Quantitative BiologyPub Date : 2020-12-24Epub Date: 2020-11-19DOI: 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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.</p>","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}
Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu
{"title":"The statistical practice of the GTEx Project: from single to multiple tissues","authors":"Xu Liao, Xiaoran Chai, Xingjie Shi, Lin S. Chen, Jin Liu","doi":"10.1007/s40484-020-0210-9","DOIUrl":"https://doi.org/10.1007/s40484-020-0210-9","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0210-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42372291","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}
Xue Jiang, Mohammad Asad, Lin Li, Zhanpeng Sun, Jean-Sébastien Milanese, Bo Liao, Edwin Wang
{"title":"Germline genomes have a dominant-heritable contribution to cancer immune evasion and immunotherapy response","authors":"Xue Jiang, Mohammad Asad, Lin Li, Zhanpeng Sun, Jean-Sébastien Milanese, Bo Liao, Edwin Wang","doi":"10.1007/s40484-020-0212-7","DOIUrl":"https://doi.org/10.1007/s40484-020-0212-7","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0212-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48750306","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}
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
{"title":"Monitoring and mathematical modeling of mitochondrial ATP in myotubes at single-cell level reveals two distinct population with different kinetics","authors":"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","doi":"10.1007/s40484-020-0211-8","DOIUrl":"https://doi.org/10.1007/s40484-020-0211-8","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0211-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49412760","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}
Kang Kang, Xue-Long Sun, Lizhong Wang, Xiaotian Yao, Senwei Tang, Junjie Deng, Xiaoli Wu, Can Yang, Gang Chen
{"title":"Direct-to-consumer genetic testing in China and its role in GWAS discovery and replication","authors":"Kang Kang, Xue-Long Sun, Lizhong Wang, Xiaotian Yao, Senwei Tang, Junjie Deng, Xiaoli Wu, Can Yang, Gang Chen","doi":"10.1007/s40484-020-0209-2","DOIUrl":"https://doi.org/10.1007/s40484-020-0209-2","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0209-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49242355","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}
Quantitative BiologyPub Date : 2020-07-13Epub Date: 2020-05-25DOI: 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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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 <i>d</i> 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","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}
Quantitative BiologyPub Date : 2020-07-13Epub Date: 2020-04-20DOI: 10.1007/s40484-020-0196-3
Hong Qian, Yu-Chen Cheng
{"title":"Counting single cells and computing their heterogeneity: from phenotypic frequencies to mean value of a quantitative biomarker.","authors":"Hong Qian, Yu-Chen Cheng","doi":"10.1007/s40484-020-0196-3","DOIUrl":"10.1007/s40484-020-0196-3","url":null,"abstract":"<p><p>This tutorial presents a mathematical theory that relates the probability of sample frequencies, of <i>M</i> phenotypes in an isogenic population of <i>N</i> cells, to the probability distribution of the sample mean of a quantitative biomarker, when the <i>N</i> is very large. An analogue to the statistical mechanics of canonical ensemble is discussed.</p>","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://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290932/pdf/nihms-1611730.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39206809","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}
{"title":"DNA sequencing using nanopores and kinetic proofreading","authors":"X. Ling","doi":"10.1007/s40484-020-0201-x","DOIUrl":"https://doi.org/10.1007/s40484-020-0201-x","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0201-x","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49224985","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}
{"title":"Transcriptome-wide association studies: a view from Mendelian randomization","authors":"Huanhuan Zhu, Xiang Zhou","doi":"10.1007/s40484-020-0207-4","DOIUrl":"https://doi.org/10.1007/s40484-020-0207-4","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2020-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0207-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45821382","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}