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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
  2. Altmetric Badge
    Chapter 1 Comparison of Gene Expression Profiles in Nonmodel Eukaryotic Organisms with RNA-Seq
  3. Altmetric Badge
    Chapter 2 Microarray Data Analysis for Transcriptome Profiling
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    Chapter 3 Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes
  5. Altmetric Badge
    Chapter 4 QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization
  6. Altmetric Badge
    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 8: Bioinformatic Analysis of MicroRNA Sequencing Data
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Chapter title
Bioinformatic Analysis of MicroRNA Sequencing Data
Chapter number 8
Book title
Transcriptome Data Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7710-9_8
Pubmed ID
Book ISBNs
978-1-4939-7709-3, 978-1-4939-7710-9
Authors

Xiaonan Fu, Daoyuan Dong, Fu, Xiaonan, Dong, Daoyuan

Abstract

The vital role of microRNAs (miRNAs) involved in gene expression regulation has been confirmed in many biological processes. With the growing power and reducing cost of next-generation sequencing, more and more researchers turn to apply this high-throughput method to solve their biological problems. For miRNAs with known sequences, their expression profiles can be generated from the sequencing data. It also allows us to identify some novel miRNAs and explore the sequence variations under different conditions. Currently, there are a handful of tools available to analyze the miRNA sequencing data with separated or combined features, such as reads preprocessing, mapping and differential expression analysis. However, to our knowledge, a hands-on guideline for miRNA sequencing data analysis covering all steps is not available. Here we will utilize a set of published tools to perform the miRNA analysis with detailed explanation. Particularly, the miRNA target prediction and annotation may provide useful information for further experimental verification.

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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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 24%
Student > Bachelor 11 16%
Researcher 8 11%
Student > Master 5 7%
Student > Postgraduate 4 6%
Other 7 10%
Unknown 18 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 37%
Agricultural and Biological Sciences 13 19%
Computer Science 4 6%
Medicine and Dentistry 4 6%
Veterinary Science and Veterinary Medicine 2 3%
Other 3 4%
Unknown 18 26%
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.
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#20,468,008
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Outputs from Methods in molecular biology
#9,951
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#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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