Biology subjects

Maiers, M.

Publications and source records attributed to Maiers, M..

16 recordsLinked to original sources

bioRxiv2026

Classical HLA Allele and Haplotype Frequency Estimates in US Populations

The human leukocyte antigen (HLA) system is the primary determinant of donor selection in allogeneic hematopoietic cell transplantation (HCT) and plays a central role in solid organ transplantation, immune-mediated disease studies, evolutionary population genetics, and immunotherapy. Large-scale sampling of registry participants reflecting major US ancestry groups allows for characterization of the complex landscape of HLA haplotype diversity for the classical HLA class I (HLA-A, HLA-B, HLA-C) and HLA class II (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) genes. Here we present nine-locus classical HLA allele and haplotype frequency estimates for five broad (Black, White, Asian or Pacific Islander, Hispanic and Native American) and 21 detailed US populations based on 9,671,082 donors with targeted genotyping by DNA-based methods. Frequency estimation used an expectation-maximization (EM) framework specifically adapted to handle mixed-resolution and ambiguous HLA genotyping data. Advancements in next-generation sequencing provide extensive HLA genotyping, offering new insights into the haplotype structure and diversity of the human MHC complex, expanding knowledge especially for HLA class II haplotypes. Population analyses reveal that the most common high-resolution haplotypes are predominantly population-specific, with only three haplotypes shared across the top-100 lists of all five broad population groups, and that Black populations exhibit the greatest nine-locus haplotypic diversity, a pattern that persists after controlling for differences in registry sample size. These frequencies, derived from the largest US cohort to date, support clinical decision-making and research in histocompatibility, immunogenetics, and transplantation and are publicly available at https://zenodo.org/records/17966993.

genetics↗

bioRxiv2026

GRIMM-II: A Two-Stage Real-Time Algorithm for Nine-Locus HLA Imputation and Matching with Up to Three Mismatches

BackgroundThe success of hematopoietic stem cell transplantation (HSCT) depends critically on human leukocyte antigen (HLA) matching between donor and recipient. While traditional matching focuses on five classical HLA loci (A, B, C, DRB1, DQB1), clinical practice increasingly considers extended typing at nine loci, including DPA1, DQA1, DPB1, and DRB3/4/5. Furthermore, emerging evidence supports transplantation with up to three HLA mismatches under post-transplant cyclophosphamide (PTCy) regimens. However, current donor search algorithms cannot efficiently identify donors with multiple mismatches across extended HLA loci in real-time. MethodsWe developed GRIMM-II (GRaph IMputation and Matching, version II), which comprises two novel algorithms: ML-GRIM (Multi-Locus GRIM) for HLA imputation across multiple loci, and ML-GRMA (Multi-Locus GRMA) for real-time donor-patient matching with up to three mismatches. Both algorithms employ a two-stage approach that combines efficient candidate reduction through graph-theoretic frameworks with detailed genotype comparison. ML-GRIM partitions genotypes into class I (HLA-A, B, C) and class II (remaining loci) components, enabling memory-efficient storage and rapid candidate identification. ML-GRMA searches a pre-imputed donor graph composed of donor genotypes and their sub-components, then computes asymmetric graft-versus-host (GvH) and host-versus-graft (HvG) mismatch probabilities to provide clinically relevant compatibility assessments. Both imputation and matching tools are available as a web application at https://grimmard.math.biu.ac.il/ and through GitHub repositories at https://github.com/nmdp-bioinformatics/py-graph-imputation (imputation) and https://github.com/nmdp-bioinformatics/py-graph-match (matching). ResultsWe validated ML-GRMA and ML-GRIM using the WMDA3 (World Marrow Donor Association) validation dataset, successfully reproducing all previously reported matches while identifying numerous additional candidate donors not detected by previous algorithms. Further validation of ML-GRMA using 3,000 patients with artificially introduced mismatches (0-3 allele substitutions) demonstrated 100% sensitivity and specificity in identifying matching donors at expected mismatch levels. We validated ML-GRIM using simulated nine-locus typings derived from 8,078,224 US donors in the NMDP registry. The algorithm successfully imputed genotypes across variable numbers of typed loci while incorporating multiethnic haplotype frequencies. The algorithm achieved real-time performance with typical imputation times under one second and matching times of 1-13 seconds per patient for up to three mismatches, even when searching databases exceeding 8 million donors. Notably, ML-GRMA identified substantially more potentially suitable donors than traditional algorithms by accounting for the biological reality that GvH and HvG mismatches often differ, particularly for donors homozygous at specific loci. To evaluate ML-GRIM performance with low-resolution typing, we tested it on simulated 3-locus typings from the same population. The resulting imputation accuracy correlated with the mutual information between typed loci and complete genotypes. ConclusionsGRIMM-II provides a scalable, memory-efficient solution for nine-locus HLA imputation and real-time identification of donors with up to three mismatches. The graph-based framework supports dynamic registry updates and can readily accommodate additional HLA loci and matching criteria as clinical knowledge evolves. By expanding the pool of acceptable donors while maintaining computational efficiency, GRIMM-II addresses a critical need in contemporary transplantation practice, particularly for patients from underrepresented ethnic minorities who face lower probabilities of finding perfectly matched donors.

bioinformatics↗

bioRxiv2026

Autoantibody Repertoires in Healthy Individuals Vary According to HLA Class II Genotype

The link between HLA genotype and the presence of pathogenic autoantibodies has previously been established across different autoimmune disorders. However, the functional process linking specific antibodies to specific HLA remains unclear. Additionally, autoantibodies - usually associated with autoimmune disease - are also present in healthy individuals. To this end, we sought to determine the spectrum of self-antigen antibody specificity (the "autoreactome") in healthy individuals, stratified by HLA genotypes. We utilized Phage ImmunoPrecipitation Sequencing (PhIP-Seq), a programmable phage display for interrogation of antibody specificity, which encompasses over 700,000 peptides tiled across the entire human proteome. Serum from 741 donors without diagnosed autoimmune disease were grouped by their five homozygous HLA-DRB1 genotypes and analyzed for differences in autoreactivity profiles. We applied a custom filter to the PhIP-Seq normalized data and obtained a set of enriched peptides for each HLA-DRB1 genotype. We found that the autoreactome in healthy individuals with different HLA-DRB1 genotypes are generally distinct. Binary logistic regression successfully identified whether a sample belongs to a specific HLA-DRB1 genotype (HLA-DRB1*01, HLA-DRB1*03, HLA-DRB1*04, HLA-DRB1*07, and HLA-DRB1*15) with the following accuracies: 96%, 92%, 90%, 94%, and 90%, and multinomial models predicted HLA-DRB1 genotype with up to 90% accuracy. Finally, gene-level analysis suggests that individuals with specific HLA autoimmune risk alleles may harbor potentially pathogenic autoantibodies in the absence of, or prior to the establishment of, overt disease. Our analysis demonstrates that autoreactivity profiles in healthy people vary according to HLA class II genotype, and may provide insight into the pathological processes associated with development of autoimmune and other immune-mediated diseases.

immunology↗

bioRxiv2024

Harnessing Global HLA Data for Enhanced Patient Matching in iPSC Haplobanks

Several countries have either developed or are developing national induced pluripotent stem cell (iPSC) banks of cell lines derived from donors with HLA homozygous genotypes (two identical haplotypes) prevalent in their local populations to provide immune matched tissues and cells to support regenerative medicine therapies. This haplobank approach relies on knowledge of the HLA genotypes of the population to identify the most beneficial haplotypes for patient coverage, and ultimately identify donors or cord blood units carrying two copies of the target haplotype. A potentially more efficient alternative to a national bank approach is to assess the haplotypes required to provide global patient coverage and to produce a single, global haplobank. Toward that end, we have developed an algorithm to prioritize HLA haplotypes that optimize coverage across the global population. We analyzed data from eighteen countries participating in the Global Alliance for iPS Therapy (GAiT). A representative pool of HLA genotypes, reflecting the HLA of patients, was derived by sampling from each countrys WMDA hematopoietic stem cell donor registry, or surrogate population. An algorithm was created based on HLA-A, -B and -DRB1 haplotype homozygous types with population HLA matching coverage defined by the absence of Host versus Graft (HvG) mismatches at these loci. HLA matching coverage was determined by iteratively selecting HLA haplotypes that provide the largest coverage against patient HLA genotypes sampled from the same population, excluding genotypes compatible with previous iterations. The top 10 haplotypes for each of the 18 countries were identified with patient coverage ranging from 19.5% in Brazil to 63.8% in Japan, with a mean coverage of 33.3%. In a global model, utilizing the 180 most frequent haplotypes across all 18 populations (equivalent to 10 lines pe country), the patient coverage ranged from 54.6% in India to 81.7% in Sweden, with a mean of 68.4%. Our findings demonstrate that global collaboration could more than double the potential for patient HLA matching coverage. Interrogation of unrelated hematopoietic stem cell donor registry and cord blood bank HLA data demonstrated that HLA-A, -B, and -DRB1 homozygous donors for the top 180 global haplotypes are widely available. These results show that a globally coordinated strategy for haplobanking would reduce redundancy and allow more patients to be treated with the same investment. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=170 SRC="FIGDIR/small/618361v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@117075eorg.highwire.dtl.DTLVardef@e4dc11org.highwire.dtl.DTLVardef@12be9c7org.highwire.dtl.DTLVardef@140d6_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIiPSC banks are being developed targeting HLA coverage to meet national needs C_LIO_LIGlobal collaboration has the potential to substantially increase the efficiency of this effort C_LI

genetics↗

bioRxiv2024

Prediction of KIR3DL1/Human Leukocyte Antigen binding

KIR3DL1 is a polymorphic inhibitory Natural Killer (NK) cell receptor that recognizes Human Leukocyte Antigen (HLA) class I allotypes that contain the Bw4 motif. Structural analyses have shown that in addition to residues 77-83 that span the Bw4 motif, polymorphism at other sites throughout the HLA molecule can influence the interaction with KIR3DL1. Given the extensive polymorphism of both KIR3DL1 and HLA class I, we built a machine learning prediction model to describe the influence of allotypic variation on the binding of KIR3DL1 to HLA class I. Nine KIR3DL1 tetramers were screened for reactivity against a panel of HLA class I molecules which revealed different patterns of specificity for each KIR3DL1 allotype. Separate models were trained for each of KIR3DL1 allotypes based on the full amino sequence of exons 2 and 3 encoding the 1 and 2 domains of the class I HLA allotypes, the set of polymorphic positions that span the Bw4 motif, or the positions that encode 1 and 2 but exclude the connecting loops. The Multi-Label-Vector-Optimization (MLVO) model trained on all alpha helix positions performed best with AUC scores ranging from 0.74 to 0.974 for the 9 KIR3DL1 allotype models. We show that a binary division into binder and non-binder is not precise, and that intermediate levels exist. Using the same models, within the binder group, high- and low-binder categories can also be predicted, the regions in HLA affecting the high vs low binder being completely distinct from the classical Bw4 motif. We further show that these positions affect binding affinity in a nonadditive way and induce deviations from linear models used to predict interaction strength. We propose that this approach should be used in lieu of simpler binding models based on a single HLA motif.

immunology↗

bioRxiv2024

Efficient test for deviation from Hardy Weinberg Equilibrium with known or ambiguous typing in highly polymorphic loci

The Hardy-Weinberg Equilibrium (HWE) assumption is essential to many population genetics models. Multiple tests were developed to test its applicability in observed genotypes. Current methods are divided into exact tests applicable to small populations and a small number of alleles, and approximate goodness of fit tests. Existing tests cannot handle ambiguous typing in multi-allelic loci. We here present a novel exact test (UMAT - Unambiguous Multi Allelic Test) practically not limited in the number of alleles and population size, based on a perturbative approach around the current observations. We show its accuracy in the detection of deviation from HWE. We then propose an additional model to handle ambiguous typing using either sampling into UMAT or a goodness of fit test with a variance estimate taking ambiguity into account, named ASTA (Asymptotic Statistical Test with Ambiguity). We show the accuracy of ASTA and the possibility to detect of the source of deviation from HWE. We apply these tests to the HLA loci to recover multiple previously reported deviations from HWE, and a large number of new ones.

genetics↗

bioRxiv2020

A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-based Multiple Sequence Alignment

Human chromosome 19q13.4 contains genes encoding killer-cell immunoglobulin-like receptors (KIR). Reported haplotype lengths range from 67 to 269 kilobases and contain 4 to 18 genes. The region has certain properties such as single nucleotide variation, structural variation, homology, and repetitive elements that make it hard to align accurately beyond single gene alleles. To the best of our knowledge, a multiple sequence alignment of KIR haplotypes has never been published or presented. Such an alignment would be useful to precisely define KIR haplotypes and loci, provide context for assigning alleles (especially fusion alleles) to genes, infer evolutionary history, impute alleles, interpret and predict co-expression, and generate markers. In order to extend the framework of KIR haplotype sequences in the human genome reference, 27 new sequences were generated including 24 haplotypes from 12 individuals of African American ancestry that were selected for genotypic diversity and novelty to the reference, to bring the total to 68 full length genomic KIR haplotype sequences. We leveraged these data and tools from our long-read KIR haplotype assembly algorithm to define and align KIR haplotypes at <5 kb resolution on average. We then used a standard alignment algorithm to refine that alignment down to single base resolution. This processing demonstrated that the high-level alignment recapitulates human-curated annotation of the human haplotypes as well as a chimpanzee haplotype. Further, assignments and alignments of gene alleles were consistent with their human curation in haplotype and allele databases. These results define KIR haplotypes as 14 loci containing 9 genes. The multiple sequence alignments have been applied in two software packages as probes to capture and annotate KIR haplotypes and as markers to genotype KIR from WGS.

bioinformatics↗

bioRxiv2020

Efficient Sequencing, Assembly, and Annotation of Human KIR Haplotypes

The homology, recombination, variation, and repetitive elements in the natural killer-cell immunoglobulin-like receptor (KIR) region has made full haplotype DNA interpretation impossible without physical separation of chromosomes. Here, we present a new approach using long-read sequencing to efficiently capture, sequence, and assemble diploid human KIR haplotypes. Sequences for capture probe design were derived from public full-length gene and haplotype sequences. IDT xGen(R) Lockdown probes were used to capture 2-8 kb of sheared DNA fragments followed by sequencing on a PacBio Sequel. The sequences were error corrected, binned, and then assembled using the Canu assembler. The assembly was evaluated on 16 individuals (8 African American and 8 Europeans) from whom ground truth was known via long-range sequencing on fosmid-isolated chromosomes. Using only 18 capture probes, the results show that the assemblies cover 97% of the GenBank reference, are 99.97% concordant, and it takes only 1.8 contigs to cover 75% of the reference. We also report the first assembly of diploid KIR haplotypes from long-read WGS, including the first sequencing of cB05[~]tB01, which pairs a KIR2DS2/KIR2DS3 fusion with the tB01 region. Our targeted hybridization probe capture and sequencing approach is the first of its kind to fully sequence and phase all diploid human KIR haplotypes, and it is efficient enough for population-scale studies and clinical use.

bioinformatics↗

bioRxiv2019

Multiple Measures Reveal The Value of Both Race And Geographic Ancestry For Self-Identification

There is long-standing tension regarding whether and how to use race or geographic ancestry in biomedical research. We examined multiple self-reported measures of race and ancestry from a cohort of over 100,000 U.S. residents alongside genetic data. We found that these measures are often non-overlapping, and that no single self-reported measure alone provides a better fit to genetic ancestry than a combination including both race and geographic ancestry. We also found that patterns of reporting for race and ancestry appear to be influenced by participation in direct-to-consumer genetic ancestry testing. Our results demonstrate that there is a place for the language of both race and geographic ancestry as we seek to empower individuals to fully describe their family history in research and medicine.\n\nOne Sentence SummarySelf-identification in the United States according to both racial and geographic terms best reflects genetic ancestry in individuals.

genetics↗

bioRxiv2018

Single haplotype admixture models using large scale HLA genotype frequencies to reproduce human admixture

The Human Leukocyte Antigen (HLA) is the most polymorphic region in humans. Anthropologists use HLA to trace populations migration and evolution. However, recent admixture between populations masks the ancestral haplotype frequency distribution.\n\nWe present an HLA-based method based on high-resolution HLA haplotype frequencies to resolve population admixture using a non-negative matrix factorization formalism and validated using haplotype frequencies from 56 populations. The result is a minimal set of original populations decoding roughly 90% of the total variance in the studied admixtures. These original populations agree with the geographical distribution, phylogenies and recent admixture events of the studied groups.\n\nWith the growing population of multi-ethnic individuals, the matching process for stem-cell and solid organ transplants is becoming more challenging. The presented algorithm provides a framework that facilitates the breakdown of highly admixed populations into original groups, which can be used to better match the rapidly growing population of multi-ethnic individuals worldwide.\n\nAuthor SummaryHuman Leukocyte Antigen (HLA) is known to be the most polymorphic region in the human genome. Anthropologists frequently use HLA to trace migration and evolution of different populations. This is due to the high linkage among HLA genes leading to the transmission of intact haplotypes from parents to offspring, hence preserving key population ancestral features.\n\nWe developed a new HLA-based method to identify admixture models in mixed populations using high-resolution HLA haplotype frequencies. Our results highlight that a single highly polymorphic locus can contain enough information to map clearly human admixture and the population genetics of the different human populations, and reproduces results based on SNP arrays.\n\nThe presented algorithm is validated using haplotype frequencies sampled from 56 worldwide populations. Under such factorization we demonstrate that 90% of the variance in these populations can be explained using a much-reduced set of 8 ethnic groups. We demonstrate that the estimated ethnic groups and admixture models agree with the geographical distribution, population phylogenies and recent historic admixture events of the studied populations.

immunology↗

bioRxiv2018

Mapping molecular HLA typing data to UNOS antigen equivalents for improved virtual crossmatch

BackgroundVirtual crossmatch utilizes HLA typing and antibody screen assay data as a part of organ offers in deceased donor allocation systems. Histocompatibility labs must convert molecular HLA typings to antigen equivalencies for entry into the United Network for Organ Sharing (UNOS) UNet system. While an Organ Procurement and Transplantation Network (OPTN) policy document provides general guidelines for conversion, the process is complex because no antigen mapping table is available. We present a UNOS antigen equivalency table for all IMGT/HLA alleles at the A, B, C, DRB1, DRB3/4/5, DQA1, and DQB1 loci.\n\nMethodsAn automated script was developed to generate a UNOS antigen equivalency table. Data sources used in the conversion algorithm included the World Marrow Donor Association(WMDA) antigen table, the HLA Dictionary, and UNOS-provided tables. To validate antigen mappings, we converted National Marrow Donor Program (NMDP) high resolution allele frequencies to antigen equivalents and compared with the UNOS Calculated Panel Reactive Antibodies (CPRA) reference panel.\n\nResultsNormalized frequency similarity scores between independent NMDP and UNOS panels for 4 US population categories (Caucasian, Hispanic, African American and Asian/Pacific Islander) ranged from 0.85 to 0.97, indicating correct antigen mapping. An open source web application (ALLele to ANtigen (\"ALLAN\")) and web services were also developed to map unambiguous and ambiguous HLA typing data to UNOS antigen equivalents based on NMDP population-specific allele frequencies (http://www.transplanttoolbox.org).\n\nConclusionsThis tool sets a foundation for using molecular HLA typing to compute the virtual crossmatch and may aid in reducing typing discrepancies in UNet.

bioinformatics↗

bioRxiv2018

GRIMM: GRaph IMputation and Matching for HLA Genotypes

Motivation: For over 10 years allele-level HLA matching for bone marrow registries has been performed in a probabilistic context. HLA typing technologies provide ambiguous results in that they could not distinguish among all known HLA allele sequences, therefore registries have implemented matching algorithms that provide lists of donor and cord blood units ordered in terms of the likelihood of allele-level matching at specific HLA loci. With the growth of registry sizes, current match algorithm implementations are unable to provide match results in real time.\n\nResults: We present here novel computationally-efficient open source implementation of an HLA imputation and match algorithm using a graph database platform. Using graph traversal, our algorithm runtime grows slowly with registry size. This implementation generates results that agree with consensus output on a publicly-available match algorithm crossvalidation dataset.\n\nAvailability: The Python, Perl and Neo4jJcode is available at https://git.com/nmdp-bioinformatics/grimm\n\nSupplementary information: Supplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Machine Learning approach to Predicting Stem-Cell Donor Availability

0. AbstractThe success of Unrelated Donor stem-cell transplants depends not only on finding genetically matched donors but also on donor availability. On average 50% of potential donors in the NMDP database are unavailable for a variety of reasons, after initially matching a patient, with significant variations in availability among subgroups (e.g., by race or age). Several studies have established univariate donor characteristics associated with availability. Individual consideration of each applicable characteristic is laborious. Extrapolating group averages to individual donor level tends to be highly inaccurate. In the current environment with enhanced donor data collection, we can make better estimates of individual donor availability. In this study, we propose a Machine Learning based approach to predict availability of every registered donor, to be used during donor selection and reduce the time taken to complete a transplant.

bioinformatics↗

bioRxiv2018

Unrelated Donor Selection for Stem Cell Transplants using Predictive Modelling

Unrelated Donor selection for a Hematopoietic Stem Cell Transplant is a complex multi-stage process. Choosing the most suitable donor from a list of Human Leukocyte Antigen (HLA) matched donors can be challenging to even the most experienced physicians and search coordinators. The process involves experts sifting through potentially thousands of genetically compatible donors based on multiple factors. We propose a Machine Learning approach to donor selection based on historical searches performed and selections made for these searches. We describe the process of building a computational model to mimic the donor selection decision process and show benefits of using the proposed model in this study.

bioinformatics↗

bioRxiv2017

Collection and Storage of HLA NGS Genotyping Data for the 17th International HLA and Immunogenetics Workshop

For over 50 years, the International HLA and Immunogenetics Workshops (IHIW) have advanced the fields of histocompatibility and immunogenetics (H&I) via community sharing of technology, experience and reagents, and the establishment of ongoing collaborative projects. In the fall of 2017, the 17th IHIW will focus on the application of next generation sequencing (NGS) technologies for clinical and research goals in the H&I fields. NGS technologies have the potential to allow dramatic insights and advances in these fields, but the scope and sheer quantity of data associated with NGS raise challenges for their analysis, collection, exchange and storage. The 17 th IHIW has adopted a centralized approach to these issues, and we have been developing the tools, services and systems to create an effective system for capturing and managing these NGS data. We have worked with NGS platform and software developers to define a set of distinct but equivalent NGS typing reports that record NGS data in a uniform fashion. The 17th IHIW database applies our standards, tools and services to collect, validate and store those structured, multi-platform data in an automated fashion. We are creating community resources to enable exploration of the vast store of curated sequence and allele-name data in the IPD-IMGT/HLA Database, with the goal of creating a long-term community resource that integrates these curated data with new NGS sequence and polymorphism data, for advanced analyses and applications.\n\nAbbreviations

bioinformatics↗

bioRxiv2017

Revealing Complete Complex KIR Haplotypes Phased By Long-Read Sequencing Technology

The killer cell immunoglobulin-like receptor (KIR) region of human chromosome 19 contains up to sixteen genes for natural killer (NK) cell receptors that recognize human leukocyte antigen (HLA)/peptide complexes and other ligands. The KIR proteins fulfill functional roles in infections, pregnancy, autoimmune diseases, and transplantation. However, their characterization remains a constant challenge. Not only are the genes highly homologous due to their recent evolution by tandem duplications, but the region is structurally dynamic due to frequent transposon-mediated recombination. A sequencing approach that precisely captures the complexity of KIR haplotypes for functional annotation is desirable.\n\nWe present a unique approach to haplotype the KIR loci using Single Molecule, Real-Time (SMRT(R)) Sequencing. Using this method, we have - for the first time - comprehensively sequenced and phased sixteen KIR haplotypes from eight individuals without imputation. The information revealed four novel haplotype structures, a novel gene-fusion allele, novel and confirmed insertion/deletion events, a homozygous individual, and overall diversity for the structural haplotypes and their alleles.\n\nThese KIR haplotypes augment our existing knowledge by providing high-quality references, evolutionary informers, and source material for imputation. The haplotype sequences and gene annotations provide alternative loci for the KIR region in the human genome reference GrCh38.p8.\n\nAuthor contributionsDR, CVG, SS, SR, DEG, MM designed the project. CWP, KE, RH performed the preparation and sequencing experiments. DR, CVG, RK, SR, DWG wrote the majority of the manuscript.

genomics↗