DIME：基于混合模型集合的差异 ChIP-seq 识别的 R 包。
DIME: R-package for identifying differential ChIP-seq based on an ensemble of mixture models.
机构信息
Department of Molecular Virology, Immunology and Medical Genetics and Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.
出版信息
Bioinformatics. 2011 Jun 1;27(11):1569-70. doi: 10.1093/bioinformatics/btr165. Epub 2011 Apr 5.
Abstract
SUMMARY
Differential Identification using Mixtures Ensemble (DIME) is a package for identification of biologically significant differential binding sites between two conditions using ChIP-seq data. It considers a collection of finite mixture models combined with a false discovery rate (FDR) criterion to find statistically significant regions. This leads to a more reliable assessment of differential binding sites based on a statistical approach. In addition to ChIP-seq, DIME is also applicable to data from other high-throughput platforms.
AVAILABILITY AND IMPLEMENTATION
DIME is implemented as an R-package, which is available at http://www.stat.osu.edu/~statgen/SOFTWARE/DIME. It may also be downloaded from http://cran.r-project.org/web/packages/DIME/.
摘要
摘要
使用混合物集成（DIME）进行差异识别是一个用于使用 ChIP-seq 数据识别两种条件之间具有生物学意义的差异结合位点的软件包。它考虑了一组有限混合模型，并结合了错误发现率（FDR）标准，以找到具有统计学意义的区域。这使得基于统计方法更可靠地评估差异结合位点成为可能。除了 ChIP-seq，DIME 还适用于来自其他高通量平台的数据。
可用性和实现
DIME 作为一个 R 包实现，可在 http://www.stat.osu.edu/~statgen/SOFTWARE/DIME 获得。也可以从 http://cran.r-project.org/web/packages/DIME/ 下载。
相似文献
1
DIME: R-package for identifying differential ChIP-seq based on an ensemble of mixture models.DIME：基于混合模型集合的差异 ChIP-seq 识别的 R 包。
Bioinformatics. 2011 Jun 1;27(11):1569-70. doi: 10.1093/bioinformatics/btr165. Epub 2011 Apr 5.
2
Comparative study on ChIP-seq data: normalization and binding pattern characterization.ChIP-seq 数据的比较研究：标准化和结合模式特征分析。
Bioinformatics. 2009 Sep 15;25(18):2334-40. doi: 10.1093/bioinformatics/btp384. Epub 2009 Jun 26.
3
Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks.控制ChIP-Seq峰中假阳性并估计置信度的经验方法。
BMC Bioinformatics. 2008 Dec 5;9:523. doi: 10.1186/1471-2105-9-523.
4
ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data.ChIPpeakAnno：一个用于注释 ChIP-seq 和 ChIP-chip 数据的 Bioconductor 软件包。
BMC Bioinformatics. 2010 May 11;11:237. doi: 10.1186/1471-2105-11-237.
5
BayesPeak--an R package for analysing ChIP-seq data.BayesPeak——一个用于分析 ChIP-seq 数据的 R 包。
Bioinformatics. 2011 Mar 1;27(5):713-4. doi: 10.1093/bioinformatics/btq685. Epub 2011 Jan 17.
6
rMAT--an R/Bioconductor package for analyzing ChIP-chip experiments.rMAT--一个用于分析 ChIP-chip 实验的 R/Bioconductor 包。
Bioinformatics. 2010 Mar 1;26(5):678-9. doi: 10.1093/bioinformatics/btq023. Epub 2010 Jan 19.
7
Role of ChIP-seq in the discovery of transcription factor binding sites, differential gene regulation mechanism, epigenetic marks and beyond.染色质免疫沉淀测序（ChIP-seq）在转录因子结合位点发现、差异基因调控机制、表观遗传标记及其他方面的作用。
Cell Cycle. 2014;13(18):2847-52. doi: 10.4161/15384101.2014.949201.
8
A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.一种用于多个ChIP-seq数据集定量比较的新型统计方法。
Bioinformatics. 2015 Jun 15;31(12):1889-96. doi: 10.1093/bioinformatics/btv094. Epub 2015 Feb 13.
9
ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization.ChIPseeker：一个用于ChIP峰注释、比较和可视化的R/Bioconductor软件包。
Bioinformatics. 2015 Jul 15;31(14):2382-3. doi: 10.1093/bioinformatics/btv145. Epub 2015 Mar 11.
10
QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions.QChIPat：一种用于鉴定两种不同实验条件下的两个生物 ChIP-seq 样本的独特结合模式的定量方法。
BMC Genomics. 2013;14 Suppl 8(Suppl 8):S3. doi: 10.1186/1471-2164-14-S8-S3. Epub 2013 Dec 9.
引用本文的文献
1
Comprehensive assessment of differential ChIP-seq tools guides optimal algorithm selection.综合评估差异 ChIP-seq 工具可指导最佳算法选择。
Genome Biol. 2022 May 24;23(1):119. doi: 10.1186/s13059-022-02686-y.
2
Statistical challenges in analyzing methylation and long-range chromosomal interaction data.分析甲基化和长程染色体相互作用数据时的统计学挑战。
Stat Biosci. 2016 Oct;8(2):284-309. doi: 10.1007/s12561-016-9145-0. Epub 2016 Mar 7.
3
A MAD-Bayes Algorithm for State-Space Inference and Clustering with Application to Querying Large Collections of ChIP-Seq Data Sets.一种用于状态空间推理和聚类的MAD-贝叶斯算法及其在查询大量ChIP-Seq数据集方面的应用
J Comput Biol. 2017 Jun;24(6):472-485. doi: 10.1089/cmb.2016.0138. Epub 2016 Nov 11.
4
Quantitative analysis of ChIP-seq data uncovers dynamic and sustained H3K4me3 and H3K27me3 modulation in cancer cells under hypoxia.ChIP-seq数据的定量分析揭示了缺氧条件下癌细胞中H3K4me3和H3K27me3的动态及持续调控。
Epigenetics Chromatin. 2016 Nov 1;9:48. doi: 10.1186/s13072-016-0090-4. eCollection 2016.
5
Systematic chromatin state comparison of epigenomes associated with diverse properties including sex and tissue type.对与包括性别和组织类型在内的多种特性相关的表观基因组进行系统的染色质状态比较。
Nat Commun. 2015 Aug 18;6:7973. doi: 10.1038/ncomms8973.
6
BOG: R-package for Bacterium and virus analysis of Orthologous Groups.BOG：用于直系同源基因簇细菌和病毒分析的R包。
Comput Struct Biotechnol J. 2015 May 21;13:366-9. doi: 10.1016/j.csbj.2015.05.002. eCollection 2015.
7
Identifying differential transcription factor binding in ChIP-seq.在染色质免疫沉淀测序（ChIP-seq）中鉴定差异转录因子结合
Front Genet. 2015 Apr 29;6:169. doi: 10.3389/fgene.2015.00169. eCollection 2015.
8
A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.一种用于多个ChIP-seq数据集定量比较的新型统计方法。
Bioinformatics. 2015 Jun 15;31(12):1889-96. doi: 10.1093/bioinformatics/btv094. Epub 2015 Feb 13.
9
Early Pregnancy Maternal Blood DNA Methylation in Repeat Pregnancies and Change in Gestational Diabetes Mellitus Status—A Pilot Study.重复妊娠中早期妊娠孕妇血液DNA甲基化与妊娠期糖尿病状态变化——一项试点研究。
Reprod Sci. 2015 Jul;22(7):904-10. doi: 10.1177/1933719115570903. Epub 2015 Feb 11.
10
dCaP: detecting differential binding events in multiple conditions and proteins.dCaP：检测多种条件和蛋白质中的差异结合事件
BMC Genomics. 2014;15 Suppl 9(Suppl 9):S12. doi: 10.1186/1471-2164-15-S9-S12. Epub 2014 Dec 8.
本文引用的文献
1
A Robust Unified Approach to Analyzing Methylation and Gene Expression Data.一种用于分析甲基化和基因表达数据的强大统一方法。
Comput Stat Data Anal. 2009 Mar 15;53(5):1701-1710. doi: 10.1016/j.csda.2008.07.010.
2
A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments.在 ChIP-seq 实验中检测转录因子结合位点的方法的实际比较。
BMC Genomics. 2009 Dec 18;10:618. doi: 10.1186/1471-2164-10-618.
3
Comparative study on ChIP-seq data: normalization and binding pattern characterization.ChIP-seq 数据的比较研究：标准化和结合模式特征分析。
Bioinformatics. 2009 Sep 15;25(18):2334-40. doi: 10.1093/bioinformatics/btp384. Epub 2009 Jun 26.
4
Normal uniform mixture differential gene expression detection for cDNA microarrays.用于cDNA微阵列的正常均匀混合物差异基因表达检测
BMC Bioinformatics. 2005 Jul 12;6:173. doi: 10.1186/1471-2105-6-173.
Zotero 插件