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Journal ArticleDOI

RNA-Seq: a revolutionary tool for transcriptomics

01 Jan 2009-Nature Reviews Genetics (Nature Publishing Group)-Vol. 10, Iss: 1, pp 57-63
TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
Abstract: RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.

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Citations
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Journal ArticleDOI
TL;DR: The results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis, and seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat are compared.
Abstract: Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

125 citations

Journal ArticleDOI
TL;DR: The results clearly showed that the DSN treatment was more efficient at removing rRNA than the Hyb method was, while preserving the original relative abundance of mRNA species in bacterial cells, and it is proposed that, for bacterial mRNA-seq experiments, D SN treatment should be preferred to Hyb-based methods.
Abstract: Next-generation sequencing has great potential for application in bacterial transcriptomics. However, unlike eukaryotes, bacteria have no clear mechanism to select mRNAs over rRNAs; therefore, rRNA removal is a critical step in sequencing-based transcriptomics. Duplex-specific nuclease (DSN) is an enzyme that, at high temperatures, degrades duplex DNA in preference to single-stranded DNA. DSN treatment has been successfully used to normalize the relative transcript abundance in mRNA-enriched cDNA libraries from eukaryotic organisms. In this study, we demonstrate the utility of this method to remove rRNA from prokaryotic total RNA. We evaluated the efficacy of DSN to remove rRNA by comparing it with the conventional subtractive hybridization (Hyb) method. Illumina deep sequencing was performed to obtain transcriptomes from Escherichia coli grown under four growth conditions. The results clearly showed that our DSN treatment was more efficient at removing rRNA than the Hyb method was, while preserving the original relative abundance of mRNA species in bacterial cells. Therefore, we propose that, for bacterial mRNA-seq experiments, DSN treatment should be preferred to Hyb-based methods.

124 citations


Cites methods from "RNA-Seq: a revolutionary tool for t..."

  • ...This method uses high-throughput nextgeneration sequencing technology and has revolutionized the way in which gene expression profiles are examined (1)....

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Journal ArticleDOI
TL;DR: It is demonstrated that integrative interpretation of transcriptome data is essential for the selection of putative alternative promoter TSCs, two of which also have protein consequences.
Abstract: We performed a genome-wide analysis of transcriptional start sites (TSSs) in human genes by multifaceted use of a massively parallel sequencer. By analyzing 800 million sequences that were obtained from various types of transcriptome analyses, we characterized 140 million TSS tags in 12 human cell types. Despite the large number of TSS clusters (TSCs), the number of TSCs was observed to decrease sharply with increasing expression levels. Highly expressed TSCs exhibited several characteristic features: Nucleosome-seq analysis revealed highly ordered nucleosome structures, ChIP-seq analysis detected clear RNA polymerase II binding signals in their surrounding regions, evaluations of previously sequenced and newly shotgun-sequenced complete cDNA sequences showed that they encode preferable transcripts for protein translation, and RNA-seq analysis of polysome-incorporated RNAs yielded direct evidence that those transcripts are actually translated into proteins. We also demonstrate that integrative interpretation of transcriptome data is essential for the selection of putative alternative promoter TSCs, two of which also have protein consequences. Furthermore, discriminative chromatin features that separate TSCs at different expression levels were found for both genic TSCs and intergenic TSCs. The collected integrative information should provide a useful basis for future biological characterization of TSCs.

124 citations


Cites background from "RNA-Seq: a revolutionary tool for t..."

  • ...2009), and the identification of RNAs in particular cellular fractions (RNA-seq) (Marioni et al. 2008; Affymetrix/Cold Spring Harbor Laboratory ENCODE Transcriptome Project. 2009; Ingolia et al. 2009; Wang et al. 2009)....

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Journal ArticleDOI
29 Jun 2012-Genes
TL;DR: It is concluded that the full extent of transcription remains an open question that will not be fully addressed until the authors decipher the complete range and biological diversity of the transcribed genomic sequences.
Abstract: Despite recent technological advances, the study of the human transcriptome is still in its early stages. Here we provide an overview of the complex human transcriptomic landscape, present the bioinformatics challenges posed by the vast quantities of transcriptomic data, and discuss some of the studies that have tried to determine how much of the human genome is transcribed. Recent evidence has suggested that more than 90% of the human genome is transcribed into RNA. However, this view has been strongly contested by groups of scientists who argued that many of the observed transcripts are simply the result of transcriptional noise. In this review, we conclude that the full extent of transcription remains an open question that will not be fully addressed until we decipher the complete range and biological diversity of the transcribed genomic sequences.

123 citations


Additional excerpts

  • ...Called a “revolutionary tool for transcriptomics”, RNA-seq is the first sequencing-based method that allows the entire transcriptome to be surveyed in a very high-throughput and quantitative manner [29]....

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Journal ArticleDOI
TL;DR: This work presents a major extension of Mayday, a very versatile open-source framework for efficient micro array data analysis designed for biologists and bioinformaticians.
Abstract: Background DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the generated data makes visual data exploration ever more important. Fast deployment of new methods as well as a combination of predefined, easy to apply methods with programmer's access to the data are important requirements for any analysis framework. Mayday is an open source platform with emphasis on visual data exploration and analysis. Many built-in methods for clustering, machine learning and classification are provided for dissecting complex datasets. Plugins can easily be written to extend Mayday's functionality in a large number of ways. As Java program, Mayday is platform-independent and can be used as Java WebStart application without any installation. Mayday can import data from several file formats, database connectivity is included for efficient data organization. Numerous interactive visualization tools, including box plots, profile plots, principal component plots and a heatmap are available, can be enhanced with metadata and exported as publication quality vector files.

123 citations


Cites background from "RNA-Seq: a revolutionary tool for t..."

  • ...Moreover, these technologies can be applied to measure gene expression (RNA-Seq) [30] and proteinDNA interactions (ChIP-Seq) [31], and many current studies use RNA-Seq and microarray data comparatively....

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References
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Journal ArticleDOI
TL;DR: Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors.
Abstract: We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41–52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3′ untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 × 10 5 distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices. The mRNA population specifies a cell’s identity and helps to govern its present and future activities. This has made transcriptome analysis a general phenotyping method, with expression microarrays of many kinds in routine use. Here we explore the possibility that transcriptome analysis, transcript discovery and transcript refinement can be done effectively in large and complex mammalian genomes by ultra-high-throughput sequencing. Expression microarrays are currently the most widely used methodology for transcriptome analysis, although some limitations persist. These include hybridization and cross-hybridization artifacts 1–3 , dye-based detection issues and design constraints that preclude or seriously limit the detection of RNA splice patterns and previously unmapped genes. These issues have made it difficult for standard array designs to provide full sequence comprehensiveness (coverage of all possible genes, including unknown ones, in large genomes) or transcriptome comprehensiveness (reliable detection of all RNAs of all prevalence classes, including the least abundant ones that are physiologically relevant). Other

12,293 citations

PatentDOI
04 Oct 2000-Science
TL;DR: Serial analysis of gene expression (SAGE) should provide a broadly applicable means for the quantitative cataloging and comparison of expressed genes in a variety of normal, developmental, and disease states.
Abstract: PROBLEM TO BE SOLVED: To provide a method for preparing a short nucleotide sequence (tag) which is useful to identify a cDNA oligonucleotide and is derived from a restricted position in a mRNA or a cDNA. SOLUTION: This is the method of preparing a tag for identifying the cDNA oligonucleotide. The above method comprises preparing the cDNA oligonucleotide bearing 5' and 3' terminals, collecting cDNA fragments by cutting the cDNA oligonucleotide with a restriction enzyme at the first restriction endonuclease site, separating a cDNA oligonucleotide bearing 5' or 3' terminal and connecting an oligonucleotide linker to the isolated cDNA fragment bearing the cDNA oligonucleotide 5' or 3' terminal. Here, the oligonucleotide linker contains the recognition site of the second restriction endonuclease enzyme and the isolated cDNA fragment is cut with the second restriction endonuclease enzyme which cuts the cDNA fragment in a section separated from the recognition site to obtain the tag for identifying the cDNA oligonucleotide.

4,437 citations

Journal ArticleDOI
TL;DR: This work describes the software MAQ, software that can build assemblies by mapping shotgun short reads to a reference genome, using quality scores to derive genotype calls of the consensus sequence of a diploid genome, e.g., from a human sample.
Abstract: New sequencing technologies promise a new era in the use of DNA sequence. However, some of these technologies produce very short reads, typically of a few tens of base pairs, and to use these reads effectively requires new algorithms and software. In particular, there is a major issue in efficiently aligning short reads to a reference genome and handling ambiguity or lack of accuracy in this alignment. Here we introduce the concept of mapping quality, a measure of the confidence that a read actually comes from the position it is aligned to by the mapping algorithm. We describe the software MAQ that can build assemblies by mapping shotgun short reads to a reference genome, using quality scores to derive genotype calls of the consensus sequence of a diploid genome, e.g., from a human sample. MAQ makes full use of mate-pair information and estimates the error probability of each read alignment. Error probabilities are also derived for the final genotype calls, using a Bayesian statistical model that incorporates the mapping qualities, error probabilities from the raw sequence quality scores, sampling of the two haplotypes, and an empirical model for correlated errors at a site. Both read mapping and genotype calling are evaluated on simulated data and real data. MAQ is accurate, efficient, versatile, and user-friendly. It is freely available at http://maq.sourceforge.net.

2,927 citations

Journal ArticleDOI
TL;DR: It is found that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane).
Abstract: Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.

2,834 citations

Journal ArticleDOI
TL;DR: The program SOAP is designed to handle the huge amounts of short reads generated by parallel sequencing using the new generation Illumina-Solexa sequencing technology, which supports multi-threaded parallel computing and has a batch module for multiple query sets.
Abstract: Summary: We have developed a program SOAP for efficient gapped and ungapped alignment of short oligonucleotides onto reference sequences. The program is designed to handle the huge amounts of short reads generated by parallel sequencing using the new generation Illumina-Solexa sequencing technology. SOAP is compatible with numerous applications, including single-read or pair-end resequencing, small RNA discovery and mRNA tag sequence mapping. SOAP is a command-driven program, which supports multi-threaded parallel computing, and has a batch module for multiple query sets. Availability: http://soap.genomics.org.cn Contact: soap@genomics.org.cn

2,729 citations


"RNA-Seq: a revolutionary tool for t..." refers methods in this paper

  • ...There are several programs for mapping reads to the genome, including ELAND, SOA...

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