Biology subjects

Danko, D. C.

Publications and source records attributed to Danko, D. C..

13 recordsLinked to original sources

bioRxiv2026

BIOTIA-DX RESISTANCE Achieved the Best Antimicrobial Resistance Phenotype Prediction Accuracy at CAMDA 2026

We present BIOTIA-DX RESISTANCE (BDXR), our submission to the CAMDA 2026 AMR Challenge. This work extends our CAMDA 2025 submission [1] to a new set of six species-drug pairs and adds k-mer-based feature engineering (both targeted and whole-genome) for pairs where the 2025 gene-presence base model underperforms. BDXR achieved a mean accuracy of 86.1% across the six pairs on the CAMDA 2026 test set, ranking first on four pairs, tied for first on Streptococcus pneumoniae (penicillin), and second on Campylobacter jejuni (nalidixic acid); per-pair test accuracy ranged from 69.9% (C. jejuni, nalidixic acid) to 98.8% (S. pneumoniae, penicillin). We refer the reader to our 2025 preprint [1] for the underlying workflow, dataset curation, and clinical motivation; this preprint focuses on the results and methodological changes that are new in 2026.

microbiology↗

bioRxiv2025

XTree enables memory-efficient, accurate short and long sequence alignment to millions of genomes across the tree of life

XTree is a k-mer-based aligner enabling rapid, memory-efficient alignment of sequencing reads to whole-genome reference databases with up to millions of genomes. Here, we detail XTrees performance on short and long read sequencing data and demonstrate its high accuracy across diverse bacterial, viral, and eukaryotic genomes. Benchmarking demonstrates superior and/or comparable precision and recall over existing tools, with more scalable indexing and efficient memory mapping. We additionally provide pre-indexed databases, including (1) the Genome Taxonomy Database (versions r214-r226), (2) representative GenBank fungi and protozoan genomes and (3) the Pan-Viral-Compendium, a bespoke data resource spanning 6.6 million, quality-controlled, viral genomes.

bioinformatics↗

bioRxiv2025

BIOTIA-DX RESISTANCE Achieved the Best Antimicrobial Resistance Phenotype Prediction Accuracy at CAMDA 2025

We have developed BIOTIA-DX RESISTANCE (BDXR), a bioinformatic tool for predicting antimicrobial resistance (AMR) from whole genome sequencing of microbial isolates. BDXR achieved the best accuracy of any submission to the CAMDA 2025 AMR Challenge. This years challenge focused on predicting AMR phenotype which is a more complex problem than the detection of AMR marker genes, the focus of some prior years. BDXR achieved an overall F1 score of 89% on the training set and 84.1% on the challenge test set. Accuracy varied across the 9 species and drug pairs in the competition from an F1 score of 98.4% (Campylobacter jejuni, tetracycline) to 78.5% (Pseudomonas aeruginosa, cefatzidime). BDXR is based on curation of global datasets, machine learning-based predictions from input data, and highly stringent prepreprocessing of input data and databases.

microbiology↗

bioRxiv2024

Blackbird: structural variant detection using synthetic and low-coverage long-reads

MotivationRecent benchmarks of structural variant (SV) detection tools revealed that the majority of human genome structural variations (SVs), especially the medium-range (50-10,000 bp) SVs cannot be re-solved with short-read sequencing, but long-read SV callers achieve great results on the same datasets. While improvements have been made, high-coverage long-read sequencing is associated with higher costs and input DNA requirements. To decrease the cost one can lower the sequence coverage, but the current long-read SV callers perform poorly with coverage below 10x. Synthetic long-read (SLR) technologies hold great potential for structural variant (SV) detection, although utilizing their long-range information for events smaller than 50 kbp has been challenging. ResultsIn this work, we propose a hybrid novel integrated alignment- and local-assembly-based algorithm, Blackbird, that uses SLR together with low-coverage long reads to improve SV detection and assembly. Without the need for a computationally expensive whole genome assembly, Blackbird uses a sliding window approach and barcode information encoded in SLR to accurately assemble small segments and use long reads for an improved gap closing and contig assembly. We evaluated Blackbird on simulated and real human genome datasets. Using the HG002 GIAB benchmark set, we demonstrated that in hybrid mode, Blackbird demonstrated results comparable to state-of-the-art long-read tools, while using less long-read coverage. Blackbird requires only 5x coverage to achieve F1 scores (0.835 and 0.808 for deletions and insertions) similar to PBSV (0.856 and 0.812) and Sniffles2 (0.839 and 0.804) using 10x Pacbio Hi-Fi long-read coverage.

bioinformatics↗

bioRxiv2024

Forecasting Dengue Fever in Brazil Using Multimodal Data, Including Climate Data

Dengue fever is a major tropical disease transmitted by Aedes mosquitoes. Dengue affects more than 120 countries with highly variable year to year infection rates. Despite high variability, dengue has a clear relationship to climate factors and human demography. Global trends to higher temperatures and greater disorderly urban development are increasing the scale and scope of dengue risk. Dengue has complex human immunity with 4 known serotypes that make multiple infections possible. Accurate forecasting of dengue fever would allow for appropriate interventions and improved public health outcomes. We demonstrate, GeoSeeq Dengue, a forecasting model for dengue fever in Brazil. GeoSeeq Dengue predicts dengue outbreaks monthly in 5,570 Brazilian municipalities at 1, 3, and 6 months ahead of the outbreak. Model accuracy compares favorably to a historical baseline model, making it a promising model for informing public health response. We evaluate how different types of input variables effect model accuracy and explore how this model could be adapted to other countries. This model could inform public health responses to dengue including targeting vector control programs, public health education messaging, and the newly launched dengue vaccine rollout.

bioinformatics↗

bioRxiv2021

Ariadne: Barcoded Linked-Read Deconvolution Using de Bruijn Graphs

Synthetic Long Read (SLR) sequencing techniques such as USTs TELL-Seq, and Loop Genomics LoopSeq combine 3' barcoding with standard short-read sequencing to expand the range of linkage resolution from hundreds to tens of thousands of base-pairs. However, the lack of a 1:1 correspondence between a long fragment and a 3' unique molecular identifier (UMI) confounds the assignment of linkage between short-reads. We introduce Ariadne, a novel assembly graph-based SLR deconvolution algorithm, that can be used to extract single-species read-clouds from SLR datasets to improve the taxonomic classification and de novo assembly of complex populations, such as metagenomes.

genomics↗

bioRxiv2020

Genetic Evidence for Selective Transfer of Microbes Between the International Space Station and an Astronaut

Microbial transfer from the environment can influence a persons health, but relevant studies often have confounding variables and short durations. Here, we used the unique environment of the International Space Station (ISS) to track movement of microbes between an astronauts commensal microbiomes and their environment. We identified several microbial taxa, including Serratia proteamaculans and Rickettsia australis which appear to have been transferred from the ISS to the commensal microbiomes of the astronaut. Strains were matched at the SNP and haplotype-level, and notably some strains persisted even after the astronauts return to Earth. Some transferred taxa correspond to secondary strains in the ISS environment, suggesting that transfer may be mediated by evolutionary selection. Finally, we show evidence that the T-Cell repertoire of the astronaut changes to become more specific to environmental taxa, suggesting that continual microbial and immune monitoring can help guide spaceflight mission planning, health monitoring, and habitat design.

microbiology↗

bioRxiv2020

Host, Viral, and Environmental Transcriptome Profiles of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

molecular biology↗

bioRxiv2019

The Microbe Directory v2.0: An Expanded Database of Ecological and Phenotypical Features of Microbes.

The Microbe Directory (TMD) is a comprehensive database of annotations for microbial species collating features such as gram-stain, capsid-symmetry, resistance to antibiotics and more. This work presents a significant improvement to the original Microbe Directory (2018). This update adds 68,852 taxa, many new annotation features, an interface for the statistical analysis of microbiomes based on TMD features, and presents a portal for the broad community to add or correct entries. This update also adds curated lists of gene annotations which are useful for characterizing microbial genomes. Much of the new data in TMD is sourced from a set of databases and independent studies collating these data into a single quality controlled and curated source. This will allow researchers and clinicians to have easier access to microbial data and provide for the possibility of serendipitous discovery of otherwise unexpected trends.

microbiology↗

bioRxiv2019

The microbiomes of seven lichen genera reveal host specificity, a reduced core community and potential as source of antimicrobials

The High Andean Paramo ecosystem is a unique neotropical mountain biome considered a diversity and evolutionary hotspot. Lichens, which are complex symbiotic structures that contain diverse commensal microbial communities, are prevalent in Paramos. There they play vital roles in soil formation and mineral fixation. In this study we analyzed the microbiomes of seven lichen genera in two Colombian Paramos using 16S rRNA gene analyses and provide the first description of the bacterial communities associated with Cora and Hypotrachyna lichens. Paramo lichen microbiomes were diverse, and in some cases were distinguished based on the identity of the lichen host. The majority of the lichen-associated microorganisms were not present in all lichens sampled and could be considered transient or specialists. We also uncovered sixteen shared taxa that suggest a core lichen microbiome among this diverse group of lichens, broadening our concept of these symbiotic structures. Additionally, we identified strains producing compounds active against clinically relevant pathogens. These results indicate that lichen microbiomes from the Paramo ecosystem are diverse and host-specific but share a taxonomic core and can be a source of new bacterial taxa and antimicrobials.

microbiology↗

bioRxiv2019

Global Genetic Cartography of Urban Metagenomes andAnti-Microbial Resistance

We have created a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities across 3 years. This is the first systematic, worldwide study cataloging the urban microbial ecosystem. We identify taxonomically-defined microorganisms collected across three years. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics AMR markers, and novel genetic elements, including 10,928 viral, 1302 bacteria, and 2 archaea novel species. We identify 4,424 species of urban microorganisms and a consistent "core" of 31 species found in nearly all samples that is largely distinct from any human commensal microbiome. Profiles of AMR genes show geographic variation in type and density. Together, these results constitute a high-resolution, global metagenomic atlas, which enables the discovery of new genetic components, highlights potential forensic applications, and provides an essential first draft of the global AMR burden of the worlds cities.

microbiology↗

bioRxiv2019

Novel Algorithms for the Taxonomic Classification of Metagenomic Linked Reads

We present KrakenLinked, a metagenomic read classifier for Linked-Reads. We have formulated two algorithms for read classification of metagenomic samples using linked reads: tree pruning and taxa promotion. Tree pruning improves specificity while taxa promotion improves sensitivity. Used together the algorithms improve taxonomic classification of linked-reads compared to short reads, particularly reducing false identification of taxa. We have implemented these algorithms as functions in KrakenUniq which we make available as KrakenLinked.

bioinformatics↗

bioRxiv2017

Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Metagenomics

Emerging Linked-Read technologies (aka Read-Cloud or barcoded short-reads) have revived interest in standard short-read technology as a viable way to understand large-scale structure in genomes and metagenomes. Linked-Read technologies, such as the 10X Chromium system, use a microfluidic system and a set of specially designed 3 barcodes (aka UIDs) to tag short DNA reads which were originally sourced from the same long fragment of DNA; subsequently, these specially barcoded reads are sequenced on standard short read platforms. This approach results in interesting compromises. Each long fragment of DNA is covered only sparsely by short reads, no information about the relative ordering of reads from the same fragment is preserved, and typically each 3 barcode matches reads from 2-20 long fragments of DNA. However, compared to long read platforms like those produced by Pacific Biosciences and Oxford Nanopore the cost per base to sequence is far lower, far less input DNA is required, and the per base error rate is that of Illumina short-reads.\n\nThe use of Linked-Reads presents a new set of algorithmic challenges. In this paper, we formally describe one particular issue common to all applications of Linked-Read technology: the deconvolution of reads with a single 3 barcode into clusters that correspond to a single long fragment of DNA. We introduce Minerva, A graph-based algorithm that approximately solves the barcode deconvolution problem for metagenomic data (where reference genomes may be incomplete or unavailable). Additionally, we develop two demonstrations where the deconvolution of barcoded reads improves downstream results: improving the specificity of taxonomic assignments, and by improving clustering of related sequences. To the best of our knowledge, we are the first to address the problem of barcode deconvolution in metagenomics.

bioinformatics↗