Genome research最新文献

{"title":"Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains","authors":"Hang Zhang, Yi Zhang, Kai Ming Ting, Jie Zhang, Qiuran Zhao","doi":"10.1101/gr.278983.124","DOIUrl":"https://doi.org/10.1101/gr.278983.124","url":null,"abstract":"Spatial transcriptomics are a collection of technologies that have enabled characterization of gene expression profiles and spatial information in tissue samples. Existing methods for clustering spatial transcriptomics data have primarily focused on data transformation techniques to represent the data suitably for subsequent clustering analysis, often using an existing clustering algorithm. These methods have limitations in handling complex data characteristics with varying densities, sizes, and shapes (in the transformed space on which clustering is performed), and they have high computational complexity, resulting in unsatisfactory clustering outcomes and slow execution time even with GPUs. Rather than focusing on data transformation techniques, we propose a new clustering algorithm called kernel-bounded clustering (KBC). It has two unique features: (1) It is the first clustering algorithm that employs a distributional kernel to recruit members of a cluster, enabling clusters of varying densities, sizes, and shapes to be discovered, and (2) it is a linear-time clustering algorithm that significantly enhances the speed of clustering analysis, enabling researchers to effectively handle large-scale spatial transcriptomics data sets. We show that (1) KBC works well with a simple data transformation technique called the Weisfeiler–Lehman scheme, and (2) a combination of KBC and the Weisfeiler–Lehman scheme produces good clustering outcomes, and it is faster and easier-to-use than many methods that employ existing clustering algorithms and data transformation techniques.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"40 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143192103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet.","authors":"Chuanyuan Wang, Zhi-Ping Liu","doi":"10.1101/gr.279551.124","DOIUrl":"10.1101/gr.279551.124","url":null,"abstract":"<p><p>A gene regulatory network (GRN) intricately encodes the interconnectedness of identities and functionalities of genes within cells, ultimately shaping cellular specificity. Despite decades of endeavors, reverse engineering of GRNs from gene expression profiling data remains a profound challenge, particularly when it comes to reconstructing cell-specific GRNs that are tailored to precise cellular and genetic contexts. Here, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRNs from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multistep recovery procedure with Markov properties. Each intermediate step has a specific model to recover a portion of the gene regulatory architectures. It thus can ensure compatibility between global network structures and regulatory modules through the unique multistep diffusion procedure. Furthermore, through iMetacell integration and non-Euclidean discrete space modeling, DigNet is robust to the presence of noise in scRNA-seq data and the sparsity of GRNs. Benchmark evaluation results against more than a dozen state-of-the-art network inference methods demonstrate that DigNet achieves superior performance across various single-cell GRN reconstruction experiments. Furthermore, DigNet provides unique insights into the immune response in breast cancer, derived from differential gene regulation identified in T cells. As an open-source software, DigNet offers a powerful and effective tool for generating cell-specific GRNs from scRNA-seq data.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The additional diagnostic yield of long-read sequencing in undiagnosed rare diseases","authors":"Giulia F. Del Gobbo, Kym M. Boycott","doi":"10.1101/gr.279970.124","DOIUrl":"https://doi.org/10.1101/gr.279970.124","url":null,"abstract":"Long-read sequencing (LRS) is a promising technology positioned to study the significant proportion of rare diseases (RDs) that remain undiagnosed as it addresses many of the limitations of short-read sequencing, detecting and clarifying additional disease-associated variants that may be missed by the current standard diagnostic workflow for RDs. Some key areas where additional diagnostic yields may be realized include: (1) detection and resolution of structural variants (SVs); (2) detection and characterization of tandem repeat expansions; (3) coverage of regions of high sequence similarity; (4) variant phasing; (5) the use of de novo genome assemblies for reference-based or graph genome variant detection; and (6) epigenetic and transcriptomic evaluations. Examples from over 50 studies support that the main areas of added diagnostic yield currently lie in SV detection and characterization, repeat expansion assessment, and phasing (with or without DNA methylation information). Several emerging studies applying LRS in cohorts of undiagnosed RDs also demonstrate that LRS can boost diagnostic yields following negative standard-of-care clinical testing and provide an added yield of 7%–17% following negative short-read genome sequencing. With this evidence of improved diagnostic yield, we discuss the incorporation of LRS into the diagnostic care pathway for undiagnosed RDs, including current challenges and considerations, with the ultimate goal of ending the diagnostic odyssey for countless individuals with RDs.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"35 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143077627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"k-mer approaches for biodiversity genomics","authors":"Katharine M. Jenike, Lucía Campos-Domínguez, Marilou Boddé, José Cerca, Christina N. Hodson, Michael C. Schatz, Kamil S. Jaron","doi":"10.1101/gr.279452.124","DOIUrl":"https://doi.org/10.1101/gr.279452.124","url":null,"abstract":"The wide array of currently available genomes displays a wonderful diversity in size, composition, and structure and is quickly expanding thanks to several global biodiversity genomics initiatives. However, sequencing of genomes, even with the latest technologies, can still be challenging for both technical (e.g., small physical size, contaminated samples, or access to appropriate sequencing platforms) and biological reasons (e.g., germline-restricted DNA, variable ploidy levels, sex chromosomes, or very large genomes). In recent years, <em>k</em>-mer-based techniques have become popular to overcome some of these challenges. They are based on the simple process of dividing the analyzed sequences (e.g., raw reads or genomes) into a set of subsequences of length <em>k</em>, called <em>k</em>-mers, and then analyzing the frequency or sequences of those <em>k</em>-mers. Analyses based on <em>k</em>-mers allow for a rapid and intuitive assessment of complex sequencing data sets. Here, we provide a comprehensive review to the theoretical properties and practical applications of <em>k</em>-mers in biodiversity genomics with a special focus on genome modeling.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"15 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing nanopore adaptive sampling for PromethION using readfish at scale","authors":"Rory Munro, Alex Payne, Nadine Holmes, Chris Moore, Inswasti Cahyani, Matt Loose","doi":"10.1101/gr.279329.124","DOIUrl":"https://doi.org/10.1101/gr.279329.124","url":null,"abstract":"A unique feature of Oxford Nanopore Technologies sequencers, adaptive sampling, allows precise DNA molecule selection from sequencing libraries. Here we present enhancements to our tool, readfish, enabling all features for the industrial scale PromethION sequencer, including standard and \"barcode-aware\" adaptive sampling. We demonstrate effective coverage enrichment and assessment of multiple human genomes for copy number and structural variation on a single PromethION flow cell.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"60 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid and accurate demultiplexing of direct RNA nanopore sequencing datasets with SeqTagger","authors":"Leszek P Pryszcz, Gregor Diensthuber, Laia Llovera, Rebeca Medina, Anna Delgado-Tejedor, Luca Cozzuto, Julia Ponomarenko, Eva Maria Novoa","doi":"10.1101/gr.279290.124","DOIUrl":"https://doi.org/10.1101/gr.279290.124","url":null,"abstract":"Nanopore direct RNA sequencing (DRS) enables direct measurement of RNA molecules, including their native RNA modifications, without prior conversion to cDNA. However, commercial methods for molecular barcoding of multiple DRS samples are lacking, and community-driven efforts, such as DeePlexiCon, are not compatible with newer RNA chemistry flowcells and the latest-generation GPU cards. To overcome these limitations, we introduce SeqTagger, a rapid and robust method that can demultiplex direct RNA sequencing datasets with 99% precision and 95% recall. We demonstrate the applicability of SeqTagger in both RNA002/R9.4 and RNA004/RNA chemistries and show its robust performance both for long and short RNA libraries, including custom libraries that do not contain standard poly(A) tails, such as Nano-tRNAseq libraries. Finally, we demonstrate that increasing the multiplexing up to 96 barcodes yields highly accurate demultiplexing models. SeqTagger can be executed in a standalone manner or through the MasterOfPores NextFlow workflow. The availability of an efficient and simple multiplexing strategy improves the cost-effectiveness of this technology and facilitates the analysis of low-input biological samples.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"29 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.","authors":"W H Adrian Tsui, Spencer C Ding, Peiyong Jiang, Y M Dennis Lo","doi":"10.1101/gr.278413.123","DOIUrl":"10.1101/gr.278413.123","url":null,"abstract":"<p><p>The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"35 1","pages":"1-19"},"PeriodicalIF":6.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11789496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143023247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of a cell-free DNA-based cancer screening cohort links fragmentomic profiles, nuclease levels, and plasma DNA concentrations.","authors":"Yasine Malki, Guannan Kang, W K Jacky Lam, Qing Zhou, Suk Hang Cheng, Peter P H Cheung, Jinyue Bai, Ming Lok Chan, Chui Ting Lee, Wenlei Peng, Yiqiong Zhang, Wanxia Gai, Winsome W S Wong, Mary-Jane L Ma, Wenshuo Li, Xinzhou Xu, Zhuoran Gao, Irene O L Tse, Huimin Shang, L Y Lois Choy, Peiyong Jiang, K C Allen Chan, Y M Dennis Lo","doi":"10.1101/gr.279667.124","DOIUrl":"10.1101/gr.279667.124","url":null,"abstract":"<p><p>The concentration of circulating cell-free DNA (cfDNA) in plasma is an important determinant of the robustness of liquid biopsies. However, biological mechanisms that lead to inter-individual differences in cfDNA concentrations remain unexplored. The concentration of plasma cfDNA is governed by an interplay between its release and clearance. We hypothesized that cfDNA clearance by nucleases might be one mechanism that contributes toward inter-individual variations in cfDNA concentrations. We performed fragmentomic analysis of the plasma cfDNA from 862 healthy individuals, with a cfDNA concentration range of 1.61-41.01 ng/mL. We observed an increase in large DNA fragments (231-600 bp), a decreased frequencies of shorter DNA fragments (20-160 bp), and an increased frequency of G-end motifs with increasing cfDNA concentrations. End motif deconvolution analysis revealed a decreased contribution of DNASE1L3 and DFFB in subjects with higher cfDNA concentration. The five subjects with the highest plasma DNA concentration (top 0.58%) had aberrantly decreased levels of DNASE1L3 protein in plasma. The cfDNA concentration could be inferred from the fragmentomic profile through machine learning and was well correlated to the measured cfDNA concentration. Such an approach could infer the fractional DNA concentration from particular tissue types, such as the fetal and tumor fraction. This work shows that individuals with different cfDNA concentrations are associated with characteristic fragmentomic patterns of the cfDNA pool and that nuclease-mediated clearance of DNA is a key parameter that affects cfDNA concentration. Understanding these mechanisms has facilitated the enhanced measurement of cfDNA species of clinical interest, including circulating fetal and tumor DNA.</p>","PeriodicalId":12678,"journal":{"name":"Genome research","volume":" ","pages":"31-42"},"PeriodicalIF":6.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11789642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142739223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Common and specific gene regulatory programs in zebrafish caudal fin regeneration at single-cell resolution","authors":"Yujie Chen, Yiran Hou, Qinglin Zeng, Irene Wang, Meiru Shang, Kwangdeok Shin, Christopher Hemauer, Xiaoyun Xing, Junsu Kang, Guoyan Zhao, Ting Wang","doi":"10.1101/gr.279372.124","DOIUrl":"https://doi.org/10.1101/gr.279372.124","url":null,"abstract":"Following amputation, zebrafish regenerate their injured caudal fin through lineage-restricted reprogramming. Although previous studies have charted various genetic and epigenetic dimensions of this process, the intricate gene regulatory programs shared by, or unique to, different regenerating cell types remain underinvestigated. Here, we mapped the regulatory landscape of fin regeneration by applying paired snRNA-seq and snATAC-seq on uninjured and regenerating fins. This map delineates the regulatory dynamics of predominant cell populations at multiple stages of regeneration. We observe a marked increase in the accessibility of chromatin regions associated with regenerative and developmental processes at 1 dpa, followed by a gradual closure across major cell types at later stages. This pattern is distinct from that of transcriptomic dynamics, which is characterized by several waves of gene upregulation and downregulation. We identified and in vivo validated cell-type-specific and position-specific regeneration-responsive enhancers and constructed regulatory networks by cell type and stage. Our single-cell resolution transcriptomic and chromatin accessibility map across regenerative stages provides new insights into regeneration regulatory mechanisms and serves as a valuable resource for the community.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"36 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of the role of spatial proximity of DNA double-strand breaks in the formation of CRISPR-Cas9-induced large structural variations","authors":"Mikkel Dahl-Jessen, Thorkild Terkelsen, Rasmus O Bak, Uffe Birk Jensen","doi":"10.1101/gr.278575.123","DOIUrl":"https://doi.org/10.1101/gr.278575.123","url":null,"abstract":"Structural variations (SVs) play important roles in genetic diversity, evolution, and carcinogenesis and are, as such, important for human health. However, it remains unclear how spatial proximity of double-strand breaks (DSBs) affects the formation of SVs. To investigate if spatial proximity between two DSBs affects DNA repair, we used data from 3C experiments (Hi-C, ChIA-PET, and ChIP-seq) to identify highly interacting loci on six different chromosomes. The target regions correlate with the borders of mega-base sized Topologically Associated Domains (TADs), and we used CRISPR-Cas9 nuclease and pairs of single guide RNAs (sgRNAs) against these targets to generate DSBs in both K562 cells and H9 human embryonic stem cells (hESC). Droplet Digital PCR (ddPCR) was used to quantify the resulting recombination events, and high-throughput sequencing was used to analyze the chimeric junctions created between the two DSBs. We observe a significantly higher formation frequency of deletions and inversions with DSBs in proximity as compared to deletions and inversions with DSBs not in proximity in K562 cells. Additionally, our results suggest that DSB proximity may affect the ligation of chimeric deletion junctions. Taken together, spatial proximity between DSBs is a significant predictor of large-scale deletion and inversion frequency induced by CRISPR-Cas9 in K562 cells. This finding has implications for understanding SVs in the human genome and for the future application of CRISPR-Cas9 in gene editing and the modelling of rare SVs.","PeriodicalId":12678,"journal":{"name":"Genome research","volume":"29 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}