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## Meet the editor

Miroslav Blumenberg, PhD, was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his PhD at MIT in Organic Chemistry; he followed up his PhD with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is a codirector of a training grant in cutaneous biology. Dr. Blumenberg's research

is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on skin. He has published more than 100 peer-reviewed research articles and graduated numerous PhD and postdoctoral students. Dr. Blumenberg lives in New York, USA, with his wife and two children.

Contents

**Section 1**

**Section 2**

**Section 3**

**Section 4**

*by Miroslav Blumenberg*

Reference Transcriptome

*by Dong Jin Lee and Chang Pyo Hong*

*by Xiangyuan Wan and Ziwen Li*

*by Ashutosh Kumar and Prasanta K. Dash*

**Preface III**

Introduction **1**

**Chapter 1 3**

Tumor Transcriptome **9**

**Chapter 2 11**

Reference Transcriptomes **23**

**Chapter 3 25**

Transcriptome Analysis in Plants **37**

**Chapter 4 39** Plant Comparative Transcriptomics Reveals Functional Mechanisms and Gene Regulatory Networks Involved in Anther Development and Male Sterility

**Chapter 5 61**

**Chapter 6 77** Revealing the Symmetry of Conifer Transcriptomes through Triplet Statistics *by Sadovsky Michael, Putintseva Yulia, Biryukov Vladislav and Senashova Maria*

Transcriptome Analysis for Abiotic Stresses in Rice (*Oryza sativa* L.)

Transcriptome Atlas by Long-Read RNA Sequencing: Contribution to a

Introductory Chapter: Transcriptome Analysis

Single-Cell Transcriptome Analysis in Tumor Tissues

*by Sadahiro Iwabuchi and Shinichi Hashimoto*

### Contents


Preface

Transcriptome analysis is the study of the transcriptome, of the complete set of RNA transcripts that are produced under specific circumstances, using highthroughput methods. Transcription profiling, which follows total changes in the behavior of a cell, is used throughout diverse areas of biomedical research, including diagnosis of disease, biomarker discovery, risk assessment of new drugs or environmental chemicals, etc. Transcription profiling can be applied to lossand gain-of-function mutants to identify the changes associated with the mutant phenotype. Transcriptomics also allows the identification of pathways that respond to or ameliorate environmental stresses. RNA sequencing (RNA-Seq) detects all transcripts in a sample, including mRNAs as well as the regulatory siRNA and lncRNA transcripts. RNA-Seq can also identify disease-associated gene fusions,

single nucleotide polymorphisms, and even allele-specific expression.

Transcriptome analysis is most commonly used to compare specific pairs of

In this volume, Dr. Pyo Hong discusses the role of long RNA sequences in

rather short reads, 35 to a few hundred nucleotides, and relied on massive redundancy to achieve required accuracy. Newer methods, which provide longer reads, have significant advantages, for example, in the analysis of previously not

sequenced genomes. Such approaches need tailored software methods.

Dr. Prasanta presents transcriptome analysis applied to rice, one of world's most essential staple foods. Rice production and yield are critically affected by environmental factors, including drought, flooding, high salinity, extreme temperatures, nutrient and mineral availability, toxins and pollutants, etc. Because of the complexity of influences on crop yield, it is essential to define the intricate regulatory gene networks and their signaling pathways involved in stress responses. High-throughput RNA-Seq data have provided an abundance of transcriptome data on rice. RNA-Seq provides data regarding not only coding mRNAs but also

the developing world.

transcriptome analysis. It should be noted that early RNA-Seq methods generated

Dr. Shinichi describes next-generation single-cell sequencing technology developed by his team. It can be used for single-cell transcriptome analysis in tumor tissues. This is an extremely important area nowadays because it is clear that most tumors are heterogeneous. Identifying the transcriptome of tumor stem cells may lead to specific targeting of these cells. Alternatively, single-cell transcriptome analysis can help in defining the tumor-infiltrating immune cells, a critical component of immunotherapies. Dr. Shinichi and his team developed a microwell device that can be easily transported and is relatively cheaper than most other RNA-Seq methods, which will be essential for the widespread use of transcription analysis, especially in

samples. The differences may be due to different external environmental conditions, for example, hormonal effects or toxins. More commonly, healthy and disease states are compared. In general, transcriptome analysis is a very powerful hypothesisgenerating tool rather than a theory-proving one. Transcriptome analyses have become indispensable in basic research and translational and clinical studies.
