Quantitative Biology最新文献_第10页

RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data. RECOGNICER：一种从 ChIP-seq 数据中识别广泛领域的粗粒度方法。

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-12-24 Epub Date: 2020-11-19 DOI: 10.1007/s40484-020-0225-2

Chongzhi Zang, Yiren Wang, Weiqun Peng

{"title":"RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data.","authors":"Chongzhi Zang, Yiren Wang, Weiqun Peng","doi":"10.1007/s40484-020-0225-2","DOIUrl":"10.1007/s40484-020-0225-2","url":null,"abstract":"Background: Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors. Some histone modifications, such as H3K27me3 and H3K9me3, usually mark broad domains in the genome ranging from kilobases (kb) to megabases (Mb) long, resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation. While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features, a principled method to identify scale-free board domains has been lacking.Methods: Here we present RECOGNICER (Recursive coarse-graining identification for ChIP-seq enriched regions), a computational method for identifying ChIP-seq enriched domains on a large range of scales. The algorithm is based on a coarse-graining approach, which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales.Results: We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data, and validate the results based on H3K27me3's association with repressive gene expression. We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces.Conclusion: RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318318/pdf/nihms-1669580.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39258476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Erratum to: Identifying miRNA-disease association based on integrating miRNA topological similarity and functional similarity 勘误:基于整合miRNA拓扑相似性和功能相似性来识别miRNA与疾病的关联

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-09-01 DOI: 10.1007/s40484-020-0220-7

Qingfeng Chen, Zhao Zhe, Wei Lan, Ruchang Zhang, Zhiqiang Wang, Cheng Luo, Yi-Ping Phoebe Chen

引用次数: 0

The statistical practice of the GTEx Project: from single to multiple tissues GTEx项目的统计实践:从单一组织到多个组织

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-08-06 DOI: 10.1007/s40484-020-0210-9

Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu

引用次数: 1

Germline genomes have a dominant-heritable contribution to cancer immune evasion and immunotherapy response 生殖系基因组对癌症免疫逃避和免疫治疗反应具有显性遗传贡献

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-31 DOI: 10.1007/s40484-020-0212-7

Xue Jiang, Mohammad Asad, Lin Li, Zhanpeng Sun, Jean-Sébastien Milanese, Bo Liao, Edwin Wang

引用次数: 2

Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics 在单细胞水平上对肌管中线粒体ATP的监测和数学建模揭示了具有不同动力学的两个不同群体

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-23 DOI: 10.1007/s40484-020-0211-8

Naoki Matsuda, Ken-ichi Hironaka, Masashi Fujii, Takumi Wada, Katsuyuki Kunida, Haruki Inoue, M. Eto, Daisuke Hoshino, Y. Furuichi, Y. Manabe, N. Fujii, H. Noji, H. Imamura, Shinya Kuroda

引用次数: 4

Direct-to-consumer genetic testing in China and its role in GWAS discovery and replication 中国直接面向消费者的基因检测及其在GWAS发现和复制中的作用

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-18 DOI: 10.1007/s40484-020-0209-2

Kang Kang, Xue-Long Sun, Lizhong Wang, Xiaotian Yao, Senwei Tang, Junjie Deng, Xiaoli Wu, Can Yang, Gang Chen

引用次数: 4

Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data. 基于下一代测序读取数据的马尔可夫链转移概率置信区间。

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-13 Epub Date: 2020-05-25 DOI: 10.1007/s40484-020-0200-y

Lin Wan, Xin Kang, Jie Ren, Fengzhu Sun

{"title":"Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.","authors":"Lin Wan, Xin Kang, Jie Ren, Fengzhu Sun","doi":"10.1007/s40484-020-0200-y","DOIUrl":"https://doi.org/10.1007/s40484-020-0200-y","url":null,"abstract":"Background: Markov chains (MC) have been widely used to model molecular sequences. The estimations of MC transition matrix and confidence intervals of the transition probabilities from long sequence data have been intensively studied in the past decades. In next generation sequencing (NGS), a large amount of short reads are generated. These short reads can overlap and some regions of the genome may not be sequenced resulting in a new type of data. Based on NGS data, the transition probabilities of MC can be estimated by moment estimators. However, the classical asymptotic distribution theory for MC transition probability estimators based on long sequences is no longer valid.Methods: In this study, we present the asymptotic distributions of several statistics related to MC based on NGS data. We show that, after scaling by the effective coverage d defined in a previous study by the authors, these statistics based on NGS data approximate to the same distributions as the corresponding statistics for long sequences.Results: We apply the asymptotic properties of these statistics for finding the theoretical confidence regions for MC transition probabilities based on NGS short reads data. We validate our theoretical confidence intervals using both simulated data and real data sets, and compare the results with those by the parametric bootstrap method.Conclusions: We find that the asymptotic distributions of these statistics and the theoretical confidence intervals of transition probabilities based on NGS data given in this study are highly accurate, providing a powerful tool for NGS data analysis.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0200-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39185013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1

Counting single cells and computing their heterogeneity: from phenotypic frequencies to mean value of a quantitative biomarker. 计数单细胞并计算其异质性：从表型频率到定量生物标志物的平均值。

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-13 Epub Date: 2020-04-20 DOI: 10.1007/s40484-020-0196-3

Hong Qian, Yu-Chen Cheng

引用次数: 0

DNA sequencing using nanopores and kinetic proofreading 利用纳米孔进行DNA测序和动力学校对

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-07-02 DOI: 10.1007/s40484-020-0201-x

X. Ling

引用次数: 0

Transcriptome-wide association studies: a view from Mendelian randomization 全转录组关联研究:孟德尔随机化的观点

IF 3.1 4区生物学

Quantitative Biology Pub Date : 2020-06-17 DOI: 10.1007/s40484-020-0207-4

Huanhuan Zhu, Xiang Zhou

引用次数: 22