TDKC (Target Distilled K-mer Classifier): Ultrafast and Memory-Efficient Sequence Classification for Target Pathogen Diagnostics.

Seungmo Lee, Vivek Agarwal, William O'Brien, Eleazar Eskin

Abstract readPreprint

In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Seungmo LeeDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA .ORCID 0009-0004-7831-8251

Vivek AgarwalDepartment of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA .

William O'BrienDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA .

Eleazar EskinDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA .ORCID 0000-0003-1149-4758

Funding

Expanding Swabseq sequencing technology to enable readiness for emerging pathogensR01AI177859 · NIAID · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, ELEAZAR ESKIN · 2023 to 2026

$2.3M

NIAID NIH HHS R01 AI177859

6 · The paper itself

Abstract

Metagenomic sequencing can identify pathogens from clinical samples without prior knowledge of the causative agent. Yet, as sequencing workflows scale to process thousands of multiplexed samples simultaneously, classifying these samples against massive reference databases creates a significant computational bottleneck. Furthermore, large-scale applications such as screening public sequence repositories remain computationally challenging. Existing metagenomic classifiers are designed for full-taxon classification, where the goal is to identify all organisms in a sample. However, many diagnostic applications focus on detecting a specific set of clinically relevant pathogens. This constraint can be exploited to significantly lower computational costs. Here we present TDKC (

Indexed as

alignment-free methodsclinical metagenomicsmemory-efficient algorithmsmetagenomic classificationpathogen detection

Identifiers

PMID42620161

DOI10.64898/2026.06.05.730319

PMCPMC13484499

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.