Biology subjects

Treangen, T. J.

Publications and source records attributed to Treangen, T. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Min-frame transformation enables more sensitive viral genome alignment

MotivationMaximal unique matches (MUMs) are a fundamental primitive in genome comparison, where they serve as high-confidence anchors for downstream multiple genome alignment. However, because MUMs rely on exact string matching, their effectiveness degrades with increased genome divergence and larger sets of genomes, inhibiting their ability to recover long homologous regions and reducing the number of base pairs covered by the multiple genome alignment. Additionally, existing approaches that improve robustness to mutation, such as spaced seeds or translated alignment methods, introduce trade-offs in specificity, scalability, or computational complexity. MethodsTo address this gap, we introduce the Min-Frame Transformation (MFT), a deterministic encoding of nucleotide sequences to sequences over a transformed alphabet that preserves the coordinate structure of the original sequence. At each position, the MFT selects a k-mer from a local window according to a fixed global ordering and assigns it a character in the transformed alphabet via a predefined mapping. This process captures local sequence context and can mask the impact of mutations, increasing the likelihood that homologous regions remain detectable as exact matches. The resulting transformed sequences can be indexed using standard string data structures, such as suffix arrays and suffix trees, enabling efficient extraction of MUMs without modifying existing algorithms. ImpactThe MFT is a novel computational approach for improving the robustness of MUM-based seeding for genome alignment by producing longer and more contiguous matches that span a greater fraction of the genome, leading to improved alignment coverage and SNP recall. Altogether, these improvements have the potential to result in improvements for downstream viral genome analysis applications such as phylogenetic inference and transmission analysis. FundingTandy Warnow: NSF grant 2316233 Todd J. Treangen: NSF grants 2126387, 2239114, NIH grants U19-AI144297, P01-AI152999

bioinformatics↗

bioRxiv2025

bronko: ultrafast, alignment-free detection of viral genome variation

As viral sequencing datasets continue to grow, traditional alignment-based variant calling pipelines are becoming computationally prohibitive. To address these challenges, we developed bronko, an ultrafast alignment-free framework for detecting viral variation directly from sequencing data. The novel computational approach implemented in bronko allows scaling to massive viral sequencing datasets and has three key components: i) a locality-sensitive bucketing function to rapidly identify single-nucleotide polymorphisms (SNPs) relative to reference(s), ii) a direct k-mer count pseudo-mapping approach that approximates a pileup without alignment, and iii) a streaming-based sliding window outlier test to estimate baseline noise across the genome and precisely differentiate real minor variants from noise. Together, these components yield near-linear computational complexity with respect to sequencing depth, enabling bronko to process thousands of viral samples rapidly on modest hardware. Our results are threefold: 1) On simulated amplicon sequencing, bronko recovers variants with higher precision and comparable recall to existing tools while running up to one to three orders of magnitude faster; 2) bronko generates sequence alignments directly from sequencing data, with SNP content similar to that of whole-genome alignment while also running in a fraction of the time, and 3) applying bronko to longitudinal sequencing data from chronically infected SARS-CoV-2 patients revealed consistent patterns of intrahost diversification and adaptive mutations over time. Altogether, these results demonstrate bronkos potential as a scalable tool for large-scale viral genomic analyses, overcoming longstanding computational barriers for intrahost and interhost characterization of viral variation. Availabilitybronko is implemented in Rust and publicly available at https://github.com/treangenlab/bronko or via conda at https://anaconda.org/channels/bioconda/packages/bronko/overview. All results, evaluations, and other code used in this study are available at https://github.com/treangenlab/bronko-test.

bioinformatics↗

bioRxiv2025

Seqwin: Ultrafast Identification of Signature Sequences in Microbial Genomes

MotivationPolymerase chain reaction (PCR) enables rapid, cost-effective diagnostics but requires prior identification of genomic regions that allow sensitive and specific detection of target microbial groups, herein referred to as microbial signature sequences. We introduce Seqwin, an open-source framework designed to automate microbial genome signature discovery. Tens of thousands of microbial genomes are now available for a single species, limiting the application of existing manual and automated approaches for identifying signatures. Modern approaches that are capable of leveraging all available microbial genomes will ensure sensitive and accurate DNA signature identification and enable robust pathogen detection for clinical, environmental, and public health applications. ResultsSeqwin builds weighted pan-genome minimizer graphs and uses a traversal algorithm to identify signature sequences that occur frequently in target genomes but remain rare in non-targets. Unlike earlier tools that depend on strict presence or absence of sequences, Seqwin accommodates natural sequence variation and scales to very large genome collections. When applied to genomes from C. difficile, M. tuberculosis, and S. enterica, Seqwin recovered more high-quality signatures than alternative methods with lower computational burden. Seqwins analysis of nearly 15,000 S. enterica genomes yielded over 200 candidate signatures in 5 minutes. Seqwin provides an open-source solution for the long-standing need for scalable microbial signature discovery and diagnostic assay design. Availability and ImplementationSeqwin is freely available for academic use (https://github.com/treangenlab/Seqwin) and can be installed via Bioconda. Benchmarking datasets, outputs, and scripts are available on Zenodo https://doi.org/10.5281/zenodo.19176444. Contacttreangen@rice.edu, xw66@rice.edu Supplementary MaterialsProvided as separate PDF and data files.

bioinformatics↗

bioRxiv2025

Strainify: Strain-Level Microbiome Profiling for Low-Coverage Short-Read Metagenomic Datasets

MotivationStrain-level microbiome profiling has revealed key insights into microbial community composition and strain dynamics. However, accurate strain-level analysis remains challenging due to limited linkage information, ambiguous read mapping, and complicating factors such as genome similarity, sequencing depth, and community complexity. These challenges are especially pronounced for short-read metagenomic data when estimating the relative abundances of multiple strains, a task critical for genotype-phenotype association studies. ResultsTo address this gap, we present Strainify, which enables accurate strain-level abundance estimation from short-read metagenomes with as little as 1% genome coverage. Specifically, Strainify combines (1) identification of informative variants via core genome alignment, (2) filtering of confounding variants via a window-based test, and (3) maximum likelihood estimation of strain abundances. A Shannon entropy-weighted version of the model further improves robustness in noisy, low-coverage settings by downweighting sites with low information content. Across simulated communities of varying complexity, Strainify consistently outperformed existing approaches. On mock community sequencing data, Strainifys estimates aligned more closely with reference abundances. When applied to a longitudinal gut microbiome dataset, Strainify successfully recapitulated the reported temporal dynamics of Bacteroides ovatus strain groups, demonstrating its ability to recover biologically meaningful patterns from real-world metagenomes. Together, these results establish Strainify as a robust and versatile solution for accurate strain-level abundance estimation in short-read, low-coverage microbiome studies. AvailabilityThe Strainify code and results are available at: https://github.com/treangenlab/Strainify

bioinformatics↗

bioRxiv2025

Inter-tool analysis of a NIST dataset for assessing baseline nucleic acid sequence screening

Nucleic acid synthesis is a dual-use technology that can benefit fields such as biology, medicine, and information storage. However, synthetic nucleic acids could also potentially be used negligently and ultimately cause harm, or be used with malicious intent to cause harm. Thus, this technology needs to be appropriately safeguarded. Sequence screening is one component of a biosecurity protocol for preventing such harm and consists of differentiating Sequences of Concern (SOCs) from benign sequences that are not associated with pathogenicity or toxicity. There exist many fit-for-purpose tools that have been developed for DNA synthesis sequence screening. However, questions remain regarding their performance with respect to consistency of screening. To aid in determining if screening tools are harmonized in regard to baseline sequence screening, NIST constructed a test dataset based on current screening recommendations. NIST then sent blinded datasets to sequence screening tool developers for testing. Overall, there was a general agreement between the tools and NIST assignments of the sequences and all tools had a baseline performance of greater than 95% sensitivity and 97% accuracy. Disagreement on specific sequences largely arose from single tools and could be traced to differences in defining a SOC and/or methodological differences in screening algorithms.

bioinformatics↗

bioRxiv2024

Lightweight taxonomic profiling of long-read sequenced metagenomes with Lemur and Magnet

The advent of long-read sequencing of microbiomes necessitates the development of new taxonomic profilers tailored to long-read shotgun metagenomic datasets. Here, we introduce Lemur and Magnet, a pair of tools optimized for lightweight and accurate taxonomic profiling for long-read shotgun metagenomic datasets. Lemur is a marker-gene-based method that leverages an EM algorithm to reduce false positive calls while preserving true positives; Magnet is a whole-genome read-mapping-based method that provides detailed presence and absence calls for bacterial genomes. We demonstrate that Lemur and Magnet can run in minutes to hours on a laptop with 32 GB of RAM, even for large inputs, a crucial feature given the portability of long-read sequencing machines. Furthermore, the marker gene database used by Lemur is only 4 GB and contains information from over 300,000 RefSeq genomes. Lemur and Magnet are open-source and available at https://github.com/treangenlab/lemur and https://github.com/treangenlab/magnet.

bioinformatics↗

bioRxiv2024

Parsnp 2.0: Scalable Core-Genome Alignment for Massive Microbial Datasets

MotivationSince 2016, the number of microbial species with available reference genomes in NCBI has more than tripled. Multiple genome alignment, the process of identifying nucleotides across multiple genomes which share a common ancestor, is used as the input to numerous downstream comparative analysis methods. Parsnp is one of the few multiple genome alignment methods able to scale to the current era of genomic data; however, there has been no major release since its initial release in 2014. ResultsTo address this gap, we developed Parsnp v2, which significantly improves on its original release. Parsnp v2 provides users with more control over executions of the program, allowing Parsnp to be better tailored for different use-cases. We introduce a partitioning option to Parsnp, which allows the input to be broken up into multiple parallel alignment processes which are then combined into a final alignment. The partitioning option can reduce memory usage by over 4x and reduce runtime by over 2x, all while maintaining a precise core-genome alignment. The partitioning workflow is also less susceptible to complications caused by assembly artifacts and minor variation, as alignment anchors only need to be conserved within their partition and not across the entire input set. We highlight the performance on datasets involving thousands of bacterial and viral genomes. AvailabilityParsnp is available at https://github.com/marbl/parsnp

bioinformatics↗

bioRxiv2024

Reference-free Structural Variant Detection inMicrobiomes via Long-read Coassembly Graphs

Bacterial genome dynamics are vital for understanding the mechanisms underlying microbial adaptation, growth, and their broader impact on host phenotype. Structural variants (SVs), genomic alterations of 10 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to absence of clear reference genomes and presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing a single metagenome coassembly graph constructed from all samples in a series. The log fold change in graph coverage between subsequent samples is then calculated to call SVs that are thriving or declining throughout the series. We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, which is particularly noticeable as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between subsequent time and temperature samples, suggesting host advantage. Our innovative approach leverages raw read patterns rather than references or MAGs to include all sequencing reads in analysis, and thus provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial genome dynamics.

bioinformatics↗

bioRxiv2023

MethPhaser: methylation-based haplotype phasing of human genomes

The assignment of variants across haplotypes, phasing, is crucial for predicting the consequences, interaction, and inheritance of mutations and is a key step in improving our understanding of phenotype and disease. However, phasing is limited by read length and stretches of homozygosity along the genome. To overcome this limitation, we designed MethPhaser, the first method that utilizes methylation signals from Oxford Nanopore Technologies to extend SNV-based phasing. Across control samples, we extend the phase length N50 by almost 3-fold while minimally increasing the phasing error by [~]0.02%. Nevertheless, methylation signals have limitations, such as random signals on sex chromosomes or tissue purity. To assess the latter, we also applied MethPhaser on blood samples from 4 patients, still showing improvements over SNV-only phasing. MethPhaser further improves phasing across HLA and multiple other medically relevant genes, improving our understanding of how mutations interact across multiple phenotypes. MethPhaser is available at https://github.com/treangenlab/methphaser.

bioinformatics↗

bioRxiv2023

Ultra-high throughput mapping of genetic design space

Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements. However, because these approaches only interrogate short sequences, it remains challenging to perform high throughput (HT) assays on constructs containing combinations of multiple sequence elements arranged across multi-kb length scales. Overcoming this barrier could accelerate synthetic biology; by screening diverse gene circuit designs and learning "composition-to-function" mappings that reveal genetic part composability rules and enable rapid identification of behavior-optimized variants. Here, we introduce CLASSIC, a genetic screening platform that combines long- and short-read next-generation sequencing (NGS) modalities to quantitatively assess pools of constructs of arbitrary length containing diverse part compositions. We show that CLAS-SIC can measure expression profiles of >105 gene circuit designs (from 5-20 kb) in a single experiment in human cells. The resulting datasets can be used to train ML models that accurately predict circuit behavior across expansive circuit design landscapes, revealing part composability rules that govern circuit performance. Our work shows that by expanding the throughput of each design-build-test-learn (DBTL) cycle, CLASSIC enhances the pace and scale of synthetic biology and establishes an experimental basis for data-driven design of complex genetic systems.

synthetic biology↗

bioRxiv2023

SeqScreen-Nano: a computational platform for rapid, in-field characterization of previously unseen pathogens.

The COVID-19 pandemic forever underscored the need for biosurveillance platforms capable of rapid detection of previously unseen pathogens. Oxford Nanopore Technology (ONT) couples long-read sequencing with in-field capability, opening the door to real-time, in-field biosurveillance. Though a promising technology, streaming assignment of accurate functional and taxonomic labels with nanopore reads remains challenging given: (i) individual reads can span multiple genes, (ii) individual reads may contain truncated genes, and pseudogenes, (iii) the error rate of the ONT platform that may introduce frameshifts and missense errors, and (iv) the computational costs of read-by-read analysis may exceed that of in-field computational equipment. Altogether, these challenges highlight a need for novel computational approaches. To this end, we describe SeqSeqscreen-Nano, a novel and portable computational platform for the characterization of novel pathogens. Based on results from simulated and synthetic microbial communities, SeqScreen-Nano can identify Open Reading Frames (ORFs) across the length of raw ONT reads and then use the predicted ORFs for accurate functional characterization and taxonomic classification. SeqScreen-Nano can run efficiently in a memory-constrained environment (less than 32GB of RAM), allowing it to be utilized in resource-limited settings. SeqScreen-Nano can also process reads directly from the ONT MinlON sequencing device, enabling rapid, in-field characterization of previously unseen pathogens. SeqScreen-Nano (v4.0) is available on GitLab at: https://gitlab.com/treangenlab/seqscreen

bioinformatics↗

bioRxiv2023

Olivar: fully automated and variant aware primer design for multiplex tiled amplicon sequencing of pathogen genomes

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 2 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer SNPs overlapping with primers and predicted PCR byproducts. We also compared Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates ([~]90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluated Olivar on real wastewater samples and found that Olivar had up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available as a web application at https://olivar.rice.edu. Olivar can also be installed locally as a command line tool with Bioconda. Source code, installation guide and usage are available at https://github.com/treangenlab/Olivar.

bioinformatics↗

bioRxiv2021

Bakdrive: Identifying the Minimum Set of Bacterial Driver Species across Multiple Microbial Communities

Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully understand and control microbial communities. In this study, we present a novel approach for identifying driver species within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohns disease patients, uncovering driver species consistent with previous work. In summary, Bakdrive provides a novel approach for teasing apart microbial interactions. Bakdrive is open-source and available at https://gitlab.com/treangenlab/bakdrive

bioinformatics↗

bioRxiv2021

Rescuing Low Frequency Variants within Intra-Host Viral Populations directly from Oxford Nanopore sequencing data

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost, exemplified by well over a half of million ONT SARS-COV-2 datasets. Tracking low frequency intra-host variants has provided important insights with respect to elucidating within host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluated Variabel on both within patient and across patient paired Illumina and ONT datasets; our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.

bioinformatics↗

bioRxiv2021

Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

BackgroundLong-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hotspots reduces read alignment accuracy and impedes structural variant detection. FindingsWe tested our hypothesis by implementing a read mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via e.g. minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long read mapper (NGMLR). In support of our hypothesis, we show Vulcan improves the alignments for Oxford Nanopore Technology (ONT) long-reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read mapping methods alone. ConclusionsVulcan is the first long-read mapping framework that combines two distinct gap penalty modes, resulting in improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan

bioinformatics↗

bioRxiv2021

Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes

Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs" across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision. Squeegee is open-source and available at: https://gitlab.com/treangenlab/squeegee

bioinformatics↗

bioRxiv2021

Emu: Species-Level Microbial Community Profiling for Full-Length Nanopore 16S Reads

16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate of long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from one simulated data set and two mock communities prove Emu capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.

bioinformatics↗

bioRxiv2021

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

The COVID-19 pandemic has emphasized the importance of detecting known and emerging pathogens from clinical and environmental samples. However, robust characterization of pathogenic sequences remains an open challenge. To this end, we developed SeqScreen, which can accurately characterize short nucleotide sequences using taxonomic and functional labels, and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed pathogen characterization and is available for download at: www.gitlab.com/treangenlab/seqscreen

bioinformatics↗