Biology subjects

Miao, Z.

Publications and source records attributed to Miao, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Stepwise developmental mimicry generates proximal-biased kidney organoids

The kidney maintains body fluid homeostasis by reabsorbing essential compounds and excreting waste. Proximal tubule cells, crucial for renal reabsorption of a range of sugars, ions, and amino acids, are highly susceptible to damage, leading to pathologies necessitating dialysis and kidney transplants. While human pluripotent stem cell-derived kidney organoids are used for modeling renal development, disease, and injury, the formation of proximal nephron cells in these 3D structures is incomplete. Here, we describe how to drive the development of proximal tubule precursors in kidney organoids by following a blueprint of in vivo human nephrogenesis. Transient manipulation of the PI3K signaling pathway activates Notch signaling in the early nephron and drives nephrons toward a proximal precursor state. These "proximal-biased" (PB) organoid nephrons proceed to generate proximal nephron precursor cells. Single-cell transcriptional analyses across the organoid nephron differentiation, comparing control and PB types, confirm the requirement of transient Notch signaling for proximal development. Indicative of functional maturity, PB organoids demonstrate dextran and albumin uptake, akin to in vivo proximal tubules. Moreover, PB organoids are highly sensitive to nephrotoxic agents, display an injury response, and drive expression of HAVCR1/KIM1, an early proximal-specific marker of kidney injury. Injured PB organoids show evidence of collapsed tubules, DNA damage, and upregulate the injury-response marker SOX9. The PB organoid model therefore has functional relevance and potential for modeling mechanisms underpinning nephron injury. These advances improve the use of iPSC-derived kidney organoids as tools to understand developmental nephrology, model disease, test novel therapeutics, and for understanding human renal physiology.

developmental biology↗

bioRxiv2024

A multimodal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks

Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, with 6 multifaceted cognitive tasks including 2 hours of naturalistic movie viewing. This datasets combination of ample sample size, extensive data per participant, more than 600 iso hours worth of data, and a wide range of experimental conditions -- including cognitive, affective, social, and somatic/interoceptive tasks -- positions it uniquely for probing important questions in cognitive neuroscience.

neuroscience↗

bioRxiv2024

CTCF mutation at R567 causes developmental disorders via 3D genome rearrangement and abnormal neurodevelopment

The three-dimensional genome structure organized by CTCF is required for development. Clinically identified mutations in CTCF have been linked to adverse developmental outcomes. Nevertheless, the underlying mechanism remains elusive. In this investigation, we explored the regulatory roles of a clinically relevant R567W point mutation, located within the 11th zinc finger of CTCF, by introducing this mutation into both murine models and human embryonic stem cell-derived cortical organoid models. Mice with homozygous CTCFR567W mutation exhibited growth impediments, resulting in postnatal mortality, and deviations in brain, heart, and lung development at the pathological and single- cell transcriptome levels. This mutation induced premature stem-like cell exhaustion, accelerated the maturation of GABAergic neurons, and disrupted neurodevelopmental and synaptic pathways. Additionally, it specifically hindered CTCF binding to peripheral motifs upstream to the core consensus site, causing alterations in local chromatin structure and gene expression, particularly at the clustered protocadherin locus. Comparative analysis using human cortical organoids mirrored the consequences induced by this mutation. In summary, this study elucidates the influence of the CTCFR567W mutation on human neurodevelopmental disorders, paving the way for potential therapeutic interventions.

molecular biology↗

bioRxiv2024

Regulation of immune signal integration and memory by inflammation-induced chromosome conformation

3-dimensional (3D) genome conformation is central to gene expression regulation, yet our understanding of its contribution to rapid transcriptional responses, signal integration, and memory in immune cells is limited. Here, we study the molecular regulation of the inflammatory response in primary macrophages using integrated transcriptomic, epigenomic, and chromosome conformation data, including base pair-resolution Micro-Capture C. We demonstrate that interleukin-4 (IL-4) primes the inflammatory response in macrophages by stably rewiring 3D genome conformation, juxtaposing endotoxin-, interferon-gamma-, and dexamethasone-responsive enhancers in close proximity to their cognate gene promoters. CRISPR-based perturbations of enhancer-promoter contacts or CCCTC-binding factor (CTCF) boundary elements demonstrated that IL-4-driven conformation changes are indispensable for enhanced and synergistic endotoxin-induced transcriptional responses, as well as transcriptional memory following stimulus removal. Moreover, transcriptional memory mediated by changes in chromosome conformation often occurred in the absence of changes in chromatin accessibility or histone modifications. Collectively, these findings demonstrate that rapid and memory transcriptional responses to immunological stimuli are encoded in the 3D genome.

genomics↗

bioRxiv2023

metaExpertPro: a computational workflow for metaproteomics spectral library construction and data-independent acquisition mass spectrometry data analysis

BackgroundAnalysis of mass spectrometry-based metaproteomic data, in particular large-scale data-independent acquisition MS (DIA-MS) data, remains a computational challenge. Here, we aim to develop a software tool for efficiently constructing spectral libraries and analyzing extensive datasets of DIA-based metaproteomics. ResultsWe present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS (DDA-MS), protein identification and quantification using DIA-MS, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. To enhance accessibility and ease of use, all modules and dependencies are encapsulated within a Docker container. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap-based and PASEF-based DDA and DIA data. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples demonstrated that metaExpertPro quantified an average of 45,000 peptides in a 60-minute diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual False Discovery Rate (FDR) of less than 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and demonstrated a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host. ConclusionsmetaExpertPro presents a robust one-stop computational solution for constructing metaproteomics spectral libraries, analyzing DIA-MS data, and annotating taxonomic as well as functional data.

microbiology↗

bioRxiv2023

Virome analysis provides new insights into the pathogenesis mechanism and treatment of SLE disease

Systemic lupus erythematosus (SLE) is a multi-system autoimmune disease. Many viruses have been reported to be associated with SLE, but the diversity of the virome in SLE and the mechanisms underlying the interactions between viral infection and SLE are still unclear. This study identified ten human virus species from 826 RNA-Seq samples of human blood from 688 SLE patients and 138 healthy controls. SLE patients were found to have higher positive rates of viruses than healthy controls, although the virus abundances were low and comparable in both SLE patients and healthy controls. Analysis of the antiviral interferon-stimulated genes (ISGs) in samples showed higher ISG expression levels in virus-positive samples compared to virus-negative samples, which confirmed viral infections in virus-positive samples. Further analysis of differential expression genes between virus-positive and negative SLE patients showed that several genes that were up-regulated in SLE patients were further up-regulated after viral infections, and they were mainly enriched in immune response-related biological processes, suggesting an excess immune response in SLE patients after viral infections. Finally, three marker genes of the SLE severity, namely STAT1, STAT5A, and STAT5B, showed higher expression levels in virus-positive SLE patients compared to those in virus-negative SLE patients, suggesting that viral infections may aggravate the SLE disease. Overall, the study deepens our understanding of the association between viruses and SLE and provides new insights into prevention and control of the disease.

bioinformatics↗

bioRxiv2023

GetPrimers: a generalized PCR-based genetic targeting primer designer enabling easy and standardized targeted gene modification across multiple systems

Genetic targeting (e.g., gene knockout and tagging) based on polymerase chain reaction (PCR) is a simple yet powerful approach for studying gene functions. Although originally developed in classic budding and fission yeast models, the same principle applies to other eukaryotic systems with efficient homologous recombination. One-step-PCR-based genetic targeting is conventionally used but the sizes of the homologous arms that it generates for recombination-mediated genetic targeting are usually limited. Alternatively, gene targeting can also be performed via fusion PCR, which can create homologous arms that are orders of magnitude larger, therefore substantially increases the efficiency of recombination-mediated genetic targeting. Here we present GetPrimers (https://www.evomicslab.org/app/getprimers/), a generalized computational framework and web tool to assist automatic targeting and verification primer design for both one-step-PCR-based and fusion-PCR-based genetic targeting experiments. Moreover, GetPrimers by design runs for any given genetic background of any species with full genome scalability. Therefore, GetPrimers is capable of empowering high-throughput functional genomics assays at multi-population and multi-species levels. Comprehensive experimental validations have been performed for targeting and verification primers designed by GetPrimers across multiple organism systems and experimental setups. We anticipate GetPrimers to become a highly useful and popular tool to facilitate easy and standardized gene modification across multiple systems. Take AwaysDeveloped GetPrimers for generalized primer design of PCR-based genetic targeting. GetPrimers shines with its high versatility and genome-wide scalability. GetPrimers was validated across multiple organisms and experimental setups.

genetics↗

bioRxiv2023

Model-based compound hypothesis testing for snATAC-seq data with PACS

Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data.

bioinformatics↗

bioRxiv2023

A comparison of convolutional neural networks and few-shot learning in classifying long-tailed distributed tropical bird songs

Biodiversity monitoring depends on reliable species identification, but it can often be difficult due to detectability or survey constraints, especially for rare and endangered species. Advances in bioacoustic monitoring and AI-assisted classification are improving our ability to carry out long-term studies, of a large proportion of the fauna, even in challenging environments, such as remote tropical rainforests. AI classifiers need training data, and this can be a challenge when working with tropical animal communities, which are characterized by high species richness but only a few common species and a long tail of rare species. Here we compare species identification results using two approaches: convolutional neural networks (CNN) and Siamese Neural Networks (SNN), a few-shot learning approach. The goal is to develop methodology that accurately identifies both common and rare species. To do this we collected more than 600 hours of audio recordings from Barro Colorado Island (BCI), Panama and we manually annotated calls from 101 bird species to create the training data set. More than 40% of the species had less than 100 annotated calls and some species had less than 10. The results showed that Siamese Networks outperformed the more widely used convolutional neural networks (CNN), especially when the number of annotated calls is low.

ecology↗

bioRxiv2023

BacScan: An Unbiased and Genome-Wide Approach to Identify Bacterial Highly Immunogenic Proteins

Bacterial pathogens are the second leading cause of death worldwide. However, the development of bacterial vaccines has been challenged by the presence of multiple serotypes and the lack of cross-protection between serotypes. Therefore, there is an urgent need to identify protective antigens conserved across serotypes in order to develop a broadly protective vaccine. Here, we have developed an unbiased and genome-wide technique, BacScan, which uses bacterial-specific serum to rapidly identify highly conserved immunogenic proteins by combining phage display, immunoprecipitation, and next-generation sequencing. As a proof of concept, we identified 19 highly immunogenic proteins from Streptococcus suis core proteins. Immunoreactivity analysis of mouse, pig, and human sera indicated that 2 proteins could be the potential targets for the development of serological diagnostics. Eight proteins provided 20%-100% protection against S. suis challenge in immunized animals, indicating the potential vaccine targets. BacScan can be applied to any bacterial pathogen and has the potential to accelerate the development of a broadly protective bacterial vaccine. TeaserA novel method to identify the highly conserved immunogenic bacterial proteins as targets for the development a broadly protective bacterial vaccine.

microbiology↗

bioRxiv2023

Comprehensive Analysis of Molecular Characteristics, Clinical Signifificance, and Cancer Immune Interactions of Patients by Anoikis-Related Genes in LUAD Combined with Single-cell Data

BackgroundLung adenocarcinoma(LUAD) is the most prevalent subtype of lung cancer today. There is a close relationship between Anoikis related genes(ARGs) and tumor prognosis, drug susceptibility, and tumor microenvironment(TME). MethodWe calculated differential expression genes using downloaded Anoikis genes and selected genes of prognostic value. Consensus clustering analysis was used and characterized between different clusters. Differences between the different groups were also explored. Risk scores and Nomogram with predictive prognostic functions were established. Immune status and drug sensitivity were also assessed between different risk groups. Single-cell data were downloaded to compare the expression profiles of selected genes, and immunohistochemical results of selected genes were also downloaded to corroborate the reliability of the manuscript. ResultTwo clusters were identified on the basis of related gene expression. We analyzed the survival time, functional enrichment between the two groups and found significant differences between the two clusters. Significant relationships were found between the different clusters and clinical variables. group B had a significantly lower KM curve than group A, as well as a significant enrichment in multiple tumor functions. A risk score with prognostic value was established. The risk score was found to have a high predictive value for prognosis and was an independent prognostic factor. Combined with clinical variables, a Nomogram was established and found to be an accurate predictor of patient prognosis. There were significant differences in immune status between the different risk groups. Patients in the low-risk group were significantly better treated than those in the high-risk group. Finally single cell data confirmed the expression of the selected genes. Also, the immunohistochemical results helped us to confirm the selected genes have increased expression in tumor tissue. ConclusionIn conclusion, this paper reveals the role of ARGs and immune status, drug susceptibility, and prediction of prognosis in LUAD. Also, an accurate prognostic prediction model was established based on genetic.

bioinformatics↗

bioRxiv2023

Assessment of three-dimensional RNA structure prediction in CASP15

The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non- canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.

biophysics↗

bioRxiv2023

Codon affinity in mitochondrial DNA shapes evolutionary and somatic fitness

Summary ParagraphSomatic variation contributes to biological heterogeneity by modulating cellular proclivity to differentiate, expand, adapt, or die. While large-scale sequencing efforts have revealed the foundational role of somatic variants to drive human tumor evolution, our understanding of the contribution of mutations to modulate cellular fitness in non-malignant contexts remains understudied. Here, we identify a mosaic synonymous variant (m.7076A>G) in the mitochondrial DNA (mtDNA) encoded cytochrome c-oxidase subunit 1 gene (MT-CO1, p.Gly391=), which was present at homoplasmy in 47% of immune cells from a healthy donor. Using single-cell multi-omics, we discover highly specific selection against the m.7076G mutant allele in the CD8+ effector memory T cell compartment in vivo, reminiscent of selection observed for pathogenic mtDNA alleles1, 2 and indicative of lineage-specific metabolic requirements. While the wildtype m.7076A allele is translated via Watson-Crick-Franklin base-pairing, the anticodon diversity of the mitochondrial transfer RNA pool is limited, requiring wobble-dependent translation of the m.7076G mutant allele. Notably, mitochondrial ribosome profiling revealed altered codon-anticodon affinity at the wobble position as evidenced by stalled translation of the synonymous m.7076G mutant allele encoding for glycine. Generalizing this observation, we provide a new ontogeny of the 8,482 synonymous variants in the human mitochondrial genome that enables interpretation of functional mtDNA variation. Specifically, via inter- and intra-species evolutionary analyses, population-level complex trait associations, and the occurrence of germline and somatic mtDNA mutations from large-scale sequencing studies, we demonstrate that synonymous variation impacting codon:anticodon affinity is actively evolving across the entire mitochondrial genome and has broad functional and phenotypic effects. In summary, our results introduce a new ontogeny for mitochondrial genetic variation and support a model where organismal principles can be discerned from somatic evolution via single-cell genomics.

genetics↗

bioRxiv2023

VRPG: an interactive web viewer for reference pangenome graphs

With the increasing availability of high-quality genome assemblies, pangenome graph is emerging as a new paradigm in the genomics field for identifying, encoding, and interpreting genomic variation landscape at both population and species levels. However, it remains a major challenge in understanding and interpretating pangenome graph via biologically informative visualization. To facilitate a better examination of pangenome graph towards novel biological insights, here we present VRPG, a web-based interactive visualization framework of pangenome graph. VRPG provides dynamic and efficient supports for exploring and annotating pangenome graphs along a reference-based coordinate system. Moreover, VRPG shines with its unique features in highlighting the directed path of any graph-constitutive assembly as well as in denoting copy number variation of genomic segments represented by graph-constitutive nodes, which are highly valuable in genome comparison and variant analysis. Additionally, VRPG enables side-by-side visualization between pangenome graph and reference-based annotation features, bridging the graph and linear genome context. To further demonstrate its features and scalability, we applied VRPG to the cutting-edge yeast and human reference pangenome graphs derived from hundreds of high-quality genome assemblies via a dedicated website (https://www.evomicslab.org/app/vrpg/). Contact: yuejiaxing@gmail.com Key pointsDeveloped an intuitive visualization framework for pangenome graph. Empowered graph-based feature highlighting and variant visualization. Enabled connecting reference-based annotation features in the context of pangenome graph.

bioinformatics↗

bioRxiv2023

Power analysis of cell-type deconvolution methods across tissues

Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods, coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples, enable the benchmark of existing methods. In this study, we conduct a comprehensive assessment of 31 methods, utilising single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the Critical Assessment of Transcriptomic Deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods. Availability: https://github.com/Papatheodorou-Group/CATD_snakemake

bioinformatics↗

bioRxiv2022

An FcRn-targeted mucosal vaccine against SARS-CoV-2 infection and transmission

SARS-CoV-2 and its variants cause COVID-19, which is primarily transmitted through droplets and airborne aerosols. To prevent viral infection and reduce viral spread, vaccine strategies must elicit protective immunity in the airways. FcRn transfers IgG across epithelial barriers; we explore FcRn-mediated respiratory delivery of SARS-CoV-2 spike (S). A monomeric IgG Fc was fused to a stabilized S protein; the resulting S-Fc bound to S-specific antibodies (Ab) and FcRn. A significant increase in Ab responses was observed following the intranasal immunization of mice with S-Fc formulated in CpG as compared to the immunization with S alone or PBS. Furthermore, we intranasally immunize adult or aged mice and hamsters with S-Fc. A significant reduction of virus replication in nasal turbinate, lung, and brain was observed following nasal challenges with SARS-CoV-2, including Delta and Omicron variants. Intranasal immunization also significantly reduced viral transmission between immunized and naive hamsters. Protection was mediated by nasal IgA, serum-neutralizing Abs, tissue-resident memory T cells, and bone marrow S-specific plasma cells. Hence FcRn delivers an S-Fc antigen effectively into the airway and induces protection against SARS-CoV-2 infection and transmission. Based on these findings, FcRn-targeted non-invasive respiratory immunizations are superior strategies for preventing highly contagious respiratory viruses from spreading.

immunology↗

bioRxiv2022

Structural basis of sRNA RsmZ regulation of Pseudomonas aeruginosa virulence

Pseudomonas aeruginosa is an opportunistic human pathogen that poses threats to hospitalized immunocompromised patients 1. A non-coding small RNA (sRNA) from the repressor of secondary metabolites (Rsm) system, RsmZ, sequesters the global repressor protein RsmA to regulate downstream gene expressions that reprogram virulence repertoires associated with acute and chronic P. aeruginosa infections 2,3. Molecular insights into the full-length RsmZ architecture remain elusive, leading to the lack of understanding of RsmZ binding to RsmA and subsequent modulations of gene expressions. Here we use cryo-electron microscopy (cryo-EM) to resolve structures of the full-length RsmZ in complexes with RsmA, in which five stem-loops (SLs) and one single-stranded junction carrying the GGA binding sites in RsmZ form three pairs of clamps, each binding to a RsmA homodimer. Disruptions of the base-pairings in all stems of RsmZ significantly reduced the binding affinity to RsmA by 17-fold, which resulted in enhanced RsmA downregulation of gene expressions and phenotypes associated to both acute and chronic virulence of P. aeruginosa. Double mutations that rescued these stems of RsmZ restored the binding to RsmA by more than 5-fold, and recovered the corresponding phenotypes. Our results reveal the molecular mechanism of RsmZ regulation of P. aeruginosa virulence and suggest RsmZ as a potential target for the development of new antimicrobial agents.

microbiology↗

bioRxiv2022

142 telomere-to-telomere assemblies reveal the genome structural landscape in Saccharomyces cerevisiae

As population genomics is transitioning from single reference genomes to pangenomes, major improvements in terms of genome contiguity, phylogenetic sampling, haplotype phasing and structural variant (SV) calling are required. Here, we generated the Saccharomyces cerevisiae Reference Assembly Panel (ScRAP) comprising 142 reference-quality genomes from strains of various geographic and ecological origins that faithfully represent the genomic diversity and complexity of the species. The ca. 4,800 non-redundant SVs we identified impact the expression of genes near the breakpoints and contribute to gene repertoire evolution through disruptions, duplications, fusions and horizontal transfers. We discovered frequent cases of complex aneuploidies, preferentially involving large chromosomes that underwent large SVs. We also characterized the evolutionary dynamics of complex genomic regions that classically remain unassembled in short read-based projects, including the 5 Ty families and the 32 individual telomeres. Overall, the ScRAP represents a crucial step towards establishing a high-quality, unified and complete S. cerevisiae pangenome.

genomics↗