Transcriptomics technologies
Transcriptomics technologies are the techniques used to study an organism's
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: microarrays, which quantify a set of predetermined sequences, and RNA-Seq, which uses 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 getting bigger and more useful as transcriptomes continue to be collected and shared by researchers. It would be almost impossible to interpret the information contained in a transcriptome without the knowledge of previous experiments.
Measuring the expression of an organism's
History
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 mRNA sequences from the human brain.[2] In 2008, two human transcriptomes, composed of millions of transcript-derived sequences covering 16,000 genes, were published,[3][4] and by 2015 transcriptomes had been published for hundreds of individuals.[5][6] Transcriptomes of different disease states, tissues, or even single cells are now routinely generated.[6][7][8] This explosion in transcriptomics has been driven by the rapid development of new technologies with improved sensitivity and economy.[9][10][11][12]
Before transcriptomics
Studies of individual
Early attempts
The word "transcriptome" was first used in the 1990s.[19][20] 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.[21] 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.[9][22] However, these methods were largely overtaken by high throughput sequencing of entire transcripts, which provided additional information on transcript structure such as splice variants.[9]
Development of contemporary techniques
RNA-Seq | Microarray | |
---|---|---|
Throughput
|
1 day to 1 week per experiment[10] | 1–2 days per experiment[10] |
Input RNA amount | Low ~ 1 ng total RNA[25] | High ~ 1 μg mRNA[26] |
Labour intensity | High (sample preparation and data analysis)[10][23] | Low[10][23] |
Prior knowledge | None required, although a reference genome/transcriptome sequence is useful[23] | Reference genome/transcriptome is required for design of probes[23] |
Quantitation accuracy | ~90% (limited by sequence coverage)[27] | >90% (limited by fluorescence detection accuracy)[27] |
Sequence resolution | RNA-Seq can detect SNPs and splice variants (limited by sequencing accuracy of ~99%)[27] | Specialised arrays can detect mRNA splice variants (limited by probe design and cross-hybridisation)[27] |
Sensitivity | 1 transcript per million (approximate, limited by sequence coverage)[27] | 1 transcript per thousand (approximate, limited by fluorescence detection)[27] |
Dynamic range | 100,000:1 (limited by sequence coverage)[28] | 1,000:1 (limited by fluorescence saturation)[28] |
Technical reproducibility | >99%[29][30] | >99%[31][32] |
The dominant contemporary techniques, microarrays and RNA-Seq, were developed in the mid-1990s and 2000s.[9][33] Microarrays that measure the abundances of a defined set of transcripts via their hybridisation to an array of complementary probes were first published in 1995.[34][35] Microarray technology allowed the assay of thousands of transcripts simultaneously and at a greatly reduced cost per gene and labour saving.[36] Both spotted oligonucleotide arrays and Affymetrix high-density arrays were the method of choice for transcriptional profiling until the late 2000s.[12][33] Over this period, a range of microarrays were produced to cover known genes in 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 detection increased the sensitivity and measurement accuracy for low abundance transcripts.[35][37]
RNA-Seq is accomplished by reverse transcribing RNA in vitro and sequencing the resulting cDNAs.[10] Transcript abundance is derived from the number of counts from each transcript. The technique has therefore been heavily influenced by the development of high-throughput sequencing technologies.[9][11] Massively parallel signature sequencing (MPSS) was an early example based on generating 16–20 bp sequences via a complex series of hybridisations,[38][note 1] and was used in 2004 to validate the expression of ten thousand genes in Arabidopsis thaliana.[39] The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using 454 technology.[40] This was sufficient coverage to quantify relative transcript abundance. RNA-Seq began to increase in popularity after 2008 when new Solexa/Illumina technologies allowed one billion transcript sequences to be recorded.[4][10][41][42] This yield now allows for the quantification and comparison of human transcriptomes.[43]
Data gathering
Generating data on RNA transcripts can be achieved via either of two main principles: sequencing of individual transcripts (ESTs, or RNA-Seq) or hybridisation of transcripts to an ordered array of nucleotide probes (microarrays).[23]
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,
Expressed sequence tags
An
Serial and cap analysis of gene expression (SAGE/CAGE)
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.[21] cDNA is generated from the RNA but is then digested into 11 bp "tag" fragments using restriction enzymes that cut DNA at a specific sequence, and 11 base pairs along from that sequence. These cDNA tags are then 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.[21] 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 differentially expressed in a disease state.[21]
The
SAGE and CAGE methods produce information on more genes than was possible when sequencing single ESTs, but sample preparation and data analysis are typically more labour-intensive.[52]
Microarrays
Principles and advances
Microarrays usually consist of a grid of short nucleotide oligomers, known as "probes", typically arranged on a glass slide.[53] Transcript abundance is determined by hybridisation of fluorescently labelled transcripts to these probes.[54] The fluorescence intensity at each probe location on the array indicates the transcript abundance for that probe sequence.[54] Groups of probes designed to measure the same transcript (i.e., hybridizing a specific transcript in different positions) are usually referred to as "probesets".
Microarrays require some genomic knowledge from the organism of interest, for example, in the form of an annotated genome sequence, or a library of ESTs that can be used to generate the probes for the array.[36]
Methods
Microarrays for transcriptomics typically fall into one of two broad categories: low-density spotted arrays or high-density short probe arrays. Transcript abundance is inferred from the intensity of fluorescence derived from fluorophore-tagged transcripts that bind to the array.[36]
Spotted low-density arrays typically feature picolitre[note 2] drops of a range of purified cDNAs arrayed on the surface of a glass slide.[55] These probes are longer than those of high-density arrays and cannot identify alternative splicing events. Spotted arrays use two different fluorophores to label the test and control samples, and the ratio of fluorescence is used to calculate a relative measure of abundance.[56] High-density arrays use a single fluorescent label, and each sample is hybridised and detected individually.[57] High-density arrays were popularised by the Affymetrix GeneChip array, where each transcript is quantified by several short 25-mer probes that together assay one gene.[58]
NimbleGen arrays were a high-density array produced by a maskless-photochemistry method, which permitted flexible manufacture of arrays in small or large numbers. These arrays had 100,000s of 45 to 85-mer probes and were hybridised with a one-colour labelled sample for expression analysis.[59] Some designs incorporated up to 12 independent arrays per slide.
RNA-Seq
Principles and advances
RNA-Seq refers to the combination of a high-throughput sequencing methodology with computational methods to capture and quantify transcripts present in an RNA extract.[10] 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 deep sampling of the transcriptome with many short fragments from a transcriptome to allow computational reconstruction of the original RNA transcript by aligning reads to a reference genome or to each other (de novo assembly).[9] Both low-abundance and high-abundance RNAs can be quantified in an RNA-Seq experiment (dynamic range of 5 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 allow examination of the transcriptome even at a single-cell resolution when combined with amplification of cDNA.[25][60] Theoretically, there is no upper limit of quantification in RNA-Seq, and background noise is very low for 100 bp reads in non-repetitive regions.[10]
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.[61] Since the first descriptions in 2006 and 2008,[40][62] RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015.[63]
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 transcriptomes are now well described and have even been extended to in situ RNA-Seq where transcriptomes of individual cells are directly interrogated in fixed tissues.[64]
Methods
RNA-Seq was established in concert with the rapid development of a range of high-throughput DNA sequencing technologies.[65] 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.[65]
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
Since mRNAs are longer than the read-lengths of typical high-throughput sequencing methods, transcripts are usually fragmented prior to sequencing.
During preparation for sequencing, cDNA copies of transcripts may be amplified by
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 isoform discovery.[10] Strand-specific RNA-Seq methods preserve the strand information of a sequenced transcript.[76] 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 genes that overlap in different directions and to make more robust gene predictions in non-model organisms.[76]
Platform | Commercial release | Typical read length | Maximum throughput per run | Single read accuracy | RNA-Seq runs deposited in the NCBI SRA (Oct 2016)[79] |
---|---|---|---|---|---|
454 Life Sciences | 2005 | 700 bp | 0.7 Gbp | 99.9% | 3548 |
Illumina | 2006 | 50–300 bp | 900 Gbp | 99.9% | 362903 |
SOLiD | 2008 | 50 bp | 320 Gbp | 99.9% | 7032 |
Ion Torrent | 2010 | 400 bp | 30 Gbp | 98% | 1953 |
PacBio | 2011 | 10,000 bp | 2 Gbp | 87% | 160 |
Legend: NCBI SRA – National center for biotechnology information sequence read archive.
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.[78][80][81] Direct sequencing of RNA using nanopore sequencing represents a current state-of-the-art RNA-Seq technique.[82][83] Nanopore sequencing of RNA can detect modified bases that would be otherwise masked when sequencing cDNA and also eliminates amplification steps that can otherwise introduce bias.[11][84]
The sensitivity and accuracy of an RNA-Seq experiment are dependent on the
Data analysis
Transcriptomics methods are highly parallel and require significant computation to produce meaningful data for both microarray and RNA-Seq experiments.
Image processing
Microarray
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.
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 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.[105] Most popular RNA-Seq programs are run from a command-line interface, either in a Unix environment or within the R/Bioconductor statistical environment.[94]
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 (
Alignment
In order to link sequence read abundance to the expression of a particular gene, transcript sequences are aligned to a reference genome or de novo aligned to one another if no reference is available.[108][109][110] The key challenges for 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 EBI.[111][112]
Alignment of
De novo assembly can be used to align reads to one another to construct full-length transcript sequences without use of a reference genome.[115] 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, have been shown to be misleading[116] and improved evaluation methods are now available.[117][118] Annotation-based metrics are better assessments of assembly completeness, such as contig reciprocal best hit count. Once assembled de novo, the assembly can be used as a reference for subsequent sequence alignment methods and quantitative gene expression analysis.
Software | Released | Last updated | Computational efficiency | Strengths and weaknesses |
---|---|---|---|---|
Velvet-Oases[119][120] | 2008 | 2011 | Low, single-threaded, high RAM requirement | The original short read assembler. It is now largely superseded. |
SOAPdenovo-trans[109] | 2011 | 2014 | Moderate, multi-thread, medium RAM requirement | An early example of a short read assembler. It has been updated for transcriptome assembly. |
Trans-ABySS[121] | 2010 | 2016 | Moderate, multi-thread, medium RAM requirement | Suited to short reads, can handle complex transcriptomes, and an MPI-parallel version is available for computing clusters. |
Trinity[122][97] | 2011 | 2017 | Moderate, multi-thread, medium RAM requirement | Suited to short reads. It can handle complex transcriptomes but is memory intensive. |
miraEST[123] | 1999 | 2016 | Moderate, multi-thread, medium RAM requirement | Can process repetitive sequences, combine different sequencing formats, and a wide range of sequence platforms are accepted. |
Newbler[124] | 2004 | 2012 | Low, single-thread, high RAM requirement | Specialised to accommodate the homo-polymer sequencing errors typical of Roche 454 sequencers. |
CLC genomics workbench[125] | 2008 | 2014 | High, multi-thread, low RAM requirement | Has a graphical user interface, can combine diverse sequencing technologies, has no transcriptome-specific features, and a licence must be purchased before use. |
SPAdes[126] | 2012 | 2017 | High, multi-thread, low RAM requirement | Used for transcriptomics experiments on single cells. |
RSEM[127] | 2011 | 2017 | High, multi-thread, low RAM requirement | Can estimate frequency of alternatively spliced transcripts. User friendly. |
StringTie[98][128] | 2015 | 2019 | High, multi-thread, low RAM requirement | Can use a combination of reference-guided and de novo assembly methods to identify transcripts. |
Legend: RAM – random access memory; MPI – message passing interface; EST – expressed sequence tag.
Quantification
Quantification of sequence alignments may be performed at the gene, exon, or transcript level.[91][87] 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.[130] 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.[114] 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.[131]
Differential expression
Once quantitative counts of each transcript are available, differential gene expression is measured by normalising, modelling, and statistically analysing the data.[108] Most tools will read a table of genes and read counts as their input, but some programs, such as cuffdiff, will accept binary alignment map format read alignments as input. The final outputs of these analyses are gene lists with associated pair-wise tests for differential expression between treatments and the probability estimates of those differences.[132]
Software | Environment | Specialisation |
---|---|---|
Cuffdiff2[108] | Unix-based | Transcript analysis that tracks alternative splicing of mRNA |
EdgeR[93] | R/Bioconductor | Any count-based genomic data |
DEseq2[133] | R/Bioconductor | Flexible data types, low replication |
Limma/Voom[92] | R/Bioconductor | Microarray or RNA-Seq data, flexible experiment design |
Ballgown[134] | R/Bioconductor | Efficient and sensitive transcript discovery, flexible. |
Legend: mRNA - messenger RNA.
Validation
Transcriptomic analyses may be validated using an independent technique, for example,
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 knock-down/rescue study in the organism of interest.[140]
Applications
Diagnostics and disease profiling
Transcriptomic strategies have seen broad application across diverse areas of biomedical research, including disease
Human and pathogen transcriptomes
RNA-Seq of human
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 networks in both interaction partners from initial contact through to invasion and the final persistence of the pathogen or clearance by the host immune system.[149][150]
Responses to environment
Transcriptomics allows identification of genes and
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,[153] identified that upregulation of the unfolded protein response and slower progression through the early stages of the asexual intraerythrocytic developmental cycle were associated with artemisinin resistance in isolates from Southeast Asia.[154]
The use of transcriptomics is also important to investigate responses in the marine environment.
Gene function annotation
All transcriptomic techniques have been particularly useful in
Assembly of RNA-Seq reads is not dependent on a
A transcriptome based aging clock
Aging-related preventive interventions are not possible without personal aging speed measurement. The most up to date and complex way to measure aging rate is by using varying biomarkers of human aging is based on the utilization of deep neural networks which may be trained on any type of omics biological data to predict the subject's age. Aging has been shown to be a strong driver of transcriptome changes.[161][162] Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. However an approach that uses temporal scaling and binarization of transcriptomes to define a gene set that predicts biological age with an accuracy allowed to reach an assessment close to the theoretical limit.[161]
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
Transcriptome databases
Transcriptomics studies generate large amounts of data that have potential applications far beyond the original aims of an experiment. As such, raw or processed data may be deposited in
Name | Host | Data | Description |
---|---|---|---|
Gene Expression Omnibus[100] | NCBI | Microarray RNA-Seq | First transcriptomics database to accept data from any source. Introduced MINSEQE community standards that define necessary experiment metadata to ensure effective interpretation and repeatability.[169][170]
|
ArrayExpress[171] | 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.[169][170] |
Expression Atlas[172] | EBI | Microarray RNA-Seq | Tissue-specific gene expression database for animals and plants. Displays secondary analyses and visualisation, such as functional enrichment of Gene Ontology terms, InterPro domains, or pathways. Links to protein abundance data where available.
|
Genevestigator[173] | Privately curated | Microarray RNA-Seq | Contains manual curations of public transcriptome datasets, focusing on medical and plant biology data. Individual experiments are normalised across the full database to allow comparison of gene expression across diverse experiments. Full functionality requires licence purchase, with free access to a limited functionality. |
RefEx[174] | DDBJ | All | Human, mouse, and rat transcriptomes from 40 different organs. Gene expression visualised as heatmaps projected onto 3D representations of anatomical structures.
|
NONCODE[175] | noncode.org | RNA-Seq | Non-coding RNAs (ncRNAs) excluding tRNA and rRNA. |
Legend: NCBI – National Center for Biotechnology Information; EBI – European Bioinformatics Institute; DDBJ – DNA Data Bank of Japan; ENA – European Nucleotide Archive; MIAME – Minimum Information About a Microarray Experiment; MINSEQE – Minimum Information about a high-throughput nucleotide SEQuencing Experiment.
See also
References
This article was adapted from the following source under a CC BY 4.0 license (2017) (reviewer reports):
Rohan Lowe; Neil Shirley; Mark Bleackley; Stephen Dolan; Thomas Shafee (18 May 2017). "Transcriptomics technologies". {{cite journal}}
: CS1 maint: unflagged free DOI (link
- ^ "Medline trend: automated yearly statistics of PubMed results for any query". dan.corlan.net. Retrieved 2016-10-05.
- ^ S2CID 13436211.
- S2CID 9228930.
- ^ S2CID 10013179.
- PMID 24037378.
- ^ PMID 25954002.
- S2CID 27632439.
- PMID 26000846.
- ^ PMID 23290152.
- ^ PMID 19015660.
- ^ PMID 21191423.
- ^ PMID 19715439.
- PMID 519770.
- PMID 6956902.
- S2CID 4364361.
- ^ PMID 9448457.
- PMID 414220.
- PMID 2479917.
- PMID 10022985.
- S2CID 11430660.
- ^ S2CID 16281846.
- PMID 9331369.
- ^ PMID 25149683.
- PMID 24454679.
- ^ PMID 22939981.
- PMID 11015604.
- ^ a b c d e f Illumina (2011-07-11). "RNA-Seq Data Comparison with Gene Expression Microarrays" (PDF). European Pharmaceutical Review.
- ^ PMID 24194394.
- PMID 18550803.
- PMID 25150838.
- PMID 17961233.
- S2CID 16088782.
- ^ PMID 11287436.
- S2CID 6720459.
- ^ PMID 17644526.
- ^ PMID 12117754.
- ISBN 978-0-471-72612-8.[page needed]
- S2CID 13884154.
- S2CID 15336496.
- ^ PMID 17010196.
- S2CID 205418589.
- S2CID 205213499.
- S2CID 10013179.
- ^ PMID 2440339.
- ^ S2CID 28653075.
- PMID 1699561.
- PMID 9664454.
- PMID 24888378.
- ^ Some examples of environmental samples include: sea water, soil, or air.
- PMID 15020760.
- ^ PMID 28545146.
- ^ PMID 14663149.
- PMID 24479125.
- ^ S2CID 13712888.
- PMID 15978318.
- PMID 8796352.
- S2CID 35232673.
- PMID 12582260.
- S2CID 39437458.
- S2CID 3560001.
- .
- ^ PMID 18451266.
- PMID 25633159.
- PMID 24578530.
- ^ S2CID 6384349.
- PMID 24981968.
- ^ PMID 22140562.
- PMID 26116762.
- PMID 27156886.
- PMID 25649271.
- PMID 21816910.
- S2CID 39225091.
- S2CID 16570747.
- S2CID 6765530.
- PMID 24531970.
- ^ PMID 20711195.
- PMID 22827831.
- ^ PMID 22829749.
- ^ "SRA". Retrieved 2016-10-06.The NCBI Sequence Read Archive (SRA) was searched using “RNA-Seq[Strategy]” and one of "LS454[Platform]”, “Illumina[platform]”, "ABI Solid[Platform]”, "Ion Torrent[Platform]”, "PacBio SMRT"[Platform]” to report the number of RNA-Seq runs deposited for each platform.
- S2CID 5300923.
- S2CID 8295541.
- S2CID 3589823.
- S2CID 15053702.
- S2CID 4426760.
- ^ PMID 23961961.
- ^ PMID 26813401.
- ^ PMID 24020486.
- PMID 22955616.
- PMID 26527727.
- ^ "ENCODE: Encyclopedia of DNA Elements". encodeproject.org.
- ^ PMID 34329375.
- ^ PMID 25605792.
- ^ PMID 19910308.
- ^ PMID 25633503.
- ISBN 9780387251462.
- OCLC 437246554.
- ^ PMID 23845962.
- ^ PMID 25690850.
- PMID 22009675.
- ^ PMID 11752295.
- S2CID 31598448.
- S2CID 31598448.
- S2CID 3684245.
- PMID 21576222.
- S2CID 205453732.
- ^ Andrews S (2010). "FastQC: A Quality Control tool for High Throughput Sequence Data". Babraham Bioinformatics. Retrieved 2017-05-23.
- PMID 25408143.
- ^ PMID 23222703.
- ^ S2CID 5152689.
- PMID 33923758.
- ^ HTS Mappers. http://www.ebi.ac.uk/~nf/hts_mappers/
- PMID 23060614.
- PMID 19289445.
- ^ PMID 20436464.
- PMID 20211242.
- PMID 23837739.
- PMID 27252236.
- PMID 25608678.
- PMID 18349386.
- PMID 22368243.
- S2CID 1034682.
- ^ PMID 21572440.
- PMID 15140833.
- PMID 16056220.
- PMID 20950480.
- PMID 22506599.
- PMID 21816040.
- doi:10.1101/694554. Retrieved 27 August 2019.
- S2CID 205419270.
- PMID 25260700.
- S2CID 205282743.
- PMID 19505943.
- PMID 25516281.
- PMID 25748911.
- PMID 21498551.
- PMID 20011106.
- PMID 12184808.
- PMID 19056941.
- PMID 20368969.
- ^ PMID 19224247.
- S2CID 52922135.
- PMID 22739340.
- S2CID 14433306.
- S2CID 9719784.
- PMID 26551575.
- ^ PMID 26996076.
- PMID 18284925.
- PMID 25517437.
- S2CID 205498287.
- PMID 25914674.
- ^ PMID 26759178.
- PMID 15075282.
- PMID 19666593.
- PMID 25502316.
- ^ ISSN 2296-7745.
- PMID 19192189.
- PMID 22047402.
- PMID 25214207.
- PMID 23445355.
- PMID 26772543.
- ^ PMID 33656257.
- PMID 30567591.
- PMID 1883196.
- PMID 16943439.
- S2CID 44527461.
- PMID 15851066.
- S2CID 13036469.
- ^ "Gene Expression Omnibus". www.ncbi.nlm.nih.gov. Retrieved 2018-03-26.
- ^ S2CID 6994467.
- ^ PMID 19484163.
- PMID 25361974.
- PMID 26481351.
- PMID 19956698.
- PMID 18835852.
- PMID 26586799.
Notes
Further reading
- Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T (May 2017). "Transcriptomics technologies". PLOS Computational Biology. 13 (5): e1005457. PMID 28545146.
- Comparative Transcriptomics Analysis in Reference Module in Life Sciences
- Software used in transcriptomics: