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      Direct estimate of the rate of germline mutation in a bird

      research-article
      1 , 2 , 1
      Genome Research
      Cold Spring Harbor Laboratory Press

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          Abstract

          The fidelity of DNA replication together with repair mechanisms ensure that the genetic material is properly copied from one generation to another. However, on extremely rare occasions when damages to DNA or replication errors are not repaired, germline mutations can be transmitted to the next generation. Because of the rarity of these events, studying the rate at which new mutations arise across organisms has been a great challenge, especially in multicellular nonmodel organisms with large genomes. We sequenced the genomes of 11 birds from a three-generation pedigree of the collared flycatcher ( Ficedula albicollis) and used highly stringent bioinformatic criteria for mutation detection and used several procedures to validate mutations, including following the stable inheritance of new mutations to subsequent generations. We identified 55 de novo mutations with a 10-fold enrichment of mutations at CpG sites and with only a modest male mutation bias. The estimated rate of mutation per site per generation was 4.6 × 10 −9, which corresponds to 2.3 × 10 −9 mutations per site per year. Compared to mammals, this is similar to mouse but about half of that reported for humans, which may be due to the higher frequency of male mutations in humans. We confirm that mutation rate scales positively with genome size and that there is a strong negative relationship between mutation rate and effective population size, in line with the drift-barrier hypothesis. Our study illustrates that it should be feasible to obtain direct estimates of the rate of mutation in essentially any organism from which family material can be obtained.

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          Most cited references74

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            A framework for variation discovery and genotyping using next-generation DNA sequencing data

            Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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              Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

              Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
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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
                September 2016
                September 2016
                : 26
                : 9
                : 1211-1218
                Affiliations
                [1 ]Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, SE-752 36 Uppsala, Sweden;
                [2 ]Department of Animal Ecology, Evolutionary Biology Centre, Uppsala University, SE-752 36 Uppsala, Sweden
                Author notes
                Corresponding author: Hans.Ellegren@ 123456ebc.uu.se
                Article
                9509184
                10.1101/gr.204669.116
                5052036
                27412854
                60406dfb-99f0-4180-bfb2-69c4e88e940b
                © 2016 Smeds et al.; Published by Cold Spring Harbor Laboratory Press

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

                History
                : 20 January 2016
                : 12 July 2016
                Page count
                Pages: 8
                Funding
                Funded by: European Research Council http://dx.doi.org/10.13039/501100000781
                Award ID: AdG 249976
                Funded by: Knut and Alice Wallenberg Foundation
                Funded by: Swedish Research Council
                Award ID: 2007–8731
                Award ID: 2010–5650
                Award ID: 2013–8271
                Categories
                Research

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