Transcriptomics technologies

(green), and serial/cap analysis of gene expression (yellow) since 1990. 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. and by 2015 transcriptomes had been published for hundreds of individuals. Transcriptomes of different disease states, tissues, or even single cells are now routinely generated. This explosion in transcriptomics has been driven by the rapid development of new technologies with improved sensitivity and economy. Before transcriptomics Studies of individual transcripts were being performed several decades before any transcriptomics approaches were available. Libraries of silkmoth mRNA transcripts were collected and converted to complementary DNA (cDNA) for storage using reverse transcriptase in the late 1970s. In the 1980s, low-throughput sequencing using the Sanger method was used to sequence random transcripts, producing expressed sequence tags (ESTs). Amounts of individual transcripts were quantified using Northern blotting, nylon membrane arrays, and later reverse transcriptase quantitative PCR (RT-qPCR) methods, but these methods are laborious and can only capture a tiny subsection of a transcriptome. 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. 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. However, these methods were largely overtaken by high throughput sequencing of entire transcripts, which provided additional information on transcript structure such as splice variants. 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. Microarray technology allowed the assay of thousands of transcripts simultaneously and at a greatly reduced cost per gene and labour saving. Both spotted oligonucleotide arrays and Affymetrix high-density arrays were the method of choice for transcriptional profiling until the late 2000s. RNA-Seq is accomplished by reverse transcribing RNA in vitro and sequencing the resulting cDNAs. and was used in 2004 to validate the expression of ten thousand genes in Arabidopsis thaliana. The earliest RNA-Seq work was published in 2006 with one hundred thousand transcripts sequenced using 454 technology. 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. This yield now allows for the quantification and comparison of human transcriptomes. ==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). disruption of macromolecules and nucleotide complexes, separation of RNA from undesired biomolecules including DNA, and concentration of the RNA via precipitation from solution or elution from a solid matrix. Isolated RNA may additionally be treated with DNase to digest any traces of DNA. It is necessary to enrich messenger RNA as total RNA extracts are typically 98% ribosomal RNA. Enrichment for transcripts can be performed by poly-A affinity methods or by depletion of ribosomal RNA using sequence-specific probes. Degraded RNA may affect downstream results; for example, mRNA enrichment from degraded samples will result in the depletion of 5' mRNA ends and an uneven signal across the length of a transcript. Snap-freezing of tissue prior to RNA isolation is typical, and care is taken to reduce exposure to RNase enzymes once isolation is complete. Serial and cap analysis of gene expression (SAGE/CAGE) .'' Within the organisms, genes are transcribed and spliced (in eukaryotes) to produce mature mRNA transcripts (red). The mRNA is extracted from the organism, and reverse transcriptase is used to copy the mRNA into stable double-stranded–cDNA (ds-cDNA; blue). In SAGE, the ds-cDNA is digested by restriction enzymes (at location 'X' and 'X'+11) to produce 11-nucleotide "tag" fragments. These tags are concatenated and sequenced using long-read Sanger sequencing (different shades of blue indicate tags from different genes). The sequences are deconvoluted to find the frequency of each tag. The tag frequency can be used to report on transcription of the gene that the tag came from. 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. Therefore, the transcriptional start site of genes can be identified when the tags are aligned to a reference genome. Identifying gene start sites is of use for promoter analysis and for the cloning of full-length cDNAs. 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. Transcript abundance is determined by hybridisation of fluorescently labelled transcripts to these probes. The fluorescence intensity at each probe location on the array indicates the transcript abundance for that probe sequence. drops of a range of purified cDNAs arrayed on the surface of a glass slide. 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. High-density arrays use a single fluorescent label, and each sample is hybridised and detected individually. 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. 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. Some designs incorporated up to 12 independent arrays per slide. RNA-Seq .'' Within the organisms, genes are transcribed and spliced (in eukaryotes) to produce mature mRNA transcripts (red). The mRNA is extracted from the organism, fragmented, and copied into stable ds-cDNA (blue). The ds-cDNA is sequenced using high-throughput, short-read sequencing methods. These sequences can then be aligned to a reference genome sequence to reconstruct which genome regions were being transcribed. This data can be used to annotate where expressed genes are, their relative expression levels, and any alternative splice variants. 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. Since the first descriptions in 2006 and 2008, RNA-Seq has been rapidly adopted and overtook microarrays as the dominant transcriptomics technique in 2015. 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. Methods RNA-Seq was established in concert with the rapid development of a range of high-throughput DNA sequencing technologies. 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. Small RNAs, such as micro RNAs, can be purified based on their size by gel electrophoresis and extraction. Since mRNAs are longer than the read-lengths of typical high-throughput sequencing methods, transcripts are usually fragmented prior to sequencing. The fragmentation method is a key aspect of sequencing library construction. Fragmentation may be achieved by chemical hydrolysis, nebulisation, sonication, or reverse transcription with chain-terminating nucleotides. During preparation for sequencing, cDNA copies of transcripts may be amplified by PCR to enrich for fragments that contain the expected 5' and 3' adapter sequences. Amplification is also used to allow sequencing of very low input amounts of RNA, down to as little as 50 pg in extreme applications. Spike-in controls of known RNAs can be used for quality control assessment to check library preparation and sequencing, in terms of GC-content, fragment length, as well as the bias due to fragment position within a transcript. Unique molecular identifiers (UMIs) are short random sequences that are used to individually tag sequence fragments during library preparation so that every tagged fragment is unique. UMIs provide an absolute scale for quantification, the opportunity to correct for subsequent amplification bias introduced during library construction, and accurately estimate the initial sample size. UMIs are particularly well-suited to single-cell RNA-Seq transcriptomics, where the amount of input RNA is restricted and extended amplification of the sample is required. 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. 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. Direct sequencing of RNA using nanopore sequencing represents a current state-of-the-art RNA-Seq technique. 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. The sensitivity and accuracy of an RNA-Seq experiment are dependent on the number of reads obtained from each sample. 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. == Data analysis ==

Transcriptomics methods are highly parallel and require significant computation to produce meaningful data for both microarray and RNA-Seq experiments. Microarray data is recorded as high-resolution images, requiring feature detection and spectral analysis. 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 genomes composed of millions to billions of base pairs. De novo assembly of reads within a dataset requires the construction of highly complex sequence graphs. This example would require approximately 1.8 gigabytes of disk space per sample when stored in a compressed fastq format. Processed count data for each gene would be much smaller, equivalent to processed microarray intensities. Sequence data may be stored in public repositories, such as the Sequence Read Archive (SRA). RNA-Seq datasets can be uploaded via the Gene Expression Omnibus. Microarray image processing must correctly identify the regular grid of features within an image and independently quantify the fluorescence intensity for each feature. 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. 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. 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'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. 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. Most popular RNA-Seq programs are run from a command-line interface, either in a Unix environment or within the R/Bioconductor statistical environment. 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. Abnormalities may be removed (trimming) or tagged for special treatment during later processes. 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. 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. Alignment of primary transcript mRNA sequences derived from eukaryotes to a reference genome requires specialised handling of intron sequences, which are absent from mature mRNA. Short read aligners perform an additional round of alignments specifically designed to identify splice junctions, 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 gene regulation may occur at the mRNA isoform level, splice-aware alignments also permit detection of isoform abundance changes that would otherwise be lost in a bulked analysis. De novo assembly can be used to align reads to one another to construct full-length transcript sequences without use of a reference genome. 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 and improved evaluation methods are now available. 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. Legend: RAM – random access memory; MPI – message passing interface; EST – expressed sequence tag. Quantification identification of gene co-expression patterns across different samples.'' Each column contains the measurements for gene expression change for a single sample. Relative gene expression is indicated by colour: high-expression (red), median-expression (white) and low-expression (blue). Genes and samples with similar expression profiles can be automatically grouped (left and top trees). Samples may be different individuals, tissues, environments or health conditions. In this example, expression of gene set 1 is high and expression of gene set 2 is low in samples 1, 2, and 3. Quantification of sequence alignments may be performed at the gene, exon, or transcript level. 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. Differential expression Once quantitative counts of each transcript are available, differential gene expression is measured by normalising, modelling, and statistically analysing the data. Legend: mRNA - messenger RNA. Validation Transcriptomic analyses may be validated using an independent technique, for example, quantitative PCR (qPCR), which is recognisable and statistically assessable. Gene expression is measured against defined standards both for the gene of interest and 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 amplicons smaller than 300 bp, usually toward the 3' end of the coding region, avoiding the 3'UTR. If validation of transcript isoforms is required, an inspection of RNA-Seq read alignments should indicate where qPCR 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. qPCR validation of RNA-Seq data has generally shown that different RNA-Seq methods are highly correlated. 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. == Applications ==

Diagnostics and disease profiling Transcriptomic strategies have seen broad application across diverse areas of biomedical research, including disease diagnosis and profiling. RNA-Seq approaches have allowed for the large-scale identification of transcriptional start sites, uncovered alternative promoter usage, and novel splicing alterations. These regulatory elements are important in human disease and, therefore, defining such variants is crucial to the interpretation of disease-association studies. RNA-Seq can also identify disease-associated single nucleotide polymorphisms (SNPs), allele-specific expression, and gene fusions, which contributes to the understanding of disease causal variants. Retrotransposons are transposable elements which proliferate within eukaryotic genomes through a process involving reverse transcription. RNA-Seq can provide information about the transcription of endogenous retrotransposons that may influence the transcription of neighboring genes by various epigenetic mechanisms that lead to disease. Similarly, the potential for using RNA-Seq to understand immune-related disease is expanding rapidly due to the ability to dissect immune cell populations and to sequence T cell and B cell receptor repertoires from patients. Human and pathogen transcriptomes RNA-Seq of human pathogens has become an established method for quantifying gene expression changes, identifying novel virulence factors, predicting antibiotic resistance, and unveiling host-pathogen immune interactions. A primary aim of this technology is to develop optimised infection control measures and targeted individualised treatment. Responses to environment Transcriptomics allows identification of genes and pathways that respond to and counteract biotic and abiotic environmental stresses. Investigation of gene expression during biofilm formation by the fungal pathogen Candida albicans revealed a co-regulated set of genes critical for biofilm establishment and maintenance. 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, 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. The use of transcriptomics is also important to investigate responses in the marine environment. In marine ecology, "stress" and "adaptation" have been among the most common research topics, especially related to anthropogenic stress, such as global change and pollution. Integration of RNA-Seq datasets across different tissues has been used to improve annotation of gene functions in commercially important organisms (e.g. cucumber) or threatened species (e.g. koala). Assembly of RNA-Seq reads is not dependent on a reference genome 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 Douglas fir breeding programs was created by de novo transcriptome analysis in the absence of a sequenced genome. 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. RNA-Seq can also be used to identify previously unknown protein coding regions in existing sequenced genomes. 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 protein translation, DNA replication, RNA splicing, and transcriptional regulation). Many of these ncRNAs affect disease states, including cancer, cardiovascular, and neurological diseases. == 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 public databases to ensure their utility for the broader scientific community. For example, as of 2018, the Gene Expression Omnibus contained millions of experiments. 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 ==