RNA-Seq

Summary of RNA-Seq. Within the organism, genes are transcribed and (in an eukaryotic organism) spliced 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.

RNA-Seq (named as an abbreviation of RNA sequencing) is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also known as transcriptome. ^{[2] [3]}

Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/SNPs and changes in gene expression over time, or differences in gene expression in different groups or treatments. ^[4] In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling. ^[5] RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing, ^[6] 3' mRNA-sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing. ^[7] Other examples of emerging RNA-Seq applications due to the advancement of bioinformatics algorithms are copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. ^[8]

Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and needing to know the sequence a priori. ^[9] Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed sequence tag libraries, to chemical tag-based methods (e.g., serial analysis of gene expression), and finally to the current technology, next-gen sequencing of complementary DNA (cDNA), notably RNA-Seq.

Experimental transcriptome sequencing technique (RNA-seq).

Methods

Library preparation

See also: Library (biology)

Typical RNA-Seq experimental workflow. RNA are isolated from multiple samples, converted to cDNA libraries, sequenced into a computer-readable format, aligned to a reference, and quantified for downstream analyses such as differential expression and alternative splicing. Overview of a typical RNA-Seq experimental workflow.

The general steps to prepare a complementary DNA (cDNA) library for sequencing are described below, but often vary between platforms. ^{[10] [3] [11]}

1. RNA Isolation: RNA is isolated from tissue and mixed with Deoxyribonuclease (DNase). DNase reduces the amount of genomic DNA. The amount of RNA degradation is checked with gel and capillary electrophoresis and is used to assign an RNA integrity number to the sample. This RNA quality and the total amount of starting RNA are taken into consideration during the subsequent library preparation, sequencing, and analysis steps.
2. RNA selection/depletion: To analyze signals of interest, the isolated RNA can either be kept as is, enriched for RNA with 3' polyadenylated (poly(A)) tails to include only eukaryotic mRNA, depleted of ribosomal RNA (rRNA), and/or filtered for RNA that binds specific sequences (RNA selection and depletion methods table, below). RNA molecules having 3' poly(A) tails in eukaryotes are mainly composed of mature, processed, coding sequences. Poly(A) selection is performed by mixing RNA with poly(T) oligomers covalently attached to a substrate, typically magnetic beads. ^{[12] [13]} Poly(A) selection has important limitations in RNA biotype detection. Many RNA biotypes are not polyadenylated, including many noncoding RNA and histone-core protein transcripts, or are regulated via their poly(A) tail length (e.g., cytokines) and thus might not be detected after poly(A) selection. ^[14] Furthermore, poly(A) selection may display increased 3' bias, especially with lower quality RNA. ^{[15] [16]} These limitations can be avoided with ribosomal depletion, removing rRNA that typically represents over 90% of the RNA in a cell. Both poly(A) enrichment and ribosomal depletion steps are labor intensive and could introduce biases, so more simple approaches have been developed to omit these steps. ^[17] Small RNA targets, such as miRNA, can be further isolated through size selection with exclusion gels, magnetic beads, or commercial kits.
3. cDNA synthesis: RNA is reverse transcribed to cDNA because DNA is more stable and to allow for amplification (which uses DNA polymerases) and leverage more mature DNA sequencing technology. Amplification subsequent to reverse transcription results in loss of strandedness, which can be avoided with chemical labeling or single molecule sequencing. Fragmentation and size selection are performed to purify sequences that are the appropriate length for the sequencing machine. The RNA, cDNA, or both are fragmented with enzymes, sonication, divalent ions, or nebulizers. Fragmentation of the RNA reduces 5' bias of randomly primed-reverse transcription and the influence of primer binding sites, ^[13] with the downside that the 5' and 3' ends are converted to DNA less efficiently. Fragmentation is followed by size selection, where either small sequences are removed or a tight range of sequence lengths are selected. Because small RNAs like miRNAs are lost, these are analyzed independently. The cDNA for each experiment can be indexed with a hexamer or octamer barcode, so that these experiments can be pooled into a single lane for multiplexed sequencing.

RNA selection and depletion methods: ^[10]

Strategy	Predominant type of RNA	Ribosomal RNA content	Unprocessed RNA content	Isolation method
Total RNA	All	High	High	None
PolyA selection	Coding	Low	Low	Hybridization with poly(dT) oligomers
rRNA depletion	Coding, noncoding	Low	High	Removal of oligomers complementary to rRNA
RNA capture	Targeted	Low	Moderate	Hybridization with probes complementary to desired transcripts

Complementary DNA sequencing (cDNA-Seq)

See also: w:DNA sequencing

The cDNA library derived from RNA biotypes is then sequenced into a computer-readable format. There are many high-throughput sequencing technologies for cDNA sequencing including platforms developed by Illumina, Thermo Fisher, BGI/MGI, PacBio, and Oxford Nanopore Technologies. ^[18] For Illumina short-read sequencing, a common technology for cDNA sequencing, adapters are ligated to the cDNA, DNA is attached to a flow cell, clusters are generated through cycles of bridge amplification and denaturing, and sequence-by-synthesis is performed in cycles of complementary strand synthesis and laser excitation of bases with reversible terminators. Sequencing platform choice and parameters are guided by experimental design and cost. Common experimental design considerations include deciding on the sequencing length, sequencing depth, use of single versus paired-end sequencing, number of replicates, multiplexing, randomization, and spike-ins. ^[19]

Small RNA/non-coding RNA sequencing

When sequencing RNA other than mRNA, the library preparation is modified. The cellular RNA is selected based on the desired size range. For small RNA targets, such as miRNA, the RNA is isolated through size selection. This can be performed with a size exclusion gel, through size selection magnetic beads, or with a commercially developed kit. Once isolated, linkers are added to the 3' and 5' end then purified. The final step is cDNA generation through reverse transcription.

Direct RNA sequencing

Because converting RNA into cDNA, ligation, amplification, and other sample manipulations have been shown to introduce biases and artifacts that may interfere with both the proper characterization and quantification of transcripts, ^[20] single molecule direct RNA sequencing has been explored by companies including Helicos (bankrupt), Oxford Nanopore Technologies, ^[21] and others. This technology sequences RNA molecules directly in a massively-parallel manner.

Single-molecule real-time RNA sequencing

See also: Single-molecule real-time sequencing

Massively parallel single molecule direct RNA-Seq has been explored as an alternative to traditional RNA-Seq, in which RNA-to-cDNA conversion, ligation, amplification, and other sample manipulation steps may introduce biases and artifacts. ^[22] Technology platforms that perform single-molecule real-time RNA-Seq include Oxford Nanopore Technologies (ONT) Nanopore sequencing, ^[21] PacBio IsoSeq, and Helicos (bankrupt). Sequencing RNA in its native form preserves modifications like methylation, allowing them to be investigated directly and simultaneously. ^[21] Another benefit of single-molecule RNA-Seq is that transcripts can be covered in full length, allowing for higher confidence isoform detection and quantification compared to short-read sequencing. Traditionally, single-molecule RNA-Seq methods have higher error rates compared to short-read sequencing, but newer methods like ONT direct RNA-Seq limit errors by avoiding fragmentation and cDNA conversion. Recent uses of ONT direct RNA-Seq for differential expression in human cell populations have demonstrated that this technology can overcome many limitations of short and long cDNA sequencing. ^[23]

Single-cell RNA sequencing (scRNA-Seq)

Main article: Single-cell transcriptomics

See also: Single cell sequencing

Standard methods such as microarrays and standard bulk RNA-Seq analysis analyze the expression of RNAs from large populations of cells. In mixed cell populations, these measurements may obscure critical differences between individual cells within these populations. ^{[24] [25]}

Single-cell RNA sequencing (scRNA-Seq) provides the expression profiles of individual cells. Although it is not possible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, patterns of gene expression can be identified through gene clustering analyses. This can uncover the existence of rare cell types within a cell population that may never have been seen before. For example, rare specialized cells in the lung called pulmonary ionocytes that express the Cystic fibrosis transmembrane conductance regulator were identified in 2018 by two groups performing scRNA-Seq on lung airway epithelia. ^{[26] [27]}

Experimental procedures

Typical single-cell RNA-Seq workflow. Single cells are isolated from a sample into either wells or droplets, cDNA libraries are generated and amplified, libraries are sequenced, and expression matrices are generated for downstream analyses like cell type identification.

Current scRNA-Seq protocols involve the following steps: isolation of single cell and RNA, reverse transcription (RT), amplification, library generation and sequencing. Single cells are either mechanically separated into microwells (e.g., BD Rhapsody, Takara ICELL8, Vycap Puncher Platform, or CellMicrosystems CellRaft) or encapsulated in droplets (e.g., 10x Genomics Chromium, Illumina Bio-Rad ddSEQ, 1CellBio InDrop, Dolomite Bio Nadia). ^[28] Single cells are labeled by adding beads with barcoded oligonucleotides; both cells and beads are supplied in limited amounts such that co-occupancy with multiple cells and beads is a very rare event. Once reverse transcription is complete, the cDNAs from many cells can be mixed together for sequencing; transcripts from a particular cell are identified by each cell's unique barcode. ^{[29] [30]} Unique molecular identifier (UMIs) can be attached to mRNA/cDNA target sequences to help identify artifacts during library preparation. ^[31]

Challenges for scRNA-Seq include preserving the initial relative abundance of mRNA in a cell and identifying rare transcripts. ^[32] The reverse transcription step is critical as the efficiency of the RT reaction determines how much of the cell's RNA population will be eventually analyzed by the sequencer. The processivity of reverse transcriptases and the priming strategies used may affect full-length cDNA production and the generation of libraries biased toward the 3’ or 5' end of genes.

In the amplification step, either PCR or in vitro transcription (IVT) is currently used to amplify cDNA. One of the advantages of PCR-based methods is the ability to generate full-length cDNA. However, different PCR efficiency on particular sequences (for instance, GC content and snapback structure) may also be exponentially amplified, producing libraries with uneven coverage. On the other hand, while libraries generated by IVT can avoid PCR-induced sequence bias, specific sequences may be transcribed inefficiently, thus causing sequence drop-out or generating incomplete sequences. ^{[33] [24]} Several scRNA-Seq protocols have been published: Tang et al., ^[34] STRT, ^[35] SMART-seq, ^[36] CEL-seq, ^[37] RAGE-seq, ^[38] Quartz-seq ^[39] and C1-CAGE. ^[40] These protocols differ in terms of strategies for reverse transcription, cDNA synthesis and amplification, and the possibility to accommodate sequence-specific barcodes (i.e. UMIs) or the ability to process pooled samples. ^[41]

In 2017, two approaches were introduced to simultaneously measure single-cell mRNA and protein expression through oligonucleotide-labeled antibodies known as REAP-seq, ^[42] and CITE-seq. ^[43]

Applications

scRNA-Seq is becoming widely used across biological disciplines including Development, Neurology, ^[44] Oncology, ^{[45] [46] [47]} Autoimmune disease, ^[48] and Infectious disease. ^[49]

scRNA-Seq has provided considerable insight into the development of embryos and organisms, including the worm Caenorhabditis elegans , ^[50] and the regenerative planarian Schmidtea mediterranea . ^{[51] [52]} The first vertebrate animals to be mapped in this way were Zebrafish ^{[53] [54]} and Xenopus laevis . ^[55] In each case multiple stages of the embryo were studied, allowing the entire process of development to be mapped on a cell-by-cell basis. ^[10] Science recognized these advances as the 2018 Breakthrough of the Year. ^[56]

Experimental considerations

A variety of parameters are considered when designing and conducting RNA-Seq experiments:

• Tissue specificity: Gene expression varies within and between tissues, and RNA-Seq measures this mix of cell types. This may make it difficult to isolate the biological mechanism of interest. Single cell sequencing can be used to study each cell individually, mitigating this issue.
• Time dependence: Gene expression changes over time, and RNA-Seq only takes a snapshot. Time course experiments can be performed to observe changes in the transcriptome.
• Coverage (also known as depth): RNA harbors the same mutations observed in DNA, and detection requires deeper coverage. With high enough coverage, RNA-Seq can be used to estimate the expression of each allele. This may provide insight into phenomena such as imprinting or cis-regulatory effects. The depth of sequencing required for specific applications can be extrapolated from a pilot experiment. ^[57]
• Data generation artifacts (also known as technical variance): The reagents (e.g., library preparation kit), personnel involved, and type of sequencer (e.g., Illumina, Pacific Biosciences) can result in technical artifacts that might be mis-interpreted as meaningful results. As with any scientific experiment, it is prudent to conduct RNA-Seq in a well controlled setting. If this is not possible or the study is a meta-analysis, another solution is to detect technical artifacts by inferring latent variables (typically principal component analysis or factor analysis) and subsequently correcting for these variables. ^[58]
• Data management: A single RNA-Seq experiment in humans is usually 1-5 Gb (compressed), or more when including intermediate files. ^[59] This large volume of data can pose storage issues. One solution is compressing the data using multi-purpose computational schemas (e.g., gzip) or genomics-specific schemas. The latter can be based on reference sequences or de novo. Another solution is to perform microarray experiments, which may be sufficient for hypothesis-driven work or replication studies (as opposed to exploratory research).

Analysis

See also: List of RNA-Seq bioinformatics tools

A standard RNA-Seq analysis workflow. Sequenced reads are aligned to a reference genome and/or transcriptome and subsequently processed for a variety of quality control, discovery, and hypothesis-driven analyses.

Transcriptome assembly

See also: Sequence alignment software § Short-Read Sequence Alignment

Two methods are used to assign raw sequence reads to genomic features (i.e., assemble the transcriptome):

• De novo: This approach does not require a reference genome to reconstruct the transcriptome, and is typically used if the genome is unknown, incomplete, or substantially altered compared to the reference. ^[60] Challenges when using short reads for de novo assembly include 1) determining which reads should be joined together into contiguous sequences (contigs), 2) robustness to sequencing errors and other artifacts, and 3) computational efficiency. The primary algorithm used for de novo assembly transitioned from overlap graphs, which identify all pair-wise overlaps between reads, to de Bruijn graphs, which break reads into sequences of length k and collapse all k-mers into a hash table. ^[61] Overlap graphs were used with Sanger sequencing, but do not scale well to the millions of reads generated with RNA-Seq. Examples of assemblers that use de Bruijn graphs are Trinity, ^[60] Oases ^[62] (derived from the genome assembler Velvet ^[63] ), Bridger, ^[64] and rnaSPAdes. ^[65] Paired-end and long-read sequencing of the same sample can mitigate the deficits in short read sequencing by serving as a template or skeleton. Metrics to assess the quality of a de novo assembly include median contig length, number of contigs and N50. ^[66]

RNA-Seq alignment with intron-split short reads. Alignment of short reads to an mRNA sequence and the reference genome. Alignment software has to account for short reads that overlap exon-exon junctions (in red) and thereby skip intronic sections of the pre-mRNA and reference genome.

• Genome guided: This approach relies on the same methods used for DNA alignment, with the additional complexity of aligning reads that cover non-continuous portions of the reference genome. ^[67] These non-continuous reads are the result of sequencing spliced transcripts (see figure). Typically, alignment algorithms have two steps: 1) align short portions of the read (i.e., seed the genome), and 2) use dynamic programming to find an optimal alignment, sometimes in combination with known annotations. Software tools that use genome-guided alignment include Bowtie, ^[68] TopHat (which builds on BowTie results to align splice junctions), ^{[69] [70]} Subread, ^[71] STAR, ^[67] HISAT2, ^[72] and GMAP. ^[73] The output of genome guided alignment (mapping) tools can be further used by tools such as Cufflinks ^[70] or StringTie ^[74] to reconstruct contiguous transcript sequences (i.e., a FASTA file). The quality of a genome guided assembly can be measured with both 1) de novo assembly metrics (e.g., N50) and 2) comparisons to known transcript, splice junction, genome, and protein sequences using precision, recall, or their combination (e.g., F1 score). ^[66] In addition, in silico assessment could be performed using simulated reads. ^{[75] [76]}

A note on assembly quality: The current consensus is that 1) assembly quality can vary depending on which metric is used, 2) assembly tools that scored well in one species do not necessarily perform well in the other species, and 3) combining different approaches might be the most reliable. ^{[77] [78] [79]}

Gene expression quantification

Expression is quantified to study cellular changes in response to external stimuli, differences between healthy and diseased states, and other research questions. Transcript levels are often used as a proxy for protein abundance, but these are often not equivalent due to post transcriptional events such as RNA interference and nonsense-mediated decay. ^[80]

Expression is quantified by counting the number of reads that mapped to each locus in the transcriptome assembly step. Expression can be quantified for exons or genes using contigs or reference transcript annotations. ^[10] These observed RNA-Seq read counts have been robustly validated against older technologies, including expression microarrays and qPCR. ^{[57] [81]} Tools that quantify counts are HTSeq, ^[82] FeatureCounts, ^[83] Rcount, ^[84] maxcounts, ^[85] FIXSEQ, ^[86] and Cuffquant. These tools determine read counts from aligned RNA-Seq data, but alignment-free counts can also be obtained with Sailfish ^[87] and Kallisto. ^[88] The read counts are then converted into appropriate metrics for hypothesis testing, regressions, and other analyses. Parameters for this conversion are:

• Sequencing depth/coverage: Although depth is pre-specified when conducting multiple RNA-Seq experiments, it will still vary widely between experiments. ^[89] Therefore, the total number of reads generated in a single experiment is typically normalized by converting counts to fragments, reads, or counts per million mapped reads (FPM, RPM, or CPM). The difference between RPM and FPM was historically derived during the evolution from single-end sequencing of fragments to paired-end sequencing. In single-end sequencing, there is only one read per fragment (i.e., RPM = FPM). In paired-end sequencing, there are two reads per fragment (i.e., RPM = 2 x FPM). Sequencing depth is sometimes referred to as library size, the number of intermediary cDNA molecules in the experiment.
• Gene length: Longer genes will have more fragments/reads/counts than shorter genes if transcript expression is the same. This is adjusted by dividing the FPM by the length of a feature (which can be a gene, transcript, or exon), resulting in the metric fragments per kilobase of feature per million mapped reads (FPKM). ^[90] When looking at groups of features across samples, FPKM is converted to transcripts per million (TPM) by dividing each FPKM by the sum of FPKMs within a sample. ^{[91] [92] [93]}
• Total sample RNA output: Because the same amount of RNA is extracted from each sample, samples with more total RNA will have less RNA per gene. These genes appear to have decreased expression, resulting in false positives in downstream analyses. ^[89] Normalization strategies including quantile, DESeq2, TMM and Median Ratio attempt to account for this difference by comparing a set of non-differentially expressed genes between samples and scaling accordingly. ^[94]
• Variance for each gene's expression: is modeled to account for sampling error (important for genes with low read counts), increase power, and decrease false positives. Variance can be estimated as a normal, Poisson, or negative binomial distribution ^{[95] [96] [97]} and is frequently decomposed into technical and biological variance.

Spike-ins for absolute quantification and detection of genome-wide effects

RNA spike-ins are samples of RNA at known concentrations that can be used as gold standards in experimental design and during downstream analyses for absolute quantification and detection of genome-wide effects.

• Absolute quantification: Absolute quantification of gene expression is not possible with most RNA-Seq experiments, which quantify expression relative to all transcripts. It is possible by performing RNA-Seq with spike-ins, samples of RNA at known concentrations. After sequencing, read counts of spike-in sequences are used to determine the relationship between each gene's read counts and absolute quantities of biological fragments. ^{[13] [98]} In one example, this technique was used in Xenopus tropicalis embryos to determine transcription kinetics. ^[99]
• Detection of genome-wide effects: Changes in global regulators including chromatin remodelers, transcription factors (e.g., MYC), acetyltransferase complexes, and nucleosome positioning are not congruent with normalization assumptions and spike-in controls can offer precise interpretation. ^{[100] [101]}

Differential expression

The simplest but often most powerful use of RNA-Seq is finding differences in gene expression between two or more conditions (e.g., treated vs not treated); this process is called differential expression. The outputs are frequently referred to as differentially expressed genes (DEGs) and these genes can either be up- or down-regulated (i.e., higher or lower in the condition of interest). There are many tools that perform differential expression. Most are run in R, Python, or the Unix command line. Commonly used tools include DESeq, ^[96] edgeR, ^[97] and voom+limma, ^{[95] [102]} all of which are available through R/Bioconductor. ^{[103] [104]} These are the common considerations when performing differential expression:

• Inputs: Differential expression inputs include (1) an RNA-Seq expression matrix (M genes x N samples) and (2) a design matrix containing experimental conditions for N samples. The simplest design matrix contains one column, corresponding to labels for the condition being tested. Other covariates (also referred to as factors, features, labels, or parameters) can include batch effects, known artifacts, and any metadata that might confound or mediate gene expression. In addition to known covariates, unknown covariates can also be estimated through unsupervised machine learning approaches including principal component, surrogate variable, ^[105] and PEER ^[58] analyses. Hidden variable analyses are often employed for human tissue RNA-Seq data, which typically have additional artifacts not captured in the metadata (e.g., ischemic time, sourcing from multiple institutions, underlying clinical traits, collecting data across many years with many personnel).
• Methods: Most tools use regression or non-parametric statistics to identify differentially expressed genes, and are either based on read counts mapped to a reference genome (DESeq2, limma, edgeR) or based on read counts derived from alignment-free quantification (sleuth, ^[106] Cuffdiff, ^[107] Ballgown ^[108] ). ^[109] Following regression, most tools employ either familywise error rate (FWER) or false discovery rate (FDR) p-value adjustments to account for multiple hypotheses (in human studies, ~20,000 protein-coding genes or ~50,000 biotypes).
• Outputs: A typical output consists of rows corresponding to the number of genes and at least three columns, each gene's log fold change (log-transform of the ratio in expression between conditions, a measure of effect size), p-value, and p-value adjusted for multiple comparisons. Genes are defined as biologically meaningful if they pass cut-offs for effect size (log fold change) and statistical significance. These cut-offs should ideally be specified a priori, but the nature of RNA-Seq experiments is often exploratory so it is difficult to predict effect sizes and pertinent cut-offs ahead of time.
• Pitfalls: The raison d'etre for these complex methods is to avoid the myriad of pitfalls that can lead to statistical errors and misleading interpretations. Pitfalls include increased false positive rates (due to multiple comparisons), sample preparation artifacts, sample heterogeneity (like mixed genetic backgrounds), highly correlated samples, unaccounted for multi-level experimental designs, and poor experimental design. One notable pitfall is viewing results in Microsoft Excel without using the import feature to ensure that the gene names remain text. ^[110] Although convenient, Excel automatically converts some gene names ( SEPT1, DEC1, MARCH2 ) into dates or floating point numbers.
• Choice of tools and benchmarking: There are numerous efforts that compare the results of these tools, with DESeq2 tending to moderately outperform other methods. ^{[111] [112] [113] [114] [19] [109] [115] [116]} As with other methods, benchmarking consists of comparing tool outputs to each other and known gold standards.

Downstream analyses for a list of differentially expressed genes come in two flavors, validating observations and making biological inferences. Owing to the pitfalls of differential expression and RNA-Seq, important observations are replicated with (1) an orthogonal method in the same samples (like real-time PCR) or (2) another, sometimes pre-registered, experiment in a new cohort. The latter helps ensure generalizability and can typically be followed up with a meta-analysis of all the pooled cohorts. The most common method for obtaining higher-level biological understanding of the results is gene set enrichment analysis, although sometimes candidate gene approaches are employed. Gene set enrichment determines if the overlap between two gene sets is statistically significant, in this case the overlap between differentially expressed genes and gene sets from known pathways/databases (e.g., Gene Ontology, KEGG, Human Phenotype Ontology) or from complementary analyses in the same data (like co-expression networks). Common tools for gene set enrichment include web interfaces (e.g., ENRICHR, g:profiler, WEBGESTALT) ^[117] and software packages. When evaluating enrichment results, one heuristic is to first look for enrichment of known biology as a sanity check and then expand the scope to look for novel biology.

Examples of alternative RNA splicing modes. Exons are represented as blue and yellow blocks, spliced introns as horizontal black lines connecting two exons, and exon-exon junctions as thin grey connecting lines between two exons.

Alternative splicing

RNA splicing is integral to eukaryotes and contributes significantly to protein regulation and diversity, occurring in >90% of human genes. ^[118] There are multiple alternative splicing modes: exon skipping (most common splicing mode in humans and higher eukaryotes), mutually exclusive exons, alternative donor or acceptor sites, intron retention (most common splicing mode in plants, fungi, and protozoa), alternative transcription start site (promoter), and alternative polyadenylation. ^[118] One goal of RNA-Seq is to identify alternative splicing events and test if they differ between conditions. Long-read sequencing captures the full transcript and thus minimizes many of issues in estimating isoform abundance, like ambiguous read mapping. For short-read RNA-Seq, there are multiple methods to detect alternative splicing that can be classified into three main groups: ^{[119] [91] [120]}

• Count-based (also event-based, differential splicing): estimate exon retention. Examples are DEXSeq, ^[121] MATS, ^[122] and SeqGSEA. ^[123]
• Isoform-based (also multi-read modules, differential isoform expression): estimate isoform abundance first, and then relative abundance between conditions. Examples are Cufflinks 2 ^[124] and DiffSplice. ^[125]
• Intron excision based: calculate alternative splicing using split reads. Examples are MAJIQ ^[126] and Leafcutter. ^[120]

Differential gene expression tools can also be used for differential isoform expression if isoforms are quantified ahead of time with other tools like RSEM. ^[127]

Coexpression networks

Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. ^[128] Their main purpose lies in hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. ^[128] RNA-Seq data has been used to infer genes involved in specific pathways based on Pearson correlation, both in plants ^[129] and mammals. ^[130] The main advantage of RNA-Seq data in this kind of analysis over the microarray platforms is the capability to cover the entire transcriptome, therefore allowing the possibility to unravel more complete representations of the gene regulatory networks. Differential regulation of the splice isoforms of the same gene can be detected and used to predict their biological functions. ^{[131] [132]} Weighted gene co-expression network analysis has been successfully used to identify co-expression modules and intramodular hub genes based on RNA seq data. Co-expression modules may correspond to cell types or pathways. Highly connected intramodular hubs can be interpreted as representatives of their respective module. An eigengene is a weighted sum of expression of all genes in a module. Eigengenes are useful biomarkers (features) for diagnosis and prognosis. ^[133] Variance-Stabilizing Transformation approaches for estimating correlation coefficients based on RNA seq data have been proposed. ^[129]

Variant discovery

RNA-Seq captures DNA variation, including single nucleotide variants, small insertions/deletions. and structural variation. Variant calling in RNA-Seq is similar to DNA variant calling and often employs the same tools (including SAMtools mpileup ^[134] and GATK HaplotypeCaller ^[135] ) with adjustments to account for splicing. One unique dimension for RNA variants is allele-specific expression (ASE): the variants from only one haplotype might be preferentially expressed due to regulatory effects including imprinting and expression quantitative trait loci, and noncoding rare variants. ^{[136] [137]} Limitations of RNA variant identification include that it only reflects expressed regions (in humans, <5% of the genome), could be subject to biases introduced by data processing (e.g., de novo transcriptome assemblies underestimate heterozygosity ^[138] ), and has lower quality when compared to direct DNA sequencing.

RNA editing (post-transcriptional alterations)

See also: RNA editing

Having the matching genomic and transcriptomic sequences of an individual can help detect post-transcriptional edits (RNA editing). ^[3] A post-transcriptional modification event is identified if the gene's transcript has an allele/variant not observed in the genomic data.

A gene fusion event and the behaviour of paired-end reads falling on both sides of the gene union. Gene fusions can occur in Trans, between genes on separate chromosomes, or in Cis, between two genes on the same chromosome.

Fusion gene detection

See also: Fusion gene

Caused by different structural modifications in the genome, fusion genes have gained attention because of their relationship with cancer. ^[139] The ability of RNA-Seq to analyze a sample's whole transcriptome in an unbiased fashion makes it an attractive tool to find these kinds of common events in cancer. ^[4]

The idea follows from the process of aligning the short transcriptomic reads to a reference genome. Most of the short reads will fall within one complete exon, and a smaller but still large set would be expected to map to known exon-exon junctions. The remaining unmapped short reads would then be further analyzed to determine whether they match an exon-exon junction where the exons come from different genes. This would be evidence of a possible fusion event, however, because of the length of the reads, this could prove to be very noisy. An alternative approach is to use paired-end reads, when a potentially large number of paired reads would map each end to a different exon, giving better coverage of these events (see figure). Nonetheless, the end result consists of multiple and potentially novel combinations of genes providing an ideal starting point for further validation.

Copy number alteration

Copy number alteration (CNA) analyses are commonly used in cancer studies. Gain and loss of the genes have signalling pathway implications and are a key biomarker of molecular dysfunction in oncology. Calling the CNA information from RNA-Seq data is not straightforward because of the differences in gene expression, which lead to the read depth variance of different magnitudes across genes. Due to these difficulties, most of these analyses are usually done using whole-genome sequencing / whole-exome sequencing (WGS/WES). But advanced bioinformatics tools can call CNA from  RNA-Seq. ^[140]

Other emerging analysis and applications

The applications of RNA-Seq are growing day by day. Other new application of RNA-Seq includes detection of microbial contaminants, ^[141] determining cell type abundance (cell type deconvolution), ^[8] measuring the expression of TEs and Neoantigen prediction etc. ^[8]

History

Pubmed manuscript matches highlight the growing popularity of RNA-Seq. Matches are for RNA-Seq (blue, search terms: "RNA Seq" OR "RNA-Seq" OR "RNA sequencing" OR "RNASeq") and RNA=Seq in medicine (gold, search terms: ("RNA Seq" OR "RNA-Seq" OR "RNA sequencing" OR "RNASeq") AND "Medicine"). The number of manuscripts on PubMed featuring RNA-Seq is still increasing.

RNA-Seq was first developed in mid 2000s with the advent of next-generation sequencing technology. ^[144] The first manuscripts that used RNA-Seq even without using the term includes those of prostate cancer cell lines ^[145] (dated 2006), Medicago truncatula ^[146] (2006), maize ^[147] (2007), and Arabidopsis thaliana ^[148] (2007), while the term "RNA-Seq" itself was first mentioned in 2008. ^{[13] [149]} The number of manuscripts referring to RNA-Seq in the title or abstract (Figure, blue line) is continuously increasing with 6754 manuscripts published in 2018. The intersection of RNA-Seq and medicine (Figure, gold line) has similar celerity. ^[150]

Applications to medicine

RNA-Seq has the potential to identify new disease biology, profile biomarkers for clinical indications, infer druggable pathways, and make genetic diagnoses. ^{[151] [152]} These results could be further personalized for subgroups or even individual patients, potentially highlighting more effective prevention, diagnostics, and therapy. The feasibility of this approach is in part dictated by costs in money and time; a related limitation is the required team of specialists (bioinformaticians, physicians/clinicians, basic researchers, technicians) to fully interpret the huge amount of data generated by this analysis. ^[153]

Large-scale sequencing efforts

A lot of emphasis has been given to RNA-Seq data after the Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA) projects have used this approach to characterize dozens of cell lines ^[154] and thousands of primary tumor samples, ^[155] respectively. ENCODE aimed to identify genome-wide regulatory regions in different cohort of cell lines and transcriptomic data are paramount to understand the downstream effect of those epigenetic and genetic regulatory layers. TCGA, instead, aimed to collect and analyze thousands of patient's samples from 30 different tumor types to understand the underlying mechanisms of malignant transformation and progression. In this context RNA-Seq data provide a unique snapshot of the transcriptomic status of the disease and look at an unbiased population of transcripts that allows the identification of novel transcripts, fusion transcripts and non-coding RNAs that could be undetected with different technologies.

See also

• Transcriptomics
• DNA microarray
• List of RNA-Seq bioinformatics tools

References

This article was submitted to WikiJournal of Science for external academic peer review in 2019 ( reviewer reports ). The updated content was reintegrated into the Wikipedia page under a CC-BY-SA-3.0 license ( 2021 ). The version of record as reviewed is: Felix Richter, et al. (17 May 2021). "A broad introduction to RNA-Seq" (PDF). WikiJournal of Science. 4 (2): 4. doi: 10.15347/WJS/2021.004 . ISSN   2470-6345. Wikidata   Q100146647.

1. Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T (May 2017). "Transcriptomics technologies". PLOS Computational Biology. 13 (5): e1005457. Bibcode:2017PLSCB..13E5457L. doi: 10.1371/journal.pcbi.1005457 . PMC   5436640 . PMID   28545146.
2. Chu Y, Corey DR (August 2012). "RNA sequencing: platform selection, experimental design, and data interpretation". Nucleic Acid Therapeutics. 22 (4): 271–4. doi:10.1089/nat.2012.0367. PMC   3426205 . PMID   22830413.
3. Wang Z, Gerstein M, Snyder M (January 2009). "RNA-Seq: a revolutionary tool for transcriptomics". Nature Reviews. Genetics. 10 (1): 57–63. doi:10.1038/nrg2484. PMC   2949280 . PMID   19015660.
4. Maher CA, Kumar-Sinha C, Cao X, Kalyana-Sundaram S, Han B, Jing X, et al. (March 2009). "Transcriptome sequencing to detect gene fusions in cancer". Nature. 458 (7234): 97–101. Bibcode:2009Natur.458...97M. doi:10.1038/nature07638. PMC   2725402 . PMID   19136943.
5. Ingolia NT, Brar GA, Rouskin S, McGeachy AM, Weissman JS (July 2012). "The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments". Nature Protocols. 7 (8): 1534–50. doi:10.1038/nprot.2012.086. PMC   3535016 . PMID   22836135.
6. Alpern D, Gardeux V, Russeil J, Mangeat B, Meireles-Filho AC, Breysse R, et al. (April 2019). "BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing". Genome Biology. 20 (1): 71. doi: 10.1186/s13059-019-1671-x . PMC   6474054 . PMID   30999927.
7. Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL, Ferrante TC, et al. (March 2014). "Highly multiplexed subcellular RNA sequencing in situ". Science. 343 (6177): 1360–3. Bibcode:2014Sci...343.1360L. doi:10.1126/science.1250212. PMC   4140943 . PMID   24578530.
8. Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, et al. (November 2021). "Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology". Briefings in Bioinformatics. 22 (6). doi:10.1093/bib/bbab259. PMID   34329375.
9. Kukurba KR, Montgomery SB (April 2015). "RNA Sequencing and Analysis". Cold Spring Harbor Protocols. 2015 (11): 951–69. doi:10.1101/pdb.top084970. PMC   4863231 . PMID   25870306.
10. Griffith M, Walker JR, Spies NC, Ainscough BJ, Griffith OL (August 2015). "Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud". PLOS Computational Biology. 11 (8): e1004393. Bibcode:2015PLSCB..11E4393G. doi: 10.1371/journal.pcbi.1004393 . PMC   4527835 . PMID   26248053.
11. "RNA-seqlopedia". rnaseq.uoregon.edu. Retrieved 8 February 2017.
12. Morin R, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh T, et al. (July 2008). "Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing". BioTechniques. 45 (1): 81–94. doi: 10.2144/000112900 . PMID   18611170.
13. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (July 2008). "Mapping and quantifying mammalian transcriptomes by RNA-Seq". Nature Methods. 5 (7): 621–8. doi:10.1038/nmeth.1226. PMID   18516045. S2CID   205418589.
14. Sun Q, Hao Q, Prasanth KV (February 2018). "Nuclear Long Noncoding RNAs: Key Regulators of Gene Expression". Trends in Genetics. 34 (2): 142–157. doi:10.1016/j.tig.2017.11.005. PMC   6002860 . PMID   29249332.
15. Sigurgeirsson B, Emanuelsson O, Lundeberg J (2014). "Sequencing degraded RNA addressed by 3' tag counting". PLOS ONE. 9 (3): e91851. Bibcode:2014PLoSO...991851S. doi: 10.1371/journal.pone.0091851 . PMC   3954844 . PMID   24632678.
16. Chen EA, Souaiaia T, Herstein JS, Evgrafov OV, Spitsyna VN, Rebolini DF, et al. (October 2014). "Effect of RNA integrity on uniquely mapped reads in RNA-Seq". BMC Research Notes. 7: 753. doi: 10.1186/1756-0500-7-753 . PMC   4213542 . PMID   25339126.
17. Moll P, Ante M, Seitz A, Reda T (December 2014). "QuantSeq 3′ mRNA sequencing for RNA quantification". Nature Methods. 11 (12): i–iii. doi:10.1038/nmeth.f.376. ISSN   1548-7105. S2CID   83424788.
18. Oikonomopoulos S, Bayega A, Fahiminiya S, Djambazian H, Berube P, Ragoussis J (2020). "Methodologies for Transcript Profiling Using Long-Read Technologies". Frontiers in Genetics. 11: 606. doi: 10.3389/fgene.2020.00606 . PMC   7358353 . PMID   32733532.
19. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. (January 2016). "A survey of best practices for RNA-seq data analysis". Genome Biology. 17 (1): 13. doi: 10.1186/s13059-016-0881-8 . PMC   4728800 . PMID   26813401.
20. Liu D, Graber JH (February 2006). "Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation". BMC Bioinformatics. 7: 77. doi: 10.1186/1471-2105-7-77 . PMC   1431573 . PMID   16503995.
21. Garalde DR, Snell EA, Jachimowicz D, Sipos B, Lloyd JH, Bruce M, et al. (March 2018). "Highly parallel direct RNA sequencing on an array of nanopores". Nature Methods. 15 (3): 201–206. doi:10.1038/nmeth.4577. PMID   29334379. S2CID   3589823.
22. Liu D, Graber JH (February 2006). "Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation". BMC Bioinformatics. 7: 77. doi: 10.1186/1471-2105-7-77 . PMC   1431573 . PMID   16503995.
23. Gleeson J, Lane TA, Harrison PJ, Haerty W, Clark MB (3 August 2020). "Nanopore direct RNA sequencing detects differential expression between human cell populations". bioRxiv: 2020.08.02.232785. doi: 10.1101/2020.08.02.232785 . S2CID   220975367.
24. "Shapiro E, Biezuner T, Linnarsson S (September 2013). "Single-cell sequencing-based technologies will revolutionize whole-organism science". Nature Reviews. Genetics. 14 (9): 618–30. doi:10.1038/nrg3542. PMID   23897237. S2CID   500845."
25. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (May 2015). "The technology and biology of single-cell RNA sequencing". Molecular Cell. 58 (4): 610–20. doi: 10.1016/j.molcel.2015.04.005 . PMID   26000846.
26. Montoro DT, Haber AL, Biton M, Vinarsky V, Lin B, Birket SE, et al. (August 2018). "A revised airway epithelial hierarchy includes CFTR-expressing ionocytes". Nature. 560 (7718): 319–324. Bibcode:2018Natur.560..319M. doi:10.1038/s41586-018-0393-7. PMC   6295155 . PMID   30069044.
27. Plasschaert LW, Žilionis R, Choo-Wing R, Savova V, Knehr J, Roma G, et al. (August 2018). "A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte". Nature. 560 (7718): 377–381. Bibcode:2018Natur.560..377P. doi:10.1038/s41586-018-0394-6. PMC   6108322 . PMID   30069046.
28. Valihrach L, Androvic P, Kubista M (March 2018). "Platforms for Single-Cell Collection and Analysis". International Journal of Molecular Sciences. 19 (3): 807. doi: 10.3390/ijms19030807 . PMC   5877668 . PMID   29534489.
29. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, et al. (May 2015). "Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells". Cell. 161 (5): 1187–1201. doi:10.1016/j.cell.2015.04.044. PMC   4441768 . PMID   26000487.
30. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. (May 2015). "Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets". Cell. 161 (5): 1202–1214. doi:10.1016/j.cell.2015.05.002. PMC   4481139 . PMID   26000488.
31. Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, et al. (February 2014). "Quantitative single-cell RNA-seq with unique molecular identifiers". Nature Methods. 11 (2): 163–6. doi:10.1038/nmeth.2772. PMID   24363023. S2CID   6765530.
32. "Hebenstreit D (November 2012). "Methods, Challenges and Potentials of Single Cell RNA-seq". Biology. 1 (3): 658–67. doi: 10.3390/biology1030658 . PMC   4009822 . PMID   24832513."
33. Eberwine J, Sul JY, Bartfai T, Kim J (January 2014). "The promise of single-cell sequencing". Nature Methods. 11 (1): 25–7. doi:10.1038/nmeth.2769. PMID   24524134. S2CID   11575439.
34. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, et al. (May 2009). "mRNA-Seq whole-transcriptome analysis of a single cell". Nature Methods. 6 (5): 377–82. doi:10.1038/NMETH.1315. PMID   19349980. S2CID   16570747.
35. Islam S, Kjällquist U, Moliner A, Zajac P, Fan JB, Lönnerberg P, et al. (July 2011). "Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq". Genome Research. 21 (7): 1160–7. doi:10.1101/gr.110882.110. PMC   3129258 . PMID   21543516.
36. Ramsköld D, Luo S, Wang YC, Li R, Deng Q, Faridani OR, et al. (August 2012). "Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells". Nature Biotechnology. 30 (8): 777–82. doi:10.1038/nbt.2282. PMC   3467340 . PMID   22820318.
37. Hashimshony T, Wagner F, Sher N, Yanai I (September 2012). "CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification". Cell Reports. 2 (3): 666–73. doi: 10.1016/j.celrep.2012.08.003 . PMID   22939981.
38. Singh M, Al-Eryani G, Carswell S, Ferguson JM, Blackburn J, Barton K, et al. (2018). "High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes". bioRxiv. 10 (1): 3120. doi: 10.1101/424945 . PMC   6635368 . PMID   31311926.
39. Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, et al. (April 2013). "Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity". Genome Biology. 14 (4): R31. doi: 10.1186/gb-2013-14-4-r31 . PMC   4054835 . PMID   23594475.
40. Kouno T, Moody J, Kwon AT, Shibayama Y, Kato S, Huang Y, et al. (January 2019). "C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution". Nature Communications. 10 (1): 360. Bibcode:2019NatCo..10..360K. doi:10.1038/s41467-018-08126-5. PMC   6341120 . PMID   30664627.
41. Dal Molin A, Di Camillo B (2019). "How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives". Briefings in Bioinformatics. 20 (4): 1384–1394. doi:10.1093/bib/bby007. PMID   29394315.
42. Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, et al. (October 2017). "Multiplexed quantification of proteins and transcripts in single cells". Nature Biotechnology. 35 (10): 936–939. doi:10.1038/nbt.3973. PMID   28854175. S2CID   205285357.
43. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. (September 2017). "Simultaneous epitope and transcriptome measurement in single cells". Nature Methods. 14 (9): 865–868. doi:10.1038/nmeth.4380. PMC   5669064 . PMID   28759029.
44. Raj B, Wagner DE, McKenna A, Pandey S, Klein AM, Shendure J, et al. (June 2018). "Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain". Nature Biotechnology. 36 (5): 442–450. doi:10.1038/nbt.4103. PMC   5938111 . PMID   29608178.
45. Olmos D, Arkenau HT, Ang JE, Ledaki I, Attard G, Carden CP, et al. (January 2009). "Circulating tumour cell (CTC) counts as intermediate end points in castration-resistant prostate cancer (CRPC): a single-centre experience". Annals of Oncology. 20 (1): 27–33. doi: 10.1093/annonc/mdn544 . PMID   18695026.
46. Levitin HM, Yuan J, Sims PA (April 2018). "Single-Cell Transcriptomic Analysis of Tumor Heterogeneity". Trends in Cancer. 4 (4): 264–268. doi:10.1016/j.trecan.2018.02.003. PMC   5993208 . PMID   29606308.
47. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. (November 2018). "A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade". Cell. 175 (4): 984–997.e24. doi:10.1016/j.cell.2018.09.006. PMC   6410377 . PMID   30388455.
48. Stephenson W, Donlin LT, Butler A, Rozo C, Bracken B, Rashidfarrokhi A, et al. (February 2018). "Single-cell RNA-seq of rheumatoid arthritis synovial tissue using low-cost microfluidic instrumentation". Nature Communications. 9 (1): 791. Bibcode:2018NatCo...9..791S. doi:10.1038/s41467-017-02659-x. PMC   5824814 . PMID   29476078.
49. Avraham R, Haseley N, Brown D, Penaranda C, Jijon HB, Trombetta JJ, et al. (September 2015). "Pathogen Cell-to-Cell Variability Drives Heterogeneity in Host Immune Responses". Cell. 162 (6): 1309–21. doi:10.1016/j.cell.2015.08.027. PMC   4578813 . PMID   26343579.
50. Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C, Daza R, et al. (August 2017). "Comprehensive single-cell transcriptional profiling of a multicellular organism". Science. 357 (6352): 661–667. Bibcode:2017Sci...357..661C. doi:10.1126/science.aam8940. PMC   5894354 . PMID   28818938.
51. Plass M, Solana J, Wolf FA, Ayoub S, Misios A, Glažar P, et al. (May 2018). "Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics". Science. 360 (6391): eaaq1723. doi: 10.1126/science.aaq1723 . PMID   29674432.
52. Fincher CT, Wurtzel O, de Hoog T, Kravarik KM, Reddien PW (May 2018). "Schmidtea mediterranea". Science. 360 (6391): eaaq1736. doi:10.1126/science.aaq1736. PMC   6563842 . PMID   29674431.
53. Wagner DE, Weinreb C, Collins ZM, Briggs JA, Megason SG, Klein AM (June 2018). "Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo". Science. 360 (6392): 981–987. Bibcode:2018Sci...360..981W. doi:10.1126/science.aar4362. PMC   6083445 . PMID   29700229.
54. Farrell JA, Wang Y, Riesenfeld SJ, Shekhar K, Regev A, Schier AF (June 2018). "Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis". Science. 360 (6392): eaar3131. doi:10.1126/science.aar3131. PMC   6247916 . PMID   29700225.
55. Briggs JA, Weinreb C, Wagner DE, Megason S, Peshkin L, Kirschner MW, et al. (June 2018). "The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution". Science. 360 (6392): eaar5780. doi:10.1126/science.aar5780. PMC   6038144 . PMID   29700227.
56. You J. "Science's 2018 Breakthrough of the Year: tracking development cell by cell". Science Magazine. American Association for the Advancement of Science.
57. Li H, Lovci MT, Kwon YS, Rosenfeld MG, Fu XD, Yeo GW (December 2008). "Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model". Proceedings of the National Academy of Sciences of the United States of America. 105 (51): 20179–84. Bibcode:2008PNAS..10520179L. doi: 10.1073/pnas.0807121105 . PMC   2603435 . PMID   19088194.
58. Stegle O, Parts L, Piipari M, Winn J, Durbin R (February 2012). "Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses". Nature Protocols. 7 (3): 500–7. doi:10.1038/nprot.2011.457. PMC   3398141 . PMID   22343431.
59. Kingsford C, Patro R (June 2015). "Reference-based compression of short-read sequences using path encoding". Bioinformatics. 31 (12): 1920–8. doi:10.1093/bioinformatics/btv071. PMC   4481695 . PMID   25649622.
60. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. (May 2011). "Full-length transcriptome assembly from RNA-Seq data without a reference genome". Nature Biotechnology. 29 (7): 644–52. doi:10.1038/nbt.1883. PMC   3571712 . PMID   21572440.
61. "De Novo Assembly Using Illumina Reads" (PDF). Retrieved 22 October 2016.
62. Oases: a transcriptome assembler for very short reads
63. Zerbino DR, Birney E (May 2008). "Velvet: algorithms for de novo short read assembly using de Bruijn graphs". Genome Research. 18 (5): 821–9. doi:10.1101/gr.074492.107. PMC   2336801 . PMID   18349386.
64. Chang Z, Li G, Liu J, Zhang Y, Ashby C, Liu D, et al. (February 2015). "Bridger: a new framework for de novo transcriptome assembly using RNA-seq data". Genome Biology. 16 (1): 30. doi: 10.1186/s13059-015-0596-2 . PMC   4342890 . PMID   25723335.
65. Bushmanova E, Antipov D, Lapidus A, Prjibelski AD (September 2019). "rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data". GigaScience. 8 (9). doi:10.1093/gigascience/giz100. PMC   6736328 . PMID   31494669.
66. Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, et al. (December 2014). "Evaluation of de novo transcriptome assemblies from RNA-Seq data". Genome Biology. 15 (12): 553. doi: 10.1186/s13059-014-0553-5 . PMC   4298084 . PMID   25608678.
67. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. (January 2013). "STAR: ultrafast universal RNA-seq aligner". Bioinformatics. 29 (1): 15–21. doi:10.1093/bioinformatics/bts635. PMC   3530905 . PMID   23104886.
68. Langmead B, Trapnell C, Pop M, Salzberg SL (2009). "Ultrafast and memory-efficient alignment of short DNA sequences to the human genome". Genome Biology. 10 (3): R25. doi: 10.1186/gb-2009-10-3-r25 . PMC   2690996 . PMID   19261174.
69. Trapnell C, Pachter L, Salzberg SL (May 2009). "TopHat: discovering splice junctions with RNA-Seq". Bioinformatics. 25 (9): 1105–11. doi:10.1093/bioinformatics/btp120. PMC   2672628 . PMID   19289445.
70. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. (March 2012). "Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks". Nature Protocols. 7 (3): 562–78. doi:10.1038/nprot.2012.016. PMC   3334321 . PMID   22383036.
71. Liao Y, Smyth GK, Shi W (May 2013). "The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote". Nucleic Acids Research. 41 (10): e108. doi:10.1093/nar/gkt214. PMC   3664803 . PMID   23558742.
72. Kim D, Langmead B, Salzberg SL (April 2015). "HISAT: a fast spliced aligner with low memory requirements". Nature Methods. 12 (4): 357–60. doi:10.1038/nmeth.3317. PMC   4655817 . PMID   25751142.
73. Wu TD, Watanabe CK (May 2005). "GMAP: a genomic mapping and alignment program for mRNA and EST sequences". Bioinformatics. 21 (9): 1859–75. doi: 10.1093/bioinformatics/bti310 . PMID   15728110.
74. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL (March 2015). "StringTie enables improved reconstruction of a transcriptome from RNA-seq reads". Nature Biotechnology. 33 (3): 290–5. doi:10.1038/nbt.3122. PMC   4643835 . PMID   25690850.
75. Baruzzo G, Hayer KE, Kim EJ, Di Camillo B, FitzGerald GA, Grant GR (February 2017). "Simulation-based comprehensive benchmarking of RNA-seq aligners". Nature Methods. 14 (2): 135–139. doi:10.1038/nmeth.4106. PMC   5792058 . PMID   27941783.
76. Engström PG, Steijger T, Sipos B, Grant GR, Kahles A, Rätsch G, et al. (December 2013). "Systematic evaluation of spliced alignment programs for RNA-seq data". Nature Methods. 10 (12): 1185–91. doi:10.1038/nmeth.2722. PMC   4018468 . PMID   24185836.
77. Lu B, Zeng Z, Shi T (February 2013). "Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq". Science China Life Sciences. 56 (2): 143–55. doi: 10.1007/s11427-013-4442-z . PMID   23393030.
78. Bradnam KR, Fass JN, Alexandrov A, Baranay P, Bechner M, Birol I, et al. (July 2013). "Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species". GigaScience. 2 (1): 10. arXiv: 1301.5406 . Bibcode:2013arXiv1301.5406B. doi: 10.1186/2047-217X-2-10 . PMC   3844414 . PMID   23870653.
79. Hölzer M, Marz M (May 2019). "De novo transcriptome assembly: A comprehensive cross-species comparison of short-read RNA-Seq assemblers". GigaScience. 8 (5). doi:10.1093/gigascience/giz039. PMC   6511074 . PMID   31077315.
80. Greenbaum D, Colangelo C, Williams K, Gerstein M (2003). "Comparing protein abundance and mRNA expression levels on a genomic scale". Genome Biology. 4 (9): 117. doi: 10.1186/gb-2003-4-9-117 . PMC   193646 . PMID   12952525.
81. Zhang ZH, Jhaveri DJ, Marshall VM, Bauer DC, Edson J, Narayanan RK, et al. (August 2014). "A comparative study of techniques for differential expression analysis on RNA-Seq data". PLOS ONE. 9 (8): e103207. Bibcode:2014PLoSO...9j3207Z. doi: 10.1371/journal.pone.0103207 . PMC   4132098 . PMID   25119138.
82. Anders S, Pyl PT, Huber W (January 2015). "HTSeq--a Python framework to work with high-throughput sequencing data". Bioinformatics. 31 (2): 166–9. doi:10.1093/bioinformatics/btu638. PMC   4287950 . PMID   25260700.
83. Liao Y, Smyth GK, Shi W (April 2014). "featureCounts: an efficient general purpose program for assigning sequence reads to genomic features". Bioinformatics. 30 (7): 923–30. arXiv: 1305.3347 . doi:10.1093/bioinformatics/btt656. PMID   24227677.
84. Schmid MW, Grossniklaus U (February 2015). "Rcount: simple and flexible RNA-Seq read counting". Bioinformatics. 31 (3): 436–7. doi: 10.1093/bioinformatics/btu680 . PMID   25322836.
85. Finotello F, Lavezzo E, Bianco L, Barzon L, Mazzon P, Fontana P, et al. (2014). "Reducing bias in RNA sequencing data: a novel approach to compute counts". BMC Bioinformatics. 15 (Suppl 1): S7. doi: 10.1186/1471-2105-15-s1-s7 . PMC   4016203 . PMID   24564404.
86. Hashimoto TB, Edwards MD, Gifford DK (March 2014). "Universal count correction for high-throughput sequencing". PLOS Computational Biology. 10 (3): e1003494. Bibcode:2014PLSCB..10E3494H. doi: 10.1371/journal.pcbi.1003494 . PMC   3945112 . PMID   24603409.
87. Patro R, Mount SM, Kingsford C (May 2014). "Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms". Nature Biotechnology. 32 (5): 462–4. arXiv: 1308.3700 . doi:10.1038/nbt.2862. PMC   4077321 . PMID   24752080.
88. Bray NL, Pimentel H, Melsted P, Pachter L (May 2016). "Near-optimal probabilistic RNA-seq quantification". Nature Biotechnology. 34 (5): 525–7. doi:10.1038/nbt.3519. PMID   27043002. S2CID   205282743.
89. Robinson MD, Oshlack A (2010). "A scaling normalization method for differential expression analysis of RNA-seq data". Genome Biology. 11 (3): R25. doi: 10.1186/gb-2010-11-3-r25 . PMC   2864565 . PMID   20196867.
90. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. (May 2010). "Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation". Nature Biotechnology. 28 (5): 511–5. doi:10.1038/nbt.1621. PMC   3146043 . PMID   20436464.
91. Pachter L (19 April 2011). "Models for transcript quantification from RNA-Seq". arXiv: 1104.3889 [q-bio.GN].
92. "What the FPKM? A review of RNA-Seq expression units". The farrago. 8 May 2014. Retrieved 28 March 2018.
93. Wagner GP, Kin K, Lynch VJ (December 2012). "Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples". Theory in Biosciences. 131 (4): 281–5. doi:10.1007/s12064-012-0162-3. PMID   22872506. S2CID   16752581.
94. Evans C, Hardin J, Stoebel DM (September 2018). "Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions". Briefings in Bioinformatics. 19 (5): 776–792. doi:10.1093/bib/bbx008. PMC   6171491 . PMID   28334202.
95. Law CW, Chen Y, Shi W, Smyth GK (February 2014). "voom: Precision weights unlock linear model analysis tools for RNA-seq read counts". Genome Biology. 15 (2): R29. doi: 10.1186/gb-2014-15-2-r29 . PMC   4053721 . PMID   24485249.
96. Anders S, Huber W (2010). "Differential expression analysis for sequence count data". Genome Biology. 11 (10): R106. doi: 10.1186/gb-2010-11-10-r106 . PMC   3218662 . PMID   20979621.
97. Robinson MD, McCarthy DJ, Smyth GK (January 2010). "edgeR: a Bioconductor package for differential expression analysis of digital gene expression data". Bioinformatics. 26 (1): 139–40. doi:10.1093/bioinformatics/btp616. PMC   2796818 . PMID   19910308.
98. Marguerat S, Schmidt A, Codlin S, Chen W, Aebersold R, Bähler J (October 2012). "Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells". Cell. 151 (3): 671–83. doi:10.1016/j.cell.2012.09.019. PMC   3482660 . PMID   23101633.
99. Owens ND, Blitz IL, Lane MA, Patrushev I, Overton JD, Gilchrist MJ, et al. (January 2016). "Measuring Absolute RNA Copy Numbers at High Temporal Resolution Reveals Transcriptome Kinetics in Development". Cell Reports. 14 (3): 632–647. doi:10.1016/j.celrep.2015.12.050. PMC   4731879 . PMID   26774488.
100. Chen K, Hu Z, Xia Z, Zhao D, Li W, Tyler JK (December 2015). "The Overlooked Fact: Fundamental Need for Spike-In Control for Virtually All Genome-Wide Analyses". Molecular and Cellular Biology. 36 (5): 662–7. doi:10.1128/MCB.00970-14. PMC   4760223 . PMID   26711261.
101. Lovén J, Orlando DA, Sigova AA, Lin CY, Rahl PB, Burge CB, et al. (October 2012). "Revisiting global gene expression analysis". Cell. 151 (3): 476–82. doi:10.1016/j.cell.2012.10.012. PMC   3505597 . PMID   23101621.
102. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. (April 2015). "limma powers differential expression analyses for RNA-sequencing and microarray studies". Nucleic Acids Research. 43 (7): e47. doi:10.1093/nar/gkv007. PMC   4402510 . PMID   25605792.
103. "Bioconductor - Open source software for bioinformatics".
104. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. (February 2015). "Orchestrating high-throughput genomic analysis with Bioconductor". Nature Methods. 12 (2): 115–21. doi:10.1038/nmeth.3252. PMC   4509590 . PMID   25633503.
105. Leek JT, Storey JD (September 2007). "Capturing heterogeneity in gene expression studies by surrogate variable analysis". PLOS Genetics. 3 (9): 1724–35. doi: 10.1371/journal.pgen.0030161 . PMC   1994707 . PMID   17907809.
106. Pimentel H, Bray NL, Puente S, Melsted P, Pachter L (July 2017). "Differential analysis of RNA-seq incorporating quantification uncertainty". Nature Methods. 14 (7): 687–690. doi:10.1038/nmeth.4324. PMID   28581496. S2CID   15063247.
107. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (January 2013). "Differential analysis of gene regulation at transcript resolution with RNA-seq". Nature Biotechnology. 31 (1): 46–53. doi:10.1038/nbt.2450. PMC   3869392 . PMID   23222703.
108. Frazee AC, Pertea G, Jaffe AE, Langmead B, Salzberg SL, Leek JT (March 2015). "Ballgown bridges the gap between transcriptome assembly and expression analysis". Nature Biotechnology. 33 (3): 243–6. doi:10.1038/nbt.3172. PMC   4792117 . PMID   25748911.
109. Sahraeian SM, Mohiyuddin M, Sebra R, Tilgner H, Afshar PT, Au KF, et al. (July 2017). "Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis". Nature Communications. 8 (1): 59. Bibcode:2017NatCo...8...59S. doi:10.1038/s41467-017-00050-4. PMC   5498581 . PMID   28680106.
110. Ziemann M, Eren Y, El-Osta A (August 2016). "Gene name errors are widespread in the scientific literature". Genome Biology. 17 (1): 177. doi: 10.1186/s13059-016-1044-7 . PMC   4994289 . PMID   27552985.
111. Soneson C, Delorenzi M (March 2013). "A comparison of methods for differential expression analysis of RNA-seq data". BMC Bioinformatics. 14: 91. doi: 10.1186/1471-2105-14-91 . PMC   3608160 . PMID   23497356.
112. Fonseca NA, Marioni J, Brazma A (30 September 2014). "RNA-Seq gene profiling--a systematic empirical comparison". PLOS ONE. 9 (9): e107026. Bibcode:2014PLoSO...9j7026F. doi: 10.1371/journal.pone.0107026 . PMC   4182317 . PMID   25268973.
113. Seyednasrollah F, Laiho A, Elo LL (January 2015). "Comparison of software packages for detecting differential expression in RNA-seq studies". Briefings in Bioinformatics. 16 (1): 59–70. doi:10.1093/bib/bbt086. PMC   4293378 . PMID   24300110.
114. Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. (2013). "Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data". Genome Biology. 14 (9): R95. doi: 10.1186/gb-2013-14-9-r95 . PMC   4054597 . PMID   24020486.
115. Costa-Silva J, Domingues D, Lopes FM (21 December 2017). "RNA-Seq differential expression analysis: An extended review and a software tool". PLOS ONE. 12 (12): e0190152. Bibcode:2017PLoSO..1290152C. doi: 10.1371/journal.pone.0190152 . PMC   5739479 . PMID   29267363.
116. Corchete LA, Rojas EA, Alonso-López D, De Las Rivas J, Gutiérrez NC, Burguillo FJ (12 November 2020). "Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis". Scientific Reports. 12 (10): 19737. Bibcode:2020NatSR..1019737C. doi: 10.1038/s41598-020-76881-x . PMC   7665074 . PMID   33184454.
117. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B (July 2019). "WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs". Nucleic Acids Research. 47 (W1): W199–W205. doi:10.1093/nar/gkz401. PMC   6602449 . PMID   31114916.
118. Keren H, Lev-Maor G, Ast G (May 2010). "Alternative splicing and evolution: diversification, exon definition and function". Nature Reviews. Genetics. 11 (5): 345–55. doi:10.1038/nrg2776. PMID   20376054. S2CID   5184582.
119. Liu R, Loraine AE, Dickerson JA (December 2014). "Comparisons of computational methods for differential alternative splicing detection using RNA-seq in plant systems". BMC Bioinformatics. 15 (1): 364. doi: 10.1186/s12859-014-0364-4 . PMC   4271460 . PMID   25511303.
120. Li YI, Knowles DA, Humphrey J, Barbeira AN, Dickinson SP, Im HK, et al. (January 2018). "Annotation-free quantification of RNA splicing using LeafCutter". Nature Genetics. 50 (1): 151–158. doi:10.1038/s41588-017-0004-9. PMC   5742080 . PMID   29229983.
121. Anders S, Reyes A, Huber W (October 2012). "Detecting differential usage of exons from RNA-seq data". Genome Research. 22 (10): 2008–17. doi:10.1101/gr.133744.111. PMC   3460195 . PMID   22722343.
122. Shen S, Park JW, Huang J, Dittmar KA, Lu ZX, Zhou Q, et al. (April 2012). "MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data". Nucleic Acids Research. 40 (8): e61. doi:10.1093/nar/gkr1291. PMC   3333886 . PMID   22266656.
123. Wang X, Cairns MJ (June 2014). "SeqGSEA: a Bioconductor package for gene set enrichment analysis of RNA-Seq data integrating differential expression and splicing". Bioinformatics. 30 (12): 1777–9. doi: 10.1093/bioinformatics/btu090 . PMID   24535097.
124. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L (January 2013). "Differential analysis of gene regulation at transcript resolution with RNA-seq". Nature Biotechnology. 31 (1): 46–53. doi:10.1038/nbt.2450. PMC   3869392 . PMID   23222703.
125. Hu Y, Huang Y, Du Y, Orellana CF, Singh D, Johnson AR, et al. (January 2013). "DiffSplice: the genome-wide detection of differential splicing events with RNA-seq". Nucleic Acids Research. 41 (2): e39. doi:10.1093/nar/gks1026. PMC   3553996 . PMID   23155066.
126. Vaquero-Garcia J, Barrera A, Gazzara MR, González-Vallinas J, Lahens NF, Hogenesch JB, et al. (February 2016). "A new view of transcriptome complexity and regulation through the lens of local splicing variations". eLife. 5: e11752. doi: 10.7554/eLife.11752 . PMC   4801060 . PMID   26829591.
127. Merino GA, Conesa A, Fernández EA (March 2019). "A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies". Briefings in Bioinformatics. 20 (2): 471–481. doi:10.1093/bib/bbx122. hdl: 11336/41247 . PMID   29040385. S2CID   22706028.
128. Marcotte EM, Pellegrini M, Thompson MJ, Yeates TO, Eisenberg D (November 1999). "A combined algorithm for genome-wide prediction of protein function". Nature. 402 (6757): 83–6. Bibcode:1999Natur.402...83M. doi:10.1038/47048. PMID   10573421. S2CID   144447.
129. Giorgi FM, Del Fabbro C, Licausi F (March 2013). "Comparative study of RNA-seq- and microarray-derived coexpression networks in Arabidopsis thaliana". Bioinformatics. 29 (6): 717–24. doi: 10.1093/bioinformatics/btt053 . hdl: 11390/990155 . PMID   23376351.
130. Iancu OD, Kawane S, Bottomly D, Searles R, Hitzemann R, McWeeney S (June 2012). "Utilizing RNA-Seq data for de novo coexpression network inference". Bioinformatics. 28 (12): 1592–7. doi:10.1093/bioinformatics/bts245. PMC   3493127 . PMID   22556371.
131. Eksi R, Li HD, Menon R, Wen Y, Omenn GS, Kretzler M, et al. (November 2013). "Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data". PLOS Computational Biology. 9 (11): e1003314. Bibcode:2013PLSCB...9E3314E. doi: 10.1371/journal.pcbi.1003314 . PMC   3820534 . PMID   24244129.
132. Li HD, Menon R, Omenn GS, Guan Y (August 2014). "The emerging era of genomic data integration for analyzing splice isoform function". Trends in Genetics. 30 (8): 340–7. doi:10.1016/j.tig.2014.05.005. PMC   4112133 . PMID   24951248.
133. Foroushani A, Agrahari R, Docking R, Chang L, Duns G, Hudoba M, et al. (March 2017). "Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications". BMC Medical Genomics. 10 (1): 16. doi: 10.1186/s12920-017-0253-6 . PMC   5353782 . PMID   28298217.
134. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. (August 2009). "The Sequence Alignment/Map format and SAMtools". Bioinformatics. 25 (16): 2078–9. doi:10.1093/bioinformatics/btp352. PMC   2723002 . PMID   19505943.
135. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. (May 2011). "A framework for variation discovery and genotyping using next-generation DNA sequencing data". Nature Genetics. 43 (5): 491–8. doi:10.1038/ng.806. PMC   3083463 . PMID   21478889.
136. Battle A, Brown CD, Engelhardt BE, Montgomery SB (October 2017). "Genetic effects on gene expression across human tissues". Nature. 550 (7675): 204–213. Bibcode:2017Natur.550..204A. doi:10.1038/nature24277. hdl: 10230/34202 . PMC   5776756 . PMID   29022597.
137. Richter F, Hoffman GE, Manheimer KB, Patel N, Sharp AJ, McKean D, et al. (October 2019). "ORE identifies extreme expression effects enriched for rare variants". Bioinformatics. 35 (20): 3906–3912. doi:10.1093/bioinformatics/btz202. PMC   6792115 . PMID   30903145.
138. Freedman AH, Clamp M, Sackton TB (January 2021). "Error, noise and bias in de novo transcriptome assemblies". Molecular Ecology Resources. 21 (1): 18–29. doi:10.1111/1755-0998.13156. PMID   32180366. S2CID   212739959.
139. Teixeira MR (December 2006). "Recurrent fusion oncogenes in carcinomas". Critical Reviews in Oncogenesis. 12 (3–4): 257–71. doi:10.1615/critrevoncog.v12.i3-4.40. PMID   17425505. S2CID   40770452.
140. Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, et al. (November 2021). "Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology". Briefings in Bioinformatics. 22 (6). doi:10.1093/bib/bbab259. PMID   34329375.
141. Sangiovanni M, Granata I, Thind AS, Guarracino MR (April 2019). "From trash to treasure: detecting unexpected contamination in unmapped NGS data". BMC Bioinformatics. 20 (Suppl 4): 168. doi: 10.1186/s12859-019-2684-x . PMC   6472186 . PMID   30999839.
142. "PubMed search: "RNA Seq" OR "RNA-Seq" OR "RNA sequencing" OR "RNASeq"". PubMed. Retrieved 20 June 2021.
143. "PubMed search: ("RNA Seq" OR "RNA-Seq" OR "RNA sequencing" OR "RNASeq") AND "Medicine"". PubMed. Retrieved 20 June 2021.
144. Weber AP (November 2015). "Discovering New Biology through Sequencing of RNA". Plant Physiology. 169 (3): 1524–31. doi:10.1104/pp.15.01081. PMC   4634082 . PMID   26353759.
145. Bainbridge MN, Warren RL, Hirst M, Romanuik T, Zeng T, Go A, et al. (September 2006). "Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach". BMC Genomics. 7: 246. doi: 10.1186/1471-2164-7-246 . PMC   1592491 . PMID   17010196.
146. Cheung F, Haas BJ, Goldberg SM, May GD, Xiao Y, Town CD (October 2006). "Sequencing Medicago truncatula expressed sequenced tags using 454 Life Sciences technology". BMC Genomics. 7: 272. doi: 10.1186/1471-2164-7-272 . PMC   1635983 . PMID   17062153.
147. Emrich SJ, Barbazuk WB, Li L, Schnable PS (January 2007). "Gene discovery and annotation using LCM-454 transcriptome sequencing". Genome Research. 17 (1): 69–73. doi:10.1101/gr.5145806. PMC   1716268 . PMID   17095711.
148. Weber AP, Weber KL, Carr K, Wilkerson C, Ohlrogge JB (May 2007). "Sampling the Arabidopsis transcriptome with massively parallel pyrosequencing". Plant Physiology. 144 (1): 32–42. doi:10.1104/pp.107.096677. PMC   1913805 . PMID   17351049.
149. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. (June 2008). "The transcriptional landscape of the yeast genome defined by RNA sequencing". Science. 320 (5881): 1344–9. Bibcode:2008Sci...320.1344N. doi:10.1126/science.1158441. PMC   2951732 . PMID   18451266.
150. Richter F (2021). "A broad introduction to RNA-Seq". WikiJournal of Science. 4 (1): 4. doi: 10.15347/WJS/2021.004 .
151. Cummings BB, Marshall JL, Tukiainen T, Lek M, Donkervoort S, Foley AR, et al. (19 April 2017). "Improving genetic diagnosis in Mendelian disease with transcriptome sequencing". Science Translational Medicine. 9 (386). doi:10.1126/scitranslmed.aal5209. hdl: 10230/35912 .
152. Kremer LS, Bader DM, Mertes C, Kopajtich R, Pichler G, Iuso A, et al. (12 June 2017). "Genetic diagnosis of Mendelian disorders via RNA sequencing". Nature Communications. 8 (1). doi:10.1038/ncomms15824.
153. Sandberg R (January 2014). "Entering the era of single-cell transcriptomics in biology and medicine". Nature Methods. 11 (1): 22–4. doi:10.1038/nmeth.2764. PMID   24524133. S2CID   27632439.
154. "ENCODE Data Matrix" . Retrieved 28 July 2013.
155. "The Cancer Genome Atlas – Data Portal" . Retrieved 28 July 2013.

Further reading

• Taguchi Y (2019). "Comparative Transcriptomics Analysis". Encyclopedia of Bioinformatics and Computational Biology. pp. 814–818. doi:10.1016/B978-0-12-809633-8.20163-5. ISBN   978-0-12-811432-2. S2CID   65302519.

External links

• Cresko B, Voelker R, Small C (2001). Bassham S, Catchen J (eds.). "RNA-seqlopedia". University of Oregon.: a high-level guide to designing and implementing an RNA-Seq experiment.