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      Global mRNA decay analysis at single nucleotide resolution reveals segmental and positional degradation patterns in a Gram-positive bacterium

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          Abstract

          Background

          Recent years have shown a marked increase in the use of next-generation sequencing technologies for quantification of gene expression (RNA sequencing, RNA-Seq). The expression level of a gene is a function of both its rate of transcription and RNA decay, and the influence of mRNA decay rates on gene expression in genome-wide studies of Gram-positive bacteria is under-investigated.

          Results

          In this work, we employed RNA-Seq in a genome-wide determination of mRNA half-lives in the Gram-positive bacterium Bacillus cereus. By utilizing a newly developed normalization protocol, RNA-Seq was used successfully to determine global mRNA decay rates at the single nucleotide level. The analysis revealed positional degradation patterns, with mRNAs being degraded from both ends of the molecule, indicating that both 5' to 3' and 3' to 5' directions of RNA decay are present in B. cereus. Other operons showed segmental degradation patterns where specific ORFs within polycistrons were degraded at variable rates, underlining the importance of RNA processing in gene regulation. We determined the half-lives for more than 2,700 ORFs in B. cereus ATCC 10987, ranging from less than one minute to more than fifteen minutes, and showed that mRNA decay rate correlates globally with mRNA expression level, GC content, and functional class of the ORF.

          Conclusions

          To our knowledge, this study presents the first global analysis of mRNA decay in a bacterium at single nucleotide resolution. We provide a proof of principle for using RNA-Seq in bacterial mRNA decay analysis, revealing RNA processing patterns at the single nucleotide level.

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          Most cited references60

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          The transcriptional landscape of the yeast genome defined by RNA sequencing.

          The identification of untranslated regions, introns, and coding regions within an organism remains challenging. We developed a quantitative sequencing-based method called RNA-Seq for mapping transcribed regions, in which complementary DNA fragments are subjected to high-throughput sequencing and mapped to the genome. We applied RNA-Seq to generate a high-resolution transcriptome map of the yeast genome and demonstrated that most (74.5%) of the nonrepetitive sequence of the yeast genome is transcribed. We confirmed many known and predicted introns and demonstrated that others are not actively used. Alternative initiation codons and upstream open reading frames also were identified for many yeast genes. We also found unexpected 3'-end heterogeneity and the presence of many overlapping genes. These results indicate that the yeast transcriptome is more complex than previously appreciated.
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            Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution.

            Recent data from several organisms indicate that the transcribed portions of genomes are larger and more complex than expected, and that many functional properties of transcripts are based not on coding sequences but on regulatory sequences in untranslated regions or non-coding RNAs. Alternative start and polyadenylation sites and regulation of intron splicing add additional dimensions to the rich transcriptional output. This transcriptional complexity has been sampled mainly using hybridization-based methods under one or few experimental conditions. Here we applied direct high-throughput sequencing of complementary DNAs (RNA-Seq), supplemented with data from high-density tiling arrays, to globally sample transcripts of the fission yeast Schizosaccharomyces pombe, independently from available gene annotations. We interrogated transcriptomes under multiple conditions, including rapid proliferation, meiotic differentiation and environmental stress, as well as in RNA processing mutants to reveal the dynamic plasticity of the transcriptional landscape as a function of environmental, developmental and genetic factors. High-throughput sequencing proved to be a powerful and quantitative method to sample transcriptomes deeply at maximal resolution. In contrast to hybridization, sequencing showed little, if any, background noise and was sensitive enough to detect widespread transcription in >90% of the genome, including traces of RNAs that were not robustly transcribed or rapidly degraded. The combined sequencing and strand-specific array data provide rich condition-specific information on novel, mostly non-coding transcripts, untranslated regions and gene structures, thus improving the existing genome annotation. Sequence reads spanning exon-exon or exon-intron junctions give unique insight into a surprising variability in splicing efficiency across introns, genes and conditions. Splicing efficiency was largely coordinated with transcript levels, and increased transcription led to increased splicing in test genes. Hundreds of introns showed such regulated splicing during cellular proliferation or differentiation.
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              Comprehensive comparative analysis of strand-specific RNA sequencing methods

              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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                Author and article information

                Contributors
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2012
                26 April 2012
                : 13
                : 4
                : R30
                Affiliations
                [1 ]Laboratory for Microbial Dynamics, Department of Pharmaceutical Biosciences, School of Pharmacy, University of Oslo, PB 1068 Blindern, 0316 Oslo, Norway
                [2 ]Department of Human Genetics, Division of Infectious Diseases, Emory University School of Medicine, 615 Michael Street, Atlanta, GA 30322-1013, USA
                [3 ]Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, 615 Michael Street, Atlanta, GA 30322-1013, USA
                [4 ]454 Life Sciences, A Roche Company, 1 Commercial Street, Branford, CT 06405, USA
                [5 ]PHARMAQ AS, PO Box 267 Skøyen, N-0213 Oslo, Norway
                Article
                gb-2012-13-4-r30
                10.1186/gb-2012-13-4-r30
                3446304
                22537947
                57ec3db1-b494-45c1-b08c-64df4aaeba86
                Copyright ©2012 Kristoffersen et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 December 2011
                : 15 April 2012
                : 26 April 2012
                Categories
                Research

                Genetics
                Genetics

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