Biology subjects

Nielsen, R.

Publications and source records attributed to Nielsen, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Error correction of SARS-CoV-2 genomic sequences using a phylogenetic prior

Genotype calling of sequencing data has improved over recent years thanks to technological and statistical breakthroughs. One such advance is the use of imputation-based genotyping methods in human genetics. For haploid and mostly non-recombining organisms, a natural equivalent is to leverage phylogenetic signal for imputation-based genotype calling. SARS-CoV-2 is one such case, and the use of improved genotyping methods in this species is particularly pertinent given the well-documented systematic problems with read alignment and assembly in rapidly evolving regions of its genome, motivating the need for statistically principled methods for genotype correction that complement existing community efforts to curate sequencing data. We present a statistical framework to combine phylogenetic prior probabilities with the information from aligned reads, and demonstrate its utility in correcting consensus calls made on short-read, viral sequencing data. We derive phylogenetically-informed posterior probabilities based on genome placement in a reference phylogeny, and read likelihoods calculated from machine-reported base quality scores. We further modify the prior at each site by estimating a maximum-likelihood scaling parameter jointly with reads at all other sites. We find that the phylogenetically-informed base calling method stays highly accurate across simulated depth and error conditions. In real datasets, the method vastly improves call rate while still maintaining similar accuracy as reference-based assembly methods. In problematic genomic regions, the phylogenetically-informed method confers both accuracy and call rate gains. Accuracy and call rate do not stratify with sequencing center and primer scheme used, suggesting broad applicability across sequencing technologies and potentially diverging sequences. Our results in real and simulated data demonstrate that phylogenetically-informed genotyping correction results in more complete sequences, achieving a higher call rate without compromising accuracy. In cases of low mean depth, sparse coverage, or difficult-to-sequence regions that may still retain a low call rate, the method improves the accuracy of genotype calls.

bioinformatics↗

bioRxiv2026

Forensic likelihood ratios for shotgun sequencing data from DNA mixtures

Shotgun sequencing of DNA mixtures has the potential to become an important tool in forensic genetics, particularly for degraded samples or complex mixtures. However, the analysis of shotgun-sequenced mixtures is challenged by several confounding factors, including sequencing errors, population structure, the potential presence of relatives, linkage, and linkage disequilibrium. Additionally, the statistical methods currently available for such data are limited. Here, we develop likelihood ratios that summarize the evidence for the presence of a suspects DNA in a mixture, assuming genotypes of the suspect and the victim are available, while accounting for DNA from unknown contributors modeled either as general population contamination or as a single contaminating individual. We show that these likelihood ratios are not robust to model assumptions and can falsely support the presence of the suspects DNA when the true contributor is even a distant relative. We therefore propose a combined evidence score, {Lambda}: the likelihood ratio minimized over contamination models, reported only when the evidence favors the suspect over a hypothesized sibling by at least a factor of C (default C = 10) under at least one contamination model, and otherwise set to 1 (log {Lambda} = 0). In simulations based on 1000 Genomes data, {Lambda} retains nearly all power to detect true contributors at mixture proportions as low as 5% while eliminating the false positives produced by model mis-specification, population structure, and by untested relatives. The methods are implemented in the open-source program DNAMIXTURE, providing the forensic and population genetics community a resource for robustly identifying small individual contributions to a DNA mixture.

bioinformatics↗

bioRxiv2026

Recent effective population size of the snowy owl (Bubo scandiacus) tracks historical climate variation

Arctic ecosystems are undergoing rapid climate warming, providing an opportunity to examine molecular evolution under environmental stress. Here, we used whole-genome resequencing to assess population structure, genetic diversity, and recent demographic history in the snowy owl (Bubo scandiacus), a highly mobile Arctic apex predator classified as Vulnerable by the IUCN. We analysed genome-wide data from individuals sampled across the species' circumpolar range using a chromosome-level reference genome. Population genomic analyses revealed an absence of detectable population structure, indicating extensive gene flow and near-panmixia across continents. Genome-wide nucleotide diversity was low ({pi}= 2.47 x 10^(-4)), while runs of homozygosity were dominated by short tracts, indicating limited standing genetic variation despite little evidence for recent close inbreeding. Our demographic inference revealed a sustained decline in effective population size over the past ~1,600 years, a pronounced reduction coinciding with the Medieval Warm Period followed by a partial recovery during the subsequent cooler interval. This direct, measurable effect of past climate warming on effective population size raises concerns for the long-term survival of the snowy owl. Our results demonstrate that high connectivity and low levels of recent inbreeding have not necessarily buffered this species against climate-driven demographic erosion of genetic diversity. This study highlights the value of using whole-genome population genomics to inform genomic monitoring and conservation decision-making in rapidly changing environments.

genetics↗

bioRxiv2026

Migrations facilitated the cultural diffusion of megaliths in Neolithic Europe

Around 6700 BP (years before present), Neolithic Europe saw the emergence of megalithic monuments in northern France, followed by a rapid spread to the rest of the continent1. However, whether this was driven by the transmission of ideas (cultural diffusion) or the movement of people remains debated. To test whether this spread coincided with population movements, we reconstructed the population history of the European Neolithic ([~]6500-4800 BP) by analysing genomic data from 2963 ancient individuals, including 46 unpublished genomes from megalithic and related contexts. To assess the alternative scenario whereby megaliths spread through contact networks, as expected under cultural diffusion, we integrated archaeological evidence with radiocarbon chronologies to reconstruct past trading networks. We identify northern France, where megaliths first emerged, as a zone of genetic admixture and cultural influence from the two major early Neolithic divisions: the Cardial of the Western Mediterranean and the Linear Pottery traditions of central Europe. From this admixed population, we uncover several migrations associated with the formation of the Funnel Beaker Culture in north-central Europe and the neolithisation of Scandinavia and the British Isles. We find that megaliths were spread exclusively within regions impacted by Cardial-related genetic ancestry, but only in the British Isles and Ireland did their emergence coincide with the time of the migration. The temporal delay between the migrations and the spread of megaliths elsewhere reveals that neither population movements nor cultural diffusion alone fully explains the geospatial distribution of the megalithic tradition. Rather, megaliths diffused across different Neolithic groups whose long-distance trade networks formed in the wake of earlier population movements. Our findings highlight the need to move beyond simplistic interpretations that present migrations or cultural diffusion as isolated events bringing technological and cultural innovations.

evolutionary biology↗

bioRxiv2026

Estimating Divergence Times and Diversification Rates with the Unresolved Fossilized Birth-Death Process

Tip-dating using Fossilized Birth-Death (FBD) models offers a favorable alternative to conventional node-dating analyses of the evolutionary timescale, bypassing various conceptual and technical difficulties. However, despite their increasing popularity in phylogenetic studies, their use has remained rather limited in taxonomic scope because of the difficulty in collecting morphological data, which is often required for tip-dating analyses, and computational intractability when a large number of fossils are present. Recent studies, however, suggest that FBD models can also be used in a principled way as priors for molecular clock analyses, even in the absence of morphological data, relying only on temporal information and given clade assignments. Heath et al. (2014) introduced an "unresolved" FBD model in which uncertainty in fossil placements is analytically integrated out when morphological data are unavailable, providing a potential solution to this problem. Here, we show that the topological enumeration scheme used by Heath et al. (2014) to marginalize over unresolved fossil attachments overcounts configurations in which multiple fossil taxa can be resolved as a clade, and we provide a correction to this factor, yielding a valid marginalized unresolved likelihood. We apply our implementation to empirical datasets of Cetacea, Emydidae, and Palaeodictyopterida, and show that our method converges and yields reasonable estimates within a reasonable amount of time, even when hundreds of fossils are present.

evolutionary biology↗

bioRxiv2026

DICAROS: Diffeomorphic Ancestral Shape Reconstruction on Phylogenies

Reconstructing ancestral morphologies on a phylogenetic tree is a central task in evolutionary morphometrics. Established reconstruction methods, including multivariate Brownian-motion approaches, rely on linear assumptions and do not directly model the correlations between landmarks within a shape, which can oversimplify the reconstructed morphology. The DICAROS method (Diffeomorphic Independent Contrasts for Ancestral Reconstruction of Shapes; Severinsen et al., 2026) instead fuses sibling shapes along branches with large-deformation diffeomorphic (LDDMM) landmark dynamics that model these correlations, so that ancestors remain on the shape manifold. DICAROS was shown to outperform ordinary least-squares, Brownian-motion, and penalized-likelihood reconstruction, particularly on non-symmetric trees. The dicaros package repackages that pipeline as a documented, pip-installable tool that runs on arbitrary landmark datasets from a single command. It handles 2D and 3D landmarks, Newick and NEXUS trees, a choice of Euclidean or Frechet species means, optional anchor-based alignment, and tips backed by a single specimen, and it returns the reconstructed shapes for all nodes together with the tree relabelled at its internal nodes. We demonstrate dicaros on two new datasets: a 2D leaf dataset (217 species) and a 3D guenon skull dataset (22 species).

evolutionary biology↗

bioRxiv2026

Beyond infinite sites: Generalized ABBA-BABA statistic for deeper phylogenies

The Pattersons D statistic detects gene flow from ABBA-BABA site patterns, but its biallelic site patterns fail under deeper divergences where multiple hits cause false positives. We propose two extensions, D+ and D*. Both incorporate multiallelic site patterns to reduce saturation bias under JC and F84 model. Simulations show that D+ and D* both remain correctly null under all conditions and detect gene flow effectively, with distinct advantages: D+ guarantees non-negativity of the denominator, while D* provides greater robustness when mutation rates vary across genomic regions. The source code and binary files are publicly available at https://github.com/chaoszhang/ASTER.

bioinformatics↗

bioRxiv2026

Molecular Clock Dating of Ancient Environmental DNA Reveals Damage Beyond Deamination

Ancient environmental DNA (aeDNA) from permafrost, lake, cave, and marine sediments provides a rich source of genetic data that captures broad perspectives of past biodiversity. Accurate dating is crucial for discovering ecologically relevant patterns from aeDNA, and molecular clock dating would allow for sample ages to be estimated from the recovered genetic material itself instead of the geological components. However, the fragmented and damaged nature of short-read ancient DNA (aDNA) from multiple taxonomic sources poses significant challenges and has limited this dating approach for aeDNA. Here we developed ratePlacer, a phylogeny-based method for analyzing aeDNA that can combine information from many short reads in a sample while accounting for DNA damage to provide maximum likelihood estimates of sample ages. Simulations demonstrate that ratePlacer accurately dates samples even under the fragmented, damaged conditions characteristic of aeDNA and outperforms Bayesian tip-dating approaches for taxonomically mixed samples commonly found in aeDNA. Yet age estimates from re-dating Kap Kobenhavn varied across taxa, highlighting the difficulty of molecular clock dating in aeDNA. This dating also revealed elevated G [->] T and C [->] A mismatches consistent with oxidative damage. These patterns reveal aDNA damage beyond deamination and that remains understudied, suggesting that aeDNA should be carefully evaluated in genomic and evolutionary analyses. The new dating method, ratePlacer, extends molecular clock dating of aDNA from single-specimen to pooled environmental DNA data, where traditional methods struggle.

bioinformatics↗

bioRxiv2026

Megalin deficiency perturbs retinal homeostasis and impairs cathepsin D processing and phagosome-lysosome maturation in the retinal pigment epithelium

The multiligand endocytic receptor, megalin (LRP2), is expressed in the retinal pigment epithelium (RPE) and patients lacking the receptor develop high myopia. Despite its established role in retinal development, the contribution of megalin to retinal homeostasis in the normally developed/mature eye remains poorly understood. Here, we investigated megalin function using an inducible knockout mouse (KO) model and human iPSC-derived RPE with megalin knockdown (KD) to distinguish post-developmental homeostatic functions from developmental effects. In vivo, megalin ablation caused progressive retinal degeneration and visual impairment, with morphological abnormalities in the RPE but no changes in myopia-associated ocular phenotypes including axial length and intraocular pressure. Proteomic profiling of megalin-KO RPE revealed reduction of autophagy-related proteins. In line with this, megalin deficiency was associated with accumulation of pro-cathepsin D, and perturbed rhodopsin turnover. This was supported in vitro, where trafficking of photoreceptor outer segment (POS) containing phagosomes to lysosomes was reduced, suggesting disturbed phagosome maturation. Megalin KD did not measurably impair initial uptake of POS discs, but delayed rhodopsin degradation, indicating defective post-ingestion processing. Together, these findings establish megalin as a key regulator of retinal homeostasis in the mature eye by controlling phagosome-lysosome fusion in the RPE and suggest that megalin dysfunction contributes to slowly progressive retinal degeneration. This positions megalin as a potential therapeutic target in lysosomal degenerative diseases in the retina.

Cell Biology↗

bioRxiv2026

A modular Bayesian framework for inferring transmission networks from polyclonal infections, with application to Plasmodium falciparum

MotivationMolecular surveillance and infectious disease transmission network reconstruction can provide compelling evidence for estimating public-health quantities that are difficult to observe directly, including importation, source-sink structure, and differences in onward transmission across locations or intervention strata. These quantities can be expressed as functions of the underlying transmission network, but individual transmission events are rarely observed and many networks may be consistent with the same data. Existing transmission network reconstruction methods leveraging genetic data are often built for settings in which each infection has one dominant source, one representative haplotype, and mutation-driven genetic divergence along transmission chains. These assumptions are poorly matched to polyclonal infections, in which hosts carry multiple genetically distinct clones and recipient infections may reflect contributions from multiple sources. Such infections are common in malaria, tuberculosis, HIV, and many parasitic infections. Methods are needed that can accommodate these data. ResultsWe present a modular Bayesian framework for estimating directed transmission on sampled cases, where an infection may have no sampled parent, one parent, or several parents, including sources outside the observed panel. Pathogen-specific modules supply likelihoods over candidate parent sets and connect to shared inference that yields marginal directed edge probabilities, posterior mean out-degree, and inclusion probabilities for unobserved parents. We demonstrate our framework with Plasmotrack, a transmission network model for Plasmodium falciparum that uses targeted amplicon sequencing data. We implemented these components with a per-locus allele-mixture transmission likelihood, an amplicon genotyping error model, and data augmentation allowing for unobserved parents. Simulations from a biologically informed generative model, under which the inferential per-locus allele-mixture likelihood is misspecified, showed recovery of aggregate network summaries including mean outdegree and mean unobserved-source inclusion, alongside high precision and recall for detecting directed transmission. Other pathogens can reuse the same modular composition after substituting transmission and observation likelihoods. AvailabilityThe Plasmotrack software and documentation are available at https://github.com/eppicenter/plasmotrack. Source code and example datasets are provided under an open-source license. Contactmaxwell.murphy@ucsf.edu

bioinformatics↗

bioRxiv2026

STEM-LM: Spatio-Temporal Ecological Modeling via Masked Language Model for Joint Species Distribution

Joint species distribution models (JSDMs) are central to biodiversity forecasting and conservation decision-making. As ecological datasets grow in size, dimensionality, and spatio-temporal resolution, there is a need for flexible yet scalable JSDMs tailored to large-scale species observation data. Recent advances in masked language modeling for text and genomics suggest a natural alternative: by treating each species presence or absence as a token, and a sites species assemblage together with its spatio-temporal and ecological covariates as a sentence, we can learn joint co-occurrence structure by reconstructing masked species from their neighboring sites. We propose STEM-LM1, a Transformer-based JSDM that frames joint species distribution modeling as masked language modeling. By varying the masking rate during training, a single trained model supports both purely spatiotemporal/ecological prediction and conditioning on arbitrary subsets of observed species for joint co-occurrence inference at a given site. On a North American butterfly and a global plant distribution dataset, STEM-LM performs better or on par with other statistical and deep-learning based methods in terms of discriminative ranking, while producing substantially better rank-calibrated occurrence probabilities. Utilizing partial species observations at the same site greatly enhances prediction performance.

ecology↗

bioRxiv2025

Robustness of Ancestral Recombination Graph Inference Tools to Phasing Errors

Ancestral Recombination Graphs (ARGs) are fundamental population genetic structures that encode the genealogical history of a sample of haplotypes along the genome. They have recently received substantial attention as they can be used to provide accurate estimates of population history, form the foundation for selection scans, provide time-labeled estimates of changes in mutation rates, and more. ARGs can be inferred using multiple different methods, but these methods all rely on computational haplotype phasing. Previous studies have shown that the accuracy of haplotype phasing can be low when done in small or medium-sized samples, which are typical for most non-model organisms. Conventional wisdom would, therefore, suggest that ARG inferences are of limited utility in most non-model organisms. To test this assumption, we benchmark the robustness of four ARG inference methods -- Relate, SINGER, Threads, and tsinfer+tsdate -- to phasing errors. Their performance with imperfect phasing was tested under simple and realistic demographic models, and evaluated in terms of estimated pairwise coalescence times, counts of inferred recombination breakpoints, and estimated branch-length-based diversity. We observe only a slight degradation in performance when using computational phasing across all four methods and all tested demographic scenarios, demonstrating a surprising robustness of ARG inference methods to phasing inaccuracies. These findings support broader application of ARG inference in settings where large reference panels are not available.

genetics↗

bioRxiv2025

Exome Sequencing and Allele Dosage Analysis of Coast Redwood, a Hexaploid Conifer, Identifies a Major Population Break South of San Francisco Bay

1The coast redwood (Sequoia sempervirens) is a long-lived, hexaploid conifer of high ecological, cultural, and economic value whose range has been greatly reduced by historical logging [1]. Effective restoration and conservation depend on understanding patterns of genetic differentiation across the redwood range to delineate populations for management prioritization. Yet, past range-wide studies provided only a partial picture of population structure in coast redwood as they relied on a limited set of genetic markers [2, 3] or limited sampling, as sequencing was done on the same range-wide provenance collection [4, 5]. Here, we analyze 334,029 SNPs from a new range-wide set of 224 individuals using a dosage-based approach that accounts for polyploidy. Principal coordinates and clustering analyses reveal clear latitudinal structure, with a steeper break south of San Francisco Bay. Outlier SNP analysis identified new candidate loci involved in salinity tolerance, climate stress response, and nutrient uptake, suggesting potential local adaptation. These results point to the central role of geography in shaping genetic variation in coast redwood and give scientific basis for designing new conservation and management strategies aimed at preserving the species genetic diversity into the future.

ecology↗

bioRxiv2025

ngsAMOVA: A Probabilistic Framework for Analysis of Molecular Variance, dXY and Neighbor-Joining Trees with Low Depth Sequencing Data

MotivationNext-generation sequencing (NGS) has transformed population genetics and evolutionary biology, but the data produced in studies of non-model organisms, ancient DNA, and environmental DNA often consist of low- or medium-depth sequencing. Analyses of these data rely on computational methods that utilize genotype likelihoods (GLs) to account for genotype uncertainty. Nevertheless, many widely-used analysis methods, such as analysis of molecular variance (AMOVA) and methods for estimating phylogenetic trees using nucleotide divergence (dXY) still lack the probabilistic frameworks necessary to accommodate GLs. ResultsWe introduce ngsAMOVA, a novel probabilistic framework for analyzing molecular variation in population hierarchies with low- and medium-depth sequencing data. It employs an Expectation Maximization algorithm to first estimate the joint genotype probabilities for pairs of individuals, accounting for genotype uncertainty using GLs. It then uses these estimates to generate a pairwise distance matrix, which can be used for AMOVA, estimation of dXY, and for estimating phylogenetic trees using Neighbor-Joining. Hypothesis testing is facilitated using genomic block-bootstrapping. Through extensive simulations, we demonstrate that ngsAMOVA provides more accurate results compared to genotype calling at low and medium read depths. Overall, ngsAMOVA represents a methodological advance in the analysis of molecular variance and divergence under sequencing uncertainty. It provides a robust framework, opening up numerous possibilities for gaining insights into the evolutionary histories through its applications. ngsAMOVA is available as a fast, efficient, and user-friendly program written in C/C++. AvailabilityngsAMOVA is freely available at https://github.com/isinaltinkaya/ngsAMOVA. Contactisin.altinkaya@sund.ku.dk Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2025

Hyper-Glycosylation as a Central Metabolic Driver of Alzheimers Disease

Alzheimers disease (AD) is a neurodegenerative disorder characterized by devastating degenerative decline. Metabolic disruptions are widely observed, yet their involvement in the molecular etiology of AD remains underexplored. Utilizing spatial metabolomics, lipidomics, and glycomics in both mouse models and human post-mortem samples, we identified a hyper-glycosylation phenotype as a hallmark of AD. To investigate the underlying mechanisms and whether the observed effect was a driver of the observed decline, we developed an advanced spatial isotopic tracing pulse-chase method to study the dynamics of N-linked glycans. Our analysis revealed enhanced glycan biosynthesis in AD mouse models. Based on these findings, we performed genetic and dietary interventions to modulate glycan biosynthesis. Genetic knockdown of glycan biosynthetic enzymes ameliorated the hyper-glycosylation and improved cognitive and behavioral outcomes in AD mice. In contrast, oral glucosamine supplementation drove hyper-glycosylation and exacerbated cognitive and behavioral deficits. To assess the clinical relevance of these findings, we conducted a retrospective analysis of a large population of patients with mild cognitive impairment (MCI), AD, and Alzheimers Disease Related Dementias (ADRD) stratified by glucosamine use, leveraging electronic health records. Consistently, glucosamine supplementation was associated with increased mortality in AD and ADRD patient cohorts, and significantly elevated progression from MCI to AD compared to age-matched controls. Collectively, our findings establish hyper-glycosylation as a pathological driver of AD and highlight glycan metabolism as an actional target in the fight against AD.

neuroscience↗

bioRxiv2025

Acyl-CoA Binding Protein is Dispensable for White and Brown Adipose Tissue Function

Acyl-CoA binding protein (ACBP) plays a vital role in lipid metabolism by mediating the intracellular flux and utilization of long-chain acyl-CoAs. In this study we generated brown- and white adipose tissue specific knockout mice (Adipoq-Acbp-/-) and brown adipose tissue specific knockout mice (Ucp1-Acbp-/-) to investigate the role of ACBP in adipose tissue function. Here we demonstrate that loss of ACBP does not affect body weight, fat and lean mass, food intake and systemic energy expenditure, even under cold stress. Transcriptomic data show only minor changes in gene expression, whereas lipidomic profiling reveals a subtle increase in acyl-carnitines levels in brown adipose tissue. However, lipolytic activity in white adipose tissue as well as plasma glycerol, non-esterified fatty acid and triacylglycerol levels remained unaffected. In addition, no changes in mitochondrial respiration in BAT were observed. Taken together, our findings suggest that ACBP is dispensable for adipose tissue function and systemic energy metabolism, including thermoregulation.

biochemistry↗

bioRxiv2025

Stochastic Phylogenetic Models of Shape

Phylogenetic modeling of morphological shape in two or three dimensions is one of the most challenging statistical problems in evolutionary biology. As shape data are inherently correlated and non-linear, most naive methods for phylogenetic analysis of morphological shape fail to capture the biological realities of evolving shapes. In this study we propose a novel framework for evolutionary analysis of morphological shape which facilitates stochastic character mapping on landmark shapes. Our framework is based on recent advances in mathematical shape analysis and models the evolution of shape as a diffusion process that accounts for the evolutionary correlation between nearby landmarks. The diffusion process we consider is parametrized in terms of meaningful parameters describing the evolutionary rate and the degree of spatial autocorrelation among landmarks. The framework we propose assumes that the phylogenetic tree is fixed and uses a Metropolis-Hastings Markov Chain Monte Carlo sampling scheme for inferring ancestral shapes and parameters of the model. We evaluate the new inference algorithm using simulations and show that the method leads to improved estimates of the shape at the root and well-calibrated credible sets of shapes at internal nodes. In addition, we also compare the diffusion parameter describing the degree of spatial autocorrelation to an existing metric of integration and find that they quantify integration in a shape in a similar way. To illustrate the method, we also apply it to a previously published data set of butterfly wing images.

evolutionary biology↗

bioRxiv2025

Assessing the combined performance of supervised learning and spike-in constructs for bias correction in eDNA metabarcoding

Environmental DNA (eDNA) metabarcoding has become increasingly popular as an approach to efficiently document biodiversity within an environment characterized by relative uncertainty. Compared to the traditional stereomicroscopic approaches, eDNA metabarcoding is simpler and less costly. Under ideal circumstances, researchers are able to directly extrapolate the true relative abundance of a particular taxon in the sampled environment by computing the proportion of sequenced reads assigned to the specific taxon. Although several previous studies have been carried out under such assumptions, some researchers have raised the possibility that there may exist both biological and technical biases in eDNA metabarcoding studies, leading to inconsistent estimations of community composition. Using mock community datasets from nine relevant studies in the past, we showed that bias correction in eDNA metabarcoding studies is indeed a predictable task. We also found reads and amp_gc to be the two most important feature predictors, such that these two features alone are enough to retain most of the model performances. Experiment-specific information were found to be necessary for bias correcting models to perform well. However, we have yet to develop an effective way of converting knowledge regarding spike-in (SP) samples into experiment-specific information that can be learned by existing models. Nonetheless, under the data-specific scenario, AdaBoost showed an optimal 35.62% improvement from the baseline established by the vanilla control model. Additionally, we showed that model performances could be rescued by the availability of experiment-specific data, under which XgBoost exhibited an optimal 81.57% improvement from the baseline. Our work suggests that future metabarcoding studies would benefit from performing supervised learning (SL)-based bias correction prior to downstream analyses. Moreover, if experiment-specific data is available at the time of the study, it is optimal to construct an XgBoost model. Otherwise, it is still recommended to construct an AdaBoost model, which showed marginal improvement from the baseline with no modeling. One Sentence SummarySupervised learning models, particularly XgBoost and AdaBoost, can effectively correct biases in eDNA metabarcoding studies, with performance improving significantly when experiment-specific data is available.

bioinformatics↗