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      Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

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          Abstract

          High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1- 3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.

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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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            miR-145 and miR-143 Regulate Smooth Muscle Cell Fate Decisions

            SUMMARY microRNAs are regulators of myriad cellular events, but evidence for a single microRNA that can efficiently differentiate multipotent cells into a specific lineage or regulate direct reprogramming of cells into an alternate cell fate has been elusive. Here, we show that miR-145 and miR-143 are co-transcribed in multipotent cardiac progenitors before becoming localized to smooth muscle cells, including neural crest stem cell–derived vascular smooth muscle cells. miR-145 and miR-143 were direct transcriptional targets of serum response factor, myocardin and Nkx2.5, and were downregulated in injured or atherosclerotic vessels containing proliferating, less differentiated smooth muscle cells. miR-145 was necessary for myocardin-induced reprogramming of adult fibroblasts into smooth muscle cells and sufficient to induce differentiation of multipotent neural crest stem cells into vascular smooth muscle. Furthermore, miR-145 and miR-143 cooperatively targeted a network of transcription factors, including Klf4, myocardin, and Elk-1 to promote differentiation and repress proliferation of smooth muscle cells. These findings demonstrate that miR-145 can direct the smooth muscle fate and that miR-145 and miR-143 function to regulate the quiescent versus proliferative phenotype of smooth muscle cells.
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              Transcriptome Sequencing to Detect Gene Fusions in Cancer

              Recurrent gene fusions, typically associated with hematological malignancies and rare bone and soft tissue tumors1, have been recently described in common solid tumors2–9. Here we employ an integrative analysis of high-throughput long and short read transcriptome sequencing of cancer cells to discover novel gene fusions. As a proof of concept we successfully utilized integrative transcriptome sequencing to “re-discover” the BCR-ABL1 10 gene fusion in a chronic myelogenous leukemia cell line and the TMPRSS2-ERG 2,3 gene fusion in a prostate cancer cell line and tissues. Additionally, we nominated, and experimentally validated, novel gene fusions resulting in chimeric transcripts in cancer cell lines and tumors. Taken together, this study establishes a robust pipeline for the discovery of novel gene chimeras using high throughput sequencing, opening up an important class of cancer-related mutations for comprehensive characterization.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                1087-0156
                1546-1696
                14 May 2010
                02 May 2010
                May 2010
                29 July 2011
                : 28
                : 5
                : 511-515
                Affiliations
                [1 ]Department of Computer Science, University of Maryland, College Park
                [2 ]Center for Bioinformatics and Computational Biology, University of Maryland
                [3 ]Division of Biology and Beckman Institute, California Institute of Technology
                [4 ]Genome Sciences Center, Washington University, St. Louis, MI
                [5 ]Department of Mathematics, University of California, Berkeley
                [6 ]Deparment of Molecular and Cell Biology, University of California, Berkeley
                [7 ]Deparment of Computer Science, University of California, Berkeley
                Article
                nihpa190938
                10.1038/nbt.1621
                3146043
                20436464
                ce33e688-197e-415b-ac31-30ab91814f0b

                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

                History
                Funding
                Funded by: National Library of Medicine : NLM
                Award ID: R01 LM006845-07 || LM
                Categories
                Article

                Biotechnology
                Biotechnology

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