Kailing Tu, Xuemei Li, Qilin Zhang, Wei Huang, Dan Xie
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引用次数: 0
摘要
动机确定染色质的可及性是研究真核生物基因组调控的关键步骤之一。外源甲基转移酶与纳米孔测序的结合提供了一种在单分子尺度上识别长基因组范围内开放染色质的策略。结果 我们系统地评估了这三个影响因素的影响,并开发了一种基于模型的计算方法--甲基转移酶可访问基因组区域搜索器(MAGNIFIER)来解决这些问题。通过纳入对照数据,MAGNIFIER 利用数据自适应比较策略削弱了这三个影响因素。我们证明,MAGNIFIER 不仅能灵敏地识别开放染色质,而且准确性大大提高,还能检测基于 NGS 方法遗漏的重复区域的染色质可及性。通过结合长线程 RNA-seq 数据,我们揭示了可访问的 Alu 元素与非经典基因同工酶之间的关联。AVAILABILITY免费提供,网址:https://github.com/Goatofmountain/MAGNIFIER.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.
A data-adaptive methods in detecting exogenous methyltransferase accessible chromatin in human genome using nanopore sequencing.
MOTIVATION
Identifying chromatin accessibility is one of the key steps in studying the regulation of eukaryotic genomes. The combination of exogenous methyltransferase and nanopore sequencing provides an strategy to identify open chromatin over long genomic ranges at the single-molecule scale. However, endogenous methylation, non-open-chromatin-specific exogenous methylation and base-calling errors limit the accuracy and hinders its application to complex genomes.
RESULTS
We systematically evaluated the impact of these three influence factors, and developed a model-based computational method, methyltransferase accessible genome region finder(MAGNIFIER), to address the issues. By incorporating control data, MAGNIFIER attenuates the three influence factors with data-adaptive comparison strategy. We demonstrate that MAGNIFIER is not only sensitive to identify the open chromatin with much improved accuracy, but also able to detect the chromatin accessibility of repetitive regions that are missed by NGS-based methods. By incorporating long-read RNA-seq data, we revealed the association between the accessible Alu elements and non-classic gene isoforms.
AVAILABILITY
Freely avaliable on web at https://github.com/Goatofmountain/MAGNIFIER.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
期刊介绍:
The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.