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      DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly

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          Abstract

          Detection of DNA copy number aberrations by shallow whole-genome sequencing (WGS) faces many challenges, including lack of completion and errors in the human reference genome, repetitive sequences, polymorphisms, variable sample quality, and biases in the sequencing procedures. Formalin-fixed paraffin-embedded (FFPE) archival material, the analysis of which is important for studies of cancer, presents particular analytical difficulties due to degradation of the DNA and frequent lack of matched reference samples. We present a robust, cost-effective WGS method for DNA copy number analysis that addresses these challenges more successfully than currently available procedures. In practice, very useful profiles can be obtained with ∼0.1× genome coverage. We improve on previous methods by first implementing a combined correction for sequence mappability and GC content, and second, by applying this procedure to sequence data from the 1000 Genomes Project in order to develop a blacklist of problematic genome regions. A small subset of these blacklisted regions was previously identified by ENCODE, but the vast majority are novel unappreciated problematic regions. Our procedures are implemented in a pipeline called QDNAseq. We have analyzed over 1000 samples, most of which were obtained from the fixed tissue archives of more than 25 institutions. We demonstrate that for most samples our sequencing and analysis procedures yield genome profiles with noise levels near the statistical limit imposed by read counting. The described procedures also provide better correction of artifacts introduced by low DNA quality than prior approaches and better copy number data than high-resolution microarrays at a substantially lower cost.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Hallmarks of Cancer: The Next Generation

            The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                December 2014
                December 2014
                : 24
                : 12
                : 2022-2032
                Affiliations
                [1 ]Department of Pathology, VU University Medical Center, 1007 MB Amsterdam, The Netherlands;
                [2 ]Department of Pathology, Haartman Institute and HUSLAB, FIN-00014 University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland;
                [3 ]Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California 94158, USA;
                [4 ]Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California 94158, USA;
                [5 ]Department of Epidemiology and Biostatistics, VU University Medical Center, 1007 MB Amsterdam, The Netherlands;
                [6 ]Department of Mathematics, VU University, 1181 HV Amsterdam, The Netherlands;
                [7 ]Department of Neurology, VU University Medical Center, 1007 MB Amsterdam, The Netherlands;
                [8 ]Department of Neurology, Academic Medical Centre, 1105 AZ Amsterdam, The Netherlands;
                [9 ]Department of Pathology, Radboud University Medical Centre, 6500 HB Nijmegen, The Netherlands;
                [10 ]Department of Laboratory Medicine, University of California San Francisco, San Francisco, California 94153, USA;
                [11 ]Bluestone Center for Clinical Research, New York University College of Dentistry, New York, New York 10010-4086, USA
                Author notes
                [12]

                These authors contributed equally to this work.

                Corresponding author: B.Ylstra@ 123456vumc.nl
                Article
                9518021
                10.1101/gr.175141.114
                4248318
                25236618
                8d546c1d-122c-4061-bcf5-fd90ab7d3f87
                © 2014 Scheinin et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0.

                Page count
                Pages: 11
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                Method

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