Research Techniques Made Simple: Methodology and Clinical Applications of RNA Sequencing

      RNA sequencing is a method of transcriptome profiling that utilizes next-generation sequencing technology. It offers several distinct advantages over hybridization-based approaches, most notably superior sensitivity and the capacity for de novo transcript discovery. This article describes RNA sequencing methodology, summarizes important technological advances and challenges, and discusses applications for this technique in the field of dermatology.

      Advantages of RNA-seq

      • Rapid, precise, quantitative measurement of gene expression
      • High sensitivity allows detection of low-abundance transcripts
      • Wide dynamic range
      • Enables identification of transcripts, facilitating discovery of single-nucleotide polymorphisms and rare mutations, previously unrecognized gene isoforms, microbial RNAs, and regulatory micro-RNAs
      • Not subject to the same biases and limitations imposed on microarrays: DNA sequences can be unambiguously mapped to unique regions of the genome instead of relying on existing genome sequence data
      • Can be performed on single cells and FFPE tissue

      Limitations of RNA-seq

      • Accurate sequence annotation and data interpretation can be computationally challenging, particularly in the absence of pre-existing reference genome(s).
      • Transcript quantitation can be affected by biases introduced during cDNA library construction and sequence alignment.
      • Lack of standardization between sequencing platforms and read depth, equivalent to the percentage of total transcripts sequenced, can compromise reproducibility.
      • Although RNA-seq has become increasingly affordable, its cost remains prohibitive for many laboratories. Start-up costs are significant, and price per individual sequencing reaction, largely dependent on read depth, can exceed $1,000.

      Introduction

      RNA sequencing (RNA-seq) is a mode of deep sequencing that enables evaluation of a complete set of an organism’s transcribed genes, or transcriptome, and noncoding RNAs such as micro-RNAs. Examination of the transcriptome offers many advantages over whole-genome analysis: whereas whole-genome sequencing provides a static view of an organism’s genetic and regulatory information, transcriptome analysis allows assessment of dynamic changes in gene expression in response to various stimuli (
      • Wang Z.
      • Gerstein M.
      • Snyder M.
      RNA-seq: a revolutionary tool for transcriptomics.
      ). In addition to quantitative measurement of gene expression, RNA-seq permits identification of unique transcripts such as alternative splice variants, single-nucleotide polymorphisms, and fusion genes (
      • Grada A.
      • Weinbrecht K.
      Next-generation sequencing: methodology and application.
      ,
      • Ray M.
      • Horne W.
      • McAleer J.P.
      • Ricks D.M.
      • Kreindler J.L.
      • Fitzsimons M.S.
      • et al.
      (2015) RNA-seq in pulmonary medicine: how much is enough?.
      ,
      • Wang Z.
      • Gerstein M.
      • Snyder M.
      RNA-seq: a revolutionary tool for transcriptomics.
      ,
      • Zeng W.
      • Mortazavi A.
      Technical considerations for functional sequencing assays.
      ) to enhance understanding of the mechanisms governing the changes in gene expression that underlie health and disease.
      Transcriptomics has emerged as a subset of functional genomics that couples the power of whole-genome sequencing with gene expression analysis to facilitate disease diagnosis, treatment, prognosis, and prevention. RNA-seq is now the preferred method of transcriptome profiling, favored over microarray analysis because of its higher sensitivity, broader dynamic range, capacity for transcript discovery, and lack of requirement for pre-existing sequence knowledge (
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Ray M.
      • Horne W.
      • McAleer J.P.
      • Ricks D.M.
      • Kreindler J.L.
      • Fitzsimons M.S.
      • et al.
      (2015) RNA-seq in pulmonary medicine: how much is enough?.
      ,
      • Regazzetti C.
      • Joly F.
      • Marty C.
      • Rivier M.
      • Mehul B.
      • Reiniche P.
      • et al.
      Transcriptional analysis of vitiligo skin reveals the alteration of WNT pathway: a promising target for repigmenting vitiligo patients.
      ,
      • Zeng W.
      • Mortazavi A.
      Technical considerations for functional sequencing assays.
      ). As we enter the era of personalized medicine, RNA-seq is being increasingly used for biomarker discovery and identification of molecular signatures that define various disease subtypes and responses to pharmacologic therapy.

      Overview of Methodology

      Template and sample preparation

      After cellular isolation, RNA can either be directly processed into complementary DNA (cDNA) to obtain a full catalog of RNAs (total RNA-seq) or selective enrichment of RNAs of interest can be performed prior to cDNA synthesis (Figure 1). Although total RNA-seq yields the broadest survey of transcripts, polyadenylated (poly(A)) RNA enrichment is commonly used in library preparation, because it excludes the abundant ribosomal RNAs (rRNAs) that confound detection of the most desirable transcripts. Referred to as messenger RNA (mRNA) sequencing (mRNA-seq), this method reliably detects coding transcripts. However, drawbacks include elimination of regulatory noncoding RNAs and poor capture of partially degraded mRNAs, precluding its use on formalin-fixed paraffin–embedded (FFPE) tissue or in other settings where there is extensive RNA degradation (
      • Ray M.
      • Horne W.
      • McAleer J.P.
      • Ricks D.M.
      • Kreindler J.L.
      • Fitzsimons M.S.
      • et al.
      (2015) RNA-seq in pulmonary medicine: how much is enough?.
      ,
      • Zeng W.
      • Mortazavi A.
      Technical considerations for functional sequencing assays.
      ,
      • Zhao W.
      • He X.
      • Hoadley K.A.
      • Parker J.S.
      • Hayes D.N.
      • Perou C.M.
      Comparison of RNA-seq by poly(A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling.
      ). Thus, rRNA (ribo-) depletion, achieved by hybridizing total RNA to bead-bound rRNAs, is being increasingly used in RNA-seq template preparation, particularly when working with formalin-fixed clinical samples.
      • Zhao W.
      • He X.
      • Hoadley K.A.
      • Parker J.S.
      • Hayes D.N.
      • Perou C.M.
      Comparison of RNA-seq by poly(A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling.
      reported comparable efficiency of rRNA removal and high concordance in transcript quantification between polyA-enriched and ribo-depleted RNA sequences obtained from fresh frozen human tumor samples, with similar numbers of reads required to achieve an adequate gene detection level.
      Figure 1
      Figure 1RNA-seq work flow. (a) Schematic diagram of RNA-seq library construction. Total RNA is extracted from cells, and a small aliquot is used to measure the integrity of the RNA. rRNA is then depleted through one of several methods to enrich subpopulations of RNA molecules, such as mRNA or small RNA. mRNA is fragmented into a uniform size distribution, and the fragment size can be monitored by RNA gel electrophoresis or a microfluidics-based bioanalyzer such as the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). The cDNA is then made into a library. (b) Mapping programs align reads to the reference genome and map splice junctions. Gene expression can be quantified as absolute read counts or normalized values such as RPKM. (c) If RNA-seq data sets are deep enough and the reads are long enough to map enough splice junctions, the mapped reads can be assembled into transcripts. (d) The sequences of the reads can be mined by comparing the transcriptome reads with the reference genome to identify nucleotide variants that are either genomic variants (e.g., SNPs) or candidates for RNA editing. A, adenine; C, cytosine; cDNA, complementary DNA; G, guanine; mRNA, messenger RNA; poly(A), polyadenylated; QPCR, quantitative reverse transcriptase PCR; RIN, RNA integrity number; RNA-seq, RNA sequencing; RPKM, reads per kilobase of transcript per million mapped reads; rRNA, ribosomal RNA; SNP, single-nucleotide polymorphism; T, thymine. Adapted with permission from Macmillan Publishers Ltd: Nature Immunology (
      • Zeng W.
      • Mortazavi A.
      Technical considerations for functional sequencing assays.
      ), copyright 2012.
      Following total RNA isolation and selective enrichment, RNA or cDNA must be fragmented to create short sequences (200–500 base pairs) that are amenable to sequencing; in the case of RNA this can be accomplished via hydrolysis or nebulization, and for DNA options include sonication or DNase I treatment. After cDNA synthesis, 3′ adenylation, ligation of adaptor molecules to nascent cDNAs, and PCR amplification are the final steps in the creation of a template for sequencing (
      • Ray M.
      • Horne W.
      • McAleer J.P.
      • Ricks D.M.
      • Kreindler J.L.
      • Fitzsimons M.S.
      • et al.
      (2015) RNA-seq in pulmonary medicine: how much is enough?.
      ,
      • Zeng W.
      • Mortazavi A.
      Technical considerations for functional sequencing assays.
      ,
      • Zhao W.
      • He X.
      • Hoadley K.A.
      • Parker J.S.
      • Hayes D.N.
      • Perou C.M.
      Comparison of RNA-seq by poly(A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling.
      ).

      Sequencing and analysis

      Sequencing of fragmented cDNAs typically produces short reads ranging from 250400 nucleotides in length. These reads are then aligned with a reference genome, and the expression level of a gene is determined by quantitating the number of reads that map to gene exons. If no existing reference genome is available, RNA-seq data sets themselves can be used to create sequence assemblies for mapping of reads. Freely available software can assist with assembly of a digital genome-scale quantitative transcriptional map, with a common output being fragments per kilobase of transcript per million mapped reads (FPKM) and the expression level of a gene representing the sum of the FPKM values of its isoforms (
      • Grada A.
      • Weinbrecht K.
      Next-generation sequencing: methodology and application.
      ,
      • Ray M.
      • Horne W.
      • McAleer J.P.
      • Ricks D.M.
      • Kreindler J.L.
      • Fitzsimons M.S.
      • et al.
      (2015) RNA-seq in pulmonary medicine: how much is enough?.
      ,
      • Zeng W.
      • Mortazavi A.
      Technical considerations for functional sequencing assays.
      ).

      Recent innovations

      One important advance in next-generation sequencing methodology is the ability to perform single-cell transcriptome profiling. Cell separation via flow cytometry, microfluidics, micropipetting, or laser capture microdissection allows isolation of individual cells that can then be subject to RNA-seq. While fluorescence-activated cell sorting produces purified cell populations as substrate for single cell RNA-seq (sc-RNA-seq), microfluidics and microdroplet techniques allow single cell capture from bulk cell suspensions obtained from tissue. Sc-RNA-seq has been exploited to study biologic phenomena for which averaged measurement of gene expression is insufficient to ascribe functional significance and/or minute numbers of cells represent discrete populations. Single-cell RNA-seq has already been applied in a variety of settings to produce a wide range of profound new discoveries, most notably in the realms of neuroscience and developmental biology. The ability to integrate electrophysiology recordings and computational structural analysis with transcriptome profiling of individual cells has critically advanced understanding of neuronal taxonomy and neurophysiology (
      • Fuzik J.
      • Zeisel A.
      • Máté Z.
      • Calvigioni D.
      • Yanagawa Y.
      • Szabó G.
      • et al.
      Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes.
      ).
      • Gaublomme J.T.
      • Yosef N.
      • Lee Y.
      • Gertner R.S.
      • Yang L.V.
      • Wu C.
      • et al.
      Single-cell genomics unveils critical regulators of Th17 cell pathogenicity.
      recently used single-cell RNA-seq to examine discrete cells within a heterogeneous population of in vitro- and in vivo-derived T helper 17 cells to characterize molecular signatures that correlate with pathogenicity in an animal model of multiple sclerosis.
      Another innovation that is of particular relevance to the field of dermatology is the capacity to perform RNA-seq on FFPE tissue. The routine storage of skin biopsy specimens in this manner offers vast availability of substrate for RNA-seq. Refinement of techniques such as exome capture and ribo-depletion to assemble cDNA sequencing libraries from degraded RNA inputs as well as use of more stringent sequence alignment criteria and increased read depth to minimize fixation-induced sequence errors have improved the quality of data derived from FFPE specimens (
      • Cieslik M.
      • Chugh R.
      • Wu Y.M.
      • Wu M.
      • Brennan C.
      • Lonigro R.
      • et al.
      The use of exome capture RNA-seq for highly degraded RNA with application to clinical cancer sequencing.
      ).

      Use of RNA-seq In Dermatology

      Next-generation sequencing–based analyses such as RNA-seq have significantly advanced understanding of a variety of dermatologic disorders. RNA-seq captured many genes differentially expressed between psoriatic lesional, nonlesional, and normal skin that were not uncovered with previous microarray analyses (
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ), highlighting the capacity of RNA-seq to extend the range of detection beyond the most abundant transcripts to include low-level transcripts and alternative splice variants. Among the psoriasis-associated transcripts that
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      identified was IL37, an anti-inflammatory IL-1 family cytokine, which they observed to be expressed at reduced levels in lesional psoriatic skin and coexpressed with the keratinocyte differentiation protein loricrin in nonlesional skin (Figure 2).
      Figure 2
      Figure 2Transcriptome analysis reveals gene regulatory circuits operational in psoriasis. (a) mRNA-seq analysis of cDNAs derived from lesional and nonlesional skin biopsy specimens obtained from 92 psoriasis patients and 82 normal individuals identified 3,577 differentially expressed genes between lesional psoriatic and normal skin. Transcripts were grouped into gene coexpression modules using weighted gene coexpression network analysis and differentially expressed genes determined for each module. RPKM (top panel), proportion of up-regulated (Up) and down-regulated differentially expressed genes (middle panel), and average Spearman’s correlation (bottom panel) are shown for each coexpressed gene module constructed for normal skin and lesional psoriatic skin. IL37 was the most down-regulated gene in psoriatic gene module P23, which was enriched for TINCR and STAU1-regulated genes involved in terminal epidermal differentiation. (b) Immunohistochemistry analysis showing expression of IL-37 protein (3,3' diaminobenzidine, brown) in the epidermis of representative specimens of nonlesional psoriatic and control skin; IL-37 was not detectable in lesional psoriatic skin. (c) Down-regulation of IL37 and loricrin mRNA in lesional compared with nonlesional psoriatic skin as determined by quantitative real-time reverse-transcriptase–PCR. **P < 0.01, ***P < 0.001. (d) Immunostaining demonstrating co-localization of IL-37 (green) and loricrin (red) proteins in the granular layer of nonlesional skin; IL-37 was not detected in lesional skin. 4,6-diamidino-2-phenylindole (DAPI) counterstaining of nuclei is shown in blue. cDNA, complementary DNA; Down, down-regulated; mRNA-seq, messenger RNA sequencing; NN, normal skin; PP, lesional psoriatic skin; RPKM, Averaged reads per kilobase per million mapped reads; Up, up-regulated. Figure and legend adapted with permission from
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      .
      Figure 2
      Figure 2Transcriptome analysis reveals gene regulatory circuits operational in psoriasis. (a) mRNA-seq analysis of cDNAs derived from lesional and nonlesional skin biopsy specimens obtained from 92 psoriasis patients and 82 normal individuals identified 3,577 differentially expressed genes between lesional psoriatic and normal skin. Transcripts were grouped into gene coexpression modules using weighted gene coexpression network analysis and differentially expressed genes determined for each module. RPKM (top panel), proportion of up-regulated (Up) and down-regulated differentially expressed genes (middle panel), and average Spearman’s correlation (bottom panel) are shown for each coexpressed gene module constructed for normal skin and lesional psoriatic skin. IL37 was the most down-regulated gene in psoriatic gene module P23, which was enriched for TINCR and STAU1-regulated genes involved in terminal epidermal differentiation. (b) Immunohistochemistry analysis showing expression of IL-37 protein (3,3' diaminobenzidine, brown) in the epidermis of representative specimens of nonlesional psoriatic and control skin; IL-37 was not detectable in lesional psoriatic skin. (c) Down-regulation of IL37 and loricrin mRNA in lesional compared with nonlesional psoriatic skin as determined by quantitative real-time reverse-transcriptase–PCR. **P < 0.01, ***P < 0.001. (d) Immunostaining demonstrating co-localization of IL-37 (green) and loricrin (red) proteins in the granular layer of nonlesional skin; IL-37 was not detected in lesional skin. 4,6-diamidino-2-phenylindole (DAPI) counterstaining of nuclei is shown in blue. cDNA, complementary DNA; Down, down-regulated; mRNA-seq, messenger RNA sequencing; NN, normal skin; PP, lesional psoriatic skin; RPKM, Averaged reads per kilobase per million mapped reads; Up, up-regulated. Figure and legend adapted with permission from
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      .
      RNA-seq has been instrumental in defining mutations and biomarkers associated with cutaneous malignancies.
      • Liu D.
      • Zhao Z.G.
      • Jiao Z.L.
      • Li H.J.
      Identifying differential expression genes and single nucleotide variations using RNA-seq in metastatic melanoma.
      performed RNA-seq analysis of cell lines derived from nonmetastatic and metastatic human melanoma tumors to define differentially expressed genes and single nucleotide variations that represent potential biomarkers for metastatic melanoma. RNA-seq was used to identify missense mutations in the spliceosome gene SF3B1 and characterize resultant RNA-splicing defects that contribute to development of uveal melanoma (
      • Alsafadi S.
      • Houy A.
      • Battistella A.
      • Popova T.
      • Wassef M.
      • Henry E.
      • et al.
      Cancer-associated SF3B1mutations affect alternative splicing by promoting alternative branchpoint usage.
      ,
      • DeBoever C.
      • Ghia E.M.
      • Shepard P.J.
      • Rassenti L.
      • Barrett C.L.
      • Jepsen K.
      • et al.
      Transcriptome sequencing reveals potential mechanism of cryptic 3’ splice site selection is SF3B1-mutated cancers.
      ). Comparison of vismodegib-resistant basal cell carcinomas (BCCs) with drug-sensitive BCCs and normal skin biopsy specimens by RNA-seq revealed that most smoothened inhibitor-resistant BCCs have acquired SMO mutations, leading to maintenance of sonic hedgehog signaling (
      • Atwood S.X.
      • Sarin K.Y.
      • Whitson R.J.
      • Li J.R.
      • Kim G.
      • Rezaee M.
      • et al.
      Smoothened variants explain the majority of drug resistance in basal cell carcinoma.
      ). This discovery has led to identification of drugs targeting other components of the sonic hedgehog pathway, including aPKC-ι/λ inhibitors and GLI2 antagonists, as promising chemotherapeutics for treatment of advanced BCCs (
      • Ally M.S.
      • Ransohoff K.
      • Sarin K.
      • Atwood S.X.
      • Rezaee M.
      • Bailey-Healy I.
      • et al.
      Effects of combined treatment with arsenic trioxide and itraconazole in patients with refractory metastatic basal cell carcinoma.
      ,
      • Atwood S.X.
      • Sarin K.Y.
      • Whitson R.J.
      • Li J.R.
      • Kim G.
      • Rezaee M.
      • et al.
      Smoothened variants explain the majority of drug resistance in basal cell carcinoma.
      ).
      Transcriptome analysis of Sézary cells compared with autologous normal CD4+ T lymphocytes uncovered 21 long noncoding RNAs and 13 coding transcripts differentially expressed in Sézary syndrome, many of which were also confirmed to be present in mycosis fungoides tumors. There was notable absence of viral RNA in Sézary cells, arguing against viral transformation as a contributing factor to neoplasia. Approximately 15% of the detected transcripts were previously uncharacterized, highlighting the ability of RNA-seq to provide new information about pathways involved in the development of carcinogenesis and unveil new therapeutic targets for cutaneous T-cell lymphoma (
      • Lee C.S.
      • Ungewickell A.
      • Bhaduri A.
      • Qu K.
      • Webster D.E.
      • Armstrong R.
      • et al.
      Transcriptome sequencing in Sézary syndrome identifies Sézary cell and mycosis fungoides-associated lncRNAs and novel transcripts.
      ).

      Summary and Future Directions

      There are a myriad of applications for next-generation sequencing–based transcriptome profiling in biomedical science. Although DNA microarray analysis remains a common method for identifying global changes in gene expression, its inferior sensitivity and inability to identify new transcripts will soon lead to its replacement by RNA-seq. As sequencing costs continue to fall and further strategies and software are developed to facilitate data analysis and interpretation, access to this technique will likely expand to allow for its routine use for purposes ranging from study of fundamental cellular processes to biomarker discovery and tailoring of therapies for cancer and autoimmune disease.
      In the future, combining high throughput microfluidic and microwell-based cell purification and isolation techniques with RNA-seq will enable transcriptome profiling of thousands of individual cells in parallel to further understanding of population and tissue heterogeneity (
      • Stegle O.
      • Teichmann S.A.
      • Marioni J.C.
      Computational and analytical challenges in single-cell transcriptomics.
      ). Growing interest in characterizing host immune interactions with colonizing microbiota will be aided by simultaneous transcriptional profiling of microbial and host cells by RNA-seq (
      • Humphrys M.S.
      • Creasy T.
      • Sun Y.
      • Shetty A.C.
      • Chibucos M.C.
      • Drabek E.F.
      • et al.
      Simultaneous transcriptional profiling of bacteria and their host cells.
      ). These analyses are currently possible, but further innovations are needed to overcome technical hurdles and improve the quality of the sequencing data obtained from these specialized cell preparations.

      Conflict of Interest

      The authors state no conflict of interest.

      CME Accreditation

      This activity has been planned and implemented by the Duke University Health System Department of Clinical Education and Professional Development and Society for Investigative Dermatology for the advancement of patient care. The Duke University Health System Department of Clinical Education & Professional Development is accredited by the American Nurses Credentialing Center (ANCC), the Accreditation Council for Pharmacy Education (ACPE), and the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing education for the health care team. Duke University Health System Department of Clinical Education and Professional Development designates this enduring activity for a maximum of 1.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity.
      To participate in the CE activity, follow the link provided. http://continuingeducation.dcri.duke.edu/sid-journal-based-translationaleducation-dermatology-research-techniques-made-simple-2015.

      Multiple Choice Questions

      • 1.
        RNA sequencing (RNA-seq) is capable of which of the following?
        • A.
          Identifying mutations
        • B.
          Detecting fusion gene products
        • C.
          Quantifying expressed transcripts
        • D.
          Characterizing regulatory noncoding RNAs
        • E.
          All of the above
      • 2.
        Substrates for RNA-seq include all of the below EXCEPT which of the following?
        • A.
          RNA obtained from formalin-fixed tissue
        • B.
          DNA derived from cells obtained by needle aspiration
        • C.
          Complementary DNA derived from individual cells obtained via flow cytometric sorting
        • D.
          Fungal and viral transcripts
      • 3.
        The basic methodological steps of RNA-seq include which of the following?
        • A.
          DNA fragmentation, template amplification, sequencing, and analysis
        • B.
          Template preparation, RNA fragmentation, sequencing, and analysis
        • C.
          RNA isolation, whole-exome capture, sequencing, and analysis
        • D.
          Template preparation, hybridization, sequencing, and analysis
        • E.
          RNA fragmentation, emulsion PCR, sequencing, and analysis
      • 4.
        Which of the following describe(s) advantages of RNA-seq over hybridization-based techniques such as microarrays?
        • a)
          Lower cost
        • b)
          Higher sensitivity and ability to detect low-frequency transcripts
        • c)
          Rapid quantitative measurement of a limited set of genes
        • d)
          De novo transcript discovery
        • e)
          B and D
        • f)
          All of the above
      • 5.
        Applications of RNA-seq in medicine include(s) which of the following?
        • a)
          Detection of mutations underlying cancer and inherited diseases
        • b)
          Biomarker discovery
        • c)
          Comparative gene expression analysis
        • d)
          Sequencing of colonizing microbial genomes
        • e)
          All of the above
      This article has been approved for 1 hour of Category 1 CME credit. To take the quiz, with or without CME credit, follow the link under the “CME ACCREDITATION” heading.

      Supplementary Material

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