The Need for a Human Pangenome Reference Sequence

Recipes and ingredients for deep learning models of 3D genome folding

Three-dimensional genome folding plays roles in gene regulation and disease. In this review, we compare and contrast recent deep learning models for predicting genome contact maps. We survey preprocessing, architecture, training, evaluation, and interpretation methods, highlighting the capabilities and limitations of different models. In each area, we highlight challenges, opportunities, and potential future directions for genome-folding models.

Population genomics advances in frontier ethnic minorities in China

China, with its large geographic span, possesses rich genetic diversity across vast frontier regions in addition to the Han Chinese majority. Importantly, demographic events and various natural and cultural environments in Chinese frontier regions have shaped the genomic diversity of ethnic minorities via local adaptations. Thus, insights into the genetic diversity and adaptive evolution of these under-represented ethnic groups are crucial for understanding evolutionary scenarios and biomedical implications in East Asian populations. Here, we focus on ethnic minorities in Chinese frontier regions and review research advances regarding genomic diversity, genetic structure, population history, genetic admixture, and local adaptation. We first provide an overview of the extensive genetic diversity across populations in different Chinese frontier regions. Next, we summarize research progress regarding genetic ancestry, demographic history, the adaptive process, and the archaic identification of multiple ethnic minorities in different Chinese frontier regions. Finally, we discuss the gaps and opportunities in genomic studies of Chinese populations and the need for a more comprehensive understanding of genomic diversity and the evolution of populations of East Asian ancestry in the post-genomic era.

The ethics of synthetic DNA

In this paper, we discuss the ethical concerns that may arise from the synthesis of human DNA. To date, only small stretches of DNA have been constructed, but the prospect of generating human genomes is becoming feasible. At the same time, the significance of genes for identity, health and reproduction is coming under increased scrutiny. We examine the implications of DNA synthesis and its impact on debates over the relationship with our DNA and the ownership of our genes, its potential to disrupt common understandings of reproduction and privacy, and the way in which synthetic DNA challenges traditional associations between genes and identity. We explore the degree to which synthetic DNA may further undermine overgeneticised accounts of identity, health, reproduction, parenthood and privacy that are prevalent in the public domain and in some areas of policy-making. While avoiding making normative claims of our own, we conclude that there is a need for reflection on the ethical implications of these developing technologies before they are on us.

Clinical Variant Reclassification in Hereditary Disease Genetic Testing

Importance

Because accurate and consistent classification of DNA sequence variants is fundamental to germline genetic testing, understanding patterns of initial variant classification (VC) and subsequent reclassification from large-scale, empirical data can help improve VC methods, promote equity among race, ethnicity, and ancestry (REA) groups, and provide insights to inform clinical practice.

Objectives

To measure the degree to which initial VCs met certainty thresholds set by professional guidelines and quantify the rates of, the factors associated with, and the impact of reclassification among more than 2 million variants.

Design, Setting, and Participants

This cohort study used clinical multigene panel and exome sequencing data from diagnostic testing for hereditary disorders, carrier screening, or preventive genetic screening from individuals for whom genetic testing was performed between January 1, 2015, and June 30, 2023.

Exposure

DNA variants were classified into 1 of 5 categories: benign, likely benign, variant of uncertain significance (VUS), likely pathogenic, or pathogenic.

Main Outcomes and Measures

The main outcomes were accuracy of classifications, rates and directions of reclassifications, evidence contributing to reclassifications, and their impact across different clinical areas and REA groups. One-way analysis of variance followed by post hoc pairwise Tukey honest significant difference tests were used to analyze differences among means, and pairwise Pearson χ2 tests with Bonferroni corrections were used to compare categorical variables among groups.

Results

The cohort comprised 3 272 035 individuals (median [range] age, 44 [0-89] years; 2 240 506 female [68.47%] and 1 030 729 male [31.50%]; 216 752 Black [6.62%]; 336 414 Hispanic [10.28%]; 1 804 273 White [55.14%]). Among 2 051 736 variants observed over 8 years in this cohort, 94 453 (4.60%) were reclassified. Some variants were reclassified more than once, resulting in 105 172 total reclassification events. The majority (64 752 events [61.65%]) were changes from VUS to either likely benign, benign, likely pathogenic, or pathogenic categories. An additional 37.66% of reclassifications (39 608 events) were gains in classification certainty to terminal categories (ie, likely benign to benign and likely pathogenic to pathogenic). Only a small fraction (663 events [0.63%]) moved toward less certainty, or very rarely (61 events [0.06%]) were classification reversals. When normalized by the number of individuals tested, VUS reclassification rates were higher among specific underrepresented REA populations (Ashkenazi Jewish, Asian, Black, Hispanic, Pacific Islander, and Sephardic Jewish). Approximately one-half of VUS reclassifications (37 074 of 64 840 events [57.18%]) resulted from improved use of data from computational modeling.

Conclusions and Relevance

In this cohort study of individuals undergoing genetic testing, the empirically estimated accuracy of pathogenic, likely pathogenic, benign, and likely benign classifications exceeded the certainty thresholds set by current VC guidelines, suggesting the need to reevaluate definitions of these classifications. The relative contribution of various strategies to resolve VUS, including emerging machine learning-based computational methods, RNA analysis, and cascade family testing, provides useful insights that can be applied toward further improving VC methods, reducing the rate of VUS, and generating more definitive results for patients.

Identifying transgene insertions in Caenorhabditis elegans genomes with Oxford Nanopore sequencing

Genetically modified organisms are commonly used in disease research and agriculture but the precise genomic alterations underlying transgenic mutations are often unknown. The position and characteristics of transgenes, including the number of independent insertions, influences the expression of both transgenic and wild-type sequences. We used long-read, Oxford Nanopore Technologies (ONT) to sequence and assemble two transgenic strains of Caenorhabditis elegans commonly used in the research of neurodegenerative diseases: BY250 (pPdat-1::GFP) and UA44 (GFP and human α-synuclein), a model for Parkinson's research. After scaffolding to the reference, the final assembled sequences were ∼102 Mb with N50s of 17.9 Mb and 18.0 Mb, respectively, and L90s of six contiguous sequences, representing chromosome-level assemblies. Each of the assembled sequences contained more than 99.2% of the Nematoda BUSCO genes found in the C. elegans reference and 99.5% of the annotated C. elegans reference protein-coding genes. We identified the locations of the transgene insertions and confirmed that all transgene sequences were inserted in intergenic regions, leaving the organismal gene content intact. The transgenic C. elegans genomes presented here will be a valuable resource for Parkinson's research as well as other neurodegenerative diseases. Our work demonstrates that long-read sequencing is a fast, cost-effective way to assemble genome sequences and characterize mutant lines and strains.

Structural variation in humans and our primate kin in the era of telomere-to-telomere genomes and pangenomics

Structural variants (SVs) account for the majority of base pair differences both within and between primate species. However, our understanding of inter- and intra-species SV has been historically hampered by the quality of draft primate genomes and the absence of genome resources for key taxa. Recently, advances in long-read sequencing and genome assembly have begun to radically reshape our understanding of SVs. Two landmark achievements include the publication of a human telomere-to-telomere (T2T) genome as well as the development of the first human pangenome reference. In this review, we first look back to the major works laying the foundation for these projects. We then examine the ways in which T2T genome assemblies and pangenomes are transforming our understanding of and approach to primate SV. Finally, we discuss what the future of primate SV research may look like in the era of T2T genomes and pangenomics.

Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References

The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.

GraphSlimmer: Preserving Read Mappability with the Minimum Number of Variants

Modern genomic datasets, like those generated under the 1000 Genome Project, contain millions of variants belonging to known haplotypes. Although these datasets are more representative than a single reference sequence and can alleviate issues like reference bias, they are significantly more computationally burdensome to work with, often involving large-indexed genome graph data structures for tasks such as read mapping. The construction, preprocessing, and mapping algorithms can require substantial computational resources depending on the size of these variant sets. Moreover, the accuracy of mapping algorithms has been shown to decrease when working with complete variant sets. Therefore, a drastically reduced set of variants that preserves important properties of the original set is desirable. This work provides a technique for finding a minimal subset of variants S such that for given parameters α and δ, all substrings up to length α in the haplotypes are guaranteed to be still alignable to the appropriate locations with either Hamming or edit distance at most δ, using only S . Our contributions include showing the NP-hardness and inapproximability of these optimization problems and providing Integer Linear Programming (ILP) formulations. Our edit distance ILP formulation carefully decomposes the problem according to variant locations, which allows it to scale to support all of chromosome 22's variants from the 1000 Genome Project. Our experiments also demonstrate a significant reduction in the number of variants. For example, for moderately long reads, e.g., α = 1000, over 75% of the variants can be removed while preserving read mappability with edit distance at most one.

Targeted phasing of 2-200 kilobase DNA fragments with a short-read sequencer and a single-tube linked-read library method

In the human genome, heterozygous sites refer to genomic positions with a different allele or nucleotide variant on the maternal and paternal chromosomes. Resolving these allelic differences by chromosomal copy, also known as phasing, is achievable on a short-read sequencer when using a library preparation method that captures long-range genomic information. TELL-Seq is a library preparation that captures long-range genomic information with the aid of molecular identifiers (barcodes). The same barcode is used to tag the reads derived from the same long DNA fragment within a range of up to 200 kilobases (kb), generating linked-reads. This strategy can be used to phase an entire genome. Here, we introduce a TELL-Seq protocol developed for targeted applications, enabling the phasing of enriched loci of varying sizes, purity levels, and heterozygosity. To validate this protocol, we phased 2-200 kb loci enriched with different methods: CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis for the longest fragments, CRISPR/Cas9-mediated protection from exonuclease digestion for mid-size fragments, and long PCR for the shortest fragments. All selected loci have known clinical relevance: BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA. Collectively, the analyses show that TELL-Seq can accurately phase 2-200 kb targets using a short-read sequencer.

Pangenome graph construction from genome alignments with Minigraph-Cactus

Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome.

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

Disparities in data underlying clinical genomic interpretation is an acknowledged problem, but there is a paucity of data demonstrating it. The All of Us Research Program is collecting data including whole-genome sequences, health records, and surveys for at least a million participants with diverse ancestry and access to healthcare, representing one of the largest biomedical research repositories of its kind. Here, we examine pathogenic and likely pathogenic variants that were identified in the All of Us cohort. The European ancestry subgroup showed the highest overall rate of pathogenic variation, with 2.26% of participants having a pathogenic variant. Other ancestry groups had lower rates of pathogenic variation, including 1.62% for the African ancestry group and 1.32% in the Latino/Admixed American ancestry group. Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer or Hypercholesterolemia. Variant frequencies in many genes were consistent with the data from the public gnomAD database, with some notable exceptions resolved using gnomAD subsets. Differences in pathogenic variant frequency observed between ancestral groups generally indicate biases of ascertainment of knowledge about those variants, but some deviations may be indicative of differences in disease prevalence. This work will allow targeted precision medicine efforts at revealed disparities.

Pangenomics: A new era in the field of neurodegenerative diseases

A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.

Disparities in care of older adults of color with cancer: A narrative review

This review describes the barriers and challenges faced by older adults of color with cancer and highlights methods to improve their overall care. In the next decade, cancer incidence rates are expected to increase in the United States for people aged ≥65 years. A large proportion will be older adults of color who often have worse outcomes than older White patients. Many issues contribute to racial disparities in older adults, including biological factors and social determinants of health (SDOH) related to healthcare access, socioeconomic concerns, systemic racism, mistrust, and the neighborhood where a person lives. These disparities are exacerbated by age-related challenges often experienced by older adults, such as decreased functional status, impaired cognition, high rates of comorbidities and polypharmacy, poor nutrition, and limited social support. Additionally, underrepresentation of both patients of color and older adults in cancer clinical research results in a lack of adequate data to guide the management of these patients. Use of geriatric assessments (GA) can aid providers in uncovering age-related concerns and personalizing interventions for older patients. Research demonstrates the ability of GA-directed care to result in fewer treatment-related toxicities and improved quality of life, thus supporting the routine incorporation of validated GA into these patients' care. GA can be enhanced by including evaluation of SDOH, which can help healthcare providers understand and address the needs of older adults of color with cancer who face disparities related to their age and race.

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data

Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.

Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree

Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10-100 times faster than assembly-based approaches and in most cases more accurate-the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.

PAPerFly: Partial Assembly-based Peak Finder for ab initio binding site reconstruction

BACKGROUND

The specific recognition of a DNA locus by a given transcription factor is a widely studied issue. It is generally agreed that the recognition can be influenced not only by the binding motif but by the larger context of the binding site. In this work, we present a novel heuristic algorithm that can reconstruct the unique binding sites captured in a sequencing experiment without using the reference genome.

RESULTS

We present PAPerFly, the Partial Assembly-based Peak Finder, a tool for the binding site and binding context reconstruction from the sequencing data without any prior knowledge. This tool operates without the need to know the reference genome of the respective organism. We employ algorithmic approaches that are used during genome assembly. The proposed algorithm constructs a de Bruijn graph from the sequencing data. Based on this graph, sequences and their enrichment are reconstructed using a novel heuristic algorithm. The reconstructed sequences are aligned and the peaks in the sequence enrichment are identified. Our approach was tested by processing several ChIP-seq experiments available in the ENCODE database and comparing the results of Paperfly and standard methods.

CONCLUSIONS

We show that PAPerFly, an algorithm tailored for experiment analysis without the reference genome, yields better results than an aggregation of ChIP-seq agnostic tools. Our tool is freely available at https://github.com/Caeph/paperfly/ or on Zenodo ( https://doi.org/10.5281/zenodo.7116424 ).

Benchmarking long-read genome sequence alignment tools for human genomics applications

Background

The utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation.

Results

For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001-100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385.

Conclusions

These results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.

The human genome as the common heritage of humanity

While debate on the international regulation of human genomic research remains unsettled, the Universal Declaration on the Human Genome and Human Rights, 1997 qualifies the human genome as "heritage of humankind" in a symbolic sense. Using document analysis this article assesses whether, how and to what extent the common heritage framework is relevant in regulation of human genomic research. The article traces the history of the Human Genome Project to reveal the international community's race against privatization of the human genome and its resulting qualification as the common heritage of humanity. Further, it reviews the archival records of UNESCO's International Bioethics Committee to discover the rationale for qualifying the human genome as common heritage of humankind. The article finds that the common heritage of mankind framework remains relevant to the application of the human genome at the collective level. However, the framework is at odds with the individual dimension of the human genome based on individual personality rights. The article thus argues that the right to benefit from scientific progress and its applications offers an alternative international regulatory framework for human genomic research.

Localizing unmapped sequences with families to validate the Telomere-to-Telomere assembly and identify new hotspots for genetic diversity

Although it is ubiquitous in genomics, the current human reference genome (GRCh38) is incomplete: It is missing large sections of heterochromatic sequence, and as a singular, linear reference genome, it does not represent the full spectrum of human genetic diversity. To characterize gaps in GRCh38 and human genetic diversity, we developed an algorithm for sequence location approximation using nuclear families (ASLAN) to identify the region of origin of reads that do not align to GRCh38. Using unmapped reads and variant calls from whole-genome sequences (WGSs), ASLAN uses a maximum likelihood model to identify the most likely region of the genome that a subsequence belongs to given the distribution of the subsequence in the unmapped reads and phasings of families. Validating ASLAN on synthetic data and on reads from the alternative haplotypes in the decoy genome, ASLAN localizes >90% of 100-bp sequences with >92% accuracy and ∼1 Mb of resolution. We then ran ASLAN on 100-mers from unmapped reads from WGS from more than 700 families, and compared ASLAN localizations to alignment of the 100-mers to the recently released T2T-CHM13 assembly. We found that many unmapped reads in GRCh38 originate from telomeres and centromeres that are gaps in GRCh38. ASLAN localizations are in high concordance with T2T-CHM13 alignments, except in the centromeres of the acrocentric chromosomes. Comparing ASLAN localizations and T2T-CHM13 alignments, we identified sequences missing from T2T-CHM13 or sequences with high divergence from their aligned region in T2T-CHM13, highlighting new hotspots for genetic diversity.

The complete and fully-phased diploid genome of a male Han Chinese

Since the release of the complete human genome, the priority of human genomic study has now been shifting towards closing gaps in ethnic diversity. Here, we present a fully phased and well-annotated diploid human genome from a Han Chinese male individual (CN1), in which the assemblies of both haploids achieve the telomere-to-telomere (T2T) level. Comparison of this diploid genome with the CHM13 haploid T2T genome revealed significant variations in the centromere. Outside the centromere, we discovered 11,413 structural variations, including numerous novel ones. We also detected thousands of CN1 alleles that have accumulated high substitution rates and a few that have been under positive selection in the East Asian population. Further, we found that CN1 outperforms CHM13 as a reference genome in mapping and variant calling for the East Asian population owing to the distinct structural variants of the two references. Comparison of SNP calling for a large cohort of 8869 Chinese genomes using CN1 and CHM13 as reference respectively showed that the reference bias profoundly impacts rare SNP calling, with nearly 2 million rare SNPs miss-called with different reference genomes. Finally, applying the CN1 as a reference, we discovered 5.80 Mb and 4.21 Mb putative introgression sequences from Neanderthal and Denisovan, respectively, including many East Asian specific ones undetected using CHM13 as the reference. Our analyses reveal the advances of using CN1 as a reference for population genomic studies and paleo-genomic studies. This complete genome will serve as an alternative reference for future genomic studies on the East Asian population.

Pan-conserved segment tags identify ultra-conserved sequences across assemblies in the human pangenome

The human pangenome, a new reference sequence, addresses many limitations of the current GRCh38 reference. The first release is based on 94 high-quality haploid assemblies from individuals with diverse backgrounds. We employed a k-mer indexing strategy for comparative analysis across multiple assemblies, including the pangenome reference, GRCh38, and CHM13, a telomere-to-telomere reference assembly. Our k-mer indexing approach enabled us to identify a valuable collection of universally conserved sequences across all assemblies, referred to as "pan-conserved segment tags" (PSTs). By examining intervals between these segments, we discerned highly conserved genomic segments and those with structurally related polymorphisms. We found 60,764 polymorphic intervals with unique geo-ethnic features in the pangenome reference. In this study, we utilized ultra-conserved sequences (PSTs) to forge a link between human pangenome assemblies and reference genomes. This methodology enables the examination of any sequence of interest within the pangenome, using the reference genome as a comparative framework.

Long-Read DNA Sequencing: Recent Advances and Remaining Challenges

DNA sequencing has revolutionized medicine over recent decades. However, analysis of large structural variation and repetitive DNA, a hallmark of human genomes, has been limited by short-read technology, with read lengths of 100-300 bp. Long-read sequencing (LRS) permits routine sequencing of human DNA fragments tens to hundreds of kilobase pairs in size, using both real-time sequencing by synthesis and nanopore-based direct electronic sequencing. LRS permits analysis of large structural variation and haplotypic phasing in human genomes and has enabled the discovery and characterization of rare pathogenic structural variants and repeat expansions. It has also recently enabled the assembly of a complete, gapless human genome that includes previously intractable regions, such as highly repetitive centromeres and homologous acrocentric short arms. With the addition of protocols for targeted enrichment, direct epigenetic DNA modification detection, and long-range chromatin profiling, LRS promises to launch a new era of understanding of genetic diversity and pathogenic mutations in human populations.

The Future of Precision Oncology

Our understanding of the molecular mechanisms underlying cancer development and evolution have evolved rapidly over recent years, and the variation from one patient to another is now widely recognized. Consequently, one-size-fits-all approaches to the treatment of cancer have been superseded by precision medicines that target specific disease characteristics, promising maximum clinical efficacy, minimal safety concerns, and reduced economic burden. While precision oncology has been very successful in the treatment of some tumors with specific characteristics, a large number of patients do not yet have access to precision medicines for their disease. The success of next-generation precision oncology depends on the discovery of new actionable disease characteristics, rapid, accurate, and comprehensive diagnosis of complex phenotypes within each patient, novel clinical trial designs with improved response rates, and worldwide access to novel targeted anticancer therapies for all patients. This review outlines some of the current technological trends, and highlights some of the complex multidisciplinary efforts that are underway to ensure that many more patients with cancer will be able to benefit from precision oncology in the near future.

Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP

BACKGROUND

With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data.

RESULTS

We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project at: https://github.com/HKU-BAL/Clair3-MP .

CONCLUSIONS

These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs

MOTIVATION

Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds.

RESULTS

We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting.

AVAILABILITY AND IMPLEMENTATION

GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

Complete sequences of six major histocompatibility complex haplotypes, including all the major MHC class II structures

Accurate and comprehensive immunogenetic reference panels are key to the successful implementation of population-scale immunogenomics. The 5Mbp Major Histocompatibility Complex (MHC) is the most polymorphic region of the human genome and associated with multiple immune-mediated diseases, transplant matching and therapy responses. Analysis of MHC genetic variation is severely complicated by complex patterns of sequence variation, linkage disequilibrium and a lack of fully resolved MHC reference haplotypes, increasing the risk of spurious findings on analyzing this medically important region. Integrating Illumina, ultra-long Nanopore, and PacBio HiFi sequencing as well as bespoke bioinformatics, we completed five of the alternative MHC reference haplotypes of the current (GRCh38/hg38) build of the human reference genome and added one other. The six assembled MHC haplotypes encompass the DR1 and DR4 haplotype structures in addition to the previously completed DR2 and DR3, as well as six distinct classes of the structurally variable C4 region. Analysis of the assembled haplotypes showed that MHC class II sequence structures, including repeat element positions, are generally conserved within the DR haplotype supergroups, and that sequence diversity peaks in three regions around HLA-A, HLA-B+C, and the HLA class II genes. Demonstrating the potential for improved short-read analysis, the number of proper read pairs recruited to the MHC was found to be increased by 0.06%-0.49% in a 1000 Genomes Project read remapping experiment with seven diverse samples. Furthermore, the assembled haplotypes can serve as references for the community and provide the basis of a structurally accurate genotyping graph of the complete MHC region.

A pangenome reference of 36 Chinese populations

Human genomics is witnessing an ongoing paradigm shift from a single reference sequence to a pangenome form, but populations of Asian ancestry are underrepresented. Here we present data from the first phase of the Chinese Pangenome Consortium, including a collection of 116 high-quality and haplotype-phased de novo assemblies based on 58 core samples representing 36 minority Chinese ethnic groups. With an average 30.65× high-fidelity long-read sequence coverage, an average contiguity N50 of more than 35.63 megabases and an average total size of 3.01 gigabases, the CPC core assemblies add 189 million base pairs of euchromatic polymorphic sequences and 1,367 protein-coding gene duplications to GRCh38. We identified 15.9 million small variants and 78,072 structural variants, of which 5.9 million small variants and 34,223 structural variants were not reported in a recently released pangenome reference1. The Chinese Pangenome Consortium data demonstrate a remarkable increase in the discovery of novel and missing sequences when individuals are included from underrepresented minority ethnic groups. The missing reference sequences were enriched with archaic-derived alleles and genes that confer essential functions related to keratinization, response to ultraviolet radiation, DNA repair, immunological responses and lifespan, implying great potential for shedding new light on human evolution and recovering missing heritability in complex disease mapping.

The Bovine Pangenome Consortium: democratizing production and accessibility of genome assemblies for global cattle breeds and other bovine species

The Bovine Pangenome Consortium (BPC) is an international collaboration dedicated to the assembly of cattle genomes to develop a more complete representation of cattle genomic diversity. The goal of the BPC is to provide genome assemblies and a community-agreed pangenome representation to replace breed-specific reference assemblies for cattle genomics. The BPC invites partners sharing our vision to participate in the production of these assemblies and the development of a common, community-approved, pangenome reference as a public resource for the research community ( https://bovinepangenome.github.io/ ). This community-driven resource will provide the context for comparison between studies and the future foundation for cattle genomic selection.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges

The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.

Comparing genomic and epigenomic features across species using the WashU Comparative Epigenome Browser

Genome browsers have become an intuitive and critical tool to visualize and analyze genomic features and data. Conventional genome browsers display data/annotations on a single reference genome/assembly; there are also genomic alignment viewer/browsers that help users visualize alignment, mismatch, and rearrangement between syntenic regions. However, there is a growing need for a comparative epigenome browser that can display genomic and epigenomic data sets across different species and enable users to compare them between syntenic regions. Here, we present the WashU Comparative Epigenome Browser. It allows users to load functional genomic data sets/annotations mapped to different genomes and display them over syntenic regions simultaneously. The browser also displays genetic differences between the genomes from single-nucleotide variants (SNVs) to structural variants (SVs) to visualize the association between epigenomic differences and genetic differences. Instead of anchoring all data sets to the reference genome coordinates, it creates independent coordinates of different genome assemblies to faithfully present features and data mapped to different genomes. It uses a simple, intuitive genome-align track to illustrate the syntenic relationship between different species. It extends the widely used WashU Epigenome Browser infrastructure and can be expanded to support multiple species. This new browser function will greatly facilitate comparative genomic/epigenomic research, as well as support the recent growing needs to directly compare and benchmark the T2T CHM13 assembly and other human genome assemblies.

Targeted Phasing of 2-200 Kilobase DNA Fragments with a Short-Read Sequencer and a Single-Tube Linked-Read Library Method

In the human genome, heterozygous sites are genomic positions with different alleles inherited from each parent. On average, there is a heterozygous site every 1-2 kilobases (kb). Resolving whether two alleles in neighboring heterozygous positions are physically linked-that is, phased-is possible with a short-read sequencer if the sequencing library captures long-range information. TELL-Seq is a library preparation method based on millions of barcoded micro-sized beads that enables instrument-free phasing of a whole human genome in a single PCR tube. TELL-Seq incorporates a unique molecular identifier (barcode) to the short reads generated from the same high-molecular-weight (HMW) DNA fragment (known as 'linked-reads'). However, genome-scale TELL-Seq is not cost-effective for applications focusing on a single locus or a few loci. Here, we present an optimized TELL-Seq protocol that enables the cost-effective phasing of enriched loci (targets) of varying sizes, purity levels, and heterozygosity. Targeted TELL-Seq maximizes linked-read efficiency and library yield while minimizing input requirements, fragment collisions on microbeads, and sequencing burden. To validate the targeted protocol, we phased seven 180-200 kb loci enriched by CRISPR/Cas9-mediated excision coupled with pulse-field electrophoresis, four 20 kb loci enriched by CRISPR/Cas9-mediated protection from exonuclease digestion, and six 2-13 kb loci amplified by PCR. The selected targets have clinical and research relevance (BRCA1, BRCA2, MLH1, MSH2, MSH6, APC, PMS2, SCN5A-SCN10A, and PKI3CA). These analyses reveal that targeted TELL-Seq provides a reliable way of phasing allelic variants within targets (2-200 kb in length) with the low cost and high accuracy of short-read sequencing.

AGC: compact representation of assembled genomes with fast queries and updates

MOTIVATION

High-quality sequence assembly is the ultimate representation of complete genetic information of an individual. Several ongoing pangenome projects are producing collections of high-quality assemblies of various species. Each project has already generated assemblies of hundreds of gigabytes on disk, greatly impeding the distribution of and access to such rich datasets.

RESULTS

Here, we show how to reduce the size of the sequenced genomes by 2-3 orders of magnitude. Our tool compresses the genomes significantly better than the existing programs and is much faster. Moreover, its unique feature is the ability to access any contig (or its part) in a fraction of a second and easily append new samples to the compressed collections. Thanks to this, AGC could be useful not only for backup or transfer purposes but also for routine analysis of pangenome sequences in common pipelines. With the rapidly reduced cost and improved accuracy of sequencing technologies, we anticipate more comprehensive pangenome projects with much larger sample sizes. AGC is likely to become a foundation tool to store, distribute and access pangenome data.

AVAILABILITY AND IMPLEMENTATION

The source code of AGC is available at https://github.com/refresh-bio/agc. The package can be installed via Bioconda at https://anaconda.org/bioconda/agc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Optimal gap-affine alignment in O(s) space

MOTIVATION

Pairwise sequence alignment remains a fundamental problem in computational biology and bioinformatics. Recent advances in genomics and sequencing technologies demand faster and scalable algorithms that can cope with the ever-increasing sequence lengths. Classical pairwise alignment algorithms based on dynamic programming are strongly limited by quadratic requirements in time and memory. The recently proposed wavefront alignment algorithm (WFA) introduced an efficient algorithm to perform exact gap-affine alignment in O(ns) time, where s is the optimal score and n is the sequence length. Notwithstanding these bounds, WFA's O(s2) memory requirements become computationally impractical for genome-scale alignments, leading to a need for further improvement.

RESULTS

In this article, we present the bidirectional WFA algorithm, the first gap-affine algorithm capable of computing optimal alignments in O(s) memory while retaining WFA's time complexity of O(ns). As a result, this work improves the lowest known memory bound O(n) to compute gap-affine alignments. In practice, our implementation never requires more than a few hundred MBs aligning noisy Oxford Nanopore Technologies reads up to 1 Mbp long while maintaining competitive execution times.

AVAILABILITY AND IMPLEMENTATION

All code is publicly available at https://github.com/smarco/BiWFA-paper.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Read2Tree: scalable and accurate phylogenetic trees from raw reads

The inference of phylogenetic trees is foundational to biology. However, state-of-the-art phylogenomics requires running complex pipelines, at significant computational and labour costs, with additional constraints in sequencing coverage, assembly and annotation quality. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes. In a benchmark encompassing a broad variety of datasets, our assembly-free approach was 10-100x faster than conventional approaches, and in most cases more accurate-the exception being when sequencing coverage was high and reference species very distant. To illustrate the broad applicability of the tool, we reconstructed a yeast tree of life of 435 species spanning 590 million years of evolution. Applied to Coronaviridae samples, Read2Tree accurately classified highly diverse animal samples and near-identical SARS-CoV-2 sequences on a single tree-thereby exhibiting remarkable breadth and depth. The speed, accuracy, and versatility of Read2Tree enables comparative genomics at scale.

Mammalian genome innovation through transposon domestication

Since the discovery of transposons, their sheer abundance in host genomes has puzzled many. While historically viewed as largely harmless 'parasitic' DNAs during evolution, transposons are not a mere record of ancient genome invasion. Instead, nearly every element of transposon biology has been integrated into host biology. Here we review how host genome sequences introduced by transposon activities provide raw material for genome innovation and document the distinct evolutionary path of each species.

Assembly of a pangenome for global cattle reveals missing sequences and novel structural variations, providing new insights into their diversity and evolutionary history

A cattle pangenome representation was created based on the genome sequences of 898 cattle representing 57 breeds. The pangenome identified 83 Mb of sequence not found in the cattle reference genome, representing 3.1% novel sequence compared with the 2.71-Gb reference. A catalog of structural variants developed from this cattle population identified 3.3 million deletions, 0.12 million inversions, and 0.18 million duplications. Estimates of breed ancestry and hybridization between cattle breeds using insertion/deletions as markers were similar to those produced by single nucleotide polymorphism-based analysis. Hundreds of deletions were observed to have stratification based on subspecies and breed. For example, an insertion of a Bov-tA1 repeat element was identified in the first intron of the APPL2 gene and correlated with cattle breed geographic distribution. This insertion falls within a segment overlapping predicted enhancer and promoter regions of the gene, and could affect important traits such as immune response, olfactory functions, cell proliferation, and glucose metabolism in muscle. The results indicate that pangenomes are a valuable resource for studying diversity and evolutionary history, and help to delineate how domestication, trait-based breeding, and adaptive introgression have shaped the cattle genome.

Thousands of human mutation clusters are explained by short-range template switching

Variation within human genomes is unevenly distributed, and variants show spatial clustering. DNA replication-related template switching is a poorly known mutational mechanism capable of causing major chromosomal rearrangements as well as creating short inverted sequence copies that appear as local mutation clusters in sequence comparisons. In this study, haplotype-resolved genome assemblies representing 25 human populations and multinucleotide variants aggregated from 140,000 human sequencing experiments were reanalyzed. Local template switching could explain thousands of complex mutation clusters across the human genome, the loci segregating within and between populations. During the study, computational tools were developed for identification of template switch events using both short-read sequencing data and genotype data, and for genotyping candidate loci using short-read data. The characteristics of template-switch mutations complicate their detection, and widely used analysis pipelines for short-read sequencing data, normally capable of identifying single nucleotide changes, were found to miss template-switch mutations of tens of base pairs, potentially invalidating medical genetic studies searching for a causative allele behind genetic diseases. Combined with the massive sequencing data now available for humans, the novel tools described here enable building catalogs of affected loci and studying the cellular mechanisms behind template switching in both healthy organisms and disease.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Population-scale genotyping of structural variation in the era of long-read sequencing

Population-scale studies of structural variation (SV) are growing rapidly worldwide with the development of long-read sequencing technology, yielding a considerable number of novel SVs and complete gap-closed genome assemblies. Herein, we highlight recent studies using a hybrid sequencing strategy and present the challenges toward large-scale genotyping for SVs due to the reference bias. Genotyping SVs at a population scale remains challenging, which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases. We summarize academic efforts to improve genotype quality through linear or graph representations of reference and alternative alleles. Graph-based genotypers capable of integrating diverse genetic information are effectively applied to large and diverse cohorts, contributing to unbiased downstream analysis. Meanwhile, there is still an urgent need in this field for efficient tools to construct complex graphs and perform sequence-to-graph alignments.

Predicting Archaic Hominin Phenotypes from Genomic Data

Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans' closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understanding the influence of variants on lower-level molecular phenotypes-such as gene expression and protein function-is a promising approach to using ancient DNA to learn about archaic hominin traits. Molecular phenotypes have simpler genetic architectures than organism-level complex phenotypes, and this approach enables moving beyond association studies by proposing hypotheses about the effects of archaic variants that are testable in model systems. The major challenge to understanding archaic hominin phenotypes is broadening our ability to accurately map genotypes to phenotypes, but ongoing advances ensure that there will be much more to learn about archaic hominin phenotypes from their genomes. Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A next-generation human genome sequence

A near-complete sequence outlines a path for a more inclusive reference.

A joint NCBI and EMBL-EBI transcript set for clinical genomics and research

Comprehensive genome annotation is essential to understand the impact of clinically relevant variants. However, the absence of a standard for clinical reporting and browser display complicates the process of consistent interpretation and reporting. To address these challenges, Ensembl/GENCODE1 and RefSeq2 launched a joint initiative, the Matched Annotation from NCBI and EMBL-EBI (MANE) collaboration, to converge on human gene and transcript annotation and to jointly define a high-value set of transcripts and corresponding proteins. Here, we describe the MANE transcript sets for use as universal standards for variant reporting and browser display. The MANE Select set identifies a representative transcript for each human protein-coding gene, whereas the MANE Plus Clinical set provides additional transcripts at loci where the Select transcripts alone are not sufficient to report all currently known clinical variants. Each MANE transcript represents an exact match between the exonic sequences of an Ensembl/GENCODE transcript and its counterpart in RefSeq such that the identifiers can be used synonymously. We have now released MANE Select transcripts for 97% of human protein-coding genes, including all American College of Medical Genetics and Genomics Secondary Findings list v3.0 (ref. 3) genes. MANE transcripts are accessible from major genome browsers and key resources. Widespread adoption of these transcript sets will increase the consistency of reporting, facilitate the exchange of data regardless of the annotation source and help to streamline clinical interpretation.

Epigenetic patterns in a complete human genome

The completion of a telomere-to-telomere human reference genome, T2T-CHM13, has resolved complex regions of the genome, including repetitive and homologous regions. Here, we present a high-resolution epigenetic study of previously unresolved sequences, representing entire acrocentric chromosome short arms, gene family expansions, and a diverse collection of repeat classes. This resource precisely maps CpG methylation (32.28 million CpGs), DNA accessibility, and short-read datasets (166,058 previously unresolved chromatin immunoprecipitation sequencing peaks) to provide evidence of activity across previously unidentified or corrected genes and reveals clinically relevant paralog-specific regulation. Probing CpG methylation across human centromeres from six diverse individuals generated an estimate of variability in kinetochore localization. This analysis provides a framework with which to investigate the most elusive regions of the human genome, granting insights into epigenetic regulation.

Complete genomic and epigenetic maps of human centromeres

Existing human genome assemblies have almost entirely excluded repetitive sequences within and near centromeres, limiting our understanding of their organization, evolution, and functions, which include facilitating proper chromosome segregation. Now, a complete, telomere-to-telomere human genome assembly (T2T-CHM13) has enabled us to comprehensively characterize pericentromeric and centromeric repeats, which constitute 6.2% of the genome (189.9 megabases). Detailed maps of these regions revealed multimegabase structural rearrangements, including in active centromeric repeat arrays. Analysis of centromere-associated sequences uncovered a strong relationship between the position of the centromere and the evolution of the surrounding DNA through layered repeat expansions. Furthermore, comparisons of chromosome X centromeres across a diverse panel of individuals illuminated high degrees of structural, epigenetic, and sequence variation in these complex and rapidly evolving regions.