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      VERSE: a novel approach to detect virus integration in host genomes through reference genome customization

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      Genome Medicine

      BioMed Central

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          Abstract

          Fueled by widespread applications of high-throughput next generation sequencing (NGS) technologies and urgent need to counter threats of pathogenic viruses, large-scale studies were conducted recently to investigate virus integration in host genomes (for example, human tumor genomes) that may cause carcinogenesis or other diseases. A limiting factor in these studies, however, is rapid virus evolution and resulting polymorphisms, which prevent reads from aligning readily to commonly used virus reference genomes, and, accordingly, make virus integration sites difficult to detect. Another confounding factor is host genomic instability as a result of virus insertions. To tackle these challenges and improve our capability to identify cryptic virus-host fusions, we present a new approach that detects Virus intEgration sites through iterative Reference SEquence customization (VERSE). To the best of our knowledge, VERSE is the first approach to improve detection through customizing reference genomes. Using 19 human tumors and cancer cell lines as test data, we demonstrated that VERSE substantially enhanced the sensitivity of virus integration site detection. VERSE is implemented in the open source package VirusFinder 2 that is available at http://bioinfo.mc.vanderbilt.edu/VirusFinder/.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13073-015-0126-6) contains supplementary material, which is available to authorized users.

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          Most cited references 25

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          Mutation rates among RNA viruses.

          The rate of spontaneous mutation is a key parameter in modeling the genetic structure and evolution of populations. The impact of the accumulated load of mutations and the consequences of increasing the mutation rate are important in assessing the genetic health of populations. Mutation frequencies are among the more directly measurable population parameters, although the information needed to convert them into mutation rates is often lacking. A previous analysis of mutation rates in RNA viruses (specifically in riboviruses rather than retroviruses) was constrained by the quality and quantity of available measurements and by the lack of a specific theoretical framework for converting mutation frequencies into mutation rates in this group of organisms. Here, we describe a simple relation between ribovirus mutation frequencies and mutation rates, apply it to the best (albeit far from satisfactory) available data, and observe a central value for the mutation rate per genome per replication of micro(g) approximately 0.76. (The rate per round of cell infection is twice this value or about 1.5.) This value is so large, and ribovirus genomes are so informationally dense, that even a modest increase extinguishes the population.
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            Iterative Correction of Reference Nucleotides (iCORN) using second generation sequencing technology

            Motivation: The accuracy of reference genomes is important for downstream analysis but a low error rate requires expensive manual interrogation of the sequence. Here, we describe a novel algorithm (Iterative Correction of Reference Nucleotides) that iteratively aligns deep coverage of short sequencing reads to correct errors in reference genome sequences and evaluate their accuracy. Results: Using Plasmodium falciparum (81% A + T content) as an extreme example, we show that the algorithm is highly accurate and corrects over 2000 errors in the reference sequence. We give examples of its application to numerous other eukaryotic and prokaryotic genomes and suggest additional applications. Availability: The software is available at http://icorn.sourceforge.net Contact: tdo@sanger.ac.uk; cnewbold@hammer.imm.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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              SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data

              Summary: We present SVDetect, a program designed to identify genomic structural variations from paired-end and mate-pair next-generation sequencing data produced by the Illumina GA and ABI SOLiD platforms. Applying both sliding-window and clustering strategies, we use anomalously mapped read pairs provided by current short read aligners to localize genomic rearrangements and classify them according to their type, e.g. large insertions–deletions, inversions, duplications and balanced or unbalanced inter-chromosomal translocations. SVDetect outputs predicted structural variants in various file formats for appropriate graphical visualization. Availability: Source code and sample data are available at http://svdetect.sourceforge.net/ Contact: svdetect@curie.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                josephw10000@gmail.com
                peilin.jia@vanderbilt.edu
                zhongming.zhao@vanderbilt.edu
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                20 January 2015
                20 January 2015
                2015
                : 7
                : 1
                Affiliations
                [ ]Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203 USA
                [ ]Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN 37232 USA
                [ ]Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
                [ ]Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
                Article
                126
                10.1186/s13073-015-0126-6
                4333248
                25699093
                © Wang et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                © The Author(s) 2015

                Molecular medicine

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