Genotyping is a core application of high-throughput sequencing. We present KAGE, a genotyper for SNPs and short indels that is inspired by recent developments within graph-based genome representations and alignment-free methods. KAGE uses a pan-genome representation of the population to efficiently and accurately predict genotypes. Two novel ideas improve both the speed and accuracy: a Bayesian model incorporates genotypes from thousands of individuals to improve prediction accuracy, and a computationally efficient method leverages correlation between variants. We show that the accuracy of KAGE is at par with the best existing alignment-free genotypers, while being an order of magnitude faster.

BACKGROUND: Overweight and obesity are defined by an anomalous or excessive fat accumulation that may compromise health. To find single-nucleotide polymorphisms (SNPs) influencing metabolic phenotypes associated with the obesity state, we analyze multiple anthropometric and clinical parameters in a cohort of 790 healthy volunteers and study potential associations with 48 manually curated SNPs, in metabolic genes functionally associated with the mechanistic target of rapamycin (mTOR) pathway. RESULTS: We identify and validate rs2291007 within a conserved region in the 3'UTR of folliculin-interacting protein FNIP2 that correlates with multiple leanness parameters. The T-to-C variant represents the major allele in Europeans and disrupts an ancestral target sequence of the miRNA miR-181b-5p, thus resulting in increased FNIP2 mRNA levels in cancer cell lines and in peripheral blood from carriers of the C allele. Because the miRNA binding site is conserved across vertebrates, we engineered the T-to-C substitution in the endogenous Fnip2 allele in mice. Primary cells derived from Fnip2 C/C mice show increased mRNA stability, and more importantly, Fnip2 C/C mice replicate the decreased adiposity and increased leanness observed in human volunteers. Finally, expression levels of FNIP2 in both human samples and mice negatively associate with leanness parameters, and moreover, are the most important contributor in a multifactorial model of body mass index prediction. CONCLUSIONS: We propose that rs2291007 influences human leanness through an evolutionarily conserved modulation of FNIP2 mRNA levels.

BACKGROUND: Histone lactylation has been recently described as a novel histone post-translational modification linking cellular metabolism to epigenetic regulation. RESULTS: Given the expected relevance of this modification and current limited knowledge of its function, we generate genome-wide datasets of H3K18la distribution in various in vitro and in vivo samples, including mouse embryonic stem cells, macrophages, adipocytes, and mouse and human skeletal muscle. We compare them to profiles of well-established histone modifications and gene expression patterns. Supervised and unsupervised bioinformatics analysis shows that global H3K18la distribution resembles H3K27ac, although we also find notable differences. H3K18la marks active CpG island-containing promoters of highly expressed genes across most tissues assessed, including many housekeeping genes, and positively correlates with H3K27ac and H3K4me3 as well as with gene expression. In addition, H3K18la is enriched at active enhancers that lie in proximity to genes that are functionally important for the respective tissue. CONCLUSIONS: Overall, our data suggests that H3K18la is not only a marker for active promoters, but also a mark of tissue specific active enhancers.

BACKGROUND: During normal zygotic division, two haploid parental genomes replicate, unite and segregate into two biparental diploid blastomeres. RESULTS: Contrary to this fundamental biological tenet, we demonstrate here that parental genomes can segregate to distinct blastomeres during the zygotic division resulting in haploid or uniparental diploid and polyploid cells, a phenomenon coined heterogoneic division. By mapping the genomic landscape of 82 blastomeres from 25 bovine zygotes, we show that multipolar zygotic division is a tell-tale of whole-genome segregation errors. Based on the haplotypes and live-imaging of zygotic divisions, we demonstrate that various combinations of androgenetic, gynogenetic, diploid, and polyploid blastomeres arise via distinct parental genome segregation errors including the formation of additional paternal, private parental, or tripolar spindles, or by extrusion of paternal genomes. Hence, we provide evidence that private parental spindles, if failing to congress before anaphase, can lead to whole-genome segregation errors. In addition, anuclear blastomeres are common, indicating that cytokinesis can be uncoupled from karyokinesis. Dissociation of blastocyst-stage embryos further demonstrates that whole-genome segregation errors might lead to mixoploid or chimeric development in both human and cow. Yet, following multipolar zygotic division, fewer embryos reach the blastocyst stage and diploidization occurs frequently indicating that alternatively, blastomeres with genome-wide errors resulting from whole-genome segregation errors can be selected against or contribute to embryonic arrest. CONCLUSIONS: Heterogoneic zygotic division provides an overarching paradigm for the development of mixoploid and chimeric individuals and moles and can be an important cause of embryonic and fetal arrest following natural conception or IVF.

Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate as low as 7%. We demonstrate that scTAM-seq can resolve DNA methylation dynamics across B-cell differentiation in blood and bone marrow, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface-protein expression, thus enabling integration of single-cell DNA methylation information with cell atlas data. In summary, scTAM-seq is a high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

Comprehensive characterisation of genome engineering technologies is relevant for their development and safe use in human gene therapy. Short-read based methods can overlook insertion events in repetitive regions. We develop INSERT-seq, a method that combines targeted amplification of integrated DNA, UMI-based correction of PCR bias and Oxford Nanopore long-read sequencing for robust analysis of DNA integration. The experimental pipeline improves the number of mappable insertions at repetitive regions by 4.8-7.3% and larger repeats are processed with a computational peak calling pipeline. INSERT-seq is a simple, cheap and robust method to quantitatively characterise DNA integration in diverse ex vivo and in vivo samples.

RNA processing, including splicing and alternative polyadenylation, is crucial to gene function and regulation, but methods to detect RNA processing from single-cell RNA sequencing data are limited by reliance on pre-existing annotations, peak calling heuristics, and collapsing measurements by cell type. We introduce ReadZS, an annotation-free statistical approach to identify regulated RNA processing in single cells. ReadZS discovers cell type-specific RNA processing in human lung and conserved, developmentally regulated RNA processing in mammalian spermatogenesis-including global 3' UTR shortening in human spermatogenesis. ReadZS also discovers global 3' UTR lengthening in Arabidopsis development, highlighting the usefulness of this method in under-annotated transcriptomes.

CRISPR tools can generate knockout and knock-in animal models easily, but the models can contain off-target genomic lesions or random insertions of donor DNAs. Simpler methods to identify off-target lesions and random insertions, using tail or earpiece DNA, are unavailable. We develop CRISPR-KRISPR (CRISPR-Knock-ins and Random Inserts Searching PRotocol), a method to identify both off-target lesions and random insertions. CRISPR-KRISPR uses as little as 3.4 μg of genomic DNA; thus, it can be easily incorporated as an additional step to genotype founder animals for further breeding.

BACKGROUND: DNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors. RESULTS: Our analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations. CONCLUSIONS: We show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

BACKGROUND: A major driver of cancer chromosomal instability is replication stress, the slowing or stalling of DNA replication. How replication stress and genomic instability are connected is not known. Aphidicolin-induced replication stress induces breakages at common fragile sites, but the exact causes of fragility are debated, and acute genomic consequences of replication stress are not fully explored. RESULTS: We characterize DNA copy number alterations (CNAs) in single, diploid non-transformed cells, caused by one cell cycle in the presence of either aphidicolin or hydroxyurea. Multiple types of CNAs are generated, associated with different genomic regions and features, and observed copy number landscapes are distinct between aphidicolin and hydroxyurea-induced replication stress. Coupling cell type-specific analysis of CNAs to gene expression and single-cell replication timing analyses pinpointed the causative large genes of the most recurrent chromosome-scale CNAs in aphidicolin. These are clustered on chromosome 7 in RPE1 epithelial cells but chromosome 1 in BJ fibroblasts. Chromosome arm level CNAs also generate acentric lagging chromatin and micronuclei containing these chromosomes. CONCLUSIONS: Chromosomal instability driven by replication stress occurs via focal CNAs and chromosome arm scale changes, with the latter confined to a very small subset of chromosome regions, potentially heavily skewing cancer genome evolution. Different inducers of replication stress lead to distinctive CNA landscapes providing the opportunity to derive copy number signatures of specific replication stress mechanisms. Single-cell CNA analysis thus reveals the impact of replication stress on the genome, providing insights into the molecular mechanisms which fuel chromosomal instability in cancer.

Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling of long-range genomic interactions. However, data from these technologies are prone to technical noise and biases that hinder downstream analysis. We develop a normalization approach, BandNorm, and a deep generative modeling framework, scVI-3D, to account for scHi-C specific biases. In benchmarking experiments, BandNorm yields leading performances in a time and memory efficient manner for cell-type separation, identification of interacting loci, and recovery of cell-type relationships, while scVI-3D exhibits advantages for rare cell types and under high sparsity scenarios. Application of BandNorm coupled with gene-associating domain analysis reveals scRNA-seq validated sub-cell type identification.

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.

BACKGROUND: The pseudoautosomal region 1 (PAR1) is a 2.7 Mb telomeric region of human sex chromosomes. PAR1 has a crucial role in ensuring proper segregation of sex chromosomes during male meiosis, exposing it to extreme recombination and mutation processes. We investigate PAR1 evolution using population genomic datasets of extant humans, eight populations of great apes, and two archaic human genome sequences. RESULTS: We find that PAR1 is fast evolving and closer to evolutionary nucleotide equilibrium than autosomal telomeres. We detect a difference between substitution patterns and extant diversity in PAR1, mainly driven by the conflict between strong mutation and recombination-associated fixation bias at CpG sites. We detect excess C-to-G mutations in PAR1 of all great apes, specific to the mutagenic effect of male recombination. Despite recent evidence for Y chromosome introgression from humans into Neanderthals, we find that the Neanderthal PAR1 retained similarity to the Denisovan sequence. We find differences between substitution spectra of these archaics suggesting rapid evolution of PAR1 in recent hominin history. Frequency analysis of alleles segregating in females and males provided no evidence for recent sexual antagonism in this region. We study repeat content and double-strand break hotspot regions in PAR1 and find that they may play roles in ensuring the obligate X-Y recombination event during male meiosis. CONCLUSIONS: Our study provides an unprecedented quantification of population genetic forces governing PAR1 biology across extant and extinct hominids. PAR1 evolutionary dynamics are predominantly governed by recombination processes with a strong impact on mutation patterns across all species.

BACKGROUND: DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown. RESULTS: We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation. CONCLUSIONS: Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

BACKGROUND: The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. RESULTS: scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. CONCLUSIONS: We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

BACKGROUND: Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools. RESULTS: We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods. CONCLUSIONS: Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub ( https://github.com/wanglabtongji/CCI ).

BACKGROUND: The early embryonic divisions of many organisms, including fish, flies, and frogs, are characterized by a very rapid S-phase caused by high rates of replication initiation. In somatic cells, S-phase is much longer due to both a reduction in the total number of initiation events and the imposition of a temporal order of origin activation. The physiological importance of changes in the rate and timing of replication initiation in S-phase remains unclear. RESULTS: Here we assess the importance of the temporal control of replication initiation using a conditional system in budding yeast to drive the early replication of the majority of origins in a single cell cycle. We show that global early replication disrupts the expression of over a quarter of all genes. By deleting individual origins, we show that delaying replication is sufficient to restore normal gene expression, directly implicating origin firing control in this regulation. Global early replication disrupts nucleosome positioning and transcription factor binding during S-phase, suggesting that the rate of S-phase is important to regulate the chromatin landscape. CONCLUSIONS: Together, these data provide new insight into the role of the temporal control of origin firing during S-phase for coordinating replication, gene expression, and chromatin establishment as occurs in the early embryo.

BACKGROUND: We and others have suggested that pioneer activity - a transcription factor's (TF's) ability to bind and open inaccessible loci - is not a qualitative trait limited to a select class of pioneer TFs. We hypothesize that most TFs display pioneering activity that depends on the TF concentration and the motif content at their target loci. RESULTS: Here, we present a quantitative in vivo measure of pioneer activity that captures the relative difference in a TF's ability to bind accessible versus inaccessible DNA. The metric is based on experiments that use CUT&Tag to measure the binding of doxycycline-inducible TFs. For each location across the genome, we determine the concentration of doxycycline required for a TF to reach half-maximal occupancy; lower concentrations reflect higher affinity. We propose that the relative difference in a TF's affinity between ATAC-seq labeled accessible and inaccessible binding sites is a measure of its pioneer activity. We estimate binding affinities at tens of thousands of genomic loci for the endodermal TFs FOXA1 and HNF4A and show that HNF4A has stronger pioneer activity than FOXA1. We show that both FOXA1 and HNF4A display higher binding affinity at inaccessible sites with more copies of their respective motifs. The quantitative analysis of binding suggests different modes of binding for FOXA1, including an anti-cooperative mode of binding at certain accessible loci. CONCLUSIONS: Our results suggest that relative binding affinities are reasonable measures of pioneer activity and support the model wherein most TFs have some degree of context-dependent pioneer activity.

We present two methods for enhancing the efficiency of mitochondrial DNA (mtDNA) editing in mice with DddA-derived cytosine base editors (DdCBEs). First, we fused DdCBEs to a nuclear export signal (DdCBE-NES) to avoid off-target C-to-T conversions in the nuclear genome and improve editing efficiency in mtDNA. Second, mtDNA-targeted TALENs (mitoTALENs) are co-injected into mouse embryos to cleave unedited mtDNA. We generated a mouse model with the m.G12918A mutation in the MT-ND5 gene, associated with mitochondrial genetic disorders in humans. The mutant mice show hunched appearances, damaged mitochondria in kidney and brown adipose tissues, and hippocampal atrophy, resulting in premature death.

Earth's environments harbor complex consortia of microbes that affect processes ranging from host health to biogeochemical cycles. Understanding their evolution and function is limited by an inability to isolate genomes in a high-throughput manner. Here, we present a workflow for bacterial whole-genome sequencing using open-source labware and the OpenTrons robotics platform, reducing costs to approximately $10 per genome. We assess genomic diversity within 45 gut bacterial species from wild-living chimpanzees and bonobos. We quantify intraspecific genomic diversity and reveal divergence of homologous plasmids between hosts. This enables population genetic analyses of bacterial strains not currently possible with metagenomic data alone.

BACKGROUND: Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth "QTL") analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. RESULTS: Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)-a well-established dimension reduction and factor discovery method-via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use. CONCLUSIONS: To help researchers use PCA in their QTL analysis, we provide an R package PCAForQTL along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL . We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.