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Transcriptome Data Analysis

Overview of attention for book
Cover of 'Transcriptome Data Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Comparison of Gene Expression Profiles in Nonmodel Eukaryotic Organisms with RNA-Seq
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    Chapter 2 Microarray Data Analysis for Transcriptome Profiling
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    Chapter 3 Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes
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    Chapter 4 QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization
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    Chapter 5 Tracking Alternatively Spliced Isoforms from Long Reads by SpliceHunter
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    Chapter 6 RNA-Seq-Based Transcript Structure Analysis with TrBorderExt
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    Chapter 7 Analysis of RNA Editing Sites from RNA-Seq Data Using GIREMI
  9. Altmetric Badge
    Chapter 8 Bioinformatic Analysis of MicroRNA Sequencing Data
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    Chapter 9 Microarray-Based MicroRNA Expression Data Analysis with Bioconductor
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    Chapter 10 Identification and Expression Analysis of Long Intergenic Noncoding RNAs
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    Chapter 11 Analysis of RNA-Seq Data Using TEtranscripts
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    Chapter 12 Computational Analysis of RNA–Protein Interactions via Deep Sequencing
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    Chapter 13 Predicting Gene Expression Noise from Gene Expression Variations
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    Chapter 14 A Protocol for Epigenetic Imprinting Analysis with RNA-Seq Data
  16. Altmetric Badge
    Chapter 15 Single-Cell Transcriptome Analysis Using SINCERA Pipeline
  17. Altmetric Badge
    Chapter 16 Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues
Attention for Chapter 12: Computational Analysis of RNA–Protein Interactions via Deep Sequencing
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Chapter title
Computational Analysis of RNA–Protein Interactions via Deep Sequencing
Chapter number 12
Book title
Transcriptome Data Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7710-9_12
Pubmed ID
29508297
Book ISBNs
978-1-4939-7709-3, 978-1-4939-7710-9
Authors

Lei Li, Konrad U. Förstner, Yanjie Chao, Li, Lei, Förstner, Konrad U., Chao, Yanjie

Abstract

RNA-binding proteins (RBPs) function in all aspects of RNA processes including stability, structure, export, localization and translation, and control gene expression at the posttranscriptional level. To investigate the roles of RBPs and their direct RNA ligands in vivo, recent global approaches combining RNA immunoprecipitation and deep sequencing (RIP-seq) as well as UV-cross-linking (CLIP-seq) have become instrumental in dissecting RNA-protein interactions. However, the computational analysis of these high-throughput sequencing data is still challenging. Here, we provide a computational pipeline to analyze CLIP-seq and RIP-seq datasets. This generic analytic procedure may help accelerate the identification of direct RNA-protein interactions from high-throughput RBP profiling experiments in a variety of bacterial species.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 54%
Unspecified 1 8%
Student > Bachelor 1 8%
Professor 1 8%
Student > Master 1 8%
Other 2 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Biochemistry, Genetics and Molecular Biology 3 23%
Computer Science 3 23%
Unspecified 1 8%
Engineering 1 8%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 March 2018.
All research outputs
#20,468,008
of 23,026,672 outputs
Outputs from Methods in molecular biology
#9,951
of 13,170 outputs
Outputs of similar age
#378,209
of 442,370 outputs
Outputs of similar age from Methods in molecular biology
#1,194
of 1,499 outputs
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