Quantitative Biology最新文献_第5页

Quantitative Biology: A Practical Introduction 定量生物学:实用导论

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-5018-5

A. Kimura

引用次数: 1

Introduction to Quantitative Biology 定量生物学概论

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-5018-5_1

A. Kimura

引用次数: 3

Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease. 传播和新发变异的综合建模确定了先天性心脏病的新风险基因。

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-06-01 DOI: 10.15302/j-qb-021-0248

Mo Li, Xue Zeng, Chentian Jin, S. Jin, W. Dong, Martina Brueckner, R. Lifton, Q. Lu, Hongyu Zhao

{"title":"Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease.","authors":"Mo Li, Xue Zeng, Chentian Jin, S. Jin, W. Dong, Martina Brueckner, R. Lifton, Q. Lu, Hongyu Zhao","doi":"10.15302/j-qb-021-0248","DOIUrl":"https://doi.org/10.15302/j-qb-021-0248","url":null,"abstract":"Background\u0000Whole-exome sequencing (WES) studies have identified multiple genes enriched for de novo mutations (DNMs) in congenital heart disease (CHD) probands. However, risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each gene.\u0000\u0000\u0000Methods\u0000In this manuscript, we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted variants. Through integrative modeling of multiple types of genetic variants, gene-level annotations, and reference data from large population cohorts, our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs only.\u0000\u0000\u0000Results\u0000Applied to WES data of 2,645 CHD proband-parent trios, our method identified 15 significant genes, half of which are novel, leading to new insights into the genetic bases of CHD.\u0000\u0000\u0000Conclusion\u0000These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"9 2 1","pages":"216-227"},"PeriodicalIF":3.1,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47316490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 4

Phase separation in synthetic biology 合成生物学中的相分离

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0262

引用次数: 0

High-throughput experimental methods for investigating biomolecular condensates 研究生物分子凝聚物的高通量实验方法

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0264

引用次数: 0

Functional characterization of disease/comorbidity-associated lncRNA 疾病/合并症相关lncRNA的功能表征

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0247

引用次数: 0

Early bioinformatics research in China 中国早期生物信息学研究

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0255

Runsheng Chen

引用次数: 1

Adaptive total variation constraint hypergraph regularized NMF for single-cell RNA-seq data analysis 单细胞RNA-seq数据分析的自适应全变异约束超图正则化NMF

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0261

引用次数: 1

Transcriptome wide association studies: general framework and methods 全转录组关联研究:一般框架和方法

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/J-QB-020-0228

Yu-Xiao Xie, N. Shan, Hongyu Zhao, Lin Hou

{"title":"Transcriptome wide association studies: general framework and methods","authors":"Yu-Xiao Xie, N. Shan, Hongyu Zhao, Lin Hou","doi":"10.15302/J-QB-020-0228","DOIUrl":"https://doi.org/10.15302/J-QB-020-0228","url":null,"abstract":"Background : Genome-wide association studies (GWAS) have succeeded in identifying tens of thousands of genetic variants associated with complex human traits during the past decade, however, they are still hampered by limited statistical power and dif ﬁ culties in biological interpretation. With the recent progress in expression quantitative trait loci (eQTL) studies, transcriptome-wide association studies (TWAS) provide a framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. Results : In this review, we will introduce the general framework of TWAS, the relevant resources, and the computational tools. Extensions of the original TWAS methods will also be discussed. Furthermore, we will brie ﬂ y introduce methods that are closely related to TWAS, including MR-based methods and colocalization approaches. Connection and difference between these approaches will be discussed. Conclusion : Finally, we will summarize strengths, limitations, and potential directions for TWAS. Author summary: Transcriptome-wide association studies (TWAS) provide an important framework to test for gene-trait associations by integrating information from GWAS and eQTL studies. In this review, we systematically review the general framework and methods of transcriptome-wide association studies, and discuss their strengths, limitations, and potential future directions.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67351219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 2

Exploring the underlying mechanism of action of a traditional Chinese medicine formula, Youdujing ointment, for cervical cancer treatment 探讨中药优毒精软膏治疗宫颈癌的作用机制

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2021-01-01 DOI: 10.15302/j-qb-021-0236

Lei Zhang, Jinli Lv, Ming Xiao, Li Yang, Le Zhang

{"title":"Exploring the underlying mechanism of action of a traditional Chinese medicine formula, Youdujing ointment, for cervical cancer treatment","authors":"Lei Zhang, Jinli Lv, Ming Xiao, Li Yang, Le Zhang","doi":"10.15302/j-qb-021-0236","DOIUrl":"https://doi.org/10.15302/j-qb-021-0236","url":null,"abstract":"Background: A traditional Chinese medicine formula, Youdujing (YDJ) ointment, is widely used for treatment of human papilloma virus-related diseases, such as cervical cancer. However, the underlying mechanisms by which active compounds of YDJ alleviates cervical cancer are still unclear. Methods: We applied a comprehensive network pharmacology approach to explore the key mechanisms of YDJ by integrating potential target identi ﬁ cation, network analysis, and enrichment analysis into classical molecular docking procedures. First, we used network and enrichment analyses to identify potential therapeutic targets. Second, we performed molecular docking to investigate the potential active compounds of YDJ. Finally, we carried out a network-based analysis to unravel potentially effective drug combinations. Results: Network analysis yielded four potential therapeutic targets: ESR1, NFKB1, TNF, and AKT1. Molecular docking highlighted that these proteins may interact with four potential active compounds of YDJ: E4, Y2, Y20, and Y21. Finally, we found that Y2 or Y21 can act alone or together with E4 to trigger apoptotic cascades via the mitochondrial apoptotic pathway and estrogen receptors. Conclusion: Our study not only explained why YDJ is effective for cervical cancer treatment, but also lays a strong foundation for future clinical studies based on this traditional medicine. summary: mechanisms underlying the effect of remained unclear, so we developed a network pharmacology method to investigate the active compounds and their possible combinations by integrating network and enrichment analyses with molecular docking. In this paper, we found four potential active compounds and four potential therapeutic targets of YDJ. However, these ﬁ ndings should be con ﬁ rmed by further experiments in vitro and in vivo , whose results can be integrated in the present bioinformatic algorithm in order to optimize our method in the future.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"1 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67350874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 6