Biology subjects

Korneliussen, T. S.

Publications and source records attributed to Korneliussen, T. S..

16 recordsLinked to original sources

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

Discovery and characterization of highly polymorphic ultra-short STRs for human identification via shotgun sequencing

Shotgun sequencing (SGS) enables simultaneous interrogation of a broad range of loci across the human genome, even from low-template and highly degraded DNA samples. While human identification traditionally relies on short tandem repeats (STRs) due to their high polymorphism, standard forensic STRs (100-450 bp) are poorly suited for the short read ([~]150 bp) constraint of SGS. The purpose of this study was to evaluate the analysis limitations of standard forensic STRs in SGS data and to identify a novel panel of STRs optimised for short-read genomic data. First, we benchmarked four STR genotyping software tools (STRait Razor, GangSTR, STRinNGS, and HipSTR) by analysing 53 standard forensic STRs in SGS data. HipSTR showed the best performance but achieved only a call rate of 64.5% and an accuracy of 83.8%, and its performance was strongly affected by STR allele length and read depth. To overcome these constraints, we screened the population-wide 1000 Genomes Project dataset and identified a panel of 265 autosomal ultra-short (< 50 bp) STRs with an effective number of alleles (Ae) ranging from 3.0 to 7.5. As few as seven of these loci were sufficient to achieve a Mean Match Probability (MMP) below 1 x 10-6. To validate these findings, we developed a custom PCR-based amplicon sequencing panel targeting 97 of the most polymorphic ultra-short STRs and evaluated these in 41 blood samples from Danish individuals. The polymorphic nature of the selected loci was confirmed (Aeranged from 2.4 to 7.2). Our results furthermore demonstrated high concordance between the amplicon panel and SGS-derived genotypes, which substantiates that these ultra-short STRs provide a robust and highly polymorphic alternative for human identification in SGS data. Author summaryShotgun sequencing (SGS) methods are increasingly being adopted in fields such as forensic genetics. SGS yields large amounts of genetic information by reading short fragments across the entire genome, enabling a wide range of analyses that may be exploited as leads in a police investigation. Human identification has traditionally been based on STR loci with a PCR amplicon length of 100-450 base pairs. However, these loci are often longer than the reads generated by SGS data, which makes them difficult to analyse in a reliable way. In this study, we evaluated four software tools designed to genotype STRs and confirmed the limited ability to genotype traditional forensic STRs in SGS data. To address this limitation, we identified a new set of highly polymorphic ultra-short STRs (less than 50 base pairs in length) that enable robust human identification using SGS data. Despite their shorter length, these loci retain the multi-allelic nature inherent to traditional STRs. This ensures a low random match probability that is comparable with the standard forensic STR panels. The ultra-short STRs may be genotyped from highly degraded DNA and may provide the possibility for complex mixture analysis and multi-donor deconvolution, which makes the STRs uniquely suited for forensic casework.

genomics↗

bioRxiv2025

Environmental DNA Reveals Reykjavik's Human and Ecological History

Iceland was among the last large islands settled by humans, with colonization (Landnam) in the late 9th century CE (Common Era) and is often portrayed as an ecological disaster driven by the Norse settlers. Here, we revisit this narrative through environmental DNA (eDNA) and multiproxy analyses of sediment cores from Lake Tjornin in central Reykjavik, one of Icelands earliest and longest-occupied settlements. Originally a marine embayment, Tjornin became a freshwater lake around 660 CE. Our record reveals a human presence decades before the long-accepted arrival date of 877 CE, marked by the Landnam volcanic tephra. Early settlement brought livestock, barley cultivation, and other introduced taxa that enhanced nutrient cycling and unexpectedly increased local biodiversity. Contrary to the conventional view of rapid deforestation, eDNA shows that birch and willow expanded during the settlement period, likely supported by deliberate management. Pronounced ecological and land use shifts occurred after 1200 CE, but these were coeval with the Little Ice Age cooling, compounded by volcanic eruptions, storm surges, and plague, rather than anthropogenic degradation. Crop cultivation ceased, arboreal taxa retracted, and grazing pressure maintained open landscapes. Even more profound ecological changes came after c. 1750 CE with urbanization and industrialization, as wastewater discharge, heavy-metal pollution, and fossil fuel use reshaped Tjornins ecosystem. These findings challenge the prevailing model of Norse-induced environmental collapse, revealing instead a dynamic human-environment relationship shaped by both cultural practices and external stressors. By applying eDNA to a long-occupied urban catchment, we demonstrate the power of genomic methods to refine settlement chronologies, reassess ecological baselines and changes, and integrate natural and cultural histories. This approach offers a model for revisiting human-environment interactions in urban centers worldwide.

genomics↗

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

AdDeam: A Fast and Scalable Tool for Estimating and Clustering Reference-Level Damage Profiles

MotivationDNA damage patterns, such as increased frequencies of C[->]T and G[->]A substitutions at fragment ends, are widely used in ancient DNA studies to assess authenticity and detect contamination. In metagenomic studies, fragments can be mapped against multiple references or de novo assembled contigs to identify those likely to be ancient. Generating and comparing damage profiles, however, can be both tedious and time-consuming. Although tools exist for estimating damage in single reference genomes and metagenomic datasets, none efficiently cluster damage patterns. ResultsTo address this methodological gap, we developed AdDeam, a tool that combines rapid damage estimation with clustering for streamlined analyses and easy identification of potential contaminants or outliers. Our tool takes aligned aDNA fragments from various samples or contigs as input, computes damage patterns, clusters them and outputs representative damage profiles per cluster, a probability of each sample of pertaining to a cluster as well as a PCA of the damage patterns for each sample for fast visualisation. We evaluated AdDeam on both simulated and empirical datasets. AdDeam effectively distinguishes different damage levels, such as UDG-treated samples, sample-specific damages from specimens of different time periods, and can also distinguish between contigs containing modern or ancient fragments, providing a clear framework for aDNA authentication and facilitating large-scale analyses. Availability and ImplementationAdDeam is publicly available at https://github.com/LouisPwr/AdDeam and can also be installed via Bioconda. It is implemented in Python and C++. All analysis scripts and datasets are available at https://github.com/LouisPwr/AdDeamAnalysis and on Zenodo under: 10.5281/zenodo.15052427.

bioinformatics↗

bioRxiv2024

vcfgl: A flexible genotype likelihood simulator for VCF/BCF files

MotivationAccurate quantification of genotype uncertainty is pivotal in ensuring the reliability of genetic inferences drawn from NGS data. Genotype uncertainty is typically modeled using Genotype Likelihoods (GLs), which can help propagate measures of statistical uncertainty in base calls to downstream analyses. However, the effects of errors and biases in the estimation of GLs, introduced by biases in the original base call quality scores or the discretization of quality scores, as well as the choice of the GL model, remain under-explored. ResultsWe present vcfgl, a versatile tool for simulating genotype likelihoods associated with simulated read data. It offers a framework for researchers to simulate and investigate the uncertainties and biases associated with the quantification of uncertainty, thereby facilitating a deeper understanding of their impacts on downstream analytical methods. Through simulations, we demonstrate the utility of vcfgl in benchmarking GL-based methods. The program can calculate GLs using various widely used genotype likelihood models and can simulate the errors in quality scores using a Beta distribution. It is compatible with modern simulators such as msprime and SLiM, and can output data in pileup, VCF/BCF and gVCF file formats. The vcfgl program is freely available as an efficient and user-friendly software written in C/C++. Availabilityvcfgl is freely available at https://github.com/isinaltinkaya/vcfgl. Contactisin.altinkaya@sund.ku.dk Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2023

Revisiting the Briggs ancient DNA damage model: a fast regression method to estimate postmortem damage

MotivationOne essential initial step in the analysis of ancient DNA is to authenticate its ancientness to ensure reliable conclusions. That is, meticulously assessing whether next-generation sequencing reads exhibit ancient characteristics, with a particular focus on the postmortem damage (PMD) signal induced by cytosine deamination in the fragments termini. We present a novel statistical method implementation in a fast multithreaded program ngsBriggs that enables the rapid quantification of PMD by calculation of the Briggs ancient damage model parameters (Briggs parameters). ResultsUsing a fast multinomial regression approach, ngsBriggs accurately models the Briggs parameters, quantifying the PMD signal from single and double-stranded DNA regions. We revisit and extend the original Briggs model, with ngsBriggs modeling PMD signals for contemporary sequencing platforms. Furthermore, ngsBriggs asserts itself as a reliable and consistent tool, by accurately estimating the Briggs parameters across a variety of contamination levels. The classification accuracy of ngsBriggs significantly exceeds the current tool available when discerning ancient-from modern sequencing reads to decontaminate samples. Our novel method and implementation ngsBriggs outperforms existing tools regarding computational speed and accuracy, establishing its practicality and usability. Our tool, ngsBriggs offers a practical and accurate toolset for researchers seeking to authenticate ancient DNA and improve the quality of their data. Availabilityhttps://github.com/lz398/metadamage_briggs

bioinformatics↗

bioRxiv2023

A 2-million-year-old microbial and viral communities from the Kap Kobenhavn Formation in North Greenland

Environmental DNA (eDNA) from the 2-million-year-old Kap Kobenhavn Formation of northern Greenland has revealed an ecosystem of plants and animals with no contemporary analogue1. Here, we reconstruct the microbial (bacterial, archaeal, and viral) communities that thrived at the site during this time. By leveraging a novel analytical framework that integrates taxonomic profiling, DNA damage estimates, and functional reconstructions, we identify and distinguish pioneer microbial communities from later permafrost microbial assemblages. We show that at the time of sediment deposition, the terrestrial input at the Kap Kobenhavn site originated from a palustrine wetland, suggesting warmer, non-permafrost conditions. During this period, the detection of methanogenic archaea and signals of their carbon metabolism is consistent with Kap Kobenhavn and similar northern ecosystems contributing moderate methane emissions. Intriguingly, we discover a remarkable nucleotide sequence similarity--exceeding 98%--between pioneer methanogens and present-day analogues in thawing permafrost. This aligns with the concept of "time-traveling" microbes2 surviving across geological time and waiting for conditions to turn favourable rather than evolving to adapt to changing conditions. Importantly, in contrast to the plant and animal communities of the Kap Kobenhavn, a striking similarity in microbial composition to that of a contemporary thawing Arctic suggests that microbial communities may serve as the first indication of broader climate-driven ecosystem disruptions.

microbiology↗

bioRxiv2022

metaDMG - A Fast and Accurate Ancient DNA Damage Toolkit for Metagenomic Data

O_LIMotivation Under favourable conditions DNA molecules can persist for hundreds of thousands of years. Such genetic remains make up invaluable resources to study past assemblages, populations, and even the evolution of species. However, DNA is subject to degradation, and hence over time decrease to ultra low concentrations which makes it highly prone to contamination by modern sources. Strict precautions are therefore necessary to ensure that DNA from modern sources does not appear in the final data is authenticated as ancient. The most generally accepted and widely applied authenticity for ancient DNA studies is to test for elevated deaminated cytosine residues towards the termini of the molecules: DNA damage. To date, this has primarily been used for single organisms and recently for read assemblies, however, these methods are not applicable for estimating DNA damage for ancient metagenomes with tens and even hundreds of thousands of species. C_LIO_LIMethods We present metaDMG, a novel framework and toolkit that allows for the estimation, quantification and visualization of postmortem damage for single reads, single genomes and even metagenomic environmental DNA by utilizing the taxonomic branching structure. It bypasses any need for initial classification, splitting reads by individual organisms, and realignment. We have implemented a Bayesian approach that combines a modified geometric damage profile with a beta-binomial model to fit the entire model to the individual misincorporations at all taxonomic levels. C_LIO_LIResults We evaluated the performance using both simulated and published environmental DNA datasets and compared to existing methods when relevant. We find metaDMG to be an order of magnitude faster than previous methods and more accurate - even for complex metagenomes. Our simulations show that metaDMG can estimate DNA damage at taxonomic levels down to 100 reads, that the estimated uncertainties decrease with increased number of reads and that the estimates are more significant with increased number of C to T misincorporations. C_LIO_LIConclusion metaDMG is a state-of-the-art program for aDNA damage estimation and allows for the computation of nucleotide misincorporation, GC-content, and DNA fragmentation for both simple and complex ancient genomic datasets, making it a complete package for ancient DNA damage authentication. C_LI

bioinformatics↗

bioRxiv2022

Genetic risk for Multiple Sclerosis originated in Pastoralist Steppe populations

Multiple sclerosis (MS) is a modern neuro-inflammatory and -degenerative disease, which is most prevalent in Northern Europe. Whilst it is known that inherited risk to MS is located within or within close proximity to immune genes, it is unknown when, where and how this genetic risk originated 1. By using the largest ancient genome dataset from the Stone Age 2, along with new Medieval and post-Medieval genomes, we show that many of the genetic risk variants for MS rose to higher frequency among pastoralists located on the Pontic Steppe, and were brought into Europe by the Yamnaya-related migration approximately 5,000 years ago. We further show that these MS-associated immunogenetic variants underwent positive selection both within the Steppe population, and later in Europe, likely driven by pathogenic challenges coinciding with dietary, lifestyle, and population density changes. This study highlights the critical importance of this period as a determinant of modern immune responses and its subsequent impact on the risk of developing MS in a changing environment.

evolutionary biology↗

bioRxiv2022

Estimation of site frequency spectra from low-coverage sequencing data using stochastic EM reduces overfitting, runtime, and memory usage

The site frequency spectrum (SFS) is an important summary statistic in population genetics used for inference on demographic history and selection. However, estimation of the SFS from called genotypes introduce bias when working with low-coverage sequencing data. Methods exist for addressing this issue, but sometimes suffer from two problems. First, they can have very high computational demands, to the point that it may not be possible to run estimation for genome-scale data. Second, existing methods are prone to overfitting, especially for multi-dimensional SFS estimation. In this article, we present a stochastic expectation-maximisation algorithm for inferring the SFS from NGS data that addresses these challenges. We show that this algorithm greatly reduces runtime and enables estimation with constant, trivial RAM usage. Further, the algorithm reduces overfitting and thereby improves downstream inference. An implementation is available at github.com/malthesr/winsfs.

bioinformatics↗

bioRxiv2021

Modelling the spatiotemporal spread of beneficial alleles using ancient genomes

Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.

evolutionary biology↗

bioRxiv2021

The pangenome of the fungal pathogen Neonectria neomacrospora

The fungal plant pathogen Neonectria neomacrospora (C. Booth & Samuels) Mantiri & Samuels (Ascomycota, Hypocreales) is a bark parasite causing twig blight, canker, and in severe cases, dieback in fir (Abies spp.). Although often described as a mild pathogen, foresty and phytosanitary agencies have expressed their concern for potential economic impact. Two epidemics caused by this species are known: one from eastern Canada and one current within Northern Europe. We present key genome features of N. neomacrospora, to facilitate the research into the biology of this pathogen. We present the first genome assembly of N. neomacrospora as well as the first pangenome within this genus. The reference genome for N. neomacrospora is a long-read sequenced Danish isolate, while the pangenome is pieced together using additional 60 short-read sequenced strains covering the known geographical distribution of the species, including Europe, North America, and China. The gapless reference genome consist of twelve chromosomes sequenced telomere to telomere to a total length of 37.1 Mb. The mitochondrial genome was assembled and circularised with a length of 22 Kb. The gapless nuclear genome contains a total of 11,291 annotated genes, where 642 only have a hypothetical function, and a 4.3 % repeat content. Two minor chromosomes are enriched in transposable elements, AT content, and effector candidates. Chromosome 12 segregates within the population, indicating an accessory nature. The pangenome compile 15,101 genes, 34% more genes than present in the single isolate reference genome of N. neomacrospora. These genes organise into 13,069 homologous clusters, of which 8,316 clusters are present in all analysed strains, 985 are private to single strains. The British Columbian population branched out before the other populations and are characterized by comparatively larger genomes. The increased genome size can be explained by an expansion of repetitive elements. The comparative analysis finds a higher number of genes with a signal peptide within N. neomacrospora and species within the genus compared to the closely related genera. A species-specific pattern is observed in the carbohydrate-active enzyme repertoire, with a reduced number of polysaccharide lyases, compared to other species within the genus. The CAZymes battery responsible for plant cell wall degradation is similar to that observed in necrotrophic and hemibiotrophic plant pathogenic fungi. The genome size of N. neomacrospora is close to the median size for Ascomycota but is the smallest genome within the Neonectria genus. Comparative analysis revealed significant intraspecies genome size differences between populations explained by a difference in repeat content. Isolates with the smallest genomes formed a monophyletic group consisting of all strains from Europe and Quebec. Based on the field observations, we assume that N. neomacrospora is a hemibiotroph. Our analysis revealed a secretome consistent with a hemibiotrophic lifestyle.

genomics↗

bioRxiv2020

Population genomics of the emerging forest pathogen Neonectria neomacrospora

The fungal pathogen Neonectria neomacrospora is of increasing concern in Europe where, within the last decade, it has caused substantial damage to forest stands and ornamental trees of the genus Abies (Mill.). Using whole-genome sequencing of a comprehensive collection of isolates, we show the extent of three major clades within N. neomacrospora, which most likely diverged around the end of the last Ice Age. We find it likely that the current European epidemic of N. neomacrospora was founded from a population belonging to the east North American clade. All European isolates (1957-2019) had a common evolutionary history, but substantial and asymmetrical gene flow from the larger American source population could be detected. The European population shows multiple signs of having gone through a bottleneck and subsequent population expansion.

genomics↗

bioRxiv2019

Population genomics of the Viking world

The Viking maritime expansion from Scandinavia (Denmark, Norway, and Sweden) marks one of the swiftest and most far-flung cultural transformations in global history. During this time (c. 750 to 1050 CE), the Vikings reached most of western Eurasia, Greenland, and North America, and left a cultural legacy that persists till today. To understand the genetic structure and influence of the Viking expansion, we sequenced the genomes of 442 ancient humans from across Europe and Greenland ranging from the Bronze Age (c. 2400 BC) to the early Modern period (c. 1600 CE), with particular emphasis on the Viking Age. We find that the period preceding the Viking Age was accompanied by foreign gene flow into Scandinavia from the south and east: spreading from Denmark and eastern Sweden to the rest of Scandinavia. Despite the close linguistic similarities of modern Scandinavian languages, we observe genetic structure within Scandinavia, suggesting that regional population differences were already present 1,000 years ago. We find evidence for a majority of Danish Viking presence in England, Swedish Viking presence in the Baltic, and Norwegian Viking presence in Ireland, Iceland, and Greenland. Additionally, we see substantial foreign European ancestry entering Scandinavia during the Viking Age. We also find that several of the members of the only archaeologically well-attested Viking expedition were close family members. By comparing Viking Scandinavian genomes with present-day Scandinavian genomes, we find that pigmentation-associated loci have undergone strong population differentiation during the last millennia. Finally, we are able to trace the allele frequency dynamics of positively selected loci with unprecedented detail, including the lactase persistence allele and various alleles associated with the immune response. We conclude that the Viking diaspora was characterized by substantial foreign engagement: distinct Viking populations influenced the genomic makeup of different regions of Europe, while Scandinavia also experienced increased contact with the rest of the continent.

genetics↗

bioRxiv2019

A likelihood method for estimating present-day human contamination in ancient DNA samples using low-depth haploid chromosome data

1MotivationThe presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested while few are aimed at low-depth data, a common feature in aDNA datasets.\n\nResultsWe develop a new X-chromosome-based maximum likelihood method for estimating present-day human contamination in low-depth sequencing data. We implement our method for general use, assess its performance under conditions typical of ancient human DNA research, and compare it to previous nuclear data-based methods through extensive simulations. For low-depth data, we show that existing methods can produce unusable estimates or substantially underestimate contamination. In contrast, our method provides accurate estimates for a depth of coverage as low as 0.5x on the X-chromosome when contamination is below 25%. Moreover, our method still yields meaningful estimates in very challenging situations, i.e., when the contaminant and the target come from closely related populations or with increased error rates. With a running time below five minutes, our method is applicable to large scale aDNA genomic studies.\n\nAvailability and implementationThe method is implemented in C++ and R and is freely available in https://github.com/sapfo/contaminationX.\n\nContactmorenomayar@gmail.com, annasapfo.malaspinas@unil.ch.

genomics↗