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Transcriptome Data Analysis

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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
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    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
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    Chapter 15 Single-Cell Transcriptome Analysis Using SINCERA Pipeline
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    Chapter 16 Mathematical Modeling and Deconvolution of Molecular Heterogeneity Identifies Novel Subpopulations in Complex Tissues
Attention for Chapter 10: Identification and Expression Analysis of Long Intergenic Noncoding RNAs
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Chapter title
Identification and Expression Analysis of Long Intergenic Noncoding RNAs
Chapter number 10
Book title
Transcriptome Data Analysis
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7710-9_10
Pubmed ID
Book ISBNs
978-1-4939-7709-3, 978-1-4939-7710-9
Authors

Ming-an Sun, Rihong Zhai, Qing Zhang, Yejun Wang

Abstract

Long intergenic noncoding RNAs (lincRNAs) have caught increasing attention in recent years. The advance of RNA-Seq has greatly facilitated the discovery of novel lincRNAs. However, the computational analysis of lincRNAs is still challenging. In this protocol, we presented a step-by-step protocol for computational analyses of lincRNAs, including read processing and alignment, transcript assembly, lincRNA identification and annotation, and differential expression analysis.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 27%
Student > Ph. D. Student 2 18%
Researcher 2 18%
Unspecified 1 9%
Student > Master 1 9%
Other 0 0%
Unknown 2 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 18%
Medicine and Dentistry 2 18%
Biochemistry, Genetics and Molecular Biology 1 9%
Unspecified 1 9%
Engineering 1 9%
Other 0 0%
Unknown 4 36%