Genome Biology最新文献_第8页

Benchmarking computational methods for single-cell chromatin data analysis 单细胞染色质数据分析计算方法基准测试

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-16 DOI: 10.1186/s13059-024-03356-x

Siyuan Luo, Pierre-Luc Germain, Mark D. Robinson, Ferdinand von Meyenn

{"title":"Benchmarking computational methods for single-cell chromatin data analysis","authors":"Siyuan Luo, Pierre-Luc Germain, Mark D. Robinson, Ferdinand von Meyenn","doi":"10.1186/s13059-024-03356-x","DOIUrl":"https://doi.org/10.1186/s13059-024-03356-x","url":null,"abstract":"Single-cell chromatin accessibility assays, such as scATAC-seq, are increasingly employed in individual and joint multi-omic profiling of single cells. As the accumulation of scATAC-seq and multi-omics datasets continue, challenges in analyzing such sparse, noisy, and high-dimensional data become pressing. Specifically, one challenge relates to optimizing the processing of chromatin-level measurements and efficiently extracting information to discern cellular heterogeneity. This is of critical importance, since the identification of cell types is a fundamental step in current single-cell data analysis practices. We benchmark 8 feature engineering pipelines derived from 5 recent methods to assess their ability to discover and discriminate cell types. By using 10 metrics calculated at the cell embedding, shared nearest neighbor graph, or partition levels, we evaluate the performance of each method at different data processing stages. This comprehensive approach allows us to thoroughly understand the strengths and weaknesses of each method and the influence of parameter selection. Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"135 1","pages":""},"PeriodicalIF":12.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991924","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}

引用次数: 0

StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases StaVia：利用高阶随机游走为细胞图谱绘制时空感知地图

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-16 DOI: 10.1186/s13059-024-03347-y

Shobana V. Stassen, Minato Kobashi, Edmund Y. Lam, Yuanhua Huang, Joshua W. K. Ho, Kevin K. Tsia

引用次数: 0

scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis scParser：用于可扩展单细胞 RNA 测序数据分析的稀疏表示学习

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-16 DOI: 10.1186/s13059-024-03345-0

Kai Zhao, Hon-Cheong So, Zhixiang Lin

引用次数: 0

Associating transcription factors to single-cell trajectories with DREAMIT 利用 DREAMIT 将转录因子与单细胞轨迹联系起来

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-14 DOI: 10.1186/s13059-024-03368-7

Nathan D. Maulding, Lucas Seninge, Joshua M. Stuart

引用次数: 0

Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation 综合网络建模方法揭示神经分化过程中增强子与启动子之间的动态相互作用

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-14 DOI: 10.1186/s13059-024-03365-w

William DeGroat, Fumitaka Inoue, Tal Ashuach, Nir Yosef, Nadav Ahituv, Anat Kreimer

{"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":"11 1","pages":""},"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}

引用次数: 0

The GC-content at the 5′ ends of human protein-coding genes is undergoing mutational decay 人类蛋白质编码基因 5′末端的 GC 含量正在发生突变衰减

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-13 DOI: 10.1186/s13059-024-03364-x

Yi Qiu, Yoon Mo Kang, Christopher Korfmann, Fanny Pouyet, Andrew Eckford, Alexander F. Palazzo

{"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":"16 1","pages":""},"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}

引用次数: 0

SynGAP: a synteny-based toolkit for gene structure annotation polishing SynGAP：基于同源关系的基因结构注释工具包

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-13 DOI: 10.1186/s13059-024-03359-8

Fengqi Wu, Yingxiao Mai, Chengjie Chen, Rui Xia

引用次数: 0

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells 单细胞转录适应的普遍性和基因调控约束

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-12 DOI: 10.1186/s13059-024-03351-2

Ian A. Mellis, Madeline E. Melzer, Nicholas Bodkin, Yogesh Goyal

{"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":"5 1","pages":""},"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}

引用次数: 0

READv2: advanced and user-friendly detection of biological relatedness in archaeogenomics READv2：考古基因组学中生物相关性的高级和用户友好检测

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-12 DOI: 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

引用次数: 0

Genomic reproducibility in the bioinformatics era 生物信息学时代的基因组重现性

IF 12.3 1区生物学

Genome Biology Pub Date : 2024-08-09 DOI: 10.1186/s13059-024-03343-2

Pelin Icer Baykal, Paweł Piotr Łabaj, Florian Markowetz, Lynn M. Schriml, Daniel J. Stekhoven, Serghei Mangul, Niko Beerenwinkel

引用次数: 0