派博傳思國際中心

標題: Titlebook: Computational Epigenomics and Epitranscriptomics; Pedro H. Oliveira Book 2023 The Editor(s) (if applicable) and The Author(s), under exclu [打印本頁]

作者: Abridge    時間: 2025-3-21 20:00
書目名稱Computational Epigenomics and Epitranscriptomics影響因子(影響力)




書目名稱Computational Epigenomics and Epitranscriptomics影響因子(影響力)學科排名




書目名稱Computational Epigenomics and Epitranscriptomics網(wǎng)絡公開度




書目名稱Computational Epigenomics and Epitranscriptomics網(wǎng)絡公開度學科排名




書目名稱Computational Epigenomics and Epitranscriptomics被引頻次




書目名稱Computational Epigenomics and Epitranscriptomics被引頻次學科排名




書目名稱Computational Epigenomics and Epitranscriptomics年度引用




書目名稱Computational Epigenomics and Epitranscriptomics年度引用學科排名




書目名稱Computational Epigenomics and Epitranscriptomics讀者反饋




書目名稱Computational Epigenomics and Epitranscriptomics讀者反饋學科排名





作者: 協(xié)議    時間: 2025-3-21 23:51

作者: macrophage    時間: 2025-3-22 01:33

作者: Medicaid    時間: 2025-3-22 07:12
Integrating Single-Cell Methylome and Transcriptome Data with MAPLE,introduced bisulfite-sequencing-based methods, it is possible to profile the entire methylome at single-cell resolution. However, analysis of single-cell methylome data is challenging due to data sparsity and moderate correlation with transcript level. Our recently developed computational framework,
作者: ELUDE    時間: 2025-3-22 09:26
Quantitative Comparison of Multiple Chromatin Immunoprecipitation-Sequencing (ChIP-seq) Experimentsmic distribution of chromatin-associated proteins, histone posttranslational modifications, and histone variants. However, in the absence of a reference control for monitoring experimental and biological variations, the standard ChIP-seq scheme is unable to accurately assess changes in the abundance
作者: 送秋波    時間: 2025-3-22 16:32

作者: 送秋波    時間: 2025-3-22 19:14
DNA Modification Patterns Filtering and Analysis Using DNAModAnnot,ng data can be used to identify modified base patterns. However, the downstream analysis of Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) data requires the integration of genomic annotation and comprehensive filtering to prevent the accumulation of artifact signals. We present i
作者: leniency    時間: 2025-3-23 01:03

作者: GOAD    時間: 2025-3-23 03:31

作者: institute    時間: 2025-3-23 06:39
Predicting Pseudouridine Sites with Porpoise,fy pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves
作者: modest    時間: 2025-3-23 13:00
Pseudouridine Identification and Functional Annotation with PIANO,gical processes. Accurately identifying the location of Ψ sites is helpful for relevant downstream researches. In this chapter, we introduce a website PIANO—for .seudouridine site (Ψ) .dentification .nd fu.ctional ann.tation, which enables researchers to predict human putative Ψ sites with a high-ac
作者: 嬰兒    時間: 2025-3-23 14:09

作者: endoscopy    時間: 2025-3-23 19:50
Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores,data. The MoP2 relies on the Nextflow DSL2 framework and Linux containers, thus enabling reproducible data analysis in transcriptomic and epitranscriptomic studies. We introduce the key concepts of MoP2 and provide a step-by-step fully reproducible and complete example of how to use the workflow for
作者: 得罪人    時間: 2025-3-23 22:14
,Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq,nce was reported in tRNA, rRNA, and sn/snoRNA as well as in mRNA/lncRNA. Multiple analytical deep sequencing-based approaches have been proposed for pseudouridine detection and quantification, among which the most popular relies on the use of soluble carbodiimide (termed CMCT). Recently, we develope
作者: ARBOR    時間: 2025-3-24 02:53

作者: 不來    時間: 2025-3-24 08:58

作者: BLANK    時間: 2025-3-24 11:18

作者: CORE    時間: 2025-3-24 18:19
Sequoia: A Framework for Visual Analysis of RNA Modifications from Direct RNA Sequencing Data, to interactively analyze signals originating from nanopore sequencers and can readily be extended to both RNA and DNA sequencing datasets. Sequoia combines a Python-based backend with a multi-view graphical interface that allows users to ingest raw nanopore sequencing data in Fast5 format, cluster
作者: 干涉    時間: 2025-3-24 21:45
Mapping of RNA Modifications by Direct Nanopore Sequencing and JACUSA2,o a sample lacking a specific modification type (e.g., “knockout” sample, here .-KO) or . a sample of interest with modifications is compared to a sample lacking all modifications (e.g., in vitro transcribed cDNA). We provide a detailed protocol on experimental and computational aspects. Extensive o
作者: 領先    時間: 2025-3-25 01:58

作者: IRK    時間: 2025-3-25 06:08
https://doi.org/10.1007/978-3-642-93418-6utional layers to achieve simultaneously a large sequence context while interpreting the DNA sequence at single base pair resolution. Using transfer learning of convolutional weights trained to predict a compendium of chromatin features across cell types allows deepC to predict cell type-specific ch
作者: gain631    時間: 2025-3-25 07:32

作者: Cytology    時間: 2025-3-25 14:07

作者: foodstuff    時間: 2025-3-25 17:52
https://doi.org/10.1007/978-3-658-28778-8cts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.
作者: foliage    時間: 2025-3-25 22:52
Walter Bien,Angela Hartl,Markus Teubnerse of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.
作者: stress-test    時間: 2025-3-26 02:23
Integrating Single-Cell Methylome and Transcriptome Data with MAPLE,cts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.
作者: COLIC    時間: 2025-3-26 08:07

作者: sparse    時間: 2025-3-26 10:12
1064-3745 ation advice from the experts.This volume details state-of-the-art computational methods designed to manage, analyze, and generally leverage epigenomic and epitranscriptomic data. Chapters guide readers through fine-mapping and quantification of modifications, visual analytics, imputation methods, s
作者: tendinitis    時間: 2025-3-26 16:08

作者: fallible    時間: 2025-3-26 19:04

作者: 斜谷    時間: 2025-3-26 23:47
Stieg Larsson‘s Millennium Trilogys under each condition and across different biological conditions. Here we describe detailed procedures to guide researchers in MeRIP-seq data analyses by providing step-by-step instructions of the dedicated bioconductor package TRESS.
作者: 微不足道    時間: 2025-3-27 03:22
https://doi.org/10.1007/978-3-319-72712-7 paucity of software to interpret the complicated data produced by these experiments. Here I describe how to use the NucleicAcidSearchEngine (NASE), a component of OpenMS as well as best practices for acquiring RNA data, and potential pitfalls in the analysis process.
作者: 比喻好    時間: 2025-3-27 05:46

作者: 毀壞    時間: 2025-3-27 09:49
A Guide to MethylationToActivity: A Deep Learning Framework That Reveals Promoter Activity LandscapH3K4me3 and H3K27ac enrichment from DNA methylomes and thus infer promoter activity. It was shown to be highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers. The following will present a user-friendly guide through the model pipeline.
作者: chastise    時間: 2025-3-27 17:34
Analyzing mRNA Epigenetic Sequencing Data with TRESS,s under each condition and across different biological conditions. Here we describe detailed procedures to guide researchers in MeRIP-seq data analyses by providing step-by-step instructions of the dedicated bioconductor package TRESS.
作者: Cupping    時間: 2025-3-27 19:05
Analysis of RNA Sequences and Modifications Using NASE, paucity of software to interpret the complicated data produced by these experiments. Here I describe how to use the NucleicAcidSearchEngine (NASE), a component of OpenMS as well as best practices for acquiring RNA data, and potential pitfalls in the analysis process.
作者: Osteoarthritis    時間: 2025-3-28 00:51

作者: JUST    時間: 2025-3-28 03:30

作者: 侵略    時間: 2025-3-28 09:58
Modell- und Hypothesenentwicklung,e experimental protocol for preparing quality control spike-in chromatin from . cells and (ii) the computational protocol to compare ChIP-seq samples with spike-in based on the use of the spikChIP software.
作者: 入會    時間: 2025-3-28 10:38
Die Erwerbsbeteiligung von Stieffamilienions. Here, we provide an application?example of this workflow with PacBio data and guide the user by explaining expected outputs via a fully integrated Rmarkdown script. This protocol is presented with tips showing how to adapt the provided code for annotating epigenomes of any organism according to the user needs.
作者: Ingenuity    時間: 2025-3-28 17:44

作者: 先驅    時間: 2025-3-28 21:54
Die Erwerbsbeteiligung von Stieffamilienrehensive query database was also provided to deposit over 4300 human Ψ modifications, which is currently the most complete collection of experimental-derived Ψ sites. The PIANO website is freely accessible at: . or ..
作者: FLIRT    時間: 2025-3-29 02:34

作者: Hypomania    時間: 2025-3-29 04:31

作者: Ancillary    時間: 2025-3-29 09:05

作者: BOOR    時間: 2025-3-29 13:01
DNA Modification Patterns Filtering and Analysis Using DNAModAnnot,ions. Here, we provide an application?example of this workflow with PacBio data and guide the user by explaining expected outputs via a fully integrated Rmarkdown script. This protocol is presented with tips showing how to adapt the provided code for annotating epigenomes of any organism according to the user needs.
作者: 通情達理    時間: 2025-3-29 16:34
Predicting Pseudouridine Sites with Porpoise, ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.
作者: Choreography    時間: 2025-3-29 23:37

作者: Bridle    時間: 2025-3-30 00:35
Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores,support of the GPU computing for the basecalling and read demultiplexing steps. The secondary analyses of the workflow focus on the estimation of RNA poly(A) tail lengths and the identification of RNA modifications. The MoP2 code is available at . and is distributed under the MIT license.
作者: 山崩    時間: 2025-3-30 05:08
,Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq,tification of the pseudouridine content. All “wet-lab” technical details of the HydraPsiSeq protocol have been described in recent publications. Here, we describe all bioinformatics analysis steps required for data processing from raw reads to the pseudouridylation profile of known or unknown RNA.
作者: 發(fā)生    時間: 2025-3-30 09:43

作者: 保留    時間: 2025-3-30 14:18

作者: bonnet    時間: 2025-3-30 20:32
Pedro H. OliveiraIncludes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts
作者: 不透氣    時間: 2025-3-30 23:05

作者: Minikin    時間: 2025-3-31 03:53

作者: Relinquish    時間: 2025-3-31 06:32

作者: 威脅你    時間: 2025-3-31 10:44
You Find Friends in Improbable Places,emical assays, including conventional bisulfite treatment-based and emerging bisulfite-free techniques, has promised high-resolution DNA methylome profiling and significantly propelled the DNA methylation research field. However, the analysis of large-scale data generated from such assays is still c
作者: figment    時間: 2025-3-31 16:38

作者: 生銹    時間: 2025-3-31 20:07
https://doi.org/10.1007/978-3-642-93418-6e architecture spans large-scale structures determined by fine-grained regulatory elements, making it challenging to predict the effects of sequence and structural variants. Experimental approaches for chromatin interaction mapping remain costly and time-consuming, limiting their use for interrogati




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