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      Comprehensive comparative analysis of strand-specific RNA sequencing methods

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          Abstract

          Strand-specific, massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in strand-specificity, library complexity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.

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          Most cited references22

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          Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

          Y. H. Yang (2002)
          There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.
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            Ab initio reconstruction of transcriptomes of pluripotent and lineage committed cells reveals gene structures of thousands of lincRNAs

            RNA-Seq provides an unbiased way to study a transcriptome, including both coding and non-coding genes. To date, most RNA-Seq studies have critically depended on existing annotations, and thus focused on expression levels and variation in known transcripts. Here, we present Scripture, a method to reconstruct the transcriptome of a mammalian cell using only RNA-Seq reads and the genome sequence. We apply it to mouse embryonic stem cells, neuronal precursor cells, and lung fibroblasts to accurately reconstruct the full-length gene structures for the vast majority of known expressed genes. We identify substantial variation in protein-coding genes, including thousands of novel 5′-start sites, 3′-ends, and internal coding exons. We then determine the gene structures of over a thousand lincRNA and antisense loci. Our results open the way to direct experimental manipulation of thousands of non-coding RNAs, and demonstrate the power of ab initio reconstruction to render a comprehensive picture of mammalian transcriptomes.
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              Bidirectional promoters generate pervasive transcription in yeast.

              Genome-wide pervasive transcription has been reported in many eukaryotic organisms, revealing a highly interleaved transcriptome organization that involves hundreds of previously unknown non-coding RNAs. These recently identified transcripts either exist stably in cells (stable unannotated transcripts, SUTs) or are rapidly degraded by the RNA surveillance pathway (cryptic unstable transcripts, CUTs). One characteristic of pervasive transcription is the extensive overlap of SUTs and CUTs with previously annotated features, which prompts questions regarding how these transcripts are generated, and whether they exert function. Single-gene studies have shown that transcription of SUTs and CUTs can be functional, through mechanisms involving the generated RNAs or their generation itself. So far, a complete transcriptome architecture including SUTs and CUTs has not been described in any organism. Knowledge about the position and genome-wide arrangement of these transcripts will be instrumental in understanding their function. Here we provide a comprehensive analysis of these transcripts in the context of multiple conditions, a mutant of the exosome machinery and different strain backgrounds of Saccharomyces cerevisiae. We show that both SUTs and CUTs display distinct patterns of distribution at specific locations. Most of the newly identified transcripts initiate from nucleosome-free regions (NFRs) associated with the promoters of other transcripts (mostly protein-coding genes), or from NFRs at the 3' ends of protein-coding genes. Likewise, about half of all coding transcripts initiate from NFRs associated with promoters of other transcripts. These data change our view of how a genome is transcribed, indicating that bidirectionality is an inherent feature of promoters. Such an arrangement of divergent and overlapping transcripts may provide a mechanism for local spreading of regulatory signals-that is, coupling the transcriptional regulation of neighbouring genes by means of transcriptional interference or histone modification.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nature methods
                1548-7091
                1548-7105
                10 November 2010
                15 August 2010
                September 2010
                1 March 2011
                : 7
                : 9
                : 709-715
                Affiliations
                [1 ]Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142 USA
                [2 ]Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [3 ]School of Engineering and Computer Science, Hebrew University, Jerusalem, Israel
                [4 ]Alexander Silberman Institute of Life Sciences, Hebrew University, Jerusalem, Israel
                [5 ]Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02140 USA
                Author notes
                Correspondence should be addressed to J.Z.L. ( jlevin@ 123456broadinstitute.org ) and A.R. ( aregev@ 123456broad.mit.edu )
                [6]

                These authors contributed equally to this work

                Article
                nihpa248954
                10.1038/nmeth.1491
                3005310
                20711195
                f0188c28-8b92-4214-a340-14dff0d15ded

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Funded by: National Cancer Institute : NCI
                Funded by: Office of the Director : NIH
                Funded by: Howard Hughes Medical Institute
                Award ID: U54 HG003067-01 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Funded by: National Cancer Institute : NCI
                Funded by: Office of the Director : NIH
                Funded by: Howard Hughes Medical Institute
                Award ID: R01 CA119176-01 ||CA
                Funded by: National Human Genome Research Institute : NHGRI
                Funded by: National Cancer Institute : NCI
                Funded by: Office of the Director : NIH
                Funded by: Howard Hughes Medical Institute
                Award ID: DP1 OD003958-01 ||OD
                Funded by: National Human Genome Research Institute : NHGRI
                Funded by: National Cancer Institute : NCI
                Funded by: Office of the Director : NIH
                Funded by: Howard Hughes Medical Institute
                Award ID: ||HHMI_
                Categories
                Article

                Life sciences
                Life sciences

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