Array ( [0] => {{Short description|Set of all RNA molecules in one cell or a population of cells}} [1] => The '''transcriptome''' is the set of all [[RNA]] transcripts, including coding and [[non-coding RNA|non-coding]], in an individual or a population of [[cell (biology)|cells]]. The term can also sometimes be used to refer to [[RNA#Types of RNA|all RNAs]], or just [[Messenger RNA|mRNA]], depending on the particular experiment. The term ''transcriptome'' is a portmanteau of the words ''transcript'' and ''genome''; it is associated with the process of transcript production during the biological process of [[Transcription (biology)|transcription]]. [2] => [3] => The early stages of transcriptome annotations began with [[cDNA]] libraries published in the 1980s. Subsequently, the advent of high-throughput technology led to faster and more efficient ways of obtaining data about the transcriptome. Two biological techniques are used to study the transcriptome, namely [[DNA microarray]], a hybridization-based technique and [[RNA-seq]], a sequence-based approach. RNA-seq is the preferred method and has been the dominant [[transcriptomics technique]] since the 2010s. [[Single-cell transcriptomics]] allows tracking of transcript changes over time within individual cells. [4] => [5] => Data obtained from the transcriptome is used in research to gain insight into processes such as [[cellular differentiation]], [[carcinogenesis]], [[transcription regulation]] and [[biomarker discovery]] among others. Transcriptome-obtained data also [[Phylogenetic inference using transcriptomic data|finds applications]] in establishing [[phylogenetics|phylogenetic relationships]] during the process of evolution and in [[in vitro fertilization|''in vitro'' fertilization]]. The transcriptome is closely related to other [[Omics|-ome]] based biological fields of study; it is complementary to the [[proteome]] and the [[metabolome]] and encompasses the [[translatome]], [[exome]], meiome and [[thanatotranscriptome]] which can be seen as ome fields studying specific types of RNA transcripts. There are quantifiable and conserved relationships between the Transcriptome and other -omes, and Transcriptomics data can be used effectively to predict other molecular species, such as metabolites.{{Cite journal |last1=Cavicchioli |first1=Maria Vittoria |last2=Santorsola |first2=Mariangela |last3=Balboni |first3=Nicola |last4=Mercatelli |first4=Daniele |last5=Giorgi |first5=Federico Manuel |date=January 2022 |title=Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines |journal=International Journal of Molecular Sciences |language=en |volume=23 |issue=7 |pages=3867 |doi=10.3390/ijms23073867| pmid=35409231 |pmc=8998886 |issn=1422-0067|doi-access=free }} There are numerous publicly available transcriptome databases. [6] => [7] => ==Etymology and history== [8] => The word ''transcriptome'' is a [[portmanteau]] of the words ''transcript'' and ''genome''. It appeared along with other [[neologism]]s formed using the suffixes ''-ome'' and ''-omics'' to denote all studies conducted on a genome-wide scale in the fields of life sciences and technology. As such, transcriptome and transcriptomics were one of the first words to emerge along with genome and proteome.{{cite journal|url=https://www.sciencedirect.com/science/article/pii/B9780444626516000040|title=Chapter 4 - Omics Tools for the Genome-Wide Analysis of Methylation and Histone Modifications|journal=Comprehensive Analytical Chemistry|doi=10.1016/B978-0-444-62651-6.00004-0|first1=Josep C.|last1=Jiménez-Chillarón|first2=Rubén|last2=Díaz|first3=Marta|last3=Ramón-Krauel|volume=64|year=2014|pages=81–110|isbn=9780444626516|access-date=25 April 2020}} The first study to present a case of a collection of a [[cDNA]] library for [[Bombyx mori|silk moth]] mRNA was published in 1979.{{cite journal|title=Use of a cDNA library for studies on evolution and developmental expression of the chorion multigene families|journal=[[Cell (journal)|Cell]]|first1=Sim|last1=GK|first2=Kafatos|last2=FC|first3=Jones|last3=CW|first4=Koehler|last4=MD|first5=Efstratiadis|last5=A|first6=Maniatis|last6=T.|date=December 1979|volume=8|issue=4|pages=1303–16|pmid=519770|doi=10.1016/0092-8674(79)90241-1|doi-access=free}} The first seminal study to mention and investigate the transcriptome of an organism was published in 1997 and it described 60,633 transcripts expressed in ''[[S. cerevisiae]]'' using [[serial analysis of gene expression]] (SAGE).{{cite journal|title=Characterization of the Yeast Transcriptome|journal=Cell|first1=Victor|last1=E Velculescu|first2=Lin|last2=Zhang|first3=Wei|last3=Zhou|first4=Jacob|last4=Vogelstein|first5=Munira|last5=A Basrai|first6=Douglas|last6=E Bassett Jr.|first7=Phil|last7=Hieter|first8=Bert|last8=Vogelstein|first9=Kenneth|last9=W Kinzler|doi=10.1016/S0092-8674(00)81845-0|date=1997|issue=88|volume=2|pages=243–51|pmid = 9008165|s2cid=11430660|doi-access=free}} With the rise of high-throughput technologies and [[bioinformatics]] and the subsequent increased computational power, it became increasingly efficient and easy to characterize and analyze enormous amount of data. Attempts to characterize the transcriptome became more prominent with the advent of automated DNA sequencing during the 1980s. During the 1990s, [[expressed sequence tag]] sequencing was used to identify genes and their fragments.{{cite journal|title=Microarray and its applications|first1=Rajeshwar|last1=Govindarajan|first2=Jeyapradha|last2=Duraiyan|first3=Karunakaran|last3=Kaliyappan|first4=Murugesan|last4=Palanisamy|journal=[[Journal of Pharmacy and Bioallied Sciences]]|year=2012|volume=4|issue = 6|pages = S310-2|doi=10.4103/0975-7406.100283|pmid=23066278|pmc = 3467903 |doi-access=free }} This was followed by techniques such as serial analysis of gene expression (SAGE), [[cap analysis of gene expression]] (CAGE), and [[massively parallel signature sequencing]] (MPSS). [9] => [10] => ==Transcription== [11] => {{See also|Transcription (biology)}} [12] => The transcriptome encompasses all the [[RNA|ribonucleic acid]] (RNA) transcripts present in a given organism or experimental sample.{{ cite book | vauthors = Brown, TA | date = 2018 | title = Genomes 4 | chapter = Chapter 12: Transcriptomics | publisher = Garland Science | place = New York, NY, USA | isbn = 9780815345084}} RNA is the main carrier of genetic information that is responsible for the process of converting [[DNA]] into an organism's phenotype. A gene can give rise to a single-stranded [[messenger RNA]] (mRNA) through a molecular process known as [[transcription (biology)|transcription]]; this mRNA is complementary to the strand of DNA it originated from.{{cite journal|title=The Human Transcriptome: An Unfinished Story |first=Mihaela|last=Peralta|journal=Genes|year=2012|volume=3|issue=3|pages = 344–360|doi=10.3390/genes3030344|pmid=22916334|pmc = 3422666|doi-access=free}} The enzyme [[RNA polymerase II]] attaches to the template DNA strand and catalyzes the addition of [[ribonucleotide]]s to the 3' end of the growing sequence of the mRNA transcript.{{cite journal|url=https://www.nature.com/scitable/topicpage/dna-transcription-426/|title=DNA Transcription|journal=Nature Education|first=Suzanne|last=Clancy|year=2008|volume=1|issue=11|page=41}} [13] => [14] => In order to initiate its function, RNA polymerase II needs to recognize a [[gene promoter|promoter sequence]], located upstream (5') of the gene. In eukaryotes, this process is mediated by [[transcription factor]]s, most notably [[Transcription factor II D]] (TFIID) which recognizes the [[TATA box]] and aids in the positioning of RNA polymerase at the appropriate start site. To finish the production of the RNA transcript, [[Terminator (genetics)|termination]] takes place usually several hundred nuclecotides away from the termination sequence and cleavage takes place. This process occurs in the nucleus of a cell along with [[RNA processing]] by which mRNA molecules are [[Five-prime cap|capped]], [[RNA splicing|spliced]] and [[Polyadenylation|polyadenylated]] to increase their stability before being subsequently taken to the cytoplasm. The mRNA gives rise to proteins through the process of [[translation (biology)|translation]] that takes place in [[ribosome]]s. [15] => [16] => ==Types of RNA transcripts== [17] => [18] => Almost all functional transcripts are derived from known genes. The only exceptions are a small number of transcripts that might play a direct role in regulating gene expression near the prompters of known genes. (See [[Enhancer RNA]].) [19] => [20] => Gene occupy most of prokaryotic genomes so most of their genomes are transcribed. Many eukaryotic genomes are very large and known genes may take up only a fraction of the genome. In mammals, for example, known genes only account for 40-50% of the genome.{{cite journal | vauthors = Francis WR, Wörheide G | title = Similar Ratios of Introns to Intergenic Sequence across Animal Genomes | journal = Genome Biology and Evolution | volume = 9 | issue = 6 | pages = 1582–1598 | date = June 2017 | pmid = 28633296 | pmc = 5534336 | doi = 10.1093/gbe/evx103 }} Nevertheless, identified transcripts often map to a much larger fraction of the genome suggesting that the transcriptome contains spurious transcripts that do not come from genes. Some of these transcripipts are known to be non-functional because they map to transcribed pseudogenes or degenerative transposons and viruses. Others map to unidentified regions of the genome that may be junk DNA. [21] => [22] => Spurious transcription is very common in eukaryotes, especially those with large genomes that might contain a lot of [[junk DNA]].{{ cite journal | vauthors = van Bakel H, Nislow C, Blencowe BJ, and Hughes TR | date = 2011 | title = Response to "the reality of pervasive transcription | journal = PLOS Biology | volume = 9 | issue = 7 | pages = e1001102 | doi = 10.1371/journal.pbio.1001102 | s2cid = 15680321 | pmc = 3134445 | doi-access = free }}{{ cite journal | vauthors = Jensen TH, Jacquier A, and Libri D | date = 2013 | title = Dealing with pervasive transcription | journal = Molecular Cell | volume = 52 | issue = 4 | pages = 473–484 | doi = 10.1016/j.molcel.2013.10.032 | pmid = 24267449 | doi-access = free }}{{ cite journal | last = Sverdlov | first = Eugene | date = 2017 | title = Transcribed Junk Remains Junk If It Does Not Acquire A Selected Function in Evolution | journal = BioEssays | volume = 39 | issue = 12 | pages = 1700164 | doi = 10.1002/bies.201700164 | pmid = 29071727 | s2cid = 35346807 }}{{ cite journal | vauthors = Wade JT, and Grainger DC | date = 2018 | title = Spurious transcription and its impact on cell function | journal = Transcription | volume = 9 | issue = 3 | pages = 182–189 | doi = 10.1080/21541264.2017.1381794 | pmid = 28980880 | pmc = 5927700 }} Some scientists claim that if a transcript has not been assigned to a known gene then the default assumption must be that it is junk RNA until it has been shown to be functional.{{ cite journal | vauthors = Palazzo AF, and Lee ES | date = 2015 | title = Non-coding RNA: what is functional and what is junk? | journal = Frontiers in Genetics | volume = 6 | page = 2 | doi = 10.3389/fgene.2015.00002 | pmid = 25674102 | pmc = 4306305 | doi-access = free }} This would mean that much of the transcriptome in species with large genomes is probably junk RNA. (See [[Non-coding RNA]]) [23] => [24] => The transcriptome includes the transcripts of protein-coding genes (mRNA plus introns) as well as the transcripts of non-coding genes (functional RNAs plus introns). [25] => [26] => *[[Ribosomal RNA]]/rRNA: Usually the most abundant RNA in the transcriptome. [27] => *[[Long non-coding RNA]]/lncRNA: Non-coding RNA transcripts that are more than 200 nucleotides long. Members of this group comprise the largest fraction of the non-coding transcriptome other than introns. It is not known how many of these transcripts are functional and how many are junk RNA. [28] => *[[transfer RNA]]/tRNA [29] => *[[micro RNA]]/miRNA: 19-24 nucleotides (nt) long. Micro RNAs up- or downregulate expression levels of mRNAs by the process of [[RNA interference]] at the post-transcriptional level. [30] => *[[small interfering RNA]]/siRNA: 20-24 nt [31] => *[[small nucleolar RNA]]/snoRNA [32] => *[[Piwi-interacting RNA]]/piRNA: 24-31 nt. They interact with [[Piwi protein]]s of the [[Argonaute]] family and have a function in targeting and cleaving [[transposon]]s. [33] => *[[enhancer RNA]]/eRNA: [34] => [35] => ==Scope of study== [36] => In the human genome, all genes get transcribed into RNA because that's how the molecular gene is defined. (See [[Gene]].) The transcriptome consists of coding regions of mRNA plus non-coding UTRs, introns, non-coding RNAs, and spurious non-functional transcripts. [37] => [38] => Several factors render the content of the transcriptome difficult to establish. These include [[alternative splicing]], [[RNA editing]] and alternative transcription among others.{{cite journal|url=https://www.nature.com/scitable/topicpage/transcriptome-connecting-the-genome-to-gene-function-605/#|title=Transcriptome: Connecting the Genome to Gene Function|first=Jill|last=U. Adams|journal=[[Nature Education]]|volume=1|issue=1|page=195|year=2008}} Additionally, transcriptome techniques are capable of capturing transcription occurring in a sample at a specific time point, although the content of the transcriptome can change during differentiation. The main aims of transcriptomics are the following: "catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions".{{cite journal|title=RNA-Seq: a revolutionary tool for transcriptomics|first1=Zhong|last1=Wang|first2=Mark|last2=Gerstein|first3=Michael|last3=Snyder|journal=[[Nature Reviews Genetics]]|doi=10.1038/nrg2484|pmid=19015660|pmc=2949280|date=January 2009|volume = 10|issue = 1|pages=57–63}} [39] => [40] => The term can be applied to the total set of transcripts in a given [[organism]], or to the specific subset of transcripts present in a particular cell type. Unlike the [[genome]], which is roughly fixed for a given cell line (excluding [[mutation]]s), the transcriptome can vary with external environmental conditions. Because it includes all mRNA transcripts in the cell, the transcriptome reflects the [[gene]]s that are being actively [[gene expression|expressed]] at any given time, with the exception of mRNA degradation phenomena such as [[attenuator (genetics)|transcriptional attenuation]]. The study of [[transcriptomics]], (which includes [[expression profiling]], [[splice variant analysis]] etc.), examines the expression level of RNAs in a given cell population, often focusing on mRNA, but sometimes including others such as tRNAs and sRNAs. [41] => [42] => ==Methods of construction== [43] => {{Main|Transcriptomics technologies}} [44] => Transcriptomics is the quantitative science that encompasses the assignment of a list of strings ("reads") to the object ("transcripts" in the genome). To calculate the expression strength, the density of reads corresponding to each object is counted. Initially, transcriptomes were analyzed and studied using [[expressed sequence tags]] libraries and serial and cap analysis of gene expression (SAGE). [45] => [46] => Currently, the two main [[Transcriptomics technologies|transcriptomics techniques]] include [[DNA microarray]]s and [[RNA-Seq]]. Both techniques require RNA isolation through [[RNA extraction]] techniques, followed by its separation from other cellular components and enrichment of mRNA.{{cite book | vauthors = Bryant S, Manning DL | title = RNA Isolation and Characterization Protocols | chapter = Isolation of messenger RNA | series = Methods in Molecular Biology | volume = 86 | pages = 61–4 | date = 1998 | pmid = 9664454 | doi = 10.1385/0-89603-494-1:61 | isbn = 978-0-89603-494-5 }}{{cite journal | vauthors = Chomczynski P, Sacchi N | title = Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction | journal = Analytical Biochemistry | volume = 162 | issue = 1 | pages = 156–9 | date = April 1987 | pmid = 2440339 | doi = 10.1016/0003-2697(87)90021-2 }} [47] => [48] => There are two general methods of inferring transcriptome sequences. One approach maps sequence reads onto a reference genome, either of the organism itself (whose transcriptome is being studied) or of a closely related species. The other approach, [[de novo transcriptome assembly|''de novo'' transcriptome assembly]], uses software to infer transcripts directly from short sequence reads and is used in organisms with genomes that are not sequenced. [49] => [50] => === DNA microarrays === [51] => {{main|DNA microarray}} [52] => [[File:Affymetrix-microarray.jpg|thumb|[[DNA microarray]] used to detect gene expression in human (''left'') and mouse (''right'') samples]] [53] => [54] => The first transcriptome studies were based on [[microarray]] techniques (also known as DNA chips). Microarrays consist of thin glass layers with spots on which [[oligonucleotide]]s, known as "probes" are arrayed; each spot contains a known DNA sequence.{{Cite journal|title=Quantitative monitoring of gene expression patterns with a complementary DNA microarray|last1=Schena|first1=M.|last2=Shalon|first2=D.|date=20 October 1995|journal=Science|location=New York, N.Y. |volume=270|number=5235|pages=467–470|issn=0036-8075|pmid=7569999|last3=Davis|first3=R. W.|last4=Brown|first4=P. O.|doi = 10.1126/science.270.5235.467|bibcode = 1995Sci...270..467S|s2cid=6720459}} [55] => [56] => When performing microarray analyses, mRNA is collected from a control and an experimental sample, the latter usually representative of a disease. The RNA of interest is converted to cDNA to increase its stability and marked with [[fluorophore]]s of two colors, usually green and red, for the two groups. The cDNA is spread onto the surface of the microarray where it hybridizes with oligonucleotides on the chip and a laser is used to scan. The fluorescence intensity on each spot of the microarray corresponds to the level of gene expression and based on the color of the fluorophores selected, it can be determined which of the samples exhibits higher levels of the mRNA of interest. [57] => [58] => One microarray usually contains enough oligonucleotides to represent all known genes; however, data obtained using microarrays does not provide information about unknown genes. During the 2010s, microarrays were almost completely replaced by next-generation techniques that are based on DNA sequencing. [59] => [60] => ===RNA sequencing=== [61] => {{Main|RNA-Seq}} [62] => RNA sequencing is a [[next-generation sequencing]] technology; as such it requires only a small amount of RNA and no previous knowledge of the genome. It allows for both qualitative and quantitative analysis of RNA transcripts, the former allowing discovery of new transcripts and the latter a measure of relative quantities for transcripts in a sample. [63] => [64] => The three main steps of sequencing transcriptomes of any biological samples include RNA purification, the synthesis of an RNA or cDNA library and sequencing the library.{{Harvnb|Cellerino|Sanguanini|2018|p=12}} The RNA purification process is different for short and long RNAs. This step is usually followed by an assessment of RNA quality, with the purpose of avoiding contaminants such as DNA or technical contaminants related to sample processing. RNA quality is measured using UV spectrometry with an absorbance peak of 260 nm.{{Harvnb|Cellerino|Sanguanini|2018|p=13}} RNA integrity can also be analyzed quantitatively comparing the ratio and intensity of [[28S RNA]] to [[18S RNA]] reported in the RNA Integrity Number (RIN) score. Since mRNA is the species of interest and it represents only 3% of its total content, the RNA sample should be treated to remove rRNA and tRNA and tissue-specific RNA transcripts. [65] => [66] => The step of library preparation with the aim of producing short cDNA fragments, begins with RNA fragmentation to transcripts in length between 50 and 300 [[base pair]]s. Fragmentation can be enzymatic (RNA [[endonuclease]]s), chemical (trismagnesium salt buffer, [[Hydrolysis|chemical hydrolysis]]) or mechanical ([[sonication]], nebulisation).{{Harvnb|Cellerino|Sanguanini|2018|p=18}} [[Reverse transcription]] is used to convert the RNA templates into cDNA and three priming methods can be used to achieve it, including oligo-DT, using random primers or ligating special adaptor oligos. [67] => [68] => ===Single-cell transcriptomics=== [69] => {{Main|Single-cell transcriptomics}} [70] => Transcription can also be studied at the level of individual cells by [[single-cell transcriptomics]]. Single-cell RNA sequencing (scRNA-seq) is a recently developed technique that allows the analysis of the transcriptome of single cells, including [[bacteria]].{{cite journal |vauthors=Toledo-Arana A, Lasa I |title=Advances in bacterial transcriptome understanding: From overlapping transcription to the excludon concept |journal=Mol Microbiol |volume=113 |issue=3 |pages=593–602 |date=March 2020 |pmid=32185833 |pmc=7154746 |doi=10.1111/mmi.14456 |url=}} With single-cell transcriptomics, subpopulations of cell types that constitute the tissue of interest are also taken into consideration.{{cite journal|last1=Kanter|first1=Itamar|last2=Kalisky|first2=Tomer|title=Single Cell Transcriptomics: Methods and Applications|journal=[[Frontiers in Oncology]]|date=10 March 2015|volume=5|pages=53|doi=10.3389/fonc.2015.00053|pmid=25806353|pmc=4354386|issn=2234-943X|doi-access=free}} This approach allows to identify whether changes in experimental samples are due to phenotypic cellular changes as opposed to proliferation, with which a specific cell type might be overexpressed in the sample.{{cite journal|url=https://www.nature.com/articles/nrg3833|title=Computational and analytical challenges in single-cell transcriptomics|journal=[[Nature Reviews Genetics]]|first1=Oliver|last1=Stegle|first2=Sarah|last2=A. Teichmann|first3=John|last3=C. Marioni|year=2015 |volume=16|issue=3|pages=133–45|doi=10.1038/nrg3833|pmid=25628217|s2cid=205486032}} Additionally, when assessing cellular progression through [[cellular differentiation|differentiation]], average expression profiles are only able to order cells by time rather than their stage of development and are consequently unable to show trends in gene expression levels specific to certain stages.{{cite journal|last1=Trapnell|first1=Cole|title=Defining cell types and states with single-cell genomics|journal=[[Genome Research]]|date=1 October 2015|volume=25|issue=10|pages=1491–1498|doi=10.1101/gr.190595.115|pmid=26430159|issn=1088-9051|pmc=4579334}} Single-cell trarnscriptomic techniques have been used to characterize rare cell populations such as [[circulating tumor cell]]s, cancer stem cells in solid tumors, and [[embryonic stem cells]] (ESCs) in mammalian [[blastocyst]]s.{{cite journal|title=Single Cell Transcriptomics: Methods and Applications|journal=Frontiers in Oncology|year=2015|volume=5|issue=13|doi=10.3389/fonc.2015.00053|pmid=25806353|first1=Itamar|last1=Kanter|first2=Tomer|last2=Kalisky|page=53|pmc = 4354386|doi-access=free}} [71] => [72] => Although there are no standardized techniques for single-cell transcriptomics, several steps need to be undertaken. The first step includes cell isolation, which can be performed using low- and high-throughput techniques. This is followed by a qPCR step and then single-cell RNAseq where the RNA of interest is converted into cDNA. Newer developments in single-cell transcriptomics allow for tissue and sub-cellular localization preservation through cryo-sectioning thin slices of tissues and sequencing the transcriptome in each slice. Another technique allows the visualization of single transcripts under a microscope while preserving the spatial information of each individual cell where they are expressed. [73] => [74] => ==Analysis== [75] => A number of organism-specific transcriptome databases have been constructed and annotated to aid in the identification of genes that are differentially expressed in distinct cell populations. [76] => [77] => [[RNA-seq]] is emerging (2013) as the method of choice for measuring transcriptomes of organisms, though the older technique of [[DNA microarray]]s is still used. RNA-seq measures the transcription of a specific gene by converting long RNAs into a library of [[cDNA]] fragments. The cDNA fragments are then sequenced using high-throughput sequencing technology and aligned to a reference genome or transcriptome which is then used to create an expression profile of the genes. [78] => [79] => ==Applications== [80] => ===Mammals=== [81] => The transcriptomes of [[stem cell]]s and [[cancer]] cells are of particular interest to researchers who seek to understand the processes of [[cellular differentiation]] and [[carcinogenesis]]. A pipeline using RNA-seq or gene array data can be used to track genetic changes occurring in [[stem cells|stem]] and [[precursor cells]] and requires at least three independent gene expression data from the former cell type and mature cells.{{cite journal|title=Assessment of stem cell differentiation based on genome-wide expression profiles|journal=[[Philosophical Transactions of the Royal Society B]]|doi=10.1098/rstb.2017.0221|date=5 July 2018|volume=373|issue=1750|pmid=29786556|first1=Patricio|last1=Godoy|first2=Wolfgang|last2=Schmidt-Heck|first3=Birte|last3=Hellwig|first4=Patrick|last4=Nell|first5=David|last5=Feuerborn|first6=Jörg|last6=Rahnenführer|first7=Kathrin|last7=Kattler|first8=Jörn|last8=Walter|first9=Nils|last9=Blüthgen|first10=Jan|last10=G. Hengstler|pages = 20170221|pmc = 5974444|doi-access=free}} [82] => [83] => Analysis of the transcriptomes of human [[oocyte]]s and [[human embryo|embryos]] is used to understand the molecular mechanisms and signaling pathways controlling early embryonic development, and could theoretically be a powerful tool in making proper [[embryo selection]] in [[in vitro fertilisation]].{{citation needed|date=April 2020}} Analyses of the transcriptome content of the placenta in the first-trimester of pregnancy in ''in vitro'' fertilization and embryo transfer (IVT-ET) revealed differences in genetic expression which are associated with higher frequency of adverse perinatal outcomes. Such insight can be used to optimize the practice.{{cite journal|title=The placental transcriptome of the first-trimester placenta is affected by in vitro fertilization and embryo transfer|journal=Reproductive Biology and Endocrinology|last1=Zhao|first1=L|last2=Zheng|first2=X|last3=Liu|first3=J|last4=Zheng|first4=R|last5=Yang|first5=R|last6=Wang|first6=Y|last7=Sun|first7=L|doi=10.1186/s12958-019-0494-7|pmid=31262321|date=1 July 2019|volume=17|issue=1|page=50|pmc = 6604150|doi-access=free}} Transcriptome analyses can also be used to optimize cryopreservation of oocytes, by lowering injuries associated with the process.{{cite journal|title=Probing lasting cryoinjuries to oocyte-embryo transcriptome|journal=PLOS ONE|first1=Binnur|last1=Eroglu|first2=Edyta|last2=A. Szurek|first3=Peter|last3=Schall|first4=Keith|last4=E. Latham|first5=Ali|last5=Eroglu|date=6 April 2020|doi=10.1371/journal.pone.0231108|pmid=32251418|volume=15|issue=4|pages = e0231108|pmc = 7135251|bibcode=2020PLoSO..1531108E|doi-access=free}} [84] => [85] => Transcriptomics is an emerging and continually growing field in [[biomarker]] discovery for use in assessing the safety of drugs or chemical [[risk assessment]].{{cite book|last=Szabo|first=David|title=Transcriptomic biomarkers in safety and risk assessment of chemicals. In Ramesh Gupta, editors:Gupta - Biomarkers in Toxicology, Oxford:Academic Press.|date=2014|isbn=978-0-12-404630-6|pages=1033–1038|doi=10.1016/B978-0-12-404630-6.00062-2|chapter=Transcriptomic biomarkers in safety and risk assessment of chemicals|s2cid=89396307 |url=https://zenodo.org/record/1258664}} [86] => [87] => Transcriptomes may also be used to [[Phylogenetic inference using transcriptomic data|infer phylogenetic relationships]] among individuals or to detect evolutionary patterns of transcriptome conservation.{{Cite journal|last1=Drost|first1=Hajk-Georg|last2=Gabel|first2=Alexander|last3=Grosse|first3=Ivo|last4=Quint|first4=Marcel|last5=Grosse|first5=Ivo|date=2018-05-01|title=myTAI: evolutionary transcriptomics with R|url= |journal=Bioinformatics|language=en|volume=34|issue=9|pages=1589–1590|doi=10.1093/molbev/msv012|issn=0737-4038|pmc=5925770|pmid=29309527}} [88] => [89] => Transcriptome analyses were used to discover the incidence of antisense transcription, their role in gene expression through interaction with surrounding genes and their abundance in different chromosomes.{{cite journal|url=https://www.science.org/doi/full/10.1126/science.1112009|title=Antisense Transcription in the Mammalian Transcriptome|first1=Katayama|display-authors=etal|last1=S|journal=[[Science (journal)|Science]]|volume=309|issue=5740|year=2005|pages=1564–6|doi=10.1126/science.1112009|pmid=16141073|bibcode=2005Sci...309.1564R|s2cid=34559885}} RNA-seq was also used to show how RNA isoforms, transcripts stemming from the same gene but with different structures, can produce complex phenotypes from limited genomes.{{cite journal|url=https://www.science.org/content/article/transcriptomics-today-microarrays-rna-seq-and-more|title=Transcriptomics today: Microarrays, RNA-seq, and more|journal=Science Magazine|first=Chris|last=Tachibana|date=31 July 2015|volume=349|issue=6247|pages=544|bibcode=2015Sci...349..544T|access-date=2 May 2020}} [90] => [91] => ===Plants=== [92] => Transcriptome analysis have been used to study the [[evolution]] and diversification process of plant species. In 2014, the [[1000 Plant Genomes Project]] was completed in which the transcriptomes of 1,124 plant species from the families [[viridiplantae]], [[glaucophyta]] and [[rhodophyta]] were sequenced. The protein coding sequences were subsequently compared to infer phylogenetic relationships between plants and to characterize the time of their [[Genetic divergence|diversification]] in the process of evolution.{{cite journal|title=One thousand plant transcriptomes and the phylogenomics of green plants|date=23 October 2019|volume=574|pages=679–685|author=One Thousand Plant Transcriptomes Initiative|author-link=1000 Plant Genomes Project|journal=Nature|issue=7780|pmid=31645766|pmc=6872490|doi=10.1038/s41586-019-1693-2}} Transcriptome studies have been used to characterize and quantify gene expression in mature [[pollen]]. Genes involved in cell wall metabolism and cytoskeleton were found to be overexpressed. Transcriptome approaches also allowed to track changes in gene expression through different developmental stages of pollen, ranging from microspore to mature pollen grains; additionally such stages could be compared across species of different plants including ''[[Arabidopsis]]'', [[rice]] and [[tobacco]].{{cite journal|title=A decade of pollen transcriptomics|first1=Nicholas|last1=Rutley|first2=David|last2=Twell|journal=Plant Reproduction|volume=28|issue=2|pages=73–89|date=12 March 2015|doi=10.1007/s00497-015-0261-7|pmid = 25761645|pmc = 4432081|doi-access=free}} [93] => [94] => ==Relation to other ome fields== [95] => [[Image:Metabolomics schema.png|thumb|350px|General schema showing the relationships of the [[genome]], transcriptome, [[proteome]], and [[metabolome]] ([[lipidome]]).]] [96] => Similar to other [[Omics|-ome]] based technologies, analysis of the transcriptome allows for an unbiased approach when validating hypotheses experimentally. This approach also allows for the discovery of novel mediators in signaling pathways.{{Harvnb|Cellerino|Sanguanini|2018|p=preface}} As with other -omics based technologies, the transcriptome can be analyzed within the scope of a [[multiomics]] approach. It is complementary to [[metabolomics]] but contrary to proteomics, a direct association between a transcript and [[metabolite]] cannot be established. [97] => [98] => There are several -ome fields that can be seen as subcategories of the transcriptome. The [[exome]] differs from the transcriptome in that it includes only those RNA molecules found in a specified cell population, and usually includes the amount or concentration of each RNA molecule in addition to the molecular identities. Additionally, the transcritpome also differs from the [[translatome]], which is the set of RNAs undergoing translation. [99] => [100] => The term meiome is used in [[functional genomics]] to describe the meiotic transcriptome or the set of RNA transcripts produced during the process of [[meiosis]].{{cite journal|title=Microarray expression analysis of meiosis and microsporogenesis in hexaploid bread wheat|journal=[[BMC Genomics]]|first1=Wayne|last1=Crismani|first2=Ute|last2=Baumann|first3=Tim|last3=Sutton|first4=Neil|last4=Shirley|first5=Tracie|last5=Webster|first6=German|last6=Spangenberg|first7=Peter|last7=Langridge|first8=Jason|last8=A Able|year = 2006|volume=7|issue=267|pages = 267|doi=10.1186/1471-2164-7-267|pmid=17052357|pmc = 1647286|doi-access=free}} Meiosis is a key feature of sexually reproducing [[eukaryote]]s, and involves the pairing of [[homologous chromosome]], synapse and recombination. Since meiosis in most organisms occurs in a short time period, meiotic transcript profiling is difficult due to the challenge of isolation (or enrichment) of meiotic cells ([[meiocyte]]s). As with transcriptome analyses, the meiome can be studied at a whole-genome level using large-scale transcriptomic techniques.{{cite journal|title=Whole genome approaches to identify early meiotic gene candidates in cereals|first1=William|last1=D. Bovill|first2=Priyanka|last2=Deveshwar|first3=Sanjay|last3=Kapoor|first4=Jason|last4=A. Able|doi=10.1007/s10142-008-0097-4|pmid=18836753|journal=Functional & Integrative Genomics|year=2009|volume=9|issue=2|pages = 219–29|s2cid=22854431}} The meiome has been well-characterized in mammal and yeast systems and somewhat less extensively characterized in plants.{{cite journal|title=Analysis of anther transcriptomes to identify genes contributing to meiosis and male gametophyte development in rice|journal=BMC Plant Biology|first1=Priyanka|last1=Deveshwar|first2=William|last2=D Bovill|first3=Rita|last3=Sharma|first4=Jason|last4=A Able|first5=Sanjay|last5=Kapoor|volume=11|issue=78|date=9 May 2011|pages=78|doi=10.1186/1471-2229-11-78|pmid=21554676|pmc=3112077 |doi-access=free }} [101] => [102] => The [[thanatotranscriptome]] consists of all RNA transcripts that continue to be expressed or that start getting re-expressed in internal organs of a dead body 24–48 hours following death. Some genes include those that are inhibited after [[fetal development]]. If the thanatotranscriptome is related to the process of programmed cell death ([[apoptosis]]), it can be referred to as the apoptotic thanatotranscriptome. Analyses of the thanatotranscriptome are used in [[forensic medicine]].{{cite journal|last1=Javan|first1=G. T.|last2=Can|first2=I.|last3=Finley|first3=S. J.|last4=Soni|first4=S|year=2015|title=The apoptotic thanatotranscriptome associated with the liver of cadavers|journal=Forensic Science, Medicine, and Pathology|volume=11|issue=4|pages=509–516|doi=10.1007/s12024-015-9704-6|pmid=26318598|s2cid=21583165}} [103] => [104] => [[Expression quantitative trait loci|eQTL]] mapping can be used to complement genomics with transcriptomics; genetic variants at DNA level and gene expression measures at RNA level.{{cite journal|title=Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences|first1=Claudia|last1=Manzoni|first2=Demis|last2=A Kia|first3=Jana|last3=Vandrovcova|first4=John|last4=Hardy|first5=Nicholas|last5=W Wood|first6=Patrick|last6=A Lewis|first7=Raffaele|last7=Ferrari|journal=[[Briefings in Bioinformatics]]|volume=19|issue=2|date=March 2018|pages=286–302|doi=10.1093/bib/bbw114|pmid=27881428|pmc=6018996}} [105] => [106] => ===Relation to proteome=== [107] => {{Further|Proteome}} [108] => The transcriptome can be seen as a subset of the [[proteome]], that is, the entire set of proteins expressed by a genome. [109] => [110] => However, the analysis of relative mRNA expression levels can be complicated by the fact that relatively small changes in mRNA expression can produce large changes in the total amount of the corresponding protein present in the cell. One analysis method, known as [[gene set enrichment analysis]], identifies coregulated gene networks rather than individual genes that are up- or down-regulated in different cell populations.{{ref|Subramanian}} [111] => [112] => Although microarray studies can reveal the relative amounts of different mRNAs in the cell, levels of mRNA are not directly proportional to the expression level of the [[protein]]s they code for.{{cite journal |last=Schwanhäusser |first=Björn |journal=Nature |volume=473 |issue=7347 |pages=337–342 |pmid=21593866 |doi=10.1038/nature10098 |title=Global quantification of mammalian gene expression control |date=May 2011|bibcode=2011Natur.473..337S |s2cid=205224972 |display-authors=etal|url=http://edoc.mdc-berlin.de/11664/1/11664oa.pdf }} The number of protein molecules synthesized using a given mRNA molecule as a template is highly dependent on translation-initiation features of the mRNA sequence; in particular, the ability of the translation initiation sequence is a key determinant in the recruiting of [[ribosome]]s for protein [[translation (genetics)|translation]]. [113] => [114] => ==Transcriptome databases== [115] => {{See also|Transcriptomics technologies#Transcriptome databases}} [116] => *Ensembl: [http://www.ensembl.org/biomart/martview/4e6c01c28faed033db52d0d30e4c21ab] [117] => *OmicTools: [https://omictools.com/mtd-3-tool] [118] => *Transcriptome Browser: [http://tagc.univ-mrs.fr/tbrowser/] [119] => *ArrayExpress: [http://www.ebi.ac.uk/arrayexpress/] [120] => [121] => ==See also== [122] => {{Portal bar|Biology|Science|Technology}} [123] => {{cmn| [124] => * [[Functional genomics]] [125] => * [[Gene expression]] [126] => * [[List of omics topics in biology]] [127] => * [[Metabolome]] [128] => * [[Serial analysis of gene expression]] [129] => * [[Transcriptomics technologies]] [130] => * [[Translatome]] [131] => * [[Transpogene]] [132] => * [[Weighted correlation network analysis|Weighted gene co-expression network analysis]] [133] => }} [134] => [135] => ==Notes== [136] => {{Reflist}} [137] => [138] => ==References== [139] => {{refbegin|indent=yes}} [140] => *{{citation|title=Transcriptome Analysis: Introduction and Examples from the Neurosciences|last1=Cellerino|first1=A|last2=Sanguanini|first2=M|doi=10.1007/978-88-7642-642-1|year=2018|isbn=978-88-7642-641-4}} [141] => {{refend}} [142] => [143] => ==Further reading== [144] => {{refbegin}} [145] => * {{note|Subramanian}} Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. ''Proc Natl Acad Sci USA'' 102(43):15545-50. [146] => * {{note|Laule}} Laule O, Hirsch-Hoffmann M, Hruz T, Gruissem W, and P Zimmermann. (2006) Web-based analysis of the mouse transcriptome using Genevestigator. ''BMC Bioinformatics'' 7:311 [147] => * {{note|Assou}} {{Cite journal | doi = 10.1093/humupd/dmq036 | title = Dynamic changes in gene expression during human early embryo development: From fundamental aspects to clinical applications | year = 2010 | last1 = Assou | first1 = S. | last2 = Boumela | first2 = I. | last3 = Haouzi | first3 = D. | last4 = Anahory | first4 = T. | pmc = 3189516 | last5 = Dechaud | first5 = H. | last6 = De Vos | first6 = J. | last7 = Hamamah | first7 = S. | journal = Human Reproduction Update | volume = 17 | issue = 2 | pages = 272–290 | pmid = 20716614| url = http://www.hal.inserm.fr/inserm-00512388/document }} [148] => * {{note|Ogorodnikov}} {{cite journal |last1=Ogorodnikov |first1=A |last2=Kargapolova |first2=Y |last3=Danckwardt |first3=S. |year=2016 |title=Processing and transcriptome expansion at the mRNA 3′ end in health and disease: finding the right end. |journal=Eur J Physiol |pmid=27220521 |doi=10.1007/s00424-016-1828-3 |volume=468 |issue=6 |pmc=4893057 |pages=993–1012}} [149] => {{refend}} [150] => [151] => {{Genomics}} [152] => [153] => [[Category:Gene expression]] [154] => [[Category:Omics]] [155] => [[Category:RNA]] [156] => [[Category:RNA splicing]] [] => )
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Transcriptome

The transcriptome is the set of all RNA transcripts, including coding and non-coding, in an individual or a population of cells. The term can also sometimes be used to refer to all RNAs, or just mRNA, depending on the particular experiment.

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