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      The Genomic and Transcriptomic Landscape of a HeLa Cell Line

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          Abstract

          HeLa is the most widely used model cell line for studying human cellular and molecular biology. To date, no genomic reference for this cell line has been released, and experiments have relied on the human reference genome. Effective design and interpretation of molecular genetic studies performed using HeLa cells require accurate genomic information. Here we present a detailed genomic and transcriptomic characterization of a HeLa cell line. We performed DNA and RNA sequencing of a HeLa Kyoto cell line and analyzed its mutational portfolio and gene expression profile. Segmentation of the genome according to copy number revealed a remarkably high level of aneuploidy and numerous large structural variants at unprecedented resolution. Some of the extensive genomic rearrangements are indicative of catastrophic chromosome shattering, known as chromothripsis. Our analysis of the HeLa gene expression profile revealed that several pathways, including cell cycle and DNA repair, exhibit significantly different expression patterns from those in normal human tissues. Our results provide the first detailed account of genomic variants in the HeLa genome, yielding insight into their impact on gene expression and cellular function as well as their origins. This study underscores the importance of accounting for the strikingly aberrant characteristics of HeLa cells when designing and interpreting experiments, and has implications for the use of HeLa as a model of human biology.

          Most cited references57

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            A faster circular binary segmentation algorithm for the analysis of array CGH data.

            Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number. The algorithm tests for change-points using a maximal t-statistic with a permutation reference distribution to obtain the corresponding P-value. The number of computations required for the maximal test statistic is O(N2), where N is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster algorithm. We present a hybrid approach to obtain the P-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analyses of array CGH data from breast cancer cell lines to show the impact of the new approaches on the analysis of real data. An R version of the CBS algorithm has been implemented in the "DNAcopy" package of the Bioconductor project. The proposed hybrid method for the P-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.
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              Paired-end mapping reveals extensive structural variation in the human genome.

              Structural variation of the genome involves kilobase- to megabase-sized deletions, duplications, insertions, inversions, and complex combinations of rearrangements. We introduce high-throughput and massive paired-end mapping (PEM), a large-scale genome-sequencing method to identify structural variants (SVs) approximately 3 kilobases (kb) or larger that combines the rescue and capture of paired ends of 3-kb fragments, massive 454 sequencing, and a computational approach to map DNA reads onto a reference genome. PEM was used to map SVs in an African and in a putatively European individual and identified shared and divergent SVs relative to the reference genome. Overall, we fine-mapped more than 1000 SVs and documented that the number of SVs among humans is much larger than initially hypothesized; many of the SVs potentially affect gene function. The breakpoint junction sequences of more than 200 SVs were determined with a novel pooling strategy and computational analysis. Our analysis provided insights into the mechanisms of SV formation in humans.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                11 March 2013
                August 2013
                : 3
                : 8
                : 1213-1224
                Affiliations
                [* ]European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
                []University Hospital Heidelberg, Institute of Human Genetics, 69120 Heidelberg, Germany
                Author notes

                Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.113.005777/-/DC1

                All data and resources from this article have been deposited with the database of Genotypes and Phenotypes under accession no. phs000643.v1.p1.

                [1]

                These authors contributed equally to this work.

                [2]

                Present address: Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, California 94080.

                [3]

                Present address: Department of Plant Physiology, Umeå Plant Science Center, S-901 87 Umeå, Sweden.

                [4]

                Present address: Department of Chemistry and Biochemistry, Ludwig-Maximilians-Universität München, 81377 Munich, Germany.

                [5 ]Corresponding authors: EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany. E-mail: wolfgang.huber@ 123456embl.de ; and EMBL Heidelberg, Meyerhofstraße 1, 69117 Heidelberg, Germany. E-mail: lars.steinmetz@ 123456embl.de
                Article
                GGG_005777
                10.1534/g3.113.005777
                3737162
                23550136
                90c53d79-620e-4544-9320-998f5d907111
                Copyright © 2013 Landry et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 January 2013
                : 8 February 2013
                Page count
                Pages: 12
                Categories
                Investigations
                Custom metadata
                v1

                Genetics
                genomics,transcriptomics,hela cell line,resource,variation
                Genetics
                genomics, transcriptomics, hela cell line, resource, variation

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