Biology subjects

Shaw, J.

Publications and source records attributed to Shaw, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Processing strategies for improving cortical thickness correspondence between low-field and high-field MRI in young people

Portable low-field MRI systems are a promising complement to conventional high-field systems, enabling broader access to MRI. However, correspondence in cortical thickness estimates between low- and high-field MRI in young people remains limited despite its importance for neurodevelopment and psychopathology. To evaluate how multiple low-field image processing approaches improve cortical thickness correspondence with high-field MRI in a large sample of young individuals, we collected T1-weighted (T1w) and T2-weighted (T2w) data using both low-field (64mT) and high-field (3T) MRI within one week of each other from a community sample of young people. We applied deep learning-based super-resolution methods (SynthSR v1 and SynthSR v2) followed by recon-all, or other reconstruction methods (recon-all-clinical and recon-any), to low-field data acquired across multiple sequences (T1w and T2w) and orientations (axial, coronal, sagittal, and multi-orientation average), with and without resampling and/or co-registration. We assessed global, lobar, and regional cortical thickness correspondence with 3T MRI measures based on the Desikan-Killiany atlas using Pearson and intraclass correlations. We compared pipelines using Steiger's Z-tests and Fisher's Z-tests. A total of 150 individuals (mean age, 18.63+/-5.07; 80 female) were included. We observed the highest global correspondence with recon-all-clinical applied to coronal T1w images (r=0.40, pFDR=3.02e-05), which significantly exceeded the best global correspondence in our previous study (Fisher's Z=1.99, p=0.047). At the lobar and regional levels, multi-orientation T2w images processed with recon-all-clinical showed the highest correspondence across the greatest number of regions (4/12 lobes; 13/68 regions). This pipeline showed the highest correspondence and largest improvements relative to recon-all alone in frontal, cingulate, and temporal regions, including the right pars triangularis (r=0.52, pFDR=4.78e-11; Steiger's Z=4.78, pFDR=4.25e-06), right caudal anterior cingulate (r=0.47, pFDR=3.83e-09; Steiger's Z=5.46, pFDR=1.32e-07), and left parahippocampal regions (r=0.58, pFDR=2.98e-14; Steiger's Z=5.17, pFDR=6.01e-07). We observed significantly improved cortical thickness correspondence in low-field MRI in young people relative to recon-all alone and our prior work. The recon-all-clinical pipeline yielded moderate correspondence, particularly in frontal, cingulate, and temporal regions. Our results demonstrate a methodological improvement in the use of low-field MRI for assessing cortical thickness in young people, providing a quantitative benchmark for current tools in this area.

neuroscience↗

bioRxiv2026

Sensitive long-read amplicon sequence variant recovery with savont

Long-read amplicon sequencing can profile longer sequences compared to short reads, but recovering amplicon sequence variants (ASVs) is a challenge for long, noisy reads. We present savont, an algorithm for recovering ASVs from modern long-read amplicons with mean accuracy [≥] 98%, including Oxford Nanopore Technologies (ONT) R10.4 and PacBio HiFi reads. Savont requires 5 to 16 times lower sequencing depth for full-length 16S rRNA ONT reads than previous methods and generates up to 95 times more ASVs for complex environments. Savont makes ASV-based analysis from shallow and noisy long-read amplicon sequencing feasible.

bioinformatics↗

bioRxiv2025

Organoid Pharmacotyping of Pancreatic Cancer Enables Functional Precision Oncology and Drug Repurposing

PurposePancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, with limited benefit from current cytotoxic regimens and poor predictive value of genomics alone. Patient-derived organoids (PDOs) represent a promising platform for functional precision oncology, yet systematic pharmacotyping of genomically annotated PDAC PDOs remains sparse. Experimental DesignWe established a clinically annotated panel of ten treatment-naive PDAC PDOs spanning well-, moderately-, and poorly differentiated tumors. PDOs were evaluated for morphologic and genomic fidelity and screened against 1,813 clinically relevant small molecules in a high-throughput 384-well format. Drug sensitivities were quantified at the compound and drug-family levels and integrated with histologic grade, pathway-level mutational profiles, and available clinical treatment information. ResultsPDOs preserved hallmark tumor features, including glandular organization and subclonal mutational architecture. Pharmacotyping revealed both shared and subtype-specific vulnerabilities. Classical (well/moderately differentiated) PDOs showed enriched mutations in DNA repair, mitotic spindle, and chromatin-regulatory pathways and were preferentially sensitive to topoisomerase inhibitors, microtubule poisons, and HDAC inhibitors. In contrast, basal (poorly differentiated) PDOs displayed coordinated defects in mitochondrial function, vesicle trafficking, and ubiquitin-mediated proteostasis, at the pathway level, that conferred a previously unrecognized vulnerability to cardiac glycosides. Sensitivities to standard PDAC agents were heterogeneous across models, underscoring the limited predictive value of genotype alone and the need for functional drug testing. ConclusionsThis integrated genomic and pharmacologic analysis demonstrates that PDO pharmacotyping identifies biologically grounded, actionable vulnerabilities in PDAC, including novel therapeutic opportunities in basal, chemo-resistant tumors. These findings support PDO-guided functional profiling as a clinically relevant platform for refining drug selection and expanding treatment options for patients with PDAC. SignificancePDAC is dominated by chemoresistance and lacks reliable genomic predictors of therapy response. By integrating high-throughput drug screening with mutation-informed pathway analysis in patient-derived organoids, we identify differentiation-linked therapeutic liabilities, including a previously unrecognized vulnerability to cardiac glycosides in basal PDAC. These results highlight PDO pharmacotyping as a powerful functional complement to genomics for guiding treatment selection in pancreatic cancer.

cancer biology↗

bioRxiv2025

Cross-chemical and cross-species toxicity prediction: benchmarkingand a novel 3D-structure-based deep learning model

Prediction of a compounds toxicity is a key step toward realizing animal-free testing of chemical compounds. Recent advances have yielded significant progress in computational toxicity prediction, including machine learning methods that utilize chemical fingerprints and deep learning-based latent representations. However, challenges remain, primarily due to the lack of clean training datasets and the inconsistent model performance. To address these challenges, we curated a comprehensive dataset of aquatic toxicity from seven data sources, which contains 50,603 records for 5,889 compounds across 2,285 different species, much larger than similar datasets used in previous studies. We also developed tox-learn, a Python library featuring tools for automated dataset cleaning, machine learning methods and performance evaluation. The library places special emphasis on avoiding overestimation of prediction accuracy caused by improper train-test data splitting. Based on this toolbox, we benchmarked various predictive models using different train-test splitting strategies on the curated dataset. Our results showed that the choice of machine learning method, molecular fingerprint, and train-test splitting strategy all significantly affect performance. We demonstrated that incorporating species information generally improved predictions, although the degree of improvement depended on how this information was represented. In addition, we developed a new 3D structure-based deep-learning model, 3DMol-Tox, which achieves regression accuracy comparable to the best 2D-structure based model (GPBoost) while exhibiting consistently higher within-one-bin (W1B) classification accuracy. Finally, we analyzed the impact of different train-test splitting strategies and provide recommendations based on our benchmarking, such as using structure-aware splitting to mitigate information leakage, a common issue that inflates reported model performance.

bioinformatics↗

bioRxiv2025

A genomic atlas of the human gut virome elucidates genetic factors shaping host interactions

Viruses are key modulators of human gut microbiome composition and function. While metagenomic sequencing has enabled culture-independent discovery of gut bacteriophage diversity, existing genomic catalogues suffer from limited geographic representation, sparse taxonomic classification, and insufficient functional annotation, hindering detailed investigation into phage biology. Here, we present the Unified Human Gastrointestinal Virome (UHGV), a collection of 873,994 viral genomes from globally diverse populations that addresses these limitations. UHGV provides high-quality virome references with extensive host predictions, comprehensive functional annotations, protein structures, a classification framework for comparative analysis, and a web portal to facilitate data access. Using UHGV to profile worldwide metagenomes, we found that host range breadth is strongly associated with phage prevalence. Additionally, we identified diversity-generating retroelements and DNA methyltransferases as key factors enabling phage populations to access diverse hosts, revealing how specific genomic features contribute to global phage distribution patterns. UHGV is available at http://uhgv.jgi.doe.gov.

genomics↗

bioRxiv2025

High-resolution metagenome assembly for modern long reads with myloasm

Long-read metagenome assembly promises complete genomic recovery from microbiomes. However, the complexity of metagenomes poses challenges. We present myloasm, a metagenome assembler for PacBio HiFi and Oxford Nanopore Technologies (ONT) R10.4 long reads. Myloasm uses polymorphic k-mers to construct a high-resolution string graph and then leverages differential abundance for graph simplification. On real-world ONT metagenomes, myloasm assembled three times more complete circular contigs than the next-best assembler. Myloasm can make ONT and HiFi comparable for assembly: for a jointly sequenced gut metagenome, myloasm with ONT assembled more complete circular genomes than any assembler with HiFi. Myloasm recovers previously inaccessible within-species diversity; we recovered six complete Prevotella copri single-contig genomes from a gut metagenome and eight complete TM7 (Saccharibacteria) contigs with >93% similarity from an oral metagenome. With this improved resolution, we resolved two 98% similar ermF antibiotic resistance genes spreading through distinct strain-specific mobile genetic elements in a human gut.

bioinformatics↗

bioRxiv2024

Structure and dynamics of Burkholderia pseudomallei OXA-57, a distinctive low-efficiency class D β-lactamase with carbapenem-hydrolyzing activity

The Gram-negative bacterium Burkholderia pseudomallei causes the severe disease melioidosis. {beta}-Lactams, including carbapenems, are the primary treatment, but are susceptible to chromosomal {beta}-lactamases, including the class D enzyme OXA-57. Here we show recombinant OXA-57 is active towards penicillins and first-generation cephalosporins, slowly hydrolyzes carbapenems including imipenem and meropenem, but is inactive towards oxyimino-cephalosporins (e.g., ceftazidime). Unlike many OXA enzymes, OXA-57 is sensitive to the mechanism-based inhibitor clavulanic acid, but less so to the diazabicyclooctane avibactam and not to the cyclic boronate vaborbactam. Crystal structures of covalent OXA-57:avibactam and OXA-57:meropenem complexes reveal limited hydrogen- bonding interactions with bound ligands. In molecular dynamics simulations, bound meropenem is mobile, while the water necessary for deacylation has only limited active site access. These observations are consistent with the low level of meropenem turnover, supporting proposals that OXA {beta}-lactamases generally possess limited carbapenemase activity, and highlight the potential importance of OXA-57 in B. pseudomallei {beta}-lactam resistance. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/630153v1_ufig1.gif" ALT="Figure 1000"> View larger version (24K): org.highwire.dtl.DTLVardef@d635d7org.highwire.dtl.DTLVardef@168d67borg.highwire.dtl.DTLVardef@1d95733org.highwire.dtl.DTLVardef@1ebeabb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

devider: long-read reconstruction of many diverse haplotypes

Reconstructing haplotypes is important when sequencing a mixture of similar sequences. Long-read sequencing can connect distant alleles to disentangle similar haplotypes, but handling se-quencing errors requires specialized techniques. We present devider, an algorithm for haplotyping small sequences--such as viruses or genes--from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. On a synthetic Nanopore dataset containing seven HIV strains, devider recovered 97% of the haplotype content compared to 86% for the next best method while taking < 4 minutes and 1 GB of memory for > 8000x coverage. Benchmarking on synthetic mixtures of antimicrobial resistance (AMR) genes showed that devider recovered 83% of haplotypes, 23 percentage points higher than the next best method. On real PacBio and Nanopore datasets, devider recapitulates previously known results in seconds, disentan-gling a bacterial community with > 10 strains and an HIV-1 co-infection dataset. We used devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering 13 distinct haplotypes for a tet(Q) tetracycline resistance gene with > 18, 000x coverage and 6 haplotypes for a CfxA2 beta-lactamase gene. We found clear recombination blocks for these AMR gene haplotypes, showcasing deviders ability to unveil ecological signals for heterogeneous mixtures.

bioinformatics↗

bioRxiv2024

Fairy: fast approximate coverage for multi-samplemetagenomic binning

BackgroundMetagenomic binning, the clustering of assembled contigs that belong to the same genome, is a crucial step for recovering metagenomeassembled genomes (MAGs). Contigs are linked by exploiting consistent read coverage patterns across a genome. Using coverage from multiple samples leads to higher-quality MAGs; however, standard pipelines require all-to-all read alignments for multiple samples to compute coverage, becoming a key computational bottleneck. ResultsWe present fairy (https://github.com/bluenote-1577/fairy), an approximate coverage calculation method for metagenomic binning. Fairy is a fast k-mer-based alignment-free method. For multi-sample binning, fairy can be > 250x faster than read alignment and accurate enough for binning. Fairy is compatible with several existing binners on host and non-host-associated datasets. Using MetaBAT2, fairy recovers 98.5% of MAGs with > 50% completeness and < 5% incompleteness relative to alignment with BWA. Notably, multi-sample binning with fairy is always better than single-sample binning using BWA (> 1.5x more > 50% complete MAGs on average) while still being faster. For a public sediment metagenome project, we demonstrate that multisample binning recovers higher quality Asgard archaea MAGs than single-sample binning and that fairys results are indistinguishable from read alignment. ConclusionsFairy is a new tool for approximately and quickly calculating multi-sample coverage for binning, resolving a longstanding computational bottleneck for metagenomics.

bioinformatics↗

bioRxiv2024

Protein complex heterogeneity and topology revealed by electron capture charge reduction and surface induced dissociation

We illustrate the utility of native mass spectrometry (nMS) combined with a fast, tunable gas-phase charge reduction, electron capture charge reduction (ECCR), for the characterization of protein complex topology and glycoprotein heterogeneity. ECCR efficiently reduces the charge states of tetradecameric GroEL, illustrating Orbitrap m/z measurements to greater than 100,000 m/z. For pentameric C-reactive protein and tetradecameric GroEL, our novel device combining ECCR with surface induced dissociation (SID) reduces the charge states and yields more topologically informative fragmentation. This is the first demonstration that ECCR yields more native-like SID fragmentation. ECCR also significantly improved mass and glycan heterogeneity measurements of heavily glycosylated SARS-CoV-2 spike protein trimer and thyroglobulin dimer. Protein glycosylation is important for structural and functional properties and plays essential roles in many biological processes. The immense heterogeneity in glycosylation sites and glycan structure poses significant analytical challenges that hinder a mechanistic understanding of the biological role of glycosylation. Without ECCR, average mass determination of glycoprotein complexes is available only through charge detection mass spectrometry or mass photometry. With narrow m/z selection windows followed by ECCR, multiple glycoform m/z values are apparent, providing quick global glycoform profiling and providing a future path for glycan localization on individual intact glycoforms.

molecular biology↗

bioRxiv2024

Intrauterine Growth-Restricted Pregnant Rats, from Hypertensive Placental Ischemic Dams Display Preeclamptic-like Symptoms: A New Rodent Model of Preeclampsia

Preeclampsia (PE) is characterized by de novo hypertension (HTN) and is one of the primary causes of intrauterine growth restriction (IUGR). PE is associated with placental ischemia, decreased nitric oxide (NO) bioavailability, oxidative stress (OS), and organ damage in the kidneys and brain. This study aims to characterize a new model of PE using IUGR rats from hypertensive placental ischemic dams. It is hypothesized that pregnant IUGR rats from hypertensive placental ischemic dams will have elevated blood pressure (BP), OS, and organ damage. MethodsPregnant Sprague Dawley rats are divided into 2 groups: normal pregnant (NP) and reduced uterine perfusion pressure (RUPP) hypertensive placental ischemic dams. Offspring from NP and RUPP dams were mated at 10 weeks of age to generate pregnant IUGR (IUGR Preg; n=3-8) and pregnant CON (CON Preg; n=3-6) rats. BP and other markers of PE were evaluated during late gestation. ResultsPregnant IUGR rats had elevated BP and systemic OS, as demonstrated by higher trending 8-isoprostanes and lower circulating antioxidant capacity. Maternal body weight of pregnant IUGR rats and their pups weights were decreased, while the brains were enlarged. Brain OS was elevated, with a rise in hydrogen peroxide (H2O2) and heat shock protein 1 (HSP- 1), along with lower Manganese Superoxide Dismutase (MnSOD) and antioxidant capacity. ConclusionPregnant IUGR rats, born from hypertensive placental ischemic dams, have HTN and increased systemic and brain OS, with larger brain sizes and smaller pups. Pregnant IUGR rats exhibit an preeclamptic-like phenotype, which suggests a new epigenetic model of PE.

physiology↗

bioRxiv2024

Floria: Fast and accurate strain haplotyping in metagenomes

Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from microbiomes remains a key challenge. We introduce Floria, a novel method designed for rapid and accurate recovery of strain haplotypes from short and long-read metagenome sequencing data, based on minimum error correction (MEC) read clustering and a strain-preserving network flow model. Floria can function as a standalone haplotyping method, outputting alleles and reads that co-occur on the same strain, as well as an end-to-end read-to-assembly pipeline (Floria-PL) for strain-level assembly. Benchmarking evaluations on synthetic metagenomes showed that Floria is > 3x faster and recovers 21% more strain content than base-level assembly methods (Strainberry), while being over an order of magnitude faster when only phasing is required. Applying Floria to a set of 109 deeply sequenced nanopore metagenomes took <20 minutes on average per sample, and identified several species that have consistent strain heterogeneity. Applying Florias short-read haplotyping to a longitudinal gut metagenomics dataset revealed a dynamic multi-strain Anaerostipes hadrus community with frequent strain loss and emergence events over 636 days. With Floria, accurate haplotyping of metagenomic datasets takes mere minutes on standard workstations, paving the way for extensive strain-level metagenomic analyses. AvailabilityFloria is available at https://github.com/bluenote-1577/floria, and the Floria-PL pipeline is available at https://github.com/jsgounot/Floria_analysis_workflow.

bioinformatics↗

bioRxiv2024

RAPID HEMATOCRIT ESTIMATION USING A FOLD-CREASE INDUCED FAST FLOWING PAPER SENSOR

Increased evaporative losses and flow obstructions can present substantial impediments to current paper analytical devices ({micro}PADs) for efficient on-site testing of biological fluids. Strategic enhancements in wicking rates of paper may thereby counter these limitations and enable on-demand healthcare monitoring. Therefore, herein we have leveraged the features of paper fold-crease regions, for the very first time, and developed a novel fast-flowing platform using laser printing to accelerate fluid flow through paper. A series of extensive experiments have been conducted to optimize the design and maximize wicking rates of {micro}PADs for smaller liquid volumes, making it well-suited for analysing biofluids. The investigation delves into structural alterations within the creased regions, employing both static and dynamic force application strategies. A first-generation Washburn type model in excellent agreement with the experimental findings is developed, providing a comprehensive insight into the fundamental physics involved. Finally, the folded channels are utilized for a distance-based hematocrit sensor employing grade-1 filter paper at very low-cost, simplified fabrication, lesser sample volume and faster analysis. The findings of this work unveil a plethora of potentialities for employing paper and paper folds to develop affordable medical devices with advanced analytical functionalities, specifically tailored for the resource-constrained settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/577541v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@4ba7e4org.highwire.dtl.DTLVardef@fb7945org.highwire.dtl.DTLVardef@1d6df10org.highwire.dtl.DTLVardef@1ceb742_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Physical constraints during Snowball Earth drive the evolution of multicellularity

Molecular and fossil evidence suggest that complex eukaryotic multicellularity evolved during the late Neoproterozoic era, coincident with Snowball Earth glaciations, where ice sheets covered most of the globe. During this period, environmental conditions--such as sea water temperature and the availability of photosynthetically active light in the oceans--likely changed dramatically. Such changes would have had significant effects on both resource availability and optimal phenotypes. Here, we construct and apply mechanistic models to explore (i) how environmental changes during Snowball Earth and biophysical constraints generated selective pressures and (ii) how these pressures may have had differential effects on organisms with different forms of biological organization. By testing a series of alternate--and commonly debated--hypotheses, we demonstrate how multicellularity was likely acquired differently in eukaryotes and prokaryotes due to selective differences in the biophysical and metabolic regimes they experience: decreasing temperatures and resource-availability instigated by the onset of glaciations generated selective pressures towards smaller sizes in organisms in a diffusive regime and towards larger sizes in motile heterotrophs. These results suggest that changing environmental conditions during Snowball Earth glaciations gave multicellular eukaryotes an evolutionary advantage, paving the way for the complex multicellular lineages that followed.

evolutionary biology↗

bioRxiv2023

Metagenome profiling and containment estimation through abundance-corrected k-mer sketching with sylph

Profiling metagenomes against databases allows for the detection and quantification of mi-crobes, even at low abundances where assembly is not possible. We introduce sylph (https://github.com/bluenote-1577/sylph), a metagenome profiler that estimates genome-to-metagenome containment average nucleotide identity (ANI) through zero-inflated Poisson k-mer statistics, enabling ANI-based taxa detection. Sylph is the most accurate method on the CAMI2 marine dataset, and compared to Kraken2 for multi-sample profiling, sylph takes 10x less CPU time and uses 30x less memory. Sylphs ANI estimates provide an orthogonal signal to abundance, enabling an ANI-based metagenome-wide association study for Parkinsons disease (PD) against 289,232 genomes while confirming known butyrate-PD associations at the strain level. Sylph takes < 1 minute and 16 GB of RAM to profile against 85,205 prokaryotic and 2,917,521 viral genomes, detecting 30x more viral sequences in the human gut compared to RefSeq. Sylph offers precise, efficient profiling with accurate containment ANI estimation for even low-coverage genomes.

bioinformatics↗

bioRxiv2023

Acute myeloid leukemia stratifies as two clinically relevant sphingolipidomic subtypes

Acute myeloid leukemia (AML) is an aggressive disease with complex and heterogeneous biology. Although several genomic classifications have been proposed, there is a growing interest in going beyond genomics to stratify AML. In this study, we profile the sphingolipid family of bioactive molecules in 213 primary AML samples and 30 common human AML cell lines. Using an integrative approach, we identify two distinct sphingolipid subtypes in AML characterized by a reciprocal abundance of hexosylceramide (Hex) and sphingomyelin (SM) species. The two Hex-SM clusters organize diverse samples more robustly than known AML driver mutations and are coupled to latent transcriptional states. Using transcriptomic data, we develop a machine-learning classifier to infer the Hex-SM status of AML cases in TCGA and BeatAML clinical repositories. The analyses show that the sphingolipid subtype with deficient Hex and abundant SM is enriched for leukemic stemness transcriptional programs and comprises an unappreciated high-risk subgroup with poor clinical outcomes. Our sphingolipid-focused examination of AML identifies patients least likely to benefit from standard of care and raises the possibility that sphingolipidomic interventions could switch the subtype of AML patients who otherwise lack targetable alternatives. Key Points1.Sphingolipidomics separates acute myeloid leukemia (AML) patients and cell lines into two subtypes. 2.The subtype with low hexosylceramide and high sphingomyelin defines a new high-risk subtype with poor clinical outcomes.

systems biology↗

bioRxiv2023

Fast and robust metagenomic sequence comparison through sparse chaining with skani

Sequence comparison algorithms for metagenome-assembled genomes (MAGs) often have difficulties dealing with data that is high-volume or low-quality. We present skani (https://github.com/bluenote-1577/skani), a method for calculating average nucleotide identity (ANI) using sparse approximate alignments. skani is more accurate than FastANI for comparing incomplete, fragmented MAGs while also being > 20 times faster. For searching a database of > 65, 000 prokaryotic genomes, skani takes only seconds per query and 6 GB of memory. skani is a versatile tool that unlocks higher-resolution insights for larger, noisier metagenomic data sets.

bioinformatics↗

bioRxiv2022

Mora: abundance aware metagenomic read re-assignment for disentangling similar strains

BackgroundTaxonomic classification of reads obtained by metagenomic sequencing is often a first step for understanding a microbial community, but correctly assigning sequencing reads to the strain or sub-species level has remained a challenging computational problem. ResultsWe introduce Mora, a MetagenOmic read Re-Assignment algorithm capable of assigning short and long metagenomic reads with high precision, even at the strain level. Mora is able to accurately re-assign reads by first estimating abundances through an expectation-maximization algorithm and then utilizing abundance information to re-assign query reads. The key idea behind Mora is to maximize read re-assignment qualities while simultaneously minimizing the difference from estimated abundance levels, allowing Mora to avoid over assigning reads to the same genomes. On simulated diverse reads, this allows Mora to achieve F1 scores comparable to other algorithms while having less runtime. However, Mora significantly outshines other algorithms on very similar reads. We show that the high penalty of over assigning reads to a common reference genome allows Mora to accurately infer correct strains for real data in the form of short E. coli reads and long Covid-19 reads. ConclusionsMora is a fast and accurate read re-assignment algorithm that is modularized, allowing it to be incorporated into general metagenomics and genomics workflows. It is freely available at https://github.com/AfZheng126/MORA.

bioinformatics↗