Global Identification of Human Transcribed Sequences with Genome Tiling Arrays

Bertone, Paul; Štolc, Viktor; Royce, Thomas; Rozowsky, Joel; Urban, Alexander; Zhu, Xiaowei; Rinn, John L.; Tongprasit, Waraporn; Samanta, Manoj P.; Weissman, Sherman M.; Gerstein, Mark; Snyder, M

doi:10.1126/science.1103388

Cited by 956 publications

(768 citation statements)

References 22 publications

(12 reference statements)

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“…While microarrays allowed the study of expression states of multiple genes simultaneously, they were still limited to the detection of known RNA molecules (often mRNAs) and therefore failed to discover novel transcripts. This was in part overcome by the introduction of high-resolution tiling arrays (Bertone et al, 2004;Selinger et al, 2003).…”

Section: Transcriptomics: Then and Nowmentioning

confidence: 99%

Dual RNA-seq of pathogen and host

2012

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SummaryThe infection of a eukaryotic host cell by a bacterial pathogen is one of the most intimate examples of cross-kingdom interactions in biology. Infection processes are highly relevant from both a basic research as well as a clinical point of view. Sophisticated mechanisms have evolved in the pathogen to manipulate the host response and vice versa host cells have developed a wide range of anti-microbial defense strategies to combat bacterial invasion and clear infections.However, it is this diversity and complexity that makes infection research so challenging to technically address as common approaches have either been optimized for bacterial or eukaryotic organisms. Instead, methods are required that are able to deal with the often dramatic discrepancy between host and pathogen with respect to various cellular properties and processes. One class of cellular macromolecules that exemplify this host-pathogen heterogeneity is given by their transcriptomes: Bacterial transcripts differ from their eukaryotic counterparts in many aspects that involve both quantitative and qualitative traits. The entity of RNA transcripts present in a cell is of paramount interest as it reflects the cell's physiological state under the given condition. Genome-wide transcriptomic techniques such as RNA-seq have therefore been used for single-organism analyses for several years, but their applicability has been limited for infection studies.The present work describes the establishment of a novel transcriptomic approach for infection biology which we have termed "Dual RNA-seq". Using this technology, it was intended to shed light particularly on the contribution of non-protein-encoding transcripts to virulence, as these classes have mostly evaded previous infection studies due to the lack of suitable methods. The performance of Dual RNA-seq was evaluated in an in vitro infection model based on the important facultative intracellular pathogen Salmonella enterica serovar Typhimurium and different human cell lines. Dual RNA-seq was found to be capable of capturing all major bacterial and human transcript classes and proved reproducible. During the course of these experiments, a previously largely uncharacterized bacterial small non-coding RNA (sRNA), referred to as STnc440, was identified as one of the most strongly induced genes in intracellular Salmonella.Interestingly, while inhibition of STnc440 expression has been previously shown to cause a virulence defect in different animal models of Salmonellosis, the underlying molecular mechanisms have remained obscure. Here, classical genetics, transcriptomics and biochemical assays proposed a complex model of Salmonella gene expression control that is orchestrated by this sRNA. In particular, STnc440 was found to be involved in the regulation of multiple bacterial target mRNAs by direct base pair interaction with consequences for Salmonella virulence and

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Section: Transcriptomics: Then and Nowmentioning

confidence: 99%

Dual RNA-seq of pathogen and host

2012

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“…In recent years there have been a number of experiments using genomic tiling microarrays that have found significantly more transcribed DNA sequences in the human genome than had been previously annotated as genes (see Kapranov et al 2002, Rinn et al 2003, Bertone et al 2004Cheng et al 2005). The biological functions of this vast quantity of additional transcribed RNA are not yet fully understood.…”

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confidence: 99%

The DART classification of unannotated transcription within the ENCODE regions: Associating transcription with known and novel loci

et al. 2007

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For the ∼1% of the human genome in the ENCODE regions, only about half of the transcriptionally active regions (TARs) identified with tiling microarrays correspond to annotated exons. Here we categorize this large amount of "unannotated transcription." We use a number of disparate features to classify the 6988 novel TARs-array expression profiles across cell lines and conditions, sequence composition, phylogenetic profiles (presence/absence of syntenic conservation across 17 species), and locations relative to genes. In the classification, we first filter out TARs with unusual sequence composition and those likely resulting from cross-hybridization. We then associate some of those remaining with proximal exons having correlated expression profiles. Finally, we cluster unclassified TARs into putative novel loci, based on similar expression and phylogenetic profiles. To encapsulate our classification, we construct a Database of Active Regions and Tools (DART.gersteinlab.org). DART has special facilities for rapidly handling and comparing many sets of TARs and their heterogeneous features, synchronizing across builds, and interfacing with other resources. Overall, we find that ∼14% of the novel TARs can be associated with known genes, while ∼21% can be clustered into ∼200 novel loci. We observe that TARs associated with genes are enriched in the potential to form structural RNAs and many novel TAR clusters are associated with nearby promoters. To benchmark our classification, we design a set of experiments for testing the connectivity of novel TARs. Overall, we find that 18 of the 46 connections tested validate by RT-PCR and four of five sequenced PCR products confirm connectivity unambiguously.

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“…Although it is straightforward to infer the existence of novel isoforms from RNA-Seq data that exhibit novel transcribed regions (Mortazavi et al, 2008;Bertone et al, 2004), it is not so obvious how to use RNA-Seq data to infer the existence of novel isoforms in known transcribed regions, because the observed reads could be sampled from either known or unknown isoforms. The problem has remained challenging for two reasons.…”

Section: Introductionmentioning

confidence: 99%

“…A large amount of work has been devoted to transcriptomics, which includes the international projects EST (Boguski et al, 1994;Boguski, 1995), FANTOM (The FANTOM Consortium, 2005), and ENCODE (The ENCODE Project Consortium, 2007;Weinstock, 2007). Many technologies have been introduced in recent years, including array-based experimental methods such as tiling arrays (Bertone et al, 2004), exon arrays (Kwan et al, 2008), and exon-junction arrays ( Johnson et al, 2003;Kapranov et al, 2007); and tag-based approaches such as MPSS (Brenner et al, 2000;Reinartz et al, 2002), SAGE (Velculescu et al, 1995;Harbers and Carninci, 2005), CAGE (Shiraki et al, 2003;Kodzius et al, 2005), PMAGE (Kim et al, 2007), and GIS (Ng et al, 2005). However, due to various constraints intrinsic to these technologies, the speed of advance in transcriptomics is far from being satisfactory, especially on eukaryotic species because of widespread alternative splicing events.…”

Section: Introductionmentioning

confidence: 99%

Inference of Isoforms from Short Sequence Reads

Feng

Jiang

2010

Lecture Notes in Computer Science

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Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages of RNASeq over traditional methods in transcriptome analysis have been confirmed by many studies, the inference of isoforms from millions of short sequence reads (e.g., Illumina/Solexa reads) has remained computationally challenging. In this work, we propose a method to calculate the expression levels of isoforms and infer isoforms from short RNA-Seq reads using exon-intron boundary, transcription start site (TSS) and poly-A site (PAS) information. We first formulate the relationship among exons, isoforms, and single-end reads as a convex quadratic program, and then use an efficient algorithm (called IsoInfer) to search for isoforms. IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known and infer novel isoforms from scratch. Our experimental tests on known mouse isoforms with both simulated expression levels and reads demonstrate that IsoInfer is able to calculate the expression levels of isoforms with an accuracy comparable to the stateof-the-art statistical method and a 60 times faster speed. Moreover, our tests on both simulated and real reads show that it achieves a good precision and sensitivity in inferring isoforms when given accurate exon-intron boundary, TSS, and PAS information, especially for isoforms whose expression levels are significantly high. The software is publicly available for free at

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Global Identification of Human Transcribed Sequences with Genome Tiling Arrays

Cited by 956 publications

References 22 publications

Dual RNA-seq of pathogen and host

Dual RNA-seq of pathogen and host

The DART classification of unannotated transcription within the ENCODE regions: Associating transcription with known and novel loci

Inference of Isoforms from Short Sequence Reads

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