{"title":"zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation","authors":"Xiuqi Gui, Jing Huang, Linjie Ruan, Yanjun Wu, Xuan Guo, Ruifang Cao, Shuhan Zhou, Fengxiang Tan, Hongwen Zhu, Mushan Li, Guoqing Zhang, Hu Zhou, Lixing Zhan, Xin Liu, Shiqi Tu, Zhen Shao","doi":"10.1186/s13059-024-03382-9","DOIUrl":"https://doi.org/10.1186/s13059-024-03382-9","url":null,"abstract":"Isobaric labeling-based mass spectrometry (ILMS) has been widely used to quantify, on a proteome-wide scale, the relative protein abundance in different biological conditions. However, large-scale ILMS data sets typically involve multiple runs of mass spectrometry, bringing great computational difficulty to the integration of ILMS samples. We present zMAP, a toolset that makes ILMS intensities comparable across mass spectrometry runs by modeling the associated mean-variance dependence and accordingly applying a variance stabilizing z-transformation. The practical utility of zMAP is demonstrated in several case studies involving the dynamics of cell differentiation and the heterogeneity across cancer patients.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431678","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}
Genome BiologyPub Date : 2024-10-14DOI: 10.1186/s13059-024-03403-7
Chris Papadopoulos, Hugo Arbes, David Cornu, Nicolas Chevrollier, Sandra Blanchet, Paul Roginski, Camille Rabier, Safiya Atia, Olivier Lespinet, Olivier Namy, Anne Lopes
{"title":"The ribosome profiling landscape of yeast reveals a high diversity in pervasive translation","authors":"Chris Papadopoulos, Hugo Arbes, David Cornu, Nicolas Chevrollier, Sandra Blanchet, Paul Roginski, Camille Rabier, Safiya Atia, Olivier Lespinet, Olivier Namy, Anne Lopes","doi":"10.1186/s13059-024-03403-7","DOIUrl":"https://doi.org/10.1186/s13059-024-03403-7","url":null,"abstract":"Pervasive translation is a widespread phenomenon that plays a critical role in the emergence of novel microproteins, but the diversity of translation patterns contributing to their generation remains unclear. Based on 54 ribosome profiling (Ribo-Seq) datasets, we investigated the yeast Ribo-Seq landscape using a representation framework that allows the comprehensive inventory and classification of the entire diversity of Ribo-Seq signals, including non-canonical ones. We show that if coding regions occupy specific areas of the Ribo-Seq landscape, noncoding regions encompass a wide diversity of Ribo-Seq signals and, conversely, populate the entire landscape. Our results show that pervasive translation can, nevertheless, be associated with high specificity, with 1055 noncoding ORFs exhibiting canonical Ribo-Seq signals. Using mass spectrometry under standard conditions or proteasome inhibition with an in-house analysis protocol, we report 239 microproteins originating from noncoding ORFs that display canonical but also non-canonical Ribo-Seq signals. Each condition yields dozens of additional microprotein candidates with comparable translation properties, suggesting a larger population of volatile microproteins that are challenging to detect. Our findings suggest that non-canonical translation signals may harbor valuable information and underscore the significance of considering them in proteogenomic studies. Finally, we show that the translation outcome of a noncoding ORF is primarily determined by the initiating codon and the codon distribution in its two alternative frames, rather than features indicative of functionality. Our results enable us to propose a topology of a species’ Ribo-Seq landscape, opening the way to comparative analyses of this translation landscape under different conditions.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431675","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}
Genome BiologyPub Date : 2024-10-14DOI: 10.1186/s13059-024-03410-8
Luxiao Chen, Zhenxing Guo, Tao Deng, Hao Wu
{"title":"scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq","authors":"Luxiao Chen, Zhenxing Guo, Tao Deng, Hao Wu","doi":"10.1186/s13059-024-03410-8","DOIUrl":"https://doi.org/10.1186/s13059-024-03410-8","url":null,"abstract":"Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431655","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}
Genome BiologyPub Date : 2024-10-14DOI: 10.1186/s13059-024-03416-2
Yunqing Liu, Ningshan Li, Ji Qi, Gang Xu, Jiayi Zhao, Nating Wang, Xiayuan Huang, Wenhao Jiang, Huanhuan Wei, Aurélien Justet, Taylor S. Adams, Robert Homer, Amei Amei, Ivan O. Rosas, Naftali Kaminski, Zuoheng Wang, Xiting Yan
{"title":"SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data","authors":"Yunqing Liu, Ningshan Li, Ji Qi, Gang Xu, Jiayi Zhao, Nating Wang, Xiayuan Huang, Wenhao Jiang, Huanhuan Wei, Aurélien Justet, Taylor S. Adams, Robert Homer, Amei Amei, Ivan O. Rosas, Naftali Kaminski, Zuoheng Wang, Xiting Yan","doi":"10.1186/s13059-024-03416-2","DOIUrl":"https://doi.org/10.1186/s13059-024-03416-2","url":null,"abstract":"Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER’s superior accuracy and robustness over existing methods.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431654","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}
Genome BiologyPub Date : 2024-10-14DOI: 10.1186/s13059-024-03414-4
Malick Ndiaye, Silvia Prieto-Baños, Lucy M. Fitzgerald, Ali Yazdizadeh Kharrazi, Sergey Oreshkov, Christophe Dessimoz, Fritz J. Sedlazeck, Natasha Glover, Sina Majidian
{"title":"When less is more: sketching with minimizers in genomics","authors":"Malick Ndiaye, Silvia Prieto-Baños, Lucy M. Fitzgerald, Ali Yazdizadeh Kharrazi, Sergey Oreshkov, Christophe Dessimoz, Fritz J. Sedlazeck, Natasha Glover, Sina Majidian","doi":"10.1186/s13059-024-03414-4","DOIUrl":"https://doi.org/10.1186/s13059-024-03414-4","url":null,"abstract":"The exponential increase in sequencing data calls for conceptual and computational advances to extract useful biological insights. One such advance, minimizers, allows for reducing the quantity of data handled while maintaining some of its key properties. We provide a basic introduction to minimizers, cover recent methodological developments, and review the diverse applications of minimizers to analyze genomic data, including de novo genome assembly, metagenomics, read alignment, read correction, and pangenomes. We also touch on alternative data sketching techniques including universal hitting sets, syncmers, or strobemers. Minimizers and their alternatives have rapidly become indispensable tools for handling vast amounts of data.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431656","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}
Genome BiologyPub Date : 2024-10-10DOI: 10.1186/s13059-024-03407-3
Varsha Thoppey Manoharan, Aly Abdelkareem, Gurveer Gill, Samuel Brown, Aaron Gillmor, Courtney Hall, Heewon Seo, Kiran Narta, Sean Grewal, Ngoc Ha Dang, Bo Young Ahn, Kata Osz, Xueqing Lun, Laura Mah, Franz Zemp, Douglas Mahoney, Donna L. Senger, Jennifer A. Chan, A. Sorana Morrissy
{"title":"Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma","authors":"Varsha Thoppey Manoharan, Aly Abdelkareem, Gurveer Gill, Samuel Brown, Aaron Gillmor, Courtney Hall, Heewon Seo, Kiran Narta, Sean Grewal, Ngoc Ha Dang, Bo Young Ahn, Kata Osz, Xueqing Lun, Laura Mah, Franz Zemp, Douglas Mahoney, Donna L. Senger, Jennifer A. Chan, A. Sorana Morrissy","doi":"10.1186/s13059-024-03407-3","DOIUrl":"https://doi.org/10.1186/s13059-024-03407-3","url":null,"abstract":"Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we apply a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in the analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion align with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program network hubs are highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of the immediate and long-term consequences of such perturbations, including the identification of compensatory programs that will inform improved combinatorial therapies.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398214","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}
Genome BiologyPub Date : 2024-10-10DOI: 10.1186/s13059-024-03405-5
Susanna Sawyer, Pere Gelabert, Benjamin Yakir, Alejandro Llanos-Lizcano, Alessandra Sperduti, Luca Bondioli, Olivia Cheronet, Christine Neugebauer-Maresch, Maria Teschler-Nicola, Mario Novak, Ildikó Pap, Ildikó Szikossy, Tamás Hajdu, Vyacheslav Moiseyev, Andrey Gromov, Gunita Zariņa, Eran Meshorer, Liran Carmel, Ron Pinhasi
{"title":"Improved detection of methylation in ancient DNA","authors":"Susanna Sawyer, Pere Gelabert, Benjamin Yakir, Alejandro Llanos-Lizcano, Alessandra Sperduti, Luca Bondioli, Olivia Cheronet, Christine Neugebauer-Maresch, Maria Teschler-Nicola, Mario Novak, Ildikó Pap, Ildikó Szikossy, Tamás Hajdu, Vyacheslav Moiseyev, Andrey Gromov, Gunita Zariņa, Eran Meshorer, Liran Carmel, Ron Pinhasi","doi":"10.1186/s13059-024-03405-5","DOIUrl":"https://doi.org/10.1186/s13059-024-03405-5","url":null,"abstract":"Reconstructing premortem DNA methylation levels in ancient DNA has led to breakthrough studies such as the prediction of anatomical features of the Denisovan. These studies rely on computationally inferring methylation levels from damage signals in naturally deaminated cytosines, which requires expensive high-coverage genomes. Here, we test two methods for direct methylation measurement developed for modern DNA based on either bisulfite or enzymatic methylation treatments. Bisulfite treatment shows the least reduction in DNA yields as well as the least biases during methylation conversion, demonstrating that this method can be successfully applied to ancient DNA.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398277","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}
{"title":"Drought-responsive dynamics of H3K9ac-marked 3D chromatin interactions are integrated by OsbZIP23-associated super-enhancer-like promoter regions in rice","authors":"Yu Chang, Jiahan Liu, Minrong Guo, Weizhi Ouyang, Jiapei Yan, Lizhong Xiong, Xingwang Li","doi":"10.1186/s13059-024-03408-2","DOIUrl":"https://doi.org/10.1186/s13059-024-03408-2","url":null,"abstract":"In response to drought stress (DS), plants undergo complex processes that entail significant transcriptome reprogramming. However, the intricate relationship between the dynamic alterations in the three-dimensional (3D) genome and the modulation of gene co-expression in drought responses remains a relatively unexplored area. In this study, we reconstruct high-resolution 3D genome maps based on genomic regions marked by H3K9ac, an active histone modification that dynamically responds to soil water variations in rice. We discover a genome-wide disconnection of 3D genome contact upon DS with over 10,000 chromatin loops lost, which are partially recovered in the subsequent re-watering. Loops integrating promoter–promoter interactions (PPI) contribute to gene expression in addition to basal H3K9ac modifications. Moreover, H3K9ac-marked promoter regions with high affinities in mediating PPIs, termed as super-promoter regions (SPRs), integrate spatially clustered PPIs in a super-enhancer-like manner. Interestingly, the knockout mutation of OsbZIP23, a well-defined DS-responsive transcription factor, leads to the disassociation of over 80% DS-specific PPIs and decreased expression of the corresponding genes under DS. As a case study, we show how OsbZIP23 integrates the PPI cluster formation and the co-expression of four dehydrin genes, RAB16A–D, through targeting the RAB16C SPR in a stress signaling-dependent manner. Our high-resolution 3D genome maps unveil the principles and details of dynamic genome folding in response to water supply variations and illustrate OsbZIP23 as an indispensable integrator of the yet unique 3D genome organization that is essential for gene co-expression under DS in rice.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398276","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}
{"title":"Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data","authors":"Chloé Bessière, Haoliang Xue, Benoit Guibert, Anthony Boureux, Florence Rufflé, Julien Viot, Rayan Chikhi, Mikaël Salson, Camille Marchet, Thérèse Commes, Daniel Gautheret","doi":"10.1186/s13059-024-03413-5","DOIUrl":"https://doi.org/10.1186/s13059-024-03413-5","url":null,"abstract":"Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398212","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}
Genome BiologyPub Date : 2024-10-10DOI: 10.1186/s13059-024-03409-1
Mir Henglin, Maryam Ghareghani, William T. Harvey, David Porubsky, Sergey Koren, Evan E. Eichler, Peter Ebert, Tobias Marschall
{"title":"Graphasing: phasing diploid genome assembly graphs with single-cell strand sequencing","authors":"Mir Henglin, Maryam Ghareghani, William T. Harvey, David Porubsky, Sergey Koren, Evan E. Eichler, Peter Ebert, Tobias Marschall","doi":"10.1186/s13059-024-03409-1","DOIUrl":"https://doi.org/10.1186/s13059-024-03409-1","url":null,"abstract":"Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.\u0000","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":null,"pages":null},"PeriodicalIF":12.3,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398213","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}
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