to short reads ([Appendix](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-app1.pdf), Table 2, Figure 1). Because the reference choice for read mapping can affect genome coverage and variant calls, we developed and tested Pathotypr’s fourth module, Match Ref, a lineage-aware reference selection ([_9_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r9 "9"),[_10_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r10 "10")). Across 162 clinical read sets, mapping to the reference selected by Pathotypr reduced nonfixed SNPs by a median of 89% relative to the MTBC ancestral reference and increased median coverage breadth by 0.84 percentage points ([_10_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r10 "10")) ([Appendix](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-app1.pdf) Figure 2). To test whether the new lineage framework could be applied to surveillance-scale datasets, we typed 88,071 UShER-TB samples ([_11_](https://wwwnc.cdc.gov/eid/article/32/11/26-1074_article#r11 "11"); K.M. Karim et al., unpub. data, [External Link](https://www.medrxiv.org/content/10.1101/2025.07.22.25331806v1)) with Pathotypr module Split-FASTQ (≈24 hours using 4 threads). To compare typing results with TB-Profiler, we excluded 1,290 genomes with multiple lineage calls from the comparison. Although only 698 (62%) of the TB-Profiler lineage markers were shared with Pathotypr, both tools assigned the same major lineage to every directly comparable sample ([Appendix](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-app1.pdf) Table 3). Pathotypr additionally identified 2 L10, 6 A1, and 23 A2 isolates that were not identified with the TB-Profiler marker set. TB-Profiler misclassified 1 L10 isolate as La1.5/_M. bovis_ and left the other L10 genome and A1 and A2 genomes unassigned. Compared with 7 other MTBC genomic analysis tools, Pathotypr uniquely combined rapid _k_ -mer/Random Forest classification (≈1 s), support for both FASTA and FASTQ file type inputs, drug-resistance prediction, desktop and command-line interfaces, and the broadest lineage coverage (L1–L10 and A1–A4), and still maintained sublineage resolution ([Table](https://wwwnc.cdc.gov/eid/article/32/11/26-1074-t1)). Figure 3 ![Resistance-informed surveillance analysis of 7,148 CRyPTIC isolates in developing Pathotypr, an alignment-free tool for assemblies and reads in genomic surveillance. A\) Performance of WHO confidence group 1–2 resistance markers compared with phenotypic drug-susceptibility results. B\) Number and resistance profiles of descendant isolates by source–destination pair. C\) Probable introductions from non-Europe ancestral states into Germany, Italy, and Ukraine. AMI, amikacin; BDQ, bedaquiline; CFZ, clofazimine; DLM, delamanid; DR, drug resistance; EMB, ethambutol; ETH, ethionamide; INH, isoniazid; KAN, kanamycin; LEV, levofloxacin; LZD, linezolid; MDR, multidrug-resistant; MXF, moxifloxacin; NPV, negative predictive value; pan-S, pansusceptible; PPV, positive predictive val
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