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 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.

In this volume, Dr. Pyo Hong discusses the role of long RNA sequences in transcriptome analysis. It should be noted that early RNA-Seq methods generated 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. 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 the developing world.

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

noncoding RNAs, components of regulatory gene networks involved in the stress response. These results may enable more optimal cultivating conditions and help to develop new tolerant varieties of rice.

Dr. Xiangyuan focused his studies on the reproductive systems of flowering plants, specifically the gene regulatory networks in anthers, the parts of the stamen that produce and contain pollen.

Prof. Sadovsky analyzed the coding sequences of few conifers, comparing the usage of triplet codons in cold-adjusted plants.

We can anticipate a greatly expanded usage of transcriptome analysis, especially when translated to the bedside, to provide better understanding and more specific diagnoses, enabling physicians to establish diagnoses quickly and reliably.

> **Miroslav Blumenberg** NYU School of Medicine, USA

> > **1**

Section 1

Introduction

Section 1 Introduction

**3**

**Chapter 1**

*Miroslav Blumenberg*

a population of cells or in an organism.

in different tissues.

**1. Transcriptome analysis**

**2. Uses of transcriptome analysis**

Introductory Chapter:

Transcriptome Analysis

The central dogma of molecular biology describes the flow of genetic information from genes to functions of the cells and organisms. This comprises a two-step process: first, DNA, the permanent, heritable, genetic information repository, is transcribed by the RNA polymerase enzymes into RNA, a short-lasting information carrier; second, a subset of RNA, the messenger RNAs, mRNAs, are translated into protein. The **transcriptome,** then, is the complete set of all RNA molecules in a cell,

Importantly, not all RNAs are translated into proteins, some serve a structural function, for example, rRNAs in the assembly of ribosomes, others are transporters, e.g., tRNAs, yet others serve regulatory functions, for example, the siRNAs, short interfering RNA, or lncRNAs, long non-coding RNAs; these are not translated into proteins [1]. However, these non-coding RNAs can and often do play roles in human diseases such as cancer, cardiovascular, and neurological disorders. While transcriptomics is most commonly applied to the mRNAs, the coding transcripts, transcriptomics also provides important data regarding content of the cell noncoding RNAs, including rRNA, tRNA, lncRNA, siRNA, and others. Specific approaches address the analysis of splice variant of the same gene

Transcriptome Analysis is the study of the transcriptome, of the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell, using high-throughput methods. Transcription profiling, which follows changes in behavior of a cell *in toto*, not of a single gene or just a few genes, is used throughout diverse areas of biomedical research, including disease diagnosis, biomarker discovery, risk assessment of new drugs or environmental chemicals etc. Transcription profiling can be applied to loss- and gain-of-function mutants to identify the changes associated with the mutant phenotype. The transcriptomic techniques have been particularly useful in identifying the functions of genes. Transcriptomics also allows identification of pathways that respond to or ameliorate environmental stresses. 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 samples. The differences may be due to different external environmental conditions, e.g., hormonal effects or toxins. More commonly, healthy and disease states

#### **Chapter 1**

## Introductory Chapter: Transcriptome Analysis

*Miroslav Blumenberg*

The central dogma of molecular biology describes the flow of genetic information from genes to functions of the cells and organisms. This comprises a two-step process: first, DNA, the permanent, heritable, genetic information repository, is transcribed by the RNA polymerase enzymes into RNA, a short-lasting information carrier; second, a subset of RNA, the messenger RNAs, mRNAs, are translated into protein. The **transcriptome,** then, is the complete set of all RNA molecules in a cell, a population of cells or in an organism.

Importantly, not all RNAs are translated into proteins, some serve a structural function, for example, rRNAs in the assembly of ribosomes, others are transporters, e.g., tRNAs, yet others serve regulatory functions, for example, the siRNAs, short interfering RNA, or lncRNAs, long non-coding RNAs; these are not translated into proteins [1]. However, these non-coding RNAs can and often do play roles in human diseases such as cancer, cardiovascular, and neurological disorders. While transcriptomics is most commonly applied to the mRNAs, the coding transcripts, transcriptomics also provides important data regarding content of the cell noncoding RNAs, including rRNA, tRNA, lncRNA, siRNA, and others. Specific approaches address the analysis of splice variant of the same gene in different tissues.

#### **1. Transcriptome analysis**

Transcriptome Analysis is the study of the transcriptome, of the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell, using high-throughput methods. Transcription profiling, which follows changes in behavior of a cell *in toto*, not of a single gene or just a few genes, is used throughout diverse areas of biomedical research, including disease diagnosis, biomarker discovery, risk assessment of new drugs or environmental chemicals etc. Transcription profiling can be applied to loss- and gain-of-function mutants to identify the changes associated with the mutant phenotype. The transcriptomic techniques have been particularly useful in identifying the functions of genes. Transcriptomics also allows identification of pathways that respond to or ameliorate environmental stresses. RNA-Seq can also identify disease-associated gene fusions, single nucleotide polymorphisms and even allele-specific expression.

#### **2. Uses of transcriptome analysis**

Transcriptome Analysis is most commonly used to compare specific pairs of samples. The differences may be due to different external environmental conditions, e.g., hormonal effects or toxins. More commonly, healthy and disease states are compared. For example, in cancer, transcriptomics analyses address classification, the mechanisms of pathogenesis and even outcome prediction. Transcriptome studies can classify cancer beyond anatomical location and histopathology. Outcome predictions can establish gene-based benchmarks to predict tumor prognosis and therapy response. These approaches are already in use for personalized medicine, individualized cancer patient therapies.

Organisms and tissues at various stages of development can be molecularly characterized. The transcriptomes of stem cells help to understand the processes of cellular differentiation or embryonic development. Because of its very broad approach transcriptome analysis is a great source for identifying targets for treatment.

#### **2.1 Methodologies**

The early approach to study whole transcriptomes used microarrays, a set of defined sequences arranged on a solid substrate [2]. Microarrays almost exclusively represented mRNAs, that is, genes that are translated into proteins.

Nowadays the microarray approach is supplanted by high-throughput RNA sequencing, RNA-Seq, which detects all transcripts in a sample, including the regulatory siRNA and lncRNA transcripts [3]. In this methodology, the bulk RNA is extracted from the sample and copied into stable double-stranded copy DNA, ds-cDNA, which is then sequenced using various sequencing methods [4]. The sequences obtained are aligned to reference genome sequences, available in data banks, to identify which genes are transcribed. Quantitatively, the results provide the expression levels for the transcribed genes. Compared to microarrays, RNA-Seq can measure both the low-abundance and high-abundance RNAs over a five orders of magnitude range and, importantly, RNA-Seq requires much less starting material (nanograms vs. micrograms and even as little as 50 pg) [5]. This made possible analyses of transcriptomes in a single cell, a great advance over bulk tissue RNA analyses [6]. RNA-seq can be used to identify alternative splicing, novel transcripts, and fusion genes (**Table 1**).

In principle, the assembly of RNA-Seq reads is not dependent on reference genomes and can be used for gene expression studies of poorly characterized species with limited genomic resources. It can also be used to identify novel protein coding regions in sequenced genomes. RNA-seq can be performed using many nextgeneration sequencing platforms, however, each platform has its own requirements of sample preparation and the instrument design.


**5**

**Figure 1.**

*of regulated genes.*

metabolic pathways.

*Introductory Chapter: Transcriptome Analysis DOI: http://dx.doi.org/10.5772/intechopen.85980*

**2.2 Data analysis, repositories and presentation**

Improved sequencing technologies necessitated improved data analysis methods to deal with the increased volume of data produced by each transcriptome experiment. Importantly, the results are deposited into transcriptome databases, essential tools for transcriptome analysis. For example Gene Expression Omnibus, www. ncbi.nml.nih.gov, contains millions of transcription profiling experiments. Such data have potential applications beyond the original aims of an experiment. Typical outputs include quantitative tables of the transcript levels. This requires specific analysis algorithms, often specific to the methodology used. There are software packages to bridge data from disparate methodologies, to identify groups of similar expressed genes, or differentially expressed functionally significant regulatory or

*Graphic representations of transcriptome analysis data. (A) Heat map with clustering tree. (B) Venn diagrams* 

**Table 1.** *Comparison of RNA-seq methodologies.*

*Introductory Chapter: Transcriptome Analysis DOI: http://dx.doi.org/10.5772/intechopen.85980*

*Transcriptome Analysis*

**2.1 Methodologies**

and fusion genes (**Table 1**).

of sample preparation and the instrument design.

are compared. For example, in cancer, transcriptomics analyses address classification, the mechanisms of pathogenesis and even outcome prediction. Transcriptome

Organisms and tissues at various stages of development can be molecularly characterized. The transcriptomes of stem cells help to understand the processes of cellular differentiation or embryonic development. Because of its very broad approach

studies can classify cancer beyond anatomical location and histopathology. Outcome predictions can establish gene-based benchmarks to predict tumor prognosis and therapy response. These approaches are already in use for personalized

transcriptome analysis is a great source for identifying targets for treatment.

represented mRNAs, that is, genes that are translated into proteins.

The early approach to study whole transcriptomes used microarrays, a set of defined sequences arranged on a solid substrate [2]. Microarrays almost exclusively

Nowadays the microarray approach is supplanted by high-throughput RNA sequencing, RNA-Seq, which detects all transcripts in a sample, including the regulatory siRNA and lncRNA transcripts [3]. In this methodology, the bulk RNA is extracted from the sample and copied into stable double-stranded copy DNA, ds-cDNA, which is then sequenced using various sequencing methods [4]. The sequences obtained are aligned to reference genome sequences, available in data banks, to identify which genes are transcribed. Quantitatively, the results provide the expression levels for the transcribed genes. Compared to microarrays, RNA-Seq can measure both the low-abundance and high-abundance RNAs over a five orders of magnitude range and, importantly, RNA-Seq requires much less starting material (nanograms vs. micrograms and even as little as 50 pg) [5]. This made possible analyses of transcriptomes in a single cell, a great advance over bulk tissue RNA analyses [6]. RNA-seq can be used to identify alternative splicing, novel transcripts,

In principle, the assembly of RNA-Seq reads is not dependent on reference genomes and can be used for gene expression studies of poorly characterized species with limited genomic resources. It can also be used to identify novel protein coding regions in sequenced genomes. RNA-seq can be performed using many nextgeneration sequencing platforms, however, each platform has its own requirements

medicine, individualized cancer patient therapies.

**4**

**Table 1.**

*Comparison of RNA-seq methodologies.*

#### **Figure 1.**

*Graphic representations of transcriptome analysis data. (A) Heat map with clustering tree. (B) Venn diagrams of regulated genes.*

#### **2.2 Data analysis, repositories and presentation**

Improved sequencing technologies necessitated improved data analysis methods to deal with the increased volume of data produced by each transcriptome experiment. Importantly, the results are deposited into transcriptome databases, essential tools for transcriptome analysis. For example Gene Expression Omnibus, www. ncbi.nml.nih.gov, contains millions of transcription profiling experiments. Such data have potential applications beyond the original aims of an experiment. Typical outputs include quantitative tables of the transcript levels. This requires specific analysis algorithms, often specific to the methodology used. There are software packages to bridge data from disparate methodologies, to identify groups of similar expressed genes, or differentially expressed functionally significant regulatory or metabolic pathways.

The results of transcriptomic analyses are graphically often presented as heat maps, a system of color-coding that represents different levels of expression of given genes in different samples (**Figure 1A**). Such presentations also frequently display a clustering of samples, this helps to identify samples with similar gene expression. Another common graphical presentation uses Venn diagrams, which count the transcripts which are equivalently regulated in multiple samples (**Figure 1B**).

Transcriptome analyses have become indispensable in basic research, translational, and clinical studies. In general, transcriptome analysis is a very powerful hypothesis-generating tool, more than a theory proving one.

#### **3. Specific example: transcriptome analysis applied to human skin**

Easily accessible, skin was among the first targets analyzed using 'omics' and dermatology embraced the approaches very early [7]. A classic example of coordinated transcriptional regulation was observed in cultured fibroblasts after serum stimulation [2]. Serum addition causes not only rapid recommencement of the cell cycle but, characteristically a wound-healing response, a physiological role of fibroblasts in wound healing [8]. Transcriptional responses of epidermal keratinocytes to UV light, hormones, vitamins, infections, inflammatory and immunomodulating cytokines, toxins and allergens have been characterized, as were the changes associated with epidermal differentiation [9, 10].

The expression signatures that define the various cell types in human skin, were used to define 20 specific gene signatures, including those for keratinocytes, melanocytes, endothelia, adipocytes, immune cells, hair follicles, sebaceous, sweat, and apocrine glands. This resource provided a resource named SkinSig, which was then used to analyze 18 skin conditions, providing in-context interpretation of, for example, influx in immune cells in inflammation or differentiation changes in disorders of cornification [11].

In the future we can anticipate a greatly expanded usage of transcriptome analysis. Translated to the bedside, it can provide better understanding and more specific diagnoses of diseases. This, of course, requires additional advances in the technology, both in the lab-bench components reducing the costs and guaranteeing reproducibility and accuracy, as well as in the computer-based components, algorithms that enable physicians to establish diagnosis quickly and reliably. In a generation, this approach will become routine.

#### **Author details**

Miroslav Blumenberg NYU School of Medicine, USA

\*Address all correspondence to: miroslav.blumenberg@nyulangone.org

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**7**

*Introductory Chapter: Transcriptome Analysis DOI: http://dx.doi.org/10.5772/intechopen.85980*

[1] Botchkareva NV. The molecular revolution in cutaneous biology:

[2] Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JC, et al. The transcriptional program in the response of human fibroblasts to serum. Science. 1999;**283**(5398):83-87. PMID: 9872747

[3] Bayega A, Fahiminiya S,

A drive toward single molecule sequencing. Methods in Molecular Biology. 2018;**1783**:209-241. DOI: 10.1007/978-1-4939-7834-2\_11. PMID:

[4] Zhang H, He L, Cai L. Transcriptome sequencing: RNA-Seq. Methods in Molecular Biology. 2018;**1754**:15-27. DOI: 10.1007/978-1- 4939-7717-8\_2. PMID: 29536435

29767365

PMID 25649271

Oikonomopoulos S, Ragoussis J. Current and future methods for mRNA analysis:

[5] Shanker S, Paulson A, Edenberg HJ, Peak A, Perera A, Alekseyev YO, et al. Evaluation of commercially available RNA amplification kits for RNA

sequencing using very low input amounts of total RNA. Journal of Biomolecular Techniques. 2015 April;**26**(1):4-18. DOI: 10.7171/jbt.15-2601-001. PMC 4310221.

[6] Stegle O, Teichmann SA, Marioni JC.

challenges in single-cell transcriptomics.

Computational and analytical

2015;**16**(3):133-145. DOI: 10.1038/

[7] Mimoso C, Lee DD, Zavadil J, Tomic-Canic M, Blumenberg M. Analysis and meta-analysis of transcriptional profiling in human epidermis. Methods in Molecular

Nature Reviews Genetics.

nrg3833. PMID: 25628217

Noncoding RNAs: New molecular players in dermatology and cutaneous biology. The Journal of Investigative Dermatology. 2017;**137**(5):e105-e111. DOI: 10.1016/j. jid.2017.02.001. PMID: 28411840

Biology. 2014;**1195**:61-97. DOI:

Science Translational Medicine. 2014;**6**(265):265sr6. DOI: 10.1126/ scitranslmed.3009337. PMID: 25473038

[9] Blumenberg M. Skinomics: Past, present and future for diagnostic microarray studies in dermatology. Expert Review of Molecular

Diagnostics. 2013;**13**(8):885-894. DOI:

[10] Santoro S, Lopez ID, Lombardi R, Zauli A, Osiceanu AM, Sorosina M, et al. Laser capture microdissection for transcriptomic profiles in human skin biopsies. BMC Molecular Biology. 2018;**19**(1):7. DOI: 10.1186/s12867- 018-0108-5. PMID: 29921228 PMCID:

[11] Shih BB, Nirmal AJ, Headon DJ, Akbar AN, Mabbott NA, Freeman TC. Derivation of marker gene signatures from human skinand their use in the interpretation of the transcriptional changes associated with dermatological disorders. The Journal of Pathology. 2017;**241**(5):600-613. DOI: 10.1002/ path.4864. Epub 2017 Feb 24. PMID: 28008606 PMCID: PMC5363360

10.1586/14737159.2013.846827.

PMID: 24151852

PMC6009967

[8] Eming SA, Martin P, Tomic-Canic M. Wound repair and regeneration: Mechanisms, signaling, and translation.

10.1007/7651\_2013\_60

**References**

*Introductory Chapter: Transcriptome Analysis DOI: http://dx.doi.org/10.5772/intechopen.85980*

#### **References**

*Transcriptome Analysis*

**6**

provided the original work is properly cited.

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: miroslav.blumenberg@nyulangone.org

The results of transcriptomic analyses are graphically often presented as heat maps, a system of color-coding that represents different levels of expression of given genes in different samples (**Figure 1A**). Such presentations also frequently display a clustering of samples, this helps to identify samples with similar gene expression. Another common graphical presentation uses Venn diagrams, which count the transcripts which are equivalently regulated in multiple samples (**Figure 1B**). Transcriptome analyses have become indispensable in basic research, translational, and clinical studies. In general, transcriptome analysis is a very powerful

hypothesis-generating tool, more than a theory proving one.

ated with epidermal differentiation [9, 10].

generation, this approach will become routine.

disorders of cornification [11].

**Author details**

Miroslav Blumenberg

NYU School of Medicine, USA

**3. Specific example: transcriptome analysis applied to human skin**

Easily accessible, skin was among the first targets analyzed using 'omics' and dermatology embraced the approaches very early [7]. A classic example of coordinated transcriptional regulation was observed in cultured fibroblasts after serum stimulation [2]. Serum addition causes not only rapid recommencement of the cell cycle but, characteristically a wound-healing response, a physiological role of fibroblasts in wound healing [8]. Transcriptional responses of epidermal keratinocytes to UV light, hormones, vitamins, infections, inflammatory and immunomodulating cytokines, toxins and allergens have been characterized, as were the changes associ-

The expression signatures that define the various cell types in human skin, were used to define 20 specific gene signatures, including those for keratinocytes, melanocytes, endothelia, adipocytes, immune cells, hair follicles, sebaceous, sweat, and apocrine glands. This resource provided a resource named SkinSig, which was then used to analyze 18 skin conditions, providing in-context interpretation of, for example, influx in immune cells in inflammation or differentiation changes in

In the future we can anticipate a greatly expanded usage of transcriptome analysis. Translated to the bedside, it can provide better understanding and more specific diagnoses of diseases. This, of course, requires additional advances in the technology, both in the lab-bench components reducing the costs and guaranteeing reproducibility and accuracy, as well as in the computer-based components, algorithms that enable physicians to establish diagnosis quickly and reliably. In a

[1] Botchkareva NV. The molecular revolution in cutaneous biology: Noncoding RNAs: New molecular players in dermatology and cutaneous biology. The Journal of Investigative Dermatology. 2017;**137**(5):e105-e111. DOI: 10.1016/j. jid.2017.02.001. PMID: 28411840

[2] Iyer VR, Eisen MB, Ross DT, Schuler G, Moore T, Lee JC, et al. The transcriptional program in the response of human fibroblasts to serum. Science. 1999;**283**(5398):83-87. PMID: 9872747

[3] Bayega A, Fahiminiya S, Oikonomopoulos S, Ragoussis J. Current and future methods for mRNA analysis: A drive toward single molecule sequencing. Methods in Molecular Biology. 2018;**1783**:209-241. DOI: 10.1007/978-1-4939-7834-2\_11. PMID: 29767365

[4] Zhang H, He L, Cai L. Transcriptome sequencing: RNA-Seq. Methods in Molecular Biology. 2018;**1754**:15-27. DOI: 10.1007/978-1- 4939-7717-8\_2. PMID: 29536435

[5] Shanker S, Paulson A, Edenberg HJ, Peak A, Perera A, Alekseyev YO, et al. Evaluation of commercially available RNA amplification kits for RNA sequencing using very low input amounts of total RNA. Journal of Biomolecular Techniques. 2015 April;**26**(1):4-18. DOI: 10.7171/jbt.15-2601-001. PMC 4310221. PMID 25649271

[6] Stegle O, Teichmann SA, Marioni JC. Computational and analytical challenges in single-cell transcriptomics. Nature Reviews Genetics. 2015;**16**(3):133-145. DOI: 10.1038/ nrg3833. PMID: 25628217

[7] Mimoso C, Lee DD, Zavadil J, Tomic-Canic M, Blumenberg M. Analysis and meta-analysis of transcriptional profiling in human epidermis. Methods in Molecular

Biology. 2014;**1195**:61-97. DOI: 10.1007/7651\_2013\_60

[8] Eming SA, Martin P, Tomic-Canic M. Wound repair and regeneration: Mechanisms, signaling, and translation. Science Translational Medicine. 2014;**6**(265):265sr6. DOI: 10.1126/ scitranslmed.3009337. PMID: 25473038

[9] Blumenberg M. Skinomics: Past, present and future for diagnostic microarray studies in dermatology. Expert Review of Molecular Diagnostics. 2013;**13**(8):885-894. DOI: 10.1586/14737159.2013.846827. PMID: 24151852

[10] Santoro S, Lopez ID, Lombardi R, Zauli A, Osiceanu AM, Sorosina M, et al. Laser capture microdissection for transcriptomic profiles in human skin biopsies. BMC Molecular Biology. 2018;**19**(1):7. DOI: 10.1186/s12867- 018-0108-5. PMID: 29921228 PMCID: PMC6009967

[11] Shih BB, Nirmal AJ, Headon DJ, Akbar AN, Mabbott NA, Freeman TC. Derivation of marker gene signatures from human skinand their use in the interpretation of the transcriptional changes associated with dermatological disorders. The Journal of Pathology. 2017;**241**(5):600-613. DOI: 10.1002/ path.4864. Epub 2017 Feb 24. PMID: 28008606 PMCID: PMC5363360

**9**

Section 2

Tumor Transcriptome

Section 2
