Pub Date : 2024-08-16DOI: 10.1186/s13059-024-03345-0
Kai Zhao, Hon-Cheong So, Zhixiang Lin
The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.
{"title":"scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis","authors":"Kai Zhao, Hon-Cheong So, Zhixiang Lin","doi":"10.1186/s13059-024-03345-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03345-0","url":null,"abstract":"The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1186/s13059-024-03368-7
Nathan D. Maulding, Lucas Seninge, Joshua M. Stuart
Inferring gene regulatory networks from single-cell RNA-sequencing trajectories has been an active area of research yet methods are still needed to identify regulators governing cell transitions. We developed DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories) to annotate transcription-factor activity along single-cell trajectory branches, using ensembles of relations to target genes. Using a benchmark representing several different tissues, as well as external validation with ATAC-Seq and Perturb-Seq data on hematopoietic cells, the method was found to have higher tissue-specific sensitivity and specificity over competing approaches.
{"title":"Associating transcription factors to single-cell trajectories with DREAMIT","authors":"Nathan D. Maulding, Lucas Seninge, Joshua M. Stuart","doi":"10.1186/s13059-024-03368-7","DOIUrl":"https://doi.org/10.1186/s13059-024-03368-7","url":null,"abstract":"Inferring gene regulatory networks from single-cell RNA-sequencing trajectories has been an active area of research yet methods are still needed to identify regulators governing cell transitions. We developed DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories) to annotate transcription-factor activity along single-cell trajectory branches, using ensembles of relations to target genes. Using a benchmark representing several different tissues, as well as external validation with ATAC-Seq and Perturb-Seq data on hematopoietic cells, the method was found to have higher tissue-specific sensitivity and specificity over competing approaches.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1186/s13059-024-03365-w
William DeGroat, Fumitaka Inoue, Tal Ashuach, Nir Yosef, Nadav Ahituv, Anat Kreimer
Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases. We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays. Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.
越来越多的证据表明，相当一部分与疾病相关的突变发生在增强子中，而增强子是非编码 DNA 中对基因调控至关重要的区域。了解这种变异所影响的调控程序的结构和机制可以揭示人类疾病的机制。我们收集了神经分化过程中七个早期时间点的表观遗传和基因表达数据集。针对这一模型系统，我们构建了增强子-启动子相互作用网络，每个网络都处于神经诱导的一个单独阶段。这些网络是一系列丰富分析的基础，我们通过这些分析展示了它们的时间动态和各种疾病相关变异的富集。我们将 Girvan-Newman 聚类算法应用于这些网络，以揭示与生物学相关的调控子结构。此外，我们还展示了利用转录因子过表达和大规模并行报告实验验证预测的增强子-启动子相互作用的方法。我们的研究结果为探索基因调控程序及其在整个发育过程中的动态提供了一个可推广的框架；其中包括一种研究疾病相关变异对转录网络影响的综合方法。应用于我们网络的技术已作为计算工具 E-P-INAnalyzer 与我们的研究结果一同发表。我们的程序可用于不同的细胞环境和疾病。
{"title":"Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation","authors":"William DeGroat, Fumitaka Inoue, Tal Ashuach, Nir Yosef, Nadav Ahituv, Anat Kreimer","doi":"10.1186/s13059-024-03365-w","DOIUrl":"https://doi.org/10.1186/s13059-024-03365-w","url":null,"abstract":"Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases. We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays. Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1186/s13059-024-03364-x
Yi Qiu, Yoon Mo Kang, Christopher Korfmann, Fanny Pouyet, Andrew Eckford, Alexander F. Palazzo
In vertebrates, most protein-coding genes have a peak of GC-content near their 5′ transcriptional start site (TSS). This feature promotes both the efficient nuclear export and translation of mRNAs. Despite the importance of GC-content for RNA metabolism, its general features, origin, and maintenance remain mysterious. We investigate the evolutionary forces shaping GC-content at the transcriptional start site (TSS) of genes through both comparative genomic analysis of nucleotide substitution rates between different species and by examining human de novo mutations. Our data suggests that GC-peaks at TSSs were present in the last common ancestor of amniotes, and likely that of vertebrates. We observe that in apes and rodents, where recombination is directed away from TSSs by PRDM9, GC-content at the 5′ end of protein-coding gene is currently undergoing mutational decay. In canids, which lack PRDM9 and perform recombination at TSSs, GC-content at the 5′ end of protein-coding is increasing. We show that these patterns extend into the 5′ end of the open reading frame, thus impacting synonymous codon position choices. Our results indicate that the dynamics of this GC-peak in amniotes is largely shaped by historic patterns of recombination. Since decay of GC-content towards the mutation rate equilibrium is the default state for non-functional DNA, the observed decrease in GC-content at TSSs in apes and rodents indicates that the GC-peak is not being maintained by selection on most protein-coding genes in those species.
{"title":"The GC-content at the 5′ ends of human protein-coding genes is undergoing mutational decay","authors":"Yi Qiu, Yoon Mo Kang, Christopher Korfmann, Fanny Pouyet, Andrew Eckford, Alexander F. Palazzo","doi":"10.1186/s13059-024-03364-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03364-x","url":null,"abstract":"In vertebrates, most protein-coding genes have a peak of GC-content near their 5′ transcriptional start site (TSS). This feature promotes both the efficient nuclear export and translation of mRNAs. Despite the importance of GC-content for RNA metabolism, its general features, origin, and maintenance remain mysterious. We investigate the evolutionary forces shaping GC-content at the transcriptional start site (TSS) of genes through both comparative genomic analysis of nucleotide substitution rates between different species and by examining human de novo mutations. Our data suggests that GC-peaks at TSSs were present in the last common ancestor of amniotes, and likely that of vertebrates. We observe that in apes and rodents, where recombination is directed away from TSSs by PRDM9, GC-content at the 5′ end of protein-coding gene is currently undergoing mutational decay. In canids, which lack PRDM9 and perform recombination at TSSs, GC-content at the 5′ end of protein-coding is increasing. We show that these patterns extend into the 5′ end of the open reading frame, thus impacting synonymous codon position choices. Our results indicate that the dynamics of this GC-peak in amniotes is largely shaped by historic patterns of recombination. Since decay of GC-content towards the mutation rate equilibrium is the default state for non-functional DNA, the observed decrease in GC-content at TSSs in apes and rodents indicates that the GC-peak is not being maintained by selection on most protein-coding genes in those species.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1186/s13059-024-03359-8
Fengqi Wu, Yingxiao Mai, Chengjie Chen, Rui Xia
Genome sequencing has become a routine task for biologists, but the challenge of gene structure annotation persists, impeding accurate genomic and genetic research. Here, we present a bioinformatics toolkit, SynGAP (Synteny-based Gene structure Annotation Polisher), which uses gene synteny information to accomplish precise and automated polishing of gene structure annotation of genomes. SynGAP offers exceptional capabilities in the improvement of gene structure annotation quality and the profiling of integrative gene synteny between species. Furthermore, an expression variation index is designed for comparative transcriptomics analysis to explore candidate genes responsible for the development of distinct traits observed in phylogenetically related species.
{"title":"SynGAP: a synteny-based toolkit for gene structure annotation polishing","authors":"Fengqi Wu, Yingxiao Mai, Chengjie Chen, Rui Xia","doi":"10.1186/s13059-024-03359-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03359-8","url":null,"abstract":"Genome sequencing has become a routine task for biologists, but the challenge of gene structure annotation persists, impeding accurate genomic and genetic research. Here, we present a bioinformatics toolkit, SynGAP (Synteny-based Gene structure Annotation Polisher), which uses gene synteny information to accomplish precise and automated polishing of gene structure annotation of genomes. SynGAP offers exceptional capabilities in the improvement of gene structure annotation quality and the profiling of integrative gene synteny between species. Furthermore, an expression variation index is designed for comparative transcriptomics analysis to explore candidate genes responsible for the development of distinct traits observed in phylogenetically related species.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1186/s13059-024-03351-2
Ian A. Mellis, Madeline E. Melzer, Nicholas Bodkin, Yogesh Goyal
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation. Our integrative approach identifies several putative hits—genes demonstrating possible transcriptional adaptation—to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
{"title":"Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells","authors":"Ian A. Mellis, Madeline E. Melzer, Nicholas Bodkin, Yogesh Goyal","doi":"10.1186/s13059-024-03351-2","DOIUrl":"https://doi.org/10.1186/s13059-024-03351-2","url":null,"abstract":"Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation. Our integrative approach identifies several putative hits—genes demonstrating possible transcriptional adaptation—to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1186/s13059-024-03350-3
Erkin Alaçamlı, Thijessen Naidoo, Merve N. Güler, Ekin Sağlıcan, Şevval Aktürk, Igor Mapelli, Kıvılcım Başak Vural, Mehmet Somel, Helena Malmström, Torsten Günther
The advent of genome-wide ancient DNA analysis has revolutionized our understanding of prehistoric societies. However, studying biological relatedness in these groups requires tailored approaches due to the challenges of analyzing ancient DNA. READv2, an optimized Python3 implementation of the most widely used tool for this purpose, addresses these challenges while surpassing its predecessor in speed and accuracy. For sufficient amounts of data, it can classify up to third-degree relatedness and differentiate between the two types of first-degree relatedness, full siblings and parent-offspring. READv2 enables user-friendly, efficient, and nuanced analysis of biological relatedness, facilitating a deeper understanding of past social structures.
全基因组古 DNA 分析的出现彻底改变了我们对史前社会的认识。然而，由于分析古 DNA 所面临的挑战，研究这些群体的生物亲缘关系需要量身定制的方法。READv2 是最广泛使用的工具在 Python3 上的优化实现，它在速度和准确性上都超越了前者，从而解决了这些难题。在数据量足够大的情况下，它可以对三等亲缘关系进行分类，并区分两类一等亲缘关系（全同胞和亲子）。READv2 可以对生物亲缘关系进行用户友好、高效和细致的分析，从而促进对过去社会结构的深入了解。
{"title":"READv2: advanced and user-friendly detection of biological relatedness in archaeogenomics","authors":"Erkin Alaçamlı, Thijessen Naidoo, Merve N. Güler, Ekin Sağlıcan, Şevval Aktürk, Igor Mapelli, Kıvılcım Başak Vural, Mehmet Somel, Helena Malmström, Torsten Günther","doi":"10.1186/s13059-024-03350-3","DOIUrl":"https://doi.org/10.1186/s13059-024-03350-3","url":null,"abstract":"The advent of genome-wide ancient DNA analysis has revolutionized our understanding of prehistoric societies. However, studying biological relatedness in these groups requires tailored approaches due to the challenges of analyzing ancient DNA. READv2, an optimized Python3 implementation of the most widely used tool for this purpose, addresses these challenges while surpassing its predecessor in speed and accuracy. For sufficient amounts of data, it can classify up to third-degree relatedness and differentiate between the two types of first-degree relatedness, full siblings and parent-offspring. READv2 enables user-friendly, efficient, and nuanced analysis of biological relatedness, facilitating a deeper understanding of past social structures.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.1186/s13059-024-03343-2
Pelin Icer Baykal, Paweł Piotr Łabaj, Florian Markowetz, Lynn M. Schriml, Daniel J. Stekhoven, Serghei Mangul, Niko Beerenwinkel
In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.
{"title":"Genomic reproducibility in the bioinformatics era","authors":"Pelin Icer Baykal, Paweł Piotr Łabaj, Florian Markowetz, Lynn M. Schriml, Daniel J. Stekhoven, Serghei Mangul, Niko Beerenwinkel","doi":"10.1186/s13059-024-03343-2","DOIUrl":"https://doi.org/10.1186/s13059-024-03343-2","url":null,"abstract":"In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development. In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets. Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.
空间转录组学（ST）正在推进我们对复杂组织和生物体的了解。然而，建立一个强大的聚类算法来定义单个组织切片中的空间一致性区域，并对来自不同来源的多个组织切片进行配准或整合，以进行必要的下游分析，这仍然具有挑战性。许多聚类、配准和整合方法都是利用空间信息专门为 ST 数据设计的。由于缺乏全面的基准研究，使得方法的选择和未来的方法开发变得更加复杂。在本研究中，我们利用各种不同规模、技术、物种和复杂程度的真实和模拟数据集，系统地对各种最先进的算法进行了基准测试。我们使用不同的定量和定性指标和分析方法来分析每种方法的优缺点，其中包括空间聚类精度和连续性、均匀流形近似和投影可视化、层间和点对点配准精度以及三维重建等八个指标，这些指标旨在评估方法性能和数据质量。用于评估的代码可在我们的 GitHub 上获取。此外，我们还提供在线笔记本教程和文档，以方便复制所有基准测试结果，并支持对新方法和新数据集的研究。通过分析，我们提出了涵盖多个方面的综合建议，帮助用户根据自己的具体需求选择最佳工具，并指导未来的方法开发。
{"title":"Benchmarking clustering, alignment, and integration methods for spatial transcriptomics","authors":"Yunfei Hu, Manfei Xie, Yikang Li, Mingxing Rao, Wenjun Shen, Can Luo, Haoran Qin, Jihoon Baek, Xin Maizie Zhou","doi":"10.1186/s13059-024-03361-0","DOIUrl":"https://doi.org/10.1186/s13059-024-03361-0","url":null,"abstract":"Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development. In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets. Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Base editing is a powerful tool for artificial evolution to create allelic diversity and improve agronomic traits. However, the great evolutionary potential for every sgRNA target has been overlooked. And there is currently no high-throughput method for generating and characterizing as many changes in a single target as possible based on large mutant pools to permit rapid gene directed evolution in plants. In this study, we establish an efficient germline-specific evolution system to screen beneficial alleles in Arabidopsis which could be applied for crop improvement. This system is based on a strong egg cell-specific cytosine base editor and the large seed production of Arabidopsis, which enables each T1 plant with unedited wild type alleles to produce thousands of independent T2 mutant lines. It has the ability of creating a wide range of mutant lines, including those containing atypical base substitutions, and as well providing a space- and labor-saving way to store and screen the resulting mutant libraries. Using this system, we efficiently generate herbicide-resistant EPSPS, ALS, and HPPD variants that could be used in crop breeding. Here, we demonstrate the significant potential of base editing-mediated artificial evolution for each sgRNA target and devised an efficient system for conducting deep evolution to harness this potential.
{"title":"Creating large-scale genetic diversity in Arabidopsis via base editing-mediated deep artificial evolution","authors":"Xiang Wang, Wenbo Pan, Chao Sun, Hong Yang, Zhentao Cheng, Fei Yan, Guojing Ma, Yun Shang, Rui Zhang, Caixia Gao, Lijing Liu, Huawei Zhang","doi":"10.1186/s13059-024-03358-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03358-9","url":null,"abstract":"Base editing is a powerful tool for artificial evolution to create allelic diversity and improve agronomic traits. However, the great evolutionary potential for every sgRNA target has been overlooked. And there is currently no high-throughput method for generating and characterizing as many changes in a single target as possible based on large mutant pools to permit rapid gene directed evolution in plants. In this study, we establish an efficient germline-specific evolution system to screen beneficial alleles in Arabidopsis which could be applied for crop improvement. This system is based on a strong egg cell-specific cytosine base editor and the large seed production of Arabidopsis, which enables each T1 plant with unedited wild type alleles to produce thousands of independent T2 mutant lines. It has the ability of creating a wide range of mutant lines, including those containing atypical base substitutions, and as well providing a space- and labor-saving way to store and screen the resulting mutant libraries. Using this system, we efficiently generate herbicide-resistant EPSPS, ALS, and HPPD variants that could be used in crop breeding. Here, we demonstrate the significant potential of base editing-mediated artificial evolution for each sgRNA target and devised an efficient system for conducting deep evolution to harness this potential.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141909082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：