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Similar, but not the same: multiomics comparison of human valve interstitial cells and osteoblast osteogenic differentiation expanded with an estimation of data-dependent and data-independent PASEF proteomics.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae110
Arseniy Lobov, Polina Kuchur, Nadezhda Boyarskaya, Daria Perepletchikova, Ivan Taraskin, Andrei Ivashkin, Daria Kostina, Irina Khvorova, Vladimir Uspensky, Egor Repkin, Evgeny Denisov, Tatiana Gerashchenko, Rashid Tikhilov, Svetlana Bozhkova, Vitaly Karelkin, Chunli Wang, Kang Xu, Anna Malashicheva

Osteogenic differentiation is crucial in normal bone formation and pathological calcification, such as calcific aortic valve disease (CAVD). Understanding the proteomic and transcriptomic landscapes underlying this differentiation can unveil potential therapeutic targets for CAVD. In this study, we employed RNA sequencing transcriptomics and proteomics on a timsTOF Pro platform to explore the multiomics profiles of valve interstitial cells (VICs) and osteoblasts during osteogenic differentiation. For proteomics, we utilized 3 data acquisition/analysis techniques: data-dependent acquisition (DDA)-parallel accumulation serial fragmentation (PASEF) and data-independent acquisition (DIA)-PASEF with a classic library-based (DIA) and machine learning-based library-free search (DIA-ML). Using RNA sequencing data as a biological reference, we compared these 3 analytical techniques in the context of actual biological experiments. We use this comprehensive dataset to reveal distinct proteomic and transcriptomic profiles between VICs and osteoblasts, highlighting specific biological processes in their osteogenic differentiation pathways. The study identified potential therapeutic targets specific for VICs osteogenic differentiation in CAVD, including the MAOA and ERK1/2 pathway. From a technical perspective, we found that DIA-based methods demonstrate even higher superiority against DDA for more sophisticated human primary cell cultures than it was shown before on HeLa samples. While the classic library-based DIA approach has proved to be a gold standard for shotgun proteomics research, the DIA-ML offers significant advantages with a relatively minor compromise in data reliability, making it the method of choice for routine proteomics.

成骨分化在正常骨形成和病理钙化(如钙化性主动脉瓣病(CAVD))中至关重要。了解这种分化背后的蛋白质组和转录组图谱可以揭示治疗 CAVD 的潜在靶点。在这项研究中,我们在timsTOF Pro平台上采用了RNA测序转录组学和蛋白质组学,以探索成骨分化过程中瓣膜间质细胞(VICs)和成骨细胞的多组学特征。在蛋白质组学方面,我们采用了3种数据采集/分析技术:数据依赖性采集(DDA)-平行累积序列片段(PASEF)和数据无关性采集(DIA)-PASEF,以及基于经典文库的搜索(DIA)和基于机器学习的无文库搜索(DIA-ML)。我们使用 RNA 测序数据作为生物参考,在实际生物实验中对这 3 种分析技术进行了比较。我们利用这个全面的数据集揭示了 VICs 和成骨细胞之间不同的蛋白质组和转录组特征,突出了它们成骨分化途径中的特定生物过程。研究发现了CAVD中VICs成骨分化的潜在治疗靶点,包括MAOA和ERK1/2通路。从技术角度看,我们发现基于 DIA 的方法在更复杂的人类原代细胞培养物上比 DDA 更有优势,这一点在 HeLa 样品上已经得到证实。虽然经典的基于文库的 DIA 方法已被证明是枪式蛋白质组学研究的黄金标准,但 DIA-ML 在数据可靠性方面的妥协相对较小,却提供了显著的优势,使其成为常规蛋白质组学研究的首选方法。
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引用次数: 0
An ecosystem for producing and sharing metadata within the web of FAIR Data. 一个在FAIR数据网络中生成和共享元数据的生态系统。
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae111
Daniel Jacob, François Ehrenmann, Romain David, Joseph Tran, Cathleen Mirande-Ney, Philippe Chaumeil

Background: Descriptive metadata are vital for reporting, discovering, leveraging, and mobilizing research datasets. However, resolving metadata issues as part of a data management plan can be complex for data producers. To organize and document data, various descriptive metadata must be created. Furthermore, when sharing data, it is important to ensure metadata interoperability in line with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Given the practical nature of these challenges, there is a need for management tools that can assist data managers effectively. Additionally, these tools should meet the needs of data producers and be user-friendly, requiring minimal training.

Results: We developed Maggot (Metadata Aggregation on Data Storage), a web-based tool to locally manage a data catalog using high-level metadata. The main goal was to facilitate easy data dissemination and deposition in data repositories. With Maggot, users can easily generate and attach high-level metadata to datasets, allowing for seamless sharing in a collaborative environment. This approach aligns with many data management plans as it effectively addresses challenges related to data organization, documentation, storage, and the sharing of metadata based on FAIR principles within and beyond the collaborative group. Furthermore, Maggot enables metadata crosswalks (i.e., generated metadata can be converted to the schema used by a specific data repository or be exported using a format suitable for data collection by third-party applications).

Conclusion: The primary purpose of Maggot is to streamline the collection of high-level metadata using carefully chosen schemas and standards. Additionally, it simplifies data accessibility via metadata, typically a requirement for publicly funded projects. As a result, Maggot can be utilized to promote effective local management with the goal of facilitating data sharing while adhering to the FAIR principles. Furthermore, it can contribute to the preparation of the future EOSC FAIR Web of Data within the European Open Science Cloud framework.

背景:描述性元数据对于报告、发现、利用和动员研究数据集至关重要。然而,将元数据问题作为数据管理计划的一部分来解决,对于数据生产者来说可能会很复杂。为了组织和记录数据,必须创建各种描述性元数据。此外,在共享数据时,重要的是确保元数据的互操作性符合FAIR(可查找、可访问、可互操作、可重用)原则。考虑到这些挑战的实际性质,需要能够有效地帮助数据管理人员的管理工具。此外,这些工具应满足数据生产者的需要,便于使用,只需要很少的培训。结果:我们开发了Maggot (Metadata Aggregation on Data Storage),这是一个基于web的工具,可以使用高级元数据在本地管理数据目录。主要目标是方便数据传播和存储在数据存储库中。使用Maggot,用户可以轻松地生成高级元数据并将其附加到数据集,从而在协作环境中实现无缝共享。这种方法与许多数据管理计划相一致,因为它有效地解决了与数据组织、文档、存储和元数据共享相关的挑战,这些挑战基于协作组内部和外部的FAIR原则。此外,Maggot支持元数据交叉(即,生成的元数据可以转换为特定数据存储库使用的模式,或者使用适合第三方应用程序收集数据的格式导出)。结论:Maggot的主要目的是使用精心选择的模式和标准来简化高级元数据的收集。此外,它简化了通过元数据的数据可访问性,这通常是公共资助项目的需求。因此,可以利用Maggot促进有效的本地管理,以促进数据共享,同时遵守公平原则。此外,它还有助于在欧洲开放科学云框架内准备未来的EOSC FAIR数据网络。
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引用次数: 0
Genomes reveal pervasive distant hybridization in nature among cyprinid fishes.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae117
Li Ren, Xiaolong Tu, Mengxue Luo, Qizhi Liu, Jialin Cui, Xin Gao, Hong Zhang, Yakui Tai, Yiyan Zeng, Mengdan Li, Chang Wu, Wuhui Li, Jing Wang, Dongdong Wu, Shaojun Liu

Background: Genomic data have unveiled a fascinating aspect of the evolutionary past, showing that the mingling of different species through hybridization has left its mark on the histories of numerous life forms. However, the relationship between hybridization events and the origins of cyprinid fishes remains unclear.

Results: In this study, we generated de novo assembled genomes of 8 cyprinid fishes and conducted phylogenetic analyses on 24 species. Widespread allele sharing across species boundaries was observed within 7 subfamilies of cyprinid fishes. Based on a systematic analysis of multiple tissues, we found that the testis exhibited a conserved pattern of divergence between the herbivorous Megalobrama amblycephala and the carnivorous Culter alburnus, suggesting a potential link to incomplete reproductive isolation. Significant differences in the expression of 4 genes (dpp2, ctrl, psb7, and ppce) in the liver and intestine, accompanied by variations in enzyme activities, indicated swift divergence in digestive enzyme secretion. Moreover, we identified introgressed genes linked to organ development in sympatric fishes with analogous feeding habits within the Cultrinae and Leuciscinae subfamilies.

Conclusions: Our findings highlight the significant role played by incomplete reproductive isolation and frequent gene flow events, particularly those associated with the development of digestive organs, in driving speciation among cyprinid fishes in diverse freshwater ecosystems.

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引用次数: 0
A high-quality assembly revealing the PMEL gene for the unique plumage phenotype in Liancheng ducks. 高质量的基因组装揭示了连城鸭独特羽色表型的 PMEL 基因。
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae114
Zhen Wang, Zhanbao Guo, Hongfei Liu, Tong Liu, Dapeng Liu, Simeng Yu, Hehe Tang, He Zhang, Qiming Mou, Bo Zhang, Junting Cao, Martine Schroyen, Shuisheng Hou, Zhengkui Zhou

Background: Plumage coloration is a distinctive trait in ducks, and the Liancheng duck, characterized by its white plumage and black beak and webbed feet, serves as an excellent subject for such studies. However, academic comprehension of the genetic mechanisms underlying duck plumage coloration remains limited. To this end, the Liancheng duck genome (GCA_039998735.1) was hereby de novo assembled using HiFi reads, and F2 segregating populations were generated from Liancheng and Pekin ducks. The aim was to identify the genetic mechanism of white plumage in Liancheng ducks.

Results: In this study, 1.29 Gb Liancheng duck genome was de novo assembled, involving a contig N50 of 12.17 Mb and a scaffold N50 of 83.98 Mb. Beyond the epistatic effect of the MITF gene, genome-wide association study analysis pinpointed a 0.8-Mb genomic region encompassing the PMEL gene. This gene encoded a protein specific to pigment cells and was essential for the formation of fibrillar sheets within melanosomes, the organelles responsible for pigmentation. Additionally, linkage disequilibrium analysis revealed 2 candidate single-nucleotide polymorphisms (Chr33: 5,303,994A>G; 5,303,997A>G) that might alter PMEL transcription, potentially influencing plumage coloration in Liancheng ducks.

Conclusions: Our study has assembled a high-quality genome for the Liancheng duck and has presented compelling evidence that the white plumage characteristic of this breed is attributable to the PMEL gene. Overall, these findings offer significant insights and direction for future studies and breeding programs aimed at understanding and manipulating avian plumage coloration.

背景:羽毛的颜色是鸭子的一个显著特征,连城鸭以其白色的羽毛、黑色的喙和蹼足为特征,是一个很好的研究对象。然而,学术界对鸭羽毛颜色的遗传机制的理解仍然有限。为此,利用HiFi reads对连城鸭基因组(GCA_039998735.1)进行从头组装,并从连城鸭和北京鸭中获得F2个分离群体。目的是探讨连城鸭白羽的遗传机制。结果:本研究共组装了1.29 Gb连城鸭基因组,其中N50为12.17 Mb,支架N50为83.98 Mb。除了MITF基因的上位性作用外,全基因组关联分析确定了包含PMEL基因的0.8 Mb基因组区域。该基因编码了一种色素细胞特有的蛋白质,对黑色素小体(负责色素沉着的细胞器)内纤维片的形成至关重要。此外,连锁不平衡分析还发现了2个候选单核苷酸多态性(Chr33: 5,303,994A>G;5,303,997A>G)可能改变PMEL转录,可能影响连城鸭的羽毛颜色。结论:本研究为连城鸭构建了一个高质量的基因组,并提供了令人信服的证据,证明该品种的白色羽毛特征可归因于PMEL基因。总的来说,这些发现为未来的研究和育种计划提供了重要的见解和方向,旨在了解和操纵鸟类的羽毛颜色。
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引用次数: 0
Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae122
Coline Royaux, Jean-Baptiste Mihoub, Marie Jossé, Dominique Pelletier, Olivier Norvez, Yves Reecht, Anne Fouilloux, Helena Rasche, Saskia Hiltemann, Bérénice Batut, Eléaume Marc, Pauline Seguineau, Guillaume Massé, Alan Amossé, Claire Bissery, Romain Lorrilliere, Alexis Martin, Yves Bas, Thimothée Virgoulay, Valentin Chambon, Elie Arnaud, Elisa Michon, Clara Urfer, Eloïse Trigodet, Marie Delannoy, Gregoire Loïs, Romain Julliard, Björn Grüning, Yvan Le Bras

Numerous conceptual frameworks exist for best practices in research data and analysis (e.g., Open Science and FAIR principles). In practice, there is a need for further progress to improve transparency, reproducibility, and confidence in ecology. Here, we propose a practical and operational framework for researchers and experts in ecology to achieve best practices for building analytical procedures from individual research projects to production-level analytical pipelines. We introduce the concept of atomization to identify analytical steps that support generalization by allowing us to go beyond single analyses. The term atomization is employed to convey the idea of single analytical steps as "atoms" composing an analytical procedure. When generalized, "atoms" can be used in more than a single case analysis. These guidelines were established during the development of the Galaxy-Ecology initiative, a web platform dedicated to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way to reach higher levels of reproducibility in ecological sciences by increasing the accessibility and reusability of analytical workflows once atomized and generalized.

{"title":"Guidance framework to apply best practices in ecological data analysis: lessons learned from building Galaxy-Ecology.","authors":"Coline Royaux, Jean-Baptiste Mihoub, Marie Jossé, Dominique Pelletier, Olivier Norvez, Yves Reecht, Anne Fouilloux, Helena Rasche, Saskia Hiltemann, Bérénice Batut, Eléaume Marc, Pauline Seguineau, Guillaume Massé, Alan Amossé, Claire Bissery, Romain Lorrilliere, Alexis Martin, Yves Bas, Thimothée Virgoulay, Valentin Chambon, Elie Arnaud, Elisa Michon, Clara Urfer, Eloïse Trigodet, Marie Delannoy, Gregoire Loïs, Romain Julliard, Björn Grüning, Yvan Le Bras","doi":"10.1093/gigascience/giae122","DOIUrl":"10.1093/gigascience/giae122","url":null,"abstract":"<p><p>Numerous conceptual frameworks exist for best practices in research data and analysis (e.g., Open Science and FAIR principles). In practice, there is a need for further progress to improve transparency, reproducibility, and confidence in ecology. Here, we propose a practical and operational framework for researchers and experts in ecology to achieve best practices for building analytical procedures from individual research projects to production-level analytical pipelines. We introduce the concept of atomization to identify analytical steps that support generalization by allowing us to go beyond single analyses. The term atomization is employed to convey the idea of single analytical steps as \"atoms\" composing an analytical procedure. When generalized, \"atoms\" can be used in more than a single case analysis. These guidelines were established during the development of the Galaxy-Ecology initiative, a web platform dedicated to data analysis in ecology. Galaxy-Ecology allows us to demonstrate a way to reach higher levels of reproducibility in ecological sciences by increasing the accessibility and reusability of analytical workflows once atomized and generalized.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11816794/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143407005","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}
引用次数: 0
Correction to: Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae031
{"title":"Correction to: Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios.","authors":"","doi":"10.1093/gigascience/giae031","DOIUrl":"https://doi.org/10.1093/gigascience/giae031","url":null,"abstract":"","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556459","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}
引用次数: 0
TransHLA: a Hybrid Transformer model for HLA-presented epitope detection.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giaf008
Tianchi Lu, Xueying Wang, Wan Nie, Miaozhe Huo, Shuaicheng Li

Background: Precise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments.

Results: We introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an area under the curve (AUC) of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes.

Conclusions: TransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA.

{"title":"TransHLA: a Hybrid Transformer model for HLA-presented epitope detection.","authors":"Tianchi Lu, Xueying Wang, Wan Nie, Miaozhe Huo, Shuaicheng Li","doi":"10.1093/gigascience/giaf008","DOIUrl":"https://doi.org/10.1093/gigascience/giaf008","url":null,"abstract":"<p><strong>Background: </strong>Precise prediction of epitope presentation on human leukocyte antigen (HLA) molecules is crucial for advancing vaccine development and immunotherapy. Conventional HLA-peptide binding affinity prediction tools often focus on specific alleles and lack a universal approach for comprehensive HLA site analysis. This limitation hinders efficient filtering of invalid peptide segments.</p><p><strong>Results: </strong>We introduce TransHLA, a pioneering tool designed for epitope prediction across all HLA alleles, integrating Transformer and Residue CNN architectures. TransHLA utilizes the ESM2 large language model for sequence and structure embeddings, achieving high predictive accuracy. For HLA class I, it reaches an accuracy of 84.72% and an area under the curve (AUC) of 91.95% on IEDB test data. For HLA class II, it achieves 79.94% accuracy and an AUC of 88.14%. Our case studies using datasets like CEDAR and VDJdb demonstrate that TransHLA surpasses existing models in specificity and sensitivity for identifying immunogenic epitopes and neoepitopes.</p><p><strong>Conclusions: </strong>TransHLA significantly enhances vaccine design and immunotherapy by efficiently identifying broadly reactive peptides. Our resources, including data and code, are publicly accessible at https://github.com/SkywalkerLuke/TransHLA.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143556462","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}
引用次数: 0
Mutation impact on mRNA versus protein expression across human cancers. 突变对人类癌症中mRNA和蛋白质表达的影响。
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae113
Yuqi Liu, Abdulkadir Elmas, Kuan-Lin Huang

Background: Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation. Proteogenomic datasets from large tumor cohorts provide an opportunity to systematically analyze the effects of somatic mutations on mRNA and protein abundance and identify mutations with distinct impacts on these molecular levels.

Results: We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across 6 cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including CDH1 and MSH3 truncations, as well as other mutations from likely "long-tail" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins are more likely to be experimentally confirmed as functional.

Conclusion: This study reveals that somatic mutations can exhibit distinct impacts on mRNA and protein levels, underscoring the necessity of integrating proteogenomic data to comprehensively identify functionally significant cancer mutations. These insights provide a framework for prioritizing mutations for further functional validation and therapeutic targeting.

背景:通常认为癌症突变会改变蛋白质,从而促进肿瘤的发生。然而,除了基因表达外,突变是如何影响蛋白质表达的,很少有系统的研究。这一点很重要,因为mRNA和蛋白质水平在翻译效率和蛋白质降解等因素的驱动下,往往只表现出适度的相关性。来自大型肿瘤队列的蛋白质基因组数据集为系统分析体细胞突变对mRNA和蛋白质丰度的影响提供了机会,并确定了对这些分子水平有不同影响的突变。结果:我们通过配对基因组学和全球蛋白质组学分析,对6种癌症类型的953例癌症病例的mRNA和蛋白质水平表达进行了全面分析。47.2%的体细胞表达数量性状位点(seQTLs)受到蛋白水平的影响,包括CDH1和MSH3截断,以及其他可能来自“长尾”驱动基因的突变。设计鉴定体细胞蛋白特异性QTLs (spsQTLs)的统计管道,我们揭示了几种基因突变,包括NF1和MAP2K4截断和TP53错义,它们对蛋白质丰度的影响不成比例,无法用转录组学解释。通过大规模平行变异效应分析(MAVE)的数据交叉验证,与高肿瘤TP53蛋白相关的TP53错感更有可能在实验上被证实是功能性的。结论:本研究揭示了体细胞突变对mRNA和蛋白水平的影响,强调了整合蛋白质基因组学数据以综合识别功能显著的癌症突变的必要性。这些见解为进一步的功能验证和治疗靶向提供了一个优先考虑突变的框架。
{"title":"Mutation impact on mRNA versus protein expression across human cancers.","authors":"Yuqi Liu, Abdulkadir Elmas, Kuan-Lin Huang","doi":"10.1093/gigascience/giae113","DOIUrl":"10.1093/gigascience/giae113","url":null,"abstract":"<p><strong>Background: </strong>Cancer mutations are often assumed to alter proteins, thus promoting tumorigenesis. However, how mutations affect protein expression-in addition to gene expression-has rarely been systematically investigated. This is significant as mRNA and protein levels frequently show only moderate correlation, driven by factors such as translation efficiency and protein degradation. Proteogenomic datasets from large tumor cohorts provide an opportunity to systematically analyze the effects of somatic mutations on mRNA and protein abundance and identify mutations with distinct impacts on these molecular levels.</p><p><strong>Results: </strong>We conduct a comprehensive analysis of mutation impacts on mRNA- and protein-level expressions of 953 cancer cases with paired genomics and global proteomic profiling across 6 cancer types. Protein-level impacts are validated for 47.2% of the somatic expression quantitative trait loci (seQTLs), including CDH1 and MSH3 truncations, as well as other mutations from likely \"long-tail\" driver genes. Devising a statistical pipeline for identifying somatic protein-specific QTLs (spsQTLs), we reveal several gene mutations, including NF1 and MAP2K4 truncations and TP53 missenses showing disproportional influence on protein abundance not readily explained by transcriptomics. Cross-validating with data from massively parallel assays of variant effects (MAVE), TP53 missenses associated with high tumor TP53 proteins are more likely to be experimentally confirmed as functional.</p><p><strong>Conclusion: </strong>This study reveals that somatic mutations can exhibit distinct impacts on mRNA and protein levels, underscoring the necessity of integrating proteogenomic data to comprehensively identify functionally significant cancer mutations. These insights provide a framework for prioritizing mutations for further functional validation and therapeutic targeting.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11702362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142947474","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}
引用次数: 0
Micromix: web infrastructure for visualizing and remixing microbial 'omics data.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae120
Regan J Hayward, Titus Ebbecke, Hanna Fricke, Vo Quang Nguyen, Lars Barquist

Micromix is a flexible web platform for sharing and integrating microbial omics data, including RNA sequencing and transposon-insertion sequencing. Currently, the lack of solutions for making data web-accessible results in omics data being fragmented across supplementary spreadsheets or languishing as raw read data in public repositories. Micromix solves this problem and can be easily deployed on a standard web server or using cloud services. It is organism-agnostic, accommodates data and annotations from various sources, and allows filtering based on KEGG pathways, Gene Ontology terms, and curated gene sets. Visualizations are provided through a plug-in system that integrates existing visualization services and allows rapid development of new services, with available plug-ins currently supporting interactive heatmap and clustering functions. Users can upload their own data in a variety of formats to perform integrative analyses in the context of existing datasets. To support collaborative research, Micromix allows sharing of interactive sessions that maintain defined filtering and/or visualization options. We demonstrate the utility of Micromix with case studies focusing on the SPI-2 pathogenicity island in Salmonella enterica and polysaccharide utilization loci in Bacteroides thetaiotaomicron, showcasing the platform's capabilities for integrating, filtering, and visualizing diverse functional genomic datasets. Micromix is available at http://micromix.systems.

{"title":"Micromix: web infrastructure for visualizing and remixing microbial 'omics data.","authors":"Regan J Hayward, Titus Ebbecke, Hanna Fricke, Vo Quang Nguyen, Lars Barquist","doi":"10.1093/gigascience/giae120","DOIUrl":"10.1093/gigascience/giae120","url":null,"abstract":"<p><p>Micromix is a flexible web platform for sharing and integrating microbial omics data, including RNA sequencing and transposon-insertion sequencing. Currently, the lack of solutions for making data web-accessible results in omics data being fragmented across supplementary spreadsheets or languishing as raw read data in public repositories. Micromix solves this problem and can be easily deployed on a standard web server or using cloud services. It is organism-agnostic, accommodates data and annotations from various sources, and allows filtering based on KEGG pathways, Gene Ontology terms, and curated gene sets. Visualizations are provided through a plug-in system that integrates existing visualization services and allows rapid development of new services, with available plug-ins currently supporting interactive heatmap and clustering functions. Users can upload their own data in a variety of formats to perform integrative analyses in the context of existing datasets. To support collaborative research, Micromix allows sharing of interactive sessions that maintain defined filtering and/or visualization options. We demonstrate the utility of Micromix with case studies focusing on the SPI-2 pathogenicity island in Salmonella enterica and polysaccharide utilization loci in Bacteroides thetaiotaomicron, showcasing the platform's capabilities for integrating, filtering, and visualizing diverse functional genomic datasets. Micromix is available at http://micromix.systems.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11788673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143079386","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}
引用次数: 0
High-quality phenotypic and genotypic dataset of barley genebank core collection to unlock untapped genetic diversity.
IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES Pub Date : 2025-01-06 DOI: 10.1093/gigascience/giae121
Zhihui Yuan, Maximilian Rembe, Martin Mascher, Nils Stein, Axel Himmelbach, Murukarthick Jayakodi, Andreas Börner, Klaus Oldach, Ahmed Jahoor, Jens Due Jensen, Julia Rudloff, Viktoria-Elisabeth Dohrendorf, Luisa Pauline Kuhfus, Emmanuelle Dyrszka, Matthieu Conte, Frederik Hinz, Salim Trouchaud, Jochen C Reif, Samira El Hanafi

Background: Genebanks around the globe serve as valuable repositories of genetic diversity, offering not only access to a broad spectrum of plant material but also critical resources for enhancing crop resilience, advancing scientific research, and supporting global food security. To this end, traditional genebanks are evolving into biodigital resource centers where the integration of phenotypic and genotypic data for accessions can drive more informed decision-making, optimize resource allocation, and unlock new opportunities for plant breeding and research. However, the curation and availability of interoperable phenotypic and genotypic data for genebank accessions is still in its infancy and represents an obstacle to rapid scientific discoveries in this field. Therefore, effectively promoting FAIR (i.e., findable, accessible, interoperable, and reusable) access to these data is vital for maximizing the potential of genebanks and driving progress in agricultural innovation.

Findings: Here we provide whole genome sequencing data of 812 barley (Hordeum vulgare L.) plant genetic resources and 298 European elite materials released between 1949 and 2021, as well as the phenotypic data for 4 disease resistance traits and 3 agronomic traits. The robustness of the investigated traits and the interoperability of genomic and phenotypic data were assessed in the current publication, aiming to make this panel publicly available as a resource for future genetic research in barley.

Conclusions: The data showed broad phenotypic variability and high association mapping potential, offering a key resource for identifying genebank donors with untapped genes to advance barley breeding while safeguarding genetic diversity.

{"title":"High-quality phenotypic and genotypic dataset of barley genebank core collection to unlock untapped genetic diversity.","authors":"Zhihui Yuan, Maximilian Rembe, Martin Mascher, Nils Stein, Axel Himmelbach, Murukarthick Jayakodi, Andreas Börner, Klaus Oldach, Ahmed Jahoor, Jens Due Jensen, Julia Rudloff, Viktoria-Elisabeth Dohrendorf, Luisa Pauline Kuhfus, Emmanuelle Dyrszka, Matthieu Conte, Frederik Hinz, Salim Trouchaud, Jochen C Reif, Samira El Hanafi","doi":"10.1093/gigascience/giae121","DOIUrl":"10.1093/gigascience/giae121","url":null,"abstract":"<p><strong>Background: </strong>Genebanks around the globe serve as valuable repositories of genetic diversity, offering not only access to a broad spectrum of plant material but also critical resources for enhancing crop resilience, advancing scientific research, and supporting global food security. To this end, traditional genebanks are evolving into biodigital resource centers where the integration of phenotypic and genotypic data for accessions can drive more informed decision-making, optimize resource allocation, and unlock new opportunities for plant breeding and research. However, the curation and availability of interoperable phenotypic and genotypic data for genebank accessions is still in its infancy and represents an obstacle to rapid scientific discoveries in this field. Therefore, effectively promoting FAIR (i.e., findable, accessible, interoperable, and reusable) access to these data is vital for maximizing the potential of genebanks and driving progress in agricultural innovation.</p><p><strong>Findings: </strong>Here we provide whole genome sequencing data of 812 barley (Hordeum vulgare L.) plant genetic resources and 298 European elite materials released between 1949 and 2021, as well as the phenotypic data for 4 disease resistance traits and 3 agronomic traits. The robustness of the investigated traits and the interoperability of genomic and phenotypic data were assessed in the current publication, aiming to make this panel publicly available as a resource for future genetic research in barley.</p><p><strong>Conclusions: </strong>The data showed broad phenotypic variability and high association mapping potential, offering a key resource for identifying genebank donors with untapped genes to advance barley breeding while safeguarding genetic diversity.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"14 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390809","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}
引用次数: 0
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