Content deleted Content added
Added the definition of "probeset" in the microarrays section. |
Citation bot (talk | contribs) Add: bibcode, url, article-number. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by Abductive | Category:Molecular biology | #UCB_Category 18/577 |
||
| (36 intermediate revisions by 18 users not shown) | |||
Line 1:
{{Short description|Study of RNA transcripts}}
{{
'''Transcriptomics technologies''' are the techniques used to study an organism's [[transcriptome]], the sum of all of its [[RNA|RNA transcripts]]. The information content of an organism is recorded in the DNA of its [[genome]] and [[Gene expression|expressed]] through [[Transcription (genetics)|transcription]]. Here, [[mRNA]] serves as a transient intermediary molecule in the information network, whilst [[non-coding RNA]]s perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a [[cell (biology)|cell]]. Transcriptomics technologies provide a broad account of which cellular processes are active and which are dormant.
A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.
The first attempts to study whole transcriptomes began in the early 1990s. Subsequent technological advances since the late 1990s have repeatedly transformed the field and made transcriptomics a widespread discipline in biological sciences. There are two key contemporary techniques in the field: [[microarray]]s, which quantify a set of predetermined sequences, and [[RNA-Seq]], which uses [[DNA sequencing#Next-generation methods|high-throughput sequencing]] to record all transcripts. As the technology improved, the volume of data produced by each transcriptome experiment increased. As a result, data analysis methods have steadily been adapted to more accurately and efficiently analyse increasingly large volumes of data. Transcriptome databases
Measuring the expression of an organism's [[gene]]s in different [[tissue (biology)|tissues]] or [[Environment (biophysical)|conditions]], or at different times, gives information on how genes are [[Regulation of gene expression|regulated]] and reveals details of an organism's biology. It can also be used to infer the [[Phenotype|functions]] of previously [[DNA annotation|unannotated]] genes. Transcriptome analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human [[disease]]. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted [[assay]]s.
==History==
[[File:Transcriptomics technique publications over time.svg|thumb|
Transcriptomics has been characterised by the development of new techniques which have redefined what is possible every decade or so and rendered previous technologies obsolete. The first attempt at capturing a partial human transcriptome was published in 1991 and reported 609 [[Messenger RNA|mRNA]] sequences from the [[human brain]].<ref name="ref2047873"/> In 2008, two human transcriptomes, composed of millions of transcript-derived sequences covering 16,000 genes, were published,<ref name="#18978789">{{cite journal | vauthors = Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ | title = Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing | journal = Nature Genetics | volume = 40 | issue = 12 | pages = 1413–5 | date = December 2008 | pmid = 18978789 | doi = 10.1038/ng.259 | s2cid = 9228930 }}</ref><ref name="#18599741">{{cite journal | vauthors = Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O'Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML | display-authors = 6 | title = A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome | journal = Science | volume = 321 | issue = 5891 | pages = 956–60 | date = August 2008 | pmid = 18599741 | doi = 10.1126/science.1160342 | bibcode = 2008Sci...321..956S | s2cid = 10013179 }}</ref> and by 2015 transcriptomes had been published for hundreds of individuals.<ref name="#24037378">{{cite journal | vauthors = Lappalainen T, Sammeth M, Friedländer MR, 't Hoen PA, Monlong J, Rivas MA, Gonzàlez-Porta M, Kurbatova N, Griebel T, Ferreira PG, Barann M, Wieland T, Greger L, van Iterson M, Almlöf J, Ribeca P, Pulyakhina I, Esser D, Giger T, Tikhonov A, Sultan M, Bertier G, MacArthur DG, Lek M, Lizano E, Buermans HP, Padioleau I, Schwarzmayr T, Karlberg O, Ongen H, Kilpinen H, Beltran S, Gut M, Kahlem K, Amstislavskiy V, Stegle O, Pirinen M, Montgomery SB, Donnelly P, McCarthy MI, Flicek P, Strom TM, Lehrach H, Schreiber S, Sudbrak R, Carracedo A, Antonarakis SE, Häsler R, Syvänen AC, van Ommen GJ, Brazma A, Meitinger T, Rosenstiel P, Guigó R, Gut IG, Estivill X, Dermitzakis ET | display-authors = 6 | title = Transcriptome and genome sequencing uncovers functional variation in humans | journal = Nature | volume = 501 | issue = 7468 | pages = 506–11 | date = September 2013 | pmid = 24037378 | pmc = 3918453 | doi = 10.1038/nature12531 | bibcode = 2013Natur.501..506L }}</ref><ref name="#25954002">{{cite journal | vauthors = Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, Johnson R, Segrè AV, Djebali S, Niarchou A, Wright FA, Lappalainen T, Calvo M, Getz G, Dermitzakis ET, Ardlie KG, Guigó R | display-authors = 6 | title = Human genomics. The human transcriptome across tissues and individuals | journal = Science | volume = 348 | issue = 6235 | pages = 660–5 | date = May 2015 | pmid = 25954002 | pmc = 4547472 | doi = 10.1126/science.aaa0355 | bibcode = 2015Sci...348..660M }}</ref> Transcriptomes of different [[disease]] states, [[Tissue (biology)|tissues]], or even single [[Cell (biology)|cells]] are now routinely generated.<ref name="#25954002" /><ref name="#24524133">{{cite journal | vauthors = Sandberg R | title = Entering the era of single-cell transcriptomics in biology and medicine | journal = Nature Methods | volume = 11 | issue = 1 | pages = 22–4 | date = January 2014 | pmid = 24524133 | doi = 10.1038/nmeth.2764 | s2cid = 27632439 | url = https://zenodo.org/record/890299 }}</ref><ref name="#26000846">{{cite journal | vauthors = Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA | title = The technology and biology of single-cell RNA sequencing | journal = Molecular Cell | volume = 58 | issue = 4 | pages = 610–20 | date = May 2015 | pmid = 26000846 | doi = 10.1016/j.molcel.2015.04.005 | doi-access = free }}</ref> This explosion in transcriptomics has been driven by the rapid development of new technologies with improved sensitivity and economy.<ref name="#23290152">{{cite journal | vauthors = McGettigan PA | title = Transcriptomics in the RNA-seq era | journal = Current Opinion in Chemical Biology | volume = 17 | issue = 1 | pages = 4–11 | date = February 2013 | pmid = 23290152 | doi = 10.1016/j.cbpa.2012.12.008 }}</ref><ref name="#19015660">{{cite journal | vauthors = Wang Z, Gerstein M, Snyder M | title = RNA-Seq: a revolutionary tool for transcriptomics | journal = Nature Reviews Genetics | volume = 10 | issue = 1 | pages = 57–63 | date = January 2009 | pmid = 19015660 | pmc = 2949280 | doi = 10.1038/nrg2484 }}</ref><ref name="#21191423">{{cite journal | vauthors = Ozsolak F, Milos PM | title = RNA sequencing: advances, challenges and opportunities | journal = Nature Reviews Genetics | volume = 12 | issue = 2 | pages = 87–98 | date = February 2011 | pmid = 21191423 | pmc = 3031867 | doi = 10.1038/nrg2934 }}</ref><ref name="#19715439">{{cite journal | vauthors = Morozova O, Hirst M, Marra MA | title = Applications of new sequencing technologies for transcriptome analysis | journal = Annual Review of Genomics and Human Genetics | volume = 10 | pages = 135–51 | date = 2009 | pmid = 19715439 | doi = 10.1146/annurev-genom-082908-145957 }}</ref>
===
Studies of individual [[Primary transcript|transcripts]] were being performed several decades before any transcriptomics approaches were available. [[CDNA library|Libraries]] of [[Antheraea polyphemus|silkmoth]] mRNA transcripts were collected and converted to [[complementary DNA]] (cDNA) for storage using [[reverse transcriptase]] in the late 1970s.<ref name="#519770">{{cite journal | vauthors = Sim GK, Kafatos FC, Jones CW, Koehler MD, Efstratiadis A, Maniatis T | title = Use of a cDNA library for studies on evolution and developmental expression of the chorion multigene families | journal = Cell | volume = 18 | issue = 4 | pages = 1303–16 | date = December 1979 | pmid = 519770 | doi = 10.1016/0092-8674(79)90241-1 | doi-access = free }}</ref> In the 1980s, low-throughput sequencing using the [[Sanger sequencing|Sanger]] method was used to sequence random transcripts, producing [[expressed sequence tag]]s (ESTs).<ref name="ref2047873">{{cite journal | vauthors = Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH, Xiao H, Merril CR, Wu A, Olde B, Moreno RF | display-authors = 6 | title = Complementary DNA sequencing: expressed sequence tags and human genome project | journal = Science | volume = 252 | issue = 5013 | pages = 1651–6 | date = June 1991 | pmid = 2047873 | doi = 10.1126/science.2047873 | bibcode = 1991Sci...252.1651A | s2cid = 13436211 }}</ref><ref name="#6956902">{{cite journal | vauthors = Sutcliffe JG, Milner RJ, Bloom FE, Lerner RA | title = Common 82-nucleotide sequence unique to brain RNA | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 79 | issue = 16 | pages = 4942–6 | date = August 1982 | pmid = 6956902 | pmc = 346801 | bibcode = 1982PNAS...79.4942S | doi = 10.1073/pnas.79.16.4942 | doi-access = free }}</ref><ref name="#6687628">{{cite journal | vauthors = Putney SD, Herlihy WC, Schimmel P | title = A new troponin T and cDNA clones for 13 different muscle proteins, found by shotgun sequencing | journal = Nature | volume = 302 | issue = 5910 | pages = 718–21 | date = April 1983 | pmid = 6687628 | doi = 10.1038/302718a0 | bibcode = 1983Natur.302..718P | s2cid = 4364361 }}</ref><ref name="#9448457" /> The [[Sanger sequencing|Sanger method of sequencing]] was predominant until the advent of [[DNA sequencing#High-throughput methods|high-throughput methods]] such as [[sequencing by synthesis]] (Solexa/Illumina). [[Expressed sequence tag|ESTs]] came to prominence during the 1990s as an efficient method to determine the [[gene annotation|gene content]] of an organism without [[Whole genome sequencing|sequencing]] the entire [[genome]].<ref name="#9448457">{{cite journal | vauthors = Marra MA, Hillier L, Waterston RH | title = Expressed sequence tags—ESTablishing bridges between genomes | journal = Trends in Genetics | volume = 14 | issue = 1 | pages = 4–7 | date = January 1998 | pmid = 9448457 | doi = 10.1016/S0168-9525(97)01355-3 }}</ref> Amounts of individual transcripts were quantified using [[Northern blotting]], [[Reverse northern blot|nylon membrane arrays]], and later [[Reverse transcription polymerase chain reaction|reverse transcriptase quantitative PCR]] (RT-qPCR) methods,<ref name="#414220">{{cite journal | vauthors = Alwine JC, Kemp DJ, Stark GR | title = Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 74 | issue = 12 | pages = 5350–4 | date = December 1977 | pmid = 414220 | pmc = 431715 | doi = 10.1073/pnas.74.12.5350 | bibcode = 1977PNAS...74.5350A | doi-access = free }}</ref><ref name="#2479917">{{cite journal | vauthors = Becker-André M, Hahlbrock K | title = Absolute mRNA quantification using the polymerase chain reaction (PCR). A novel approach by a PCR aided transcript titration assay (PATTY) | journal = Nucleic Acids Research | volume = 17 | issue = 22 | pages = 9437–46 | date = November 1989 | pmid = 2479917 | pmc = 335144 | doi = 10.1093/nar/17.22.9437 }}</ref> but these methods are laborious and can only capture a tiny subsection of a transcriptome.<ref name="#19715439" /> Consequently, the manner in which a transcriptome as a whole is expressed and regulated remained unknown until higher-throughput techniques were developed.
===
The word "transcriptome" was first used in the 1990s.<ref name="#10022985">{{cite journal | vauthors = Piétu G, Mariage-Samson R, Fayein NA, Matingou C, Eveno E, Houlgatte R, Decraene C, Vandenbrouck Y, Tahi F, Devignes MD, Wirkner U, Ansorge W, Cox D, Nagase T, Nomura N, Auffray C | title = The Genexpress IMAGE knowledge base of the human brain transcriptome: a prototype integrated resource for functional and computational genomics | journal = Genome Research | volume = 9 | issue = 2 | pages = 195–209 | date = February 1999 | pmid = 10022985 | pmc = 310711 | doi=10.1101/gr.9.2.195}}</ref><ref name="#9008165">{{cite journal | vauthors = Velculescu VE, Zhang L, Zhou W, Vogelstein J, Basrai MA, Bassett DE, Hieter P, Vogelstein B, Kinzler KW | title = Characterization of the yeast transcriptome | journal = Cell | volume = 88 | issue = 2 | pages = 243–51 | date = January 1997 | pmid = 9008165 | doi = 10.1016/S0092-8674(00)81845-0 | s2cid = 11430660 | doi-access = free }}</ref> In 1995, one of the earliest sequencing-based transcriptomic methods was developed, [[serial analysis of gene expression]] (SAGE), which worked by [[Sanger sequencing]] of concatenated random transcript fragments.<ref name="#7570003">{{cite journal | vauthors = Velculescu VE, Zhang L, Vogelstein B, Kinzler KW | title = Serial analysis of gene expression | journal = Science | volume = 270 | issue = 5235 | pages = 484–7 | date = October 1995 | pmid = 7570003 | doi = 10.1126/science.270.5235.484 | bibcode = 1995Sci...270..484V | s2cid = 16281846 }}</ref> Transcripts were quantified by matching the fragments to known genes. A variant of SAGE using high-throughput sequencing techniques, called digital gene expression analysis, was also briefly used.<ref name="#23290152" /><ref name="#9331369">{{cite journal | vauthors = Audic S, Claverie JM | title = The significance of digital gene expression profiles | journal = Genome Research | volume = 7 | issue = 10 | pages = 986–95 | date = October 1997 | pmid = 9331369 | doi = 10.1101/gr.7.10.986 | doi-access = free }}</ref> However, these methods were largely overtaken by high throughput sequencing of entire transcripts, which provided additional information on transcript structure such as [[alternative splicing|splice variants]].<ref name="#23290152" />
===
{| class="wikitable floatright" style="width:500px"
|+ '''Comparison of contemporary methods'''<ref name="#25149683">{{cite journal | vauthors = Mantione KJ, Kream RM, Kuzelova H, Ptacek R, Raboch J, Samuel JM, Stefano GB | title = Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq | journal = Medical Science Monitor Basic Research | volume = 20 | pages = 138–42 | date = August 2014 | pmid = 25149683 | pmc = 4152252 | doi = 10.12659/MSMBR.892101 }}</ref><ref name="#24454679">{{cite journal | vauthors = Zhao S, Fung-Leung WP, Bittner A, Ngo K, Liu X | title = Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells | journal = PLOS ONE | volume = 9 | issue = 1 | pages = e78644 | date = 2014 | pmid = 24454679 | pmc = 3894192 | doi = 10.1371/journal.pone.0078644 | bibcode = 2014PLoSO...978644Z | doi-access = free }}</ref><ref name="#19015660" />
Line 24:
!RNA-Seq
!Microarray
|-
|[[Throughput]]
|1 day to 1 week per experiment<ref name="#19015660" />
|1–2 days per experiment<ref name="#19015660" />
|-
|[[Nucleic acid quantitation|Input RNA amount]]
|Low ~ 1 [[Orders of magnitude (mass)|ng]] total RNA<ref name="#22939981"/>
|High ~ 1 μg mRNA<ref name="#11015604">{{cite journal | vauthors = Stears RL, Getts RC, Gullans SR | title = A novel, sensitive detection system for high-density microarrays using dendrimer technology | journal = Physiological Genomics | volume = 3 | issue = 2 | pages = 93–9 | date = August 2000 | pmid = 11015604 | doi = 10.1152/physiolgenomics.2000.3.2.93 }}</ref>
|-
|Labour intensity
|High (sample preparation and data analysis)<ref name="#19015660" /><ref name="#25149683" />
Line 40:
|None required, although a reference genome/transcriptome sequence is useful<ref name="#25149683" />
|Reference genome/transcriptome is required for design of [[Molecular probe|probes]]<ref name="#25149683" />
|-
|[[Quantification (science)|Quantitation]] accuracy
|~90% (limited by sequence coverage)<ref name="EPR">{{Cite web|url=http://www.europeanpharmaceuticalreview.com/wp-content/uploads/Illumina_whitepaper.pdf|title=RNA-Seq Data Comparison with Gene Expression Microarrays|last=Illumina|publisher=European Pharmaceutical Review|date=2011-07-11}}</ref>
Line 54:
|-
|[[Dynamic range]]
|100,000:1 (limited by sequence coverage)<ref name="#24194394">{{cite journal | vauthors = Black MB, Parks BB, Pluta L, Chu TM, Allen BC, Wolfinger RD, Thomas RS | title = Comparison of microarrays and RNA-seq for gene expression analyses of dose-response experiments | journal = Toxicological Sciences | volume = 137 | issue = 2 | pages = 385–403 | date = February 2014 | pmid = 24194394 | doi = 10.1093/toxsci/kft249 | doi-access =
|1,000:1 (limited by fluorescence saturation)<ref name="#24194394" />
|-
|[[Reproducibility|Technical reproducibility]]
|>99%<ref name="#18550803">{{cite journal | vauthors = Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y | title = RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays | journal = Genome Research | volume = 18 | issue = 9 | pages = 1509–17 | date = September 2008 | pmid = 18550803 | pmc = 2527709 | doi = 10.1101/gr.079558.108 }}</ref><ref name="#25150838">{{cite journal | title = A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium | journal = Nature Biotechnology | volume = 32 | issue = 9 | pages = 903–14 | date = September 2014 | pmid = 25150838 | pmc = 4321899 | doi = 10.1038/nbt.2957 | author1 = SEQC/MAQC-III Consortium }}</ref>
|>99%<ref name="#17961233">{{cite journal | vauthors = Chen JJ, Hsueh HM, Delongchamp RR, Lin CJ, Tsai CA | title = Reproducibility of microarray data: a further analysis of microarray quality control (MAQC) data | journal = BMC Bioinformatics | volume = 8 |
|}
The dominant contemporary techniques, [[DNA microarray|microarrays]] and [[RNA-Seq]], were developed in the mid-1990s and 2000s.<ref name="#23290152" /><ref name="#11287436">{{cite journal | vauthors = Nelson NJ | title = Microarrays have arrived: gene expression tool matures | journal = Journal of the National Cancer Institute | volume = 93 | issue = 7 | pages = 492–4 | date = April 2001 | pmid = 11287436 | doi = 10.1093/jnci/93.7.492 }}</ref> Microarrays that measure the abundances of a defined set of transcripts via their [[Nucleic acid hybridization|hybridisation]] to an array of [[Complementarity (molecular biology)|complementary]] [[Molecular probe|probes]] were first published in 1995.<ref name="#7569999">{{cite journal | vauthors = Schena M, Shalon D, Davis RW, Brown PO | title = Quantitative monitoring of gene expression patterns with a complementary DNA microarray | journal = Science | volume = 270 | issue = 5235 | pages = 467–70 | date = October 1995 | pmid = 7569999 | doi = 10.1126/science.270.5235.467 | bibcode = 1995Sci...270..467S | s2cid = 6720459 }}</ref><ref name="#17644526">{{cite journal | vauthors = Pozhitkov AE, Tautz D, Noble PA | title = Oligonucleotide microarrays: widely applied—poorly understood | journal = Briefings in Functional Genomics & Proteomics | volume = 6 | issue = 2 | pages = 141–8 | date = June 2007 | pmid = 17644526 | doi = 10.1093/bfgp/elm014 | url = http://edoc.mpg.de/get.epl?fid=85687&did=572223&ver=0 | doi-access = free | hdl = 11858/00-001M-0000-000F-D7B3-3 | hdl-access = free }}</ref> Microarray technology allowed the assay of thousands of transcripts simultaneously and at a greatly reduced cost per gene and labour saving.<ref name=pmid12117754>{{cite journal | vauthors = Heller MJ | title = DNA microarray technology: devices, systems, and applications | journal = Annual Review of Biomedical Engineering | volume = 4 | pages = 129–53 | year = 2002 | pmid = 12117754 | doi = 10.1146/annurev.bioeng.4.020702.153438 }}</ref> Both [[DNA microarray#Spotted vs. in situ synthesised arrays|spotted oligonucleotide arrays]] and [[Affymetrix]] high-density arrays were the method of choice for transcriptional profiling until the late 2000s.<ref name="#19715439" /><ref name="#11287436" /> Over this period, a range of microarrays were produced to cover known genes in [[model organism|model]] or economically important organisms. Advances in design and manufacture of arrays improved the specificity of probes and allowed more genes to be tested on a single array. Advances in [[Fluorescence spectroscopy|fluorescence detection]] increased the sensitivity and measurement accuracy for low abundance transcripts.<ref name="#17644526" /><ref>{{cite book | first1 = Geoffrey J. | last1 = McLachlan | first2 = Kim-Anh | last2 = Do | author2-link = Kim-Anh Do | last3 = Ambroise | first3 = Christopher| name-list-style = vanc | title = Analyzing Microarray Gene Expression Data |date=2005|publisher=John Wiley & Sons|___location=Hoboken|isbn=978-0-471-72612-8}}{{page needed|date=June 2017}}</ref>
RNA-Seq is accomplished by reverse transcribing RNA ''in vitro'' and sequencing the resulting [[Complementary DNA|cDNAs]].<ref name="#19015660" /> Transcript abundance is derived from the number of counts from each transcript. The technique has therefore been heavily influenced by the development of [[DNA sequencing#High-throughput methods|high-throughput sequencing technologies]].<ref name="#23290152" /><ref name="#21191423" /> [[Massively parallel signature sequencing]] (MPSS) was an early example based on generating 16–20 [[Base pair#Length measurements|bp]] sequences via a complex series of [[Nucleic acid hybridization|hybridisation]]s,<ref name="#10835600">{{cite journal | vauthors = Brenner S, Johnson M, Bridgham J, Golda G, Lloyd DH, Johnson D, Luo S, McCurdy S, Foy M, Ewan M, Roth R, George D, Eletr S, Albrecht G, Vermaas E, Williams SR, Moon K, Burcham T, Pallas M, DuBridge RB, Kirchner J, Fearon K, Mao J, Corcoran K | title = Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays | journal = Nature Biotechnology | volume = 18 | issue = 6 | pages = 630–4 | date = June 2000 | pmid = 10835600 | doi = 10.1038/76469 | s2cid = 13884154 }}</ref><ref group="note">In molecular biology, '''hybridisation''' is a phenomenon in which single-stranded deoxyribonucleic acid ([[DNA]]) or ribonucleic acid ([[RNA]]) molecules [[Nucleic acid thermodynamics#Annealing|anneal]] to [[Complementarity (molecular biology)|complementary DNA or RNA]].</ref> and was used in 2004 to validate the expression of ten thousand genes in ''[[Arabidopsis thaliana]]''.<ref name="#15247925">{{cite journal | vauthors = Meyers BC, Vu TH, Tej SS, Ghazal H, Matvienko M, Agrawal V, Ning J, Haudenschild CD | title = Analysis of the transcriptional complexity of Arabidopsis thaliana by massively parallel signature sequencing | journal = Nature Biotechnology | volume = 22 | issue = 8 | pages = 1006–11 | date = August 2004 | pmid = 15247925 | doi = 10.1038/nbt992 | s2cid = 15336496 }}</ref> The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using [[454 Life Sciences#Technology|454 technology]].<ref name="#17010196">{{cite journal | vauthors = Bainbridge MN, Warren RL, Hirst M, Romanuik T, Zeng T, Go A, Delaney A, Griffith M, Hickenbotham M, Magrini V, Mardis ER, Sadar MD, Siddiqui AS, Marra MA, Jones SJ | title = Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach | journal = BMC Genomics | volume = 7 |
==Data gathering==
Line 70:
===Isolation of RNA===
All transcriptomic methods require RNA to first be isolated from the experimental organism before transcripts can be recorded. Although biological systems are incredibly diverse, [[RNA extraction]] techniques are broadly similar and involve mechanical [[Cell disruption|disruption of cells]] or tissues, disruption of [[RNAse|RNase]] with [[Chaotropic agent|chaotropic salts]],<ref name="#2440339">{{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 }}</ref> disruption of macromolecules and nucleotide complexes, separation of RNA from undesired [[biomolecule]]s including DNA, and concentration of the RNA via [[ethanol precipitation|precipitation]] from solution or [[Spin column-based nucleic acid purification|elution from a solid matrix]].<ref name="#2440339" /><ref name="#17406285">{{cite journal | vauthors = Chomczynski P, Sacchi N | title = The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on | journal = Nature Protocols | volume = 1 | issue = 2 | pages = 581–5 | date = 2006 | pmid = 17406285 | doi = 10.1038/nprot.2006.83 | s2cid = 28653075 }}</ref> Isolated RNA may additionally be treated with [[DNAse|DNase]] to digest any traces of DNA.<ref name="#1699561">{{cite journal | vauthors = Grillo M, Margolis FL | title = Use of reverse transcriptase polymerase chain reaction to monitor expression of intronless genes | journal = BioTechniques | volume = 9 | issue = 3 | pages = 262, 264, 266–8 | date = September 1990 | pmid = 1699561 }}</ref> It is necessary to enrich messenger RNA as total RNA extracts are typically 98% [[ribosomal RNA]].<ref name="#9664454">{{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 }}</ref> Enrichment for transcripts can be performed by [[Polyadenylation|poly-A]] affinity methods or by depletion of ribosomal RNA using sequence-specific probes.<ref name="#24888378">{{cite journal | vauthors = Zhao W, He X, Hoadley KA, Parker JS, Hayes DN, Perou CM | title = Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling | journal = BMC Genomics | volume = 15 |
===Expressed sequence tags===
Line 76:
===Serial and cap analysis of gene expression (SAGE/CAGE)===
[[File:Summary of SAGE.svg|thumb|
[[Serial analysis of gene expression]] (SAGE) was a development of EST methodology to increase the throughput of the tags generated and allow some quantitation of transcript abundance.<ref name="#7570003" /> [[Complementary DNA|cDNA]] is generated from the [[RNA]] but is then digested into 11 bp "tag" fragments using [[restriction enzyme]]s that cut DNA at a specific sequence, and 11 base pairs along from that sequence. These cDNA tags are then [[Ligation (molecular biology)|joined]] head-to-tail into long strands (>500 bp) and sequenced using low-throughput, but long read-length methods such as [[Sanger sequencing]]. The sequences are then divided back into their original 11 bp tags using computer software in a process called [[deconvolution]].<ref name="#7570003" /> If a high-quality [[reference genome]] is available, these tags may be matched to their corresponding gene in the genome. If a reference genome is unavailable, the tags can be directly used as diagnostic markers if found to be [[Gene expression profiling|differentially expressed]] in a disease state.<ref name="#7570003" />
Line 85:
===Microarrays===
[[File:Summary of RNA Microarray.svg|thumb|
====Principles and advances====
Line 102:
===RNA-Seq===
[[File:Summary of RNA-Seq.svg|thumb|
====Principles and advances====
[[RNA-Seq]] refers to the combination of a [[DNA sequencing#High-throughput methods|high-throughput sequencing]] methodology with computational methods to capture and quantify transcripts present in an RNA extract.<ref name="#19015660" /> The nucleotide sequences generated are typically around 100 bp in length, but can range from 30 bp to over 10,000 bp depending on the sequencing method used. RNA-Seq leverages [[Coverage (genetics)|deep sampling]] of the transcriptome with many short fragments from a transcriptome to allow computational reconstruction of the original RNA transcript by [[Sequence alignment|aligning]] reads to a reference genome or to each other ([[De novo transcriptome assembly|de novo assembly]]).<ref name="#23290152" /> Both low-abundance and high-abundance RNAs can be quantified in an RNA-Seq experiment ([[dynamic range]] of 5 [[Order of magnitude|orders of magnitude]])—a key advantage over microarray transcriptomes. In addition, input RNA amounts are much lower for RNA-Seq (nanogram quantity) compared to microarrays (microgram quantity), which
RNA-Seq may be used to identify genes within a [[genome]], or identify which genes are active at a particular point in time, and read counts can be used to accurately model the relative gene expression level. RNA-Seq methodology has constantly improved, primarily through the development of DNA sequencing technologies to increase throughput, accuracy, and read length.<ref>{{Cite journal|last=Tachibana|first=Chris|name-list-style = vanc |date=2015-08-18|title=Transcriptomics today: Microarrays, RNA-seq, and more|journal=Science|volume=349|issue=6247|page=544|doi=10.1126/science.opms.p1500095|bibcode=2015Sci...349..544T|doi-access=free}}</ref> Since the first descriptions in 2006 and 2008,<ref name="#17010196" /><ref name="#18451266">{{cite journal | vauthors = Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M | title = The transcriptional landscape of the yeast genome defined by RNA sequencing | journal = Science | volume = 320 | issue = 5881 | pages = 1344–9 | date = June 2008 | pmid = 18451266 | pmc = 2951732 | doi = 10.1126/science.1158441 | bibcode = 2008Sci...320.1344N }}</ref> RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015.<ref name="#25633159">{{cite journal | vauthors = Su Z, Fang H, Hong H, Shi L, Zhang W, Zhang W, Zhang Y, Dong Z, Lancashire LJ, Bessarabova M, Yang X, Ning B, Gong B, Meehan J, Xu J, Ge W, Perkins R, Fischer M, Tong W | title = An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era | journal = Genome Biology | volume = 15 | issue = 12 |
The quest for transcriptome data at the level of individual cells has driven advances in RNA-Seq library preparation methods, resulting in dramatic advances in sensitivity. [[Single-cell transcriptomics|Single-cell transcriptomes]] are now well described and have even been extended to ''[[In situ#Biology and biomedical engineering|in situ]]'' RNA-Seq where transcriptomes of individual cells are directly interrogated in [[Fixation (histology)|fixed]] tissues.<ref name="#24578530">{{cite journal | vauthors = Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL, Ferrante TC, Terry R, Jeanty SS, Li C, Amamoto R, Peters DT, Turczyk BM, Marblestone AH, Inverso SA, Bernard A, Mali P, Rios X, Aach J, Church GM | title = Highly multiplexed subcellular RNA sequencing in situ | journal = Science | volume = 343 | issue = 6177 | pages = 1360–3 | date = March 2014 | pmid = 24578530 | pmc = 4140943 | doi = 10.1126/science.1250212 | bibcode = 2014Sci...343.1360L }}</ref>
Line 115:
RNA-Seq was established in concert with the rapid development of a range of high-throughput DNA sequencing technologies.<ref name="#18846087">{{cite journal | vauthors = Shendure J, Ji H | title = Next-generation DNA sequencing | journal = Nature Biotechnology | volume = 26 | issue = 10 | pages = 1135–45 | date = October 2008 | pmid = 18846087 | doi = 10.1038/nbt1486 | s2cid = 6384349 }}</ref> However, before the extracted RNA transcripts are sequenced, several key processing steps are performed. Methods differ in the use of transcript enrichment, fragmentation, amplification, single or paired-end sequencing, and whether to preserve strand information.<ref name="#18846087" />
The sensitivity of an RNA-Seq experiment can be increased by enriching classes of RNA that are of interest and depleting known abundant RNAs. The mRNA molecules can be separated using oligonucleotides probes which bind their [[Polyadenylation|poly-A tails]]. Alternatively, ribo-depletion can be used to specifically remove abundant but uninformative [[ribosomal RNA]]s (rRNAs) by hybridisation to probes tailored to the [[taxon|taxon's]] specific rRNA sequences (e.g. mammal rRNA, plant rRNA). However, ribo-depletion can also introduce some bias via non-specific depletion of off-target transcripts.<ref name="#24981968">{{cite journal | vauthors = Lahens NF, Kavakli IH, Zhang R, Hayer K, Black MB, Dueck H, Pizarro A, Kim J, Irizarry R, Thomas RS, Grant GR, Hogenesch JB | title = IVT-seq reveals extreme bias in RNA sequencing | journal = Genome Biology | volume = 15 | issue = 6 |
Since mRNAs are longer than the read-lengths of typical high-throughput sequencing methods, transcripts are usually fragmented prior to sequencing.<ref name="#22140562">{{cite journal|vauthors=Knierim E, Lucke B, Schwarz JM, Schuelke M, Seelow D|date=2011|title=Systematic comparison of three methods for fragmentation of long-range PCR products for next generation sequencing|journal=PLOS ONE|volume=6|issue=11|pages=e28240|bibcode=2011PLoSO...628240K|doi=10.1371/journal.pone.0028240|pmc=3227650|pmid=22140562|doi-access=free}}</ref> The fragmentation method is a key aspect of sequencing library construction. [[DNA fragmentation|Fragmentation]] may be achieved by [[hydrolysis|chemical hydrolysis]], [[Atomizer nozzle|nebulisation]], [[sonication]], or [[Reverse transcriptase|reverse transcription]] with [[DNA sequencing#Chain-termination methods|chain-terminating nucleotides]].<ref name="#22140562" /> Alternatively, fragmentation and cDNA tagging may be done simultaneously by using [[Transposase|transposase enzymes]].<ref>{{cite journal | vauthors = Routh A, Head SR, Ordoukhanian P, Johnson JE | title = ClickSeq: Fragmentation-Free Next-Generation Sequencing via Click Ligation of Adaptors to Stochastically Terminated 3'-Azido cDNAs | journal = Journal of Molecular Biology | volume = 427 | issue = 16 | pages = 2610–6 | date = August 2015 | pmid = 26116762 | doi = 10.1016/j.jmb.2015.06.011 | pmc = 4523409 }}</ref>
During preparation for sequencing, cDNA copies of transcripts may be amplified by [[Polymerase chain reaction|PCR]] to enrich for fragments that contain the expected 5’ and 3’ adapter sequences.<ref name="#27156886">{{cite journal | vauthors = Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I | title = The impact of amplification on differential expression analyses by RNA-seq | journal = Scientific Reports | volume = 6 |
Once the transcript molecules have been prepared they can be sequenced in just one direction (single-end) or both directions (paired-end). A single-end sequence is usually quicker to produce, cheaper than paired-end sequencing and sufficient for quantification of gene expression levels. Paired-end sequencing produces more robust alignments/assemblies, which is beneficial for gene annotation and transcript [[Protein isoform|isoform]] discovery.<ref name="#19015660" /> Strand-specific RNA-Seq methods preserve the [[Directionality (molecular biology)|strand]] information of a sequenced transcript.<ref name="#20711195">{{cite journal | vauthors = Levin JZ, Yassour M, Adiconis X, Nusbaum C, Thompson DA, Friedman N, Gnirke A, Regev A | title = Comprehensive comparative analysis of strand-specific RNA sequencing methods | journal = Nature Methods | volume = 7 | issue = 9 | pages = 709–15 | date = September 2010 | pmid = 20711195 | pmc = 3005310 | doi = 10.1038/nmeth.1491 }}</ref> Without strand information, reads can be aligned to a gene locus but do not inform in which direction the gene is transcribed. Stranded-RNA-Seq is useful for deciphering transcription for [[Overlapping gene|genes that overlap]] in different directions and to make more robust gene predictions in non-model organisms.<ref name="#20711195" />
{| class="wikitable sortable"
|+ '''Sequencing technology platforms commonly used for RNA-Seq'''<ref name="#22827831">{{cite journal | vauthors = Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, Bertoni A, Swerdlow HP, Gu Y | title = A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers | journal = BMC Genomics | volume = 13 | pages = 341 | date = July 2012 | pmid = 22827831 | pmc = 3431227 | doi = 10.1186/1471-2164-13-341 | doi-access = free }}</ref><ref name="#22829749"/>
!Platform
!Commercial release
Line 148:
|[[ABI Solid Sequencing|SOLiD]]
|2008
|50 bp
|320 Gbp
|99.9%
Line 170:
<small>Legend: NCBI SRA – National center for biotechnology information sequence read archive.</small>
Currently RNA-Seq relies on copying RNA molecules into cDNA molecules prior to sequencing; therefore, the subsequent platforms are the same for transcriptomic and genomic data. Consequently, the development of DNA sequencing technologies has been a defining feature of RNA-Seq.<ref name="#22829749">{{cite journal | vauthors = Liu L, Li Y, Li S, Hu N, He Y, Pong R, Lin D, Lu L, Law M | title = Comparison of next-generation sequencing systems | journal = Journal of Biomedicine & Biotechnology | volume = 2012 | pages = 251364 | date = 2012 | pmid = 22829749 | pmc = 3398667 | doi = 10.1155/2012/251364 | doi-access = free }}</ref><ref name="#22522955">{{cite journal | vauthors = Loman NJ, Misra RV, Dallman TJ, Constantinidou C, Gharbia SE, Wain J, Pallen MJ | title = Performance comparison of benchtop high-throughput sequencing platforms | journal = Nature Biotechnology | volume = 30 | issue = 5 | pages = 434–9 | date = May 2012 | pmid = 22522955 | doi = 10.1038/nbt.2198 | s2cid = 5300923 }}</ref><ref name="#27184599">{{cite journal | vauthors = Goodwin S, McPherson JD, McCombie WR | title = Coming of age: ten years of next-generation sequencing technologies | journal = Nature Reviews Genetics | volume = 17 | issue = 6 | pages = 333–51 | date = May 2016 | pmid = 27184599 | doi = 10.1038/nrg.2016.49 | s2cid = 8295541 | pmc = 10373632 }}</ref> Direct sequencing of RNA using [[nanopore sequencing]] represents a current state-of-the-art RNA-Seq technique.<ref>{{cite journal | vauthors = Garalde DR, Snell EA, Jachimowicz D, Sipos B, Lloyd JH, Bruce M, Pantic N, Admassu T, James P, Warland A, Jordan M, Ciccone J, Serra S, Keenan J, Martin S, McNeill L, Wallace EJ, Jayasinghe L, Wright C, Blasco J, Young S, Brocklebank D, Juul S, Clarke J, Heron AJ, Turner DJ | title = Highly parallel direct RNA sequencing on an array of nanopores | language = En | journal = Nature Methods | volume = 15 | issue = 3 | pages = 201–206 | date = March 2018 | pmid = 29334379 | doi = 10.1038/nmeth.4577 | s2cid = 3589823 }}</ref><ref name="#26076426">{{cite journal | vauthors = Loman NJ, Quick J, Simpson JT | title = A complete bacterial genome assembled de novo using only nanopore sequencing data | journal = Nature Methods | volume = 12 | issue = 8 | pages = 733–5 | date = August 2015 | pmid = 26076426 | doi = 10.1038/nmeth.3444 | s2cid = 15053702 }}</ref> Nanopore sequencing of RNA can detect [[RNA#Structure|modified bases]] that would be otherwise masked when sequencing cDNA and also eliminates [[DNA replication|amplification]] steps that can otherwise introduce bias.<ref name="#21191423" /><ref name="#19776739">{{cite journal | vauthors = Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, McInerney P, Thompson JF, Bowers J, Jarosz M, Milos PM | title = Direct RNA sequencing | journal = Nature | volume = 461 | issue = 7265 | pages = 814–8 | date = October 2009 | pmid = 19776739 | doi = 10.1038/nature08390 | bibcode = 2009Natur.461..814O | s2cid = 4426760 }}</ref>
The sensitivity and accuracy of an RNA-Seq experiment are dependent on the [[sequencing depth|number of reads]] obtained from each sample.<ref name="#23961961" /><ref name="#26813401" /> A large number of reads are needed to ensure sufficient coverage of the transcriptome, enabling detection of low abundance transcripts. Experimental design is further complicated by sequencing technologies with a limited output range, the variable efficiency of sequence creation, and variable sequence quality. Added to those considerations is that every species has a different [[number of genes]] and therefore requires a tailored sequence yield for an effective transcriptome. Early studies determined suitable thresholds empirically, but as the technology matured suitable coverage was predicted computationally by transcriptome saturation. Somewhat counter-intuitively, the most effective way to improve detection of differential expression in low expression genes is to add more [[Replicate (biology)|biological replicates]] rather than adding more reads.<ref name="#24020486">{{cite journal | vauthors = Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D | title = Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data | journal = Genome Biology | volume = 14 | issue = 9 | pages = R95 | date = 2013 | article-number = 3158 | pmid = 24020486 | pmc = 4054597 | doi = 10.1186/gb-2013-14-9-r95 | doi-access = free }}</ref> The current benchmarks recommended by the [[Encyclopedia of DNA Elements]] (ENCODE) Project are for 70-fold exome coverage for standard RNA-Seq and up to 500-fold exome coverage to detect rare transcripts and isoforms.<ref name=pmid22955616>{{cite journal | title = An integrated encyclopedia of DNA elements in the human genome | journal = Nature | volume = 489 | issue = 7414 | pages = 57–74 | date = September 2012 | pmid = 22955616 | pmc = 3439153 | doi = 10.1038/nature11247 | bibcode = 2012Natur.489...57T | author1 = ENCODE Project Consortium | last2 = Aldred | first2 = Shelley F. | last3 = Collins | first3 = Patrick J. | last4 = Davis | first4 = Carrie A. | last5 = Doyle | first5 = Francis | last6 = Epstein | first6 = Charles B. | last7 = Frietze | first7 = Seth | last8 = Harrow | first8 = Jennifer | last9 = Kaul | first9 = Rajinder | last10 = Khatun | first10 = Jainab | last11 = Lajoie | first11 = Bryan R. | last12 = Landt | first12 = Stephen G. | last13 = Lee | first13 = Bum-Kyu | last14 = Pauli | first14 = Florencia | last15 = Rosenbloom | first15 = Kate R. | last16 = Sabo | first16 = Peter | last17 = Safi | first17 = Alexias | last18 = Sanyal | first18 = Amartya | last19 = Shoresh | first19 = Noam | last20 = Simon | first20 = Jeremy M. | last21 = Song | first21 = Lingyun | last22 = Altshuler | first22 = Robert C. | last23 = Birney | first23 = Ewan | last24 = Brown | first24 = James B. | last25 = Cheng | first25 = Chao | last26 = Djebali | first26 = Sarah | last27 = Dong | first27 = Xianjun | last28 = Dunham | first28 = Ian | last29 = Ernst | first29 = Jason | last30 = Furey | first30 = Terrence S. | display-authors = 29 }}</ref><ref name="#26527727">{{cite journal | vauthors = Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, Gabdank I, Narayanan AK, Ho M, Lee BT, Rowe LD, Dreszer TR, Roe G, Podduturi NR, Tanaka F, Hong EL, Cherry JM | display-authors = 6 | title = ENCODE data at the ENCODE portal | journal = Nucleic Acids Research | volume = 44 | issue = D1 | pages = D726–32 | date = January 2016 | pmid = 26527727 | pmc = 4702836 | doi = 10.1093/nar/gkv1160 }}</ref><ref>{{Cite web|url=http://www.encodeproject.org|title=ENCODE: Encyclopedia of DNA Elements|website=encodeproject.org}}</ref>
== Data analysis ==
Transcriptomics methods are highly parallel and require significant computation to produce meaningful data for both microarray and RNA-Seq experiments.<ref name="Thind">{{cite journal | vauthors = Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B| title = Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology | journal = Briefings in Bioinformatics | volume = 22 | issue = 6 | date = Nov 2021 | pmid = 34329375 | doi = 10.1093/bib/bbab259}}</ref><ref name="#25605792" /><ref name="#19910308" /><ref name="#25633503" /><ref>{{Cite book|title=Bioinformatics and Computational Biology Solutions Using R and Bioconductor|last=Smyth|first=G. K.|date=2005|publisher=Springer, New York, NY|isbn=9780387251462|series=Statistics for Biology and Health|pages=397–420|language=en|doi=10.1007/0-387-29362-0_23|chapter = Limma: Linear Models for Microarray Data|citeseerx = 10.1.1.361.8519}}</ref> Microarray data is recorded as [[Image resolution|high-resolution]] images, requiring [[Feature detection (computer vision)|feature detection]] and spectral analysis.<ref>{{Cite book|title=Microarray Technology in Practice.|last=Steve.|first=Russell|date=2008|publisher=Elsevier|others=Meadows, Lisa A.|isbn=9780080919768|___location=Burlington|oclc=437246554}}</ref> Microarray raw image files are each about 750 MB in size, while the processed intensities are around 60 MB in size. Multiple short probes matching a single transcript can reveal details about the [[intron]]-[[exon]] structure, requiring statistical models to determine the authenticity of the resulting signal. RNA-Seq studies produce billions of short DNA sequences, which must be aligned to [[reference genome]]s composed of millions to billions of base pairs. [[De novo transcriptome assembly|''De novo'' assembly of reads]] within a dataset requires the construction of highly complex [[sequence graph]]s.<ref name="#23845962" /> RNA-Seq operations are highly repetitious and benefit from [[Parallel computing|parallelised computation]] but modern algorithms mean consumer computing hardware is sufficient for simple transcriptomics experiments that do not require ''de novo'' assembly of reads.<ref name="Pertea_2015" /> A human transcriptome could be accurately captured using RNA-Seq with 30 million 100 bp sequences per sample.<ref name="#23961961">{{cite journal | vauthors = Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP | title = Calculating sample size estimates for RNA sequencing data | journal = Journal of Computational Biology | volume = 20 | issue = 12 | pages = 970–8 | date = December 2013 | pmid = 23961961 | pmc = 3842884 | doi = 10.1089/cmb.2012.0283 }}</ref><ref name="#26813401">{{cite journal | vauthors = Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A | title = A survey of best practices for RNA-seq data analysis | journal = Genome Biology | volume = 17 |
=== Image processing ===
[[File:
Microarray [[image processing]] must correctly identify the [[regular grid]] of features within an image and independently quantify the fluorescence [[Luminous intensity|intensity]] for each feature. [[Visual artefact|Image artefacts]] must be additionally identified and removed from the overall analysis. Fluorescence intensities directly indicate the abundance of each sequence, since the sequence of each probe on the array is already known.<ref name="PetrovShams2004">{{cite journal | year=2004|title=Microarray Image Processing and Quality Control|journal=The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology|volume=38|issue=3|pages=211–226|doi=10.1023/B:VLSI.0000042488.08307.ad|last1=Petrov|first1=Anton |last2=Shams|first2=Soheil |bibcode=2004JSPSy..38..211P |s2cid=31598448| name-list-style = vanc }}</ref>
The first steps of RNA-seq also include similar image processing; however, conversion of images to sequence data is typically handled automatically by the instrument software. The Illumina sequencing-by-synthesis method results in an array of clusters distributed over the surface of a flow cell.<ref>{{cite book |title=High-Throughput Next Generation Sequencing|volume=733|last1=Kwon|first1=Young Min|last2=Ricke|first2=Steven | name-list-style = vanc |publisher=SpringerLink|year=2011|isbn=978-1-61779-088-1|doi=10.1007/978-1-61779-089-8|series=Methods in Molecular Biology|s2cid=3684245}}</ref> The flow cell is imaged up to four times during each sequencing cycle, with tens to hundreds of cycles in total. Flow cell clusters are analogous to microarray spots and must be correctly identified during the early stages of the sequencing process. In [[Roche Diagnostics|Roche]]’s [[pyrosequencing]] method, the intensity of emitted light determines the number of consecutive nucleotides in a homopolymer repeat. There are many variants on these methods, each with a different error profile for the resulting data.<ref name="#21576222">{{cite journal | vauthors = Nakamura K, Oshima T, Morimoto T, Ikeda S, Yoshikawa H, Shiwa Y, Ishikawa S, Linak MC, Hirai A, Takahashi H, Altaf-Ul-Amin M, Ogasawara N, Kanaya S | title = Sequence-specific error profile of Illumina sequencers | journal = Nucleic Acids Research | volume = 39 | issue = 13 | pages = e90 | date = July 2011 | pmid = 21576222 | pmc = 3141275 | doi = 10.1093/nar/gkr344 }}</ref>
=== RNA-Seq data analysis ===
RNA-Seq experiments generate a large volume of raw sequence reads which have to be processed to yield useful information. Data analysis usually requires a combination of [[List of open-source bioinformatics software|bioinformatics software]] tools (see also [[List of RNA-Seq bioinformatics tools]]) that vary according to the experimental design and goals. The process can be broken down into four stages: quality control, alignment, quantification, and differential expression.<ref name="#23481128">{{cite journal | vauthors = Van Verk MC, Hickman R, Pieterse CM, Van Wees SC | title = RNA-Seq: revelation of the messengers | journal = Trends in Plant Science | volume = 18 | issue = 4 | pages = 175–9 | date = April 2013 | pmid = 23481128 | doi = 10.1016/j.tplants.2013.02.001 | bibcode = 2013TPS....18..175V | hdl = 1874/309456 | s2cid = 205453732 | hdl-access = free }}</ref> Most popular RNA-Seq programs are run from a [[command-line interface]], either in a [[Unix]] environment or within the [[R (programming language)|R]]/[[Bioconductor]] statistical environment.<ref name="#25633503">{{cite journal | vauthors = Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M | display-authors = 6 | title = Orchestrating high-throughput genomic analysis with Bioconductor | journal = Nature Methods | volume = 12 | issue = 2 | pages = 115–21 | date = February 2015 | pmid = 25633503 | pmc = 4509590 | doi = 10.1038/nmeth.3252 }}</ref>
==== Quality control ====
Sequence reads are not perfect, so the accuracy of each base in the sequence needs to be estimated for downstream analyses. Raw data is examined to ensure: quality scores for base calls are high, the GC content matches the expected distribution, short sequence motifs ([[k-mers]]) are not over-represented, and the read duplication rate is acceptably low.<ref name="#26813401" /> Several software options exist for sequence quality analysis, including FastQC and FaQCs.<ref>{{Cite web|url=http://www.bioinformatics.babraham.ac.uk/projects/fastqc/|title=FastQC: A Quality Control tool for High Throughput Sequence Data|publisher=Babraham Bioinformatics|date=2010|access-date=2017-05-23 | vauthors = Andrews S }}</ref><ref name="#25408143">{{cite journal | vauthors = Lo CC, Chain PS | title = Rapid evaluation and quality control of next generation sequencing data with FaQCs | journal = BMC Bioinformatics | volume = 15 |
==== Alignment ====
In order to link sequence read abundance to the expression of a particular gene, transcript sequences are [[Sequence alignment|aligned]] to a reference genome or [[De novo transcriptome assembly|''de novo'' aligned]] to one another if no reference is available.<ref name="#23222703" /><ref name="#24532719" /><ref>{{Cite journal|last1=Siadjeu|first1=Christian|last2=Mayland-Quellhorst|first2=Eike|last3=Pande|first3=Shruti|last4=Laubinger|first4=Sascha|last5=Albach|first5=Dirk C.|date=2021|title=Transcriptome Sequence Reveals Candidate Genes Involving in the Post-Harvest Hardening of Trifoliate Yam Dioscorea dumetorum|journal=Plants|language=en|volume=10|issue=4|pages=787|doi=10.3390/plants10040787|pmid=33923758|pmc=8074181|bibcode=2021Plnts..10..787S |doi-access=free}}</ref> The key challenges for [[List of sequence alignment software|alignment software]] include sufficient speed to permit billions of short sequences to be aligned in a meaningful timeframe, flexibility to recognise and deal with intron splicing of eukaryotic mRNA, and correct assignment of reads that map to multiple locations. Software advances have greatly addressed these issues, and increases in sequencing read length reduce the chance of ambiguous read alignments. A list of currently available high-throughput sequence aligners is maintained by the [[European Bioinformatics Institute|EBI]].<ref>HTS Mappers. http://www.ebi.ac.uk/~nf/hts_mappers/</ref><ref name="#23060614">{{cite journal | vauthors = Fonseca NA, Rung J, Brazma A, Marioni JC | title = Tools for mapping high-throughput sequencing data | journal = Bioinformatics | volume = 28 | issue = 24 | pages = 3169–77 | date = December 2012 | pmid = 23060614 | doi = 10.1093/bioinformatics/bts605 | doi-access = free }}</ref>
Alignment of [[Primary transcript|primary transcript mRNA]] sequences derived from [[eukaryote]]s to a reference genome requires specialised handling of [[intron]] sequences, which are absent from mature mRNA.<ref>{{cite journal | vauthors = Trapnell C, Pachter L, Salzberg SL | title = TopHat: discovering splice junctions with RNA-Seq | journal = Bioinformatics | volume = 25 | issue = 9 | pages = 1105–11 | date = May 2009 | pmid = 19289445 | doi = 10.1093/bioinformatics/btp120 | pmc = 2672628 }}</ref> Short read aligners perform an additional round of alignments specifically designed to identify [[splice junction]]s, informed by canonical splice site sequences and known intron splice site information. Identification of intron splice junctions prevents reads from being misaligned across splice junctions or erroneously discarded, allowing more reads to be aligned to the reference genome and improving the accuracy of gene expression estimates. Since [[Regulation of gene expression|gene regulation]] may occur at the [[Alternative splicing|mRNA isoform]] level, splice-aware alignments also permit detection of isoform abundance changes that would otherwise be lost in a bulked analysis.<ref name="#20436464">{{cite journal | vauthors = Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L | title = Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation | journal = Nature Biotechnology | volume = 28 | issue = 5 | pages = 511–5 | date = May 2010 | pmid = 20436464 | pmc = 3146043 | doi = 10.1038/nbt.1621 }}</ref>
''De novo'' assembly can be used to align reads to one another to construct full-length transcript sequences without use of a reference genome.<ref name="#20211242">{{cite journal | vauthors = Miller JR, Koren S, Sutton G | title = Assembly algorithms for next-generation sequencing data | journal = Genomics | volume = 95 | issue = 6 | pages = 315–27 | date = June 2010 | pmid = 20211242 | pmc = 2874646 | doi = 10.1016/j.ygeno.2010.03.001 }}</ref> Challenges particular to ''de novo'' assembly include larger computational requirements compared to a reference-based transcriptome, additional validation of gene variants or fragments, and additional annotation of assembled transcripts. The first metrics used to describe transcriptome assemblies, such as [[N50, L50, and related statistics|N50]], have been shown to be misleading<ref name="#23837739">{{cite journal | vauthors = O'Neil ST, Emrich SJ | title = Assessing De Novo transcriptome assembly metrics for consistency and utility | journal = BMC Genomics | volume = 14 | pages = 465 | date = July 2013 | pmid = 23837739 | pmc = 3733778 | doi = 10.1186/1471-2164-14-465 | doi-access = free }}</ref> and improved evaluation methods are now available.<ref name="#27252236">{{cite journal | vauthors = Smith-Unna R, Boursnell C, Patro R, Hibberd JM, Kelly S | title = TransRate: reference-free quality assessment of de novo transcriptome assemblies | journal = Genome Research | volume = 26 | issue = 8 | pages = 1134–44 | date = August 2016 | pmid = 27252236 | pmc = 4971766 | doi = 10.1101/gr.196469.115 }}</ref><ref name="#25608678">{{cite journal | vauthors = Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, Dewey CN | title = Evaluation of de novo transcriptome assemblies from RNA-Seq data | journal = Genome Biology | volume = 15 | issue = 12 |
{| class="wikitable sortable"
Line 207:
|2008
|2011
|Low, single-threaded, high RAM requirement
|The original short read assembler. It is now largely superseded.
|-
Line 240:
|Specialised to accommodate the homo-polymer sequencing errors typical of Roche 454 sequencers.
|-
|CLC genomics workbench<ref name="#20950480">{{cite journal | vauthors = Kumar S, Blaxter ML | title = Comparing de novo assemblers for 454 transcriptome data | journal = BMC Genomics | volume = 11 |
|2008
|2014
Line 252:
|Used for transcriptomics experiments on single cells.
|-
|RSEM<ref>{{cite journal | vauthors = Li B, Dewey CN | title = RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome | journal = BMC Bioinformatics | volume = 12 |
|2011
|2017
Line 267:
==== Quantification ====
[[File:
Quantification of sequence alignments may be performed at the gene, exon, or transcript level.<ref name="Thind"/><ref name="#24020486" /> Typical outputs include a table of read counts for each feature supplied to the software; for example, for genes in a [[general feature format]] file. Gene and exon read counts may be calculated quite easily using HTSeq, for example.<ref name="#25260700">{{cite journal | vauthors = Anders S, Pyl PT, Huber W | title = HTSeq—a Python framework to work with high-throughput sequencing data | journal = Bioinformatics | volume = 31 | issue = 2 | pages = 166–9 | date = January 2015 | pmid = 25260700 | pmc = 4287950 | doi = 10.1093/bioinformatics/btu638 }}</ref> Quantitation at the transcript level is more complicated and requires probabilistic methods to estimate transcript isoform abundance from short read information; for example, using cufflinks software.<ref name="#20436464" /> Reads that align equally well to multiple locations must be identified and either removed, aligned to one of the possible locations, or aligned to the most probable ___location.
Some quantification methods can circumvent the need for an exact alignment of a read to a reference sequence altogether. The kallisto software method combines pseudoalignment and quantification into a single step that runs 2 orders of magnitude faster than contemporary methods such as those used by tophat/cufflinks software, with less computational burden.<ref name="#27043002">{{cite journal | vauthors = Bray NL, Pimentel H, Melsted P, Pachter L | title = Near-optimal probabilistic RNA-seq quantification | journal = Nature Biotechnology | volume = 34 | issue = 5 | pages = 525–7 | date = May 2016 | pmid = 27043002 | doi = 10.1038/nbt.3519 | s2cid = 205282743 | url = https://resolver.caltech.edu/CaltechAUTHORS:20190506-110012992 }}</ref>
==== Differential expression ====
Line 289:
|Any count-based genomic data
|-
|DEseq2<ref name="#25516281">{{cite journal | vauthors = Love MI, Huber W, Anders S | title = Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 | journal = Genome Biology | volume = 15 | issue = 12 |
|R/Bioconductor
|Flexible data types, low replication
Line 304:
=== Validation ===
Transcriptomic analyses may be validated using an independent technique, for example, [[Real-time polymerase chain reaction|quantitative PCR]] (qPCR), which is recognisable and statistically assessable.<ref name="#21498551">{{cite journal | vauthors = Fang Z, Cui X | title = Design and validation issues in RNA-seq experiments | journal = Briefings in Bioinformatics | volume = 12 | issue = 3 | pages = 280–7 | date = May 2011 | pmid = 21498551 | doi = 10.1093/bib/bbr004 | doi-access = free }}</ref> Gene expression is measured against defined standards both for the gene of interest and [[Scientific control|control]] genes. The measurement by qPCR is similar to that obtained by RNA-Seq wherein a value can be calculated for the concentration of a target region in a given sample. qPCR is, however, restricted to [[amplicon]]s smaller than 300 bp, usually toward the 3’ end of the coding region, avoiding the [[3’UTR]].<ref name="#20011106">{{cite journal | vauthors = Ramsköld D, Wang ET, Burge CB, Sandberg R | title = An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data | journal = PLOS Computational Biology | volume = 5 | issue = 12 | pages = e1000598 | date = December 2009 | pmid = 20011106 | pmc = 2781110 | doi = 10.1371/journal.pcbi.1000598 | bibcode = 2009PLSCB...5E0598R | doi-access = free }}</ref> If validation of transcript isoforms is required, an inspection of RNA-Seq read alignments should indicate where qPCR [[Primer (molecular biology)#Uses of synthetic primers|primers]] might be placed for maximum discrimination. The measurement of multiple control genes along with the genes of interest produces a stable reference within a biological context.<ref name="#12184808">{{cite journal | vauthors = Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F | title = Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes | journal = Genome Biology | volume = 3 | issue = 7 | pages = RESEARCH0034 | date = June 2002 | article-number = research0034.1 | pmid = 12184808 | pmc = 126239 | doi=10.1186/gb-2002-3-7-research0034 | doi-access = free }}</ref> qPCR validation of RNA-Seq data has generally shown that different RNA-Seq methods are highly correlated.<ref name="#18451266" /><ref name="#19056941">{{cite journal | vauthors = Core LJ, Waterfall JJ, Lis JT | title = Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters | journal = Science | volume = 322 | issue = 5909 | pages = 1845–8 | date = December 2008 | pmid = 19056941 | pmc = 2833333 | doi = 10.1126/science.1162228 | bibcode = 2008Sci...322.1845C }}</ref><ref name="#20368969">{{cite journal | vauthors = Camarena L, Bruno V, Euskirchen G, Poggio S, Snyder M | title = Molecular mechanisms of ethanol-induced pathogenesis revealed by RNA-sequencing | journal = PLOS Pathogens | volume = 6 | issue = 4 | pages = e1000834 | date = April 2010 | pmid = 20368969 | pmc = 2848557 | doi = 10.1371/journal.ppat.1000834 | doi-access = free }}</ref>
Functional validation of key genes is an important consideration for post transcriptome planning. Observed gene expression patterns may be functionally linked to a [[phenotype]] by an independent [[Gene knockdown|knock-down]]/[[Synthetic rescue|rescue]] study in the organism of interest.<ref name="Govind_2009">{{cite journal | vauthors = Govind G, Harshavardhan VT, ThammeGowda HV, Patricia JK, Kalaiarasi PJ, Dhanalakshmi R, Iyer DR, Senthil Kumar M, Muthappa SK, Sreenivasulu N, Nese S, Udayakumar M, Makarla UK | title = Identification and functional validation of a unique set of drought induced genes preferentially expressed in response to gradual water stress in peanut | journal = Molecular Genetics and Genomics | volume = 281 | issue = 6 | pages = 591–605 | date = June 2009 | pmid = 19224247 | pmc = 2757612 | doi = 10.1007/s00438-009-0432-z }}</ref>
Line 316:
=== Human and pathogen transcriptomes ===
RNA-Seq of human [[pathogen]]s has become an established method for quantifying gene expression changes, identifying novel [[virulence factors]], predicting [[Antimicrobial resistance|antibiotic resistance]], and unveiling [[Host–pathogen interaction|host-pathogen immune interactions]].<ref name="#18284925">{{cite journal | vauthors = Wu HJ, Wang AH, Jennings MP | title = Discovery of virulence factors of pathogenic bacteria | journal = Current Opinion in Chemical Biology | volume = 12 | issue = 1 | pages = 93–101 | date = February 2008 | pmid = 18284925 | doi = 10.1016/j.cbpa.2008.01.023 | url = http://ntur.lib.ntu.edu.tw/bitstream/246246/219228/-1/26.pdf }}</ref><ref name="#25517437">{{cite journal | vauthors = Suzuki S, Horinouchi T, Furusawa C | title = Prediction of antibiotic resistance by gene expression profiles | journal = Nature Communications | volume = 5 |
Transcriptomic analysis has predominantly focused on either the host or the pathogen. Dual RNA-Seq has been applied to simultaneously profile RNA expression in both the pathogen and host throughout the infection process. This technique enables the study of the dynamic response and interspecies [[gene regulatory network]]s in both interaction partners from initial contact through to invasion and the final persistence of the pathogen or clearance by the host immune system.<ref name="#22890146">{{cite journal | vauthors = Westermann AJ, Gorski SA, Vogel J | title = Dual RNA-seq of pathogen and host | journal = Nature Reviews. Microbiology | volume = 10 | issue = 9 | pages = 618–30 | date = September 2012 | pmid = 22890146 | doi = 10.1038/nrmicro2852 | s2cid = 205498287 | url = https://opus.bibliothek.uni-wuerzburg.de/files/11246/Dissertation_Westermann_Alexander.pdf }}</ref><ref name="#25914674">{{cite journal | vauthors = Durmuş S, Çakır T, Özgür A, Guthke R | title = A review on computational systems biology of pathogen-host interactions | journal = Frontiers in Microbiology | volume = 6 | pages = 235 | date = 2015 | pmid = 25914674 | pmc = 4391036 | doi = 10.3389/fmicb.2015.00235 | doi-access = free }}</ref>
=== Responses to environment ===
Transcriptomics allows identification of genes and [[Metabolic pathways|pathways]] that respond to and counteract [[Biotic stress|biotic]] and [[Abiotic stress|abiotic environmental stresses.]]<ref name="#26759178" /><ref name="Govind_2009" /> The non-targeted nature of transcriptomics allows the identification of novel transcriptional networks in complex systems. For example, comparative analysis of a range of [[Cicer arietinum|chickpea]] lines at different developmental stages identified distinct transcriptional profiles associated with [[drought]] and [[salinity]] stresses, including identifying the role of [[Alternative splicing|transcript isoforms]] of [[Apetala 2|AP2]]-[[Ethylene-responsive element binding protein|EREBP]].<ref name="#26759178">{{cite journal | vauthors = Garg R, Shankar R, Thakkar B, Kudapa H, Krishnamurthy L, Mantri N, Varshney RK, Bhatia S, Jain M | title = Transcriptome analyses reveal genotype- and developmental stage-specific molecular responses to drought and salinity stresses in chickpea | journal = Scientific Reports | volume = 6 |
Transcriptomic profiling also provides crucial information on mechanisms of [[drug resistance]]. Analysis of over 1000 isolates of ''[[Plasmodium falciparum]]'', a virulent parasite responsible for malaria in humans,<ref name="Rich et al">{{cite journal | vauthors = Rich SM, Leendertz FH, Xu G, LeBreton M, Djoko CF, Aminake MN, Takang EE, Diffo JL, Pike BL, Rosenthal BM, Formenty P, Boesch C, Ayala FJ, Wolfe ND | title = The origin of malignant malaria | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 106 | issue = 35 | pages = 14902–7 | date = September 2009 | pmid = 19666593 | pmc = 2720412 | doi = 10.1073/pnas.0907740106 | bibcode = 2009PNAS..10614902R | doi-access = free }}</ref> identified that upregulation of the [[unfolded protein response]] and slower progression through the early stages of the asexual intraerythrocytic [[Plasmodium falciparum#
The use of transcriptomics is also important to investigate responses in the marine environment.<ref name=":0">{{Cite journal |last1=Page |first1=Tessa M. |last2=Lawley |first2=Jonathan W. |date=2022 |title=The Next Generation Is Here: A Review of Transcriptomic Approaches in Marine Ecology |journal=Frontiers in Marine Science |volume=9 |doi=10.3389/fmars.2022.757921 |bibcode=2022FrMaS...957921P |issn=2296-7745|doi-access=free |hdl=10072/428702 |hdl-access=free }}</ref> In marine ecology, "[[Stress (biology)|stress]]" and "[[adaptation]]" have been among the most common research topics, especially related to anthropogenic stress, such as [[global change]] and [[pollution]].<ref name=":0" /> Most of the studies in this area have been done in [[animal]]s, although [[invertebrate]]s have been underrepresented.<ref name=":0" /> One issue still is a deficiency in functional genetic studies, which hamper [[gene annotation]]s, especially for non-model species, and can lead to vague conclusions on the effects of responses studied.<ref name=":0" />
=== Gene function annotation ===▼
All transcriptomic techniques have been particularly useful in [[Gene annotation|identifying the functions of genes]] and identifying those responsible for particular phenotypes. Transcriptomics of ''Arabidopsis'' [[ecotype]]s that [[Hyperaccumulator|hyperaccumulate metals]] correlated genes involved in [[Bioinorganic chemistry#Metal ion transport and storage|metal uptake]], tolerance, and [[homeostasis]] with the phenotype.<ref name="#19192189">{{cite journal | vauthors = Verbruggen N, Hermans C, Schat H | title = Molecular mechanisms of metal hyperaccumulation in plants | journal = The New Phytologist | volume = 181 | issue = 4 | pages = 759–76 | date = March 2009 | pmid = 19192189 | doi = 10.1111/j.1469-8137.2008.02748.x | url = https://dipot.ulb.ac.be/dspace/bitstream/2013/58126/3/58126.pdf }}</ref> Integration of RNA-Seq datasets across different tissues has been used to improve annotation of gene functions in commercially important organisms (e.g. [[Cucumis sativus|cucumber]])<ref name="#22047402">{{cite journal | vauthors = Li Z, Zhang Z, Yan P, Huang S, Fei Z, Lin K | title = RNA-Seq improves annotation of protein-coding genes in the cucumber genome | journal = BMC Genomics | volume = 12 | pages = 540 | date = November 2011 | pmid = 22047402 | pmc = 3219749 | doi = 10.1186/1471-2164-12-540 }}</ref> or threatened species (e.g. [[koala]]).<ref name="#25214207">{{cite journal | vauthors = Hobbs M, Pavasovic A, King AG, Prentis PJ, Eldridge MD, Chen Z, Colgan DJ, Polkinghorne A, Wilkins MR, Flanagan C, Gillett A, Hanger J, Johnson RN, Timms P | title = A transcriptome resource for the koala (Phascolarctos cinereus): insights into koala retrovirus transcription and sequence diversity | journal = BMC Genomics | volume = 15 | pages = 786 | date = September 2014 | pmid = 25214207 | pmc = 4247155 | doi = 10.1186/1471-2164-15-786 }}</ref>▼
▲=== Gene function annotation ===
Assembly of RNA-Seq reads is not dependent on a [[reference genome]]<ref name="#21572440">{{cite journal | vauthors = Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A | title = Full-length transcriptome assembly from RNA-Seq data without a reference genome | journal = Nature Biotechnology | volume = 29 | issue = 7 | pages = 644–52 | date = May 2011 | pmid = 21572440 | pmc = 3571712 | doi = 10.1038/nbt.1883 }}</ref> and so is ideal for gene expression studies of non-model organisms with non-existing or poorly developed genomic resources. For example, a database of SNPs used in [[Pseudotsuga menziesii|Douglas fir]] breeding programs was created by ''de novo'' transcriptome analysis in the absence of a [[Genome sequencing|sequenced genome]].<ref name="#23445355">{{cite journal | vauthors = Howe GT, Yu J, Knaus B, Cronn R, Kolpak S, Dolan P, Lorenz WW, Dean JF | title = A SNP resource for Douglas-fir: de novo transcriptome assembly and SNP detection and validation | journal = BMC Genomics | volume = 14 | pages = 137 | date = February 2013 | pmid = 23445355 | pmc = 3673906 | doi = 10.1186/1471-2164-14-137 }}</ref> Similarly, genes that function in the development of cardiac, muscle, and nervous tissue in lobsters were identified by comparing the transcriptomes of the various tissue types without use of a genome sequence.<ref name="#26772543">{{cite journal | vauthors = McGrath LL, Vollmer SV, Kaluziak ST, Ayers J | title = De novo transcriptome assembly for the lobster Homarus americanus and characterization of differential gene expression across nervous system tissues | journal = BMC Genomics | volume = 17 | pages = 63 | date = January 2016 | pmid = 26772543 | pmc = 4715275 | doi = 10.1186/s12864-016-2373-3 }}</ref> RNA-Seq can also be used to identify previously unknown [[protein coding region]]s in existing sequenced genomes.▼
▲All transcriptomic techniques have been particularly useful in [[Gene annotation|identifying the functions of genes]] and identifying those responsible for particular phenotypes. Transcriptomics of ''Arabidopsis'' [[ecotype]]s that [[Hyperaccumulator|hyperaccumulate metals]] correlated genes involved in [[Bioinorganic chemistry#Metal ion transport and storage|metal uptake]], tolerance, and [[homeostasis]] with the phenotype.<ref name="#19192189">{{cite journal | vauthors = Verbruggen N, Hermans C, Schat H | title = Molecular mechanisms of metal hyperaccumulation in plants | journal = The New Phytologist | volume = 181 | issue = 4 | pages = 759–76 | date = March 2009 | pmid = 19192189 | doi = 10.1111/j.1469-8137.2008.02748.x | bibcode = 2009NewPh.181..759V | url = https://dipot.ulb.ac.be/dspace/bitstream/2013/58126/3/58126.pdf }}</ref> Integration of RNA-Seq datasets across different tissues has been used to improve annotation of gene functions in commercially important organisms (e.g. [[Cucumis sativus|cucumber]])<ref name="#22047402">{{cite journal | vauthors = Li Z, Zhang Z, Yan P, Huang S, Fei Z, Lin K | title = RNA-Seq improves annotation of protein-coding genes in the cucumber genome | journal = BMC Genomics | volume = 12 |
▲Assembly of RNA-Seq reads is not dependent on a [[reference genome]]<ref name="#21572440">{{cite journal | vauthors = Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A | title = Full-length transcriptome assembly from RNA-Seq data without a reference genome | journal = Nature Biotechnology | volume = 29 | issue = 7 | pages = 644–52 | date = May 2011 | pmid = 21572440 | pmc = 3571712 | doi = 10.1038/nbt.1883 }}</ref> and so is ideal for gene expression studies of non-model organisms with non-existing or poorly developed genomic resources. For example, a database of SNPs used in [[Pseudotsuga menziesii|Douglas fir]] breeding programs was created by ''de novo'' transcriptome analysis in the absence of a [[Genome sequencing|sequenced genome]].<ref name="#23445355">{{cite journal | vauthors = Howe GT, Yu J, Knaus B, Cronn R, Kolpak S, Dolan P, Lorenz WW, Dean JF | title = A SNP resource for Douglas-fir: de novo transcriptome assembly and SNP detection and validation | journal = BMC Genomics | volume = 14 | pages = 137 | date = February 2013 | pmid = 23445355 | pmc = 3673906 | doi = 10.1186/1471-2164-14-137 | doi-access = free }}</ref> Similarly, genes that function in the development of cardiac, muscle, and nervous tissue in lobsters were identified by comparing the transcriptomes of the various tissue types without use of a genome sequence.<ref name="#26772543">{{cite journal | vauthors = McGrath LL, Vollmer SV, Kaluziak ST, Ayers J | title = De novo transcriptome assembly for the lobster Homarus americanus and characterization of differential gene expression across nervous system tissues | journal = BMC Genomics | volume = 17 |
=== Non-coding RNA ===
Transcriptomics is most commonly applied to the mRNA content of the cell. However, the same techniques are equally applicable to non-coding RNAs (ncRNAs) that are not translated into a protein, but instead have direct functions (e.g. roles in [[Translation (genetics)|protein translation]], [[DNA replication]], [[RNA splicing]], and [[transcriptional regulation]]).<ref name="#1883196">{{cite journal | vauthors = Noller HF | title = Ribosomal RNA and translation | journal = Annual Review of Biochemistry | volume = 60 | pages = 191–227 | date = 1991 | pmid = 1883196 | doi = 10.1146/annurev.bi.60.070191.001203 }}</ref><ref name="#16943439">{{cite journal | vauthors = Christov CP, Gardiner TJ, Szüts D, Krude T | title = Functional requirement of noncoding Y RNAs for human chromosomal DNA replication | journal = Molecular and Cellular Biology | volume = 26 | issue = 18 | pages = 6993–7004 | date = September 2006 | pmid = 16943439 | pmc = 1592862 | doi = 10.1128/MCB.01060-06 }}</ref><ref name="#16357227">{{cite journal | vauthors = Kishore S, Stamm S | title = The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C | journal = Science | volume = 311 | issue = 5758 | pages = 230–2 | date = January 2006 | pmid = 16357227 | doi = 10.1126/science.1118265 | bibcode = 2006Sci...311..230K | s2cid = 44527461 | doi-access = free }}</ref><ref name="#15851066">{{cite journal | vauthors = Hüttenhofer A, Schattner P, Polacek N | title = Non-coding RNAs: hope or hype? | journal = Trends in Genetics | volume = 21 | issue = 5 | pages = 289–97 | date = May 2005 | pmid = 15851066 | doi = 10.1016/j.tig.2005.03.007 }}</ref> Many of these ncRNAs affect disease states, including cancer, cardiovascular, and neurological diseases.<ref name="#22094949">{{cite journal | vauthors = Esteller M | title = Non-coding RNAs in human disease | journal = Nature Reviews Genetics | volume = 12 | issue = 12 | pages = 861–74 | date = November 2011 | pmid = 22094949 | doi = 10.1038/nrg3074 | s2cid = 13036469 }}</ref>
== Transcriptome databases ==
Line 353 ⟶ 352:
|[[European Nucleotide Archive|ENA]]
|Microarray
|Imports datasets from the Gene Expression Omnibus and accepts direct submissions. Processed data and experiment metadata is stored at ArrayExpress, while the raw sequence reads are held at the ENA. Complies with MIAME and MINSEQE standards.<ref name="#11726920" /><ref name="#19484163">{{cite journal | vauthors = Brazma A | title = Minimum Information About a Microarray Experiment (MIAME)--successes, failures, challenges | journal = TheScientificWorldJournal | volume = 9 | pages = 420–3 | date = May 2009 | pmid = 19484163 | pmc = 5823224 | doi = 10.1100/tsw.2009.57 | doi-access = free }}</ref>
|-
|[[Expression Atlas]]<ref name="#26481351">{{cite journal | vauthors = Petryszak R, Keays M, Tang YA, Fonseca NA, Barrera E, Burdett T, Füllgrabe A, Fuentes AM, Jupp S, Koskinen S, Mannion O, Huerta L, Megy K, Snow C, Williams E, Barzine M, Hastings E, Weisser H, Wright J, Jaiswal P, Huber W, Choudhary J, Parkinson HE, Brazma A | title = Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants | journal = Nucleic Acids Research | volume = 44 | issue = D1 | pages = D746–52 | date = January 2016 | pmid = 26481351 | pmc = 4702781 | doi = 10.1093/nar/gkv1045 }}</ref>
Line 382 ⟶ 381:
** [[Proteomics]]
** [[Metabolomics]]
** [[
== References ==
{{Academic peer reviewed|Q33703532|doi-access=free}}
{{reflist}}
Line 393 ⟶ 392:
== Further reading ==
* {{cite journal | vauthors = Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T | title = Transcriptomics technologies | journal = PLOS Computational Biology | volume = 13 | issue = 5 | pages = e1005457 | date = May 2017 | pmid = 28545146 | pmc = 5436640 | doi = 10.1371/journal.pcbi.1005457 | bibcode = 2017PLSCB..13E5457L | doi-access = free }}
* [https://doi.org/10.1016/B978-0-12-809633-8.20163-5 Comparative Transcriptomics Analysis] in [https://www.sciencedirect.com/science/referenceworks/9780128096338 Reference Module in Life Sciences]
* Software used in transcriptomics:
| |||