11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Precise estimates of mutation rate and spectrum in yeast

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          Spontaneous mutations are rare and difficult to observe in large numbers experimentally. By sequencing the genomes of 145 diploid mutation accumulation (MA) lines of the budding yeast Saccharomyces cerevisiae , we identified nearly 1,000 mutations, a larger number than in any prior eukaryotic MA experiment as far as we are aware. For the first time, to our knowledge, in MA data, we were able to estimate rates of context-dependent single-nucleotide mutations. We were also able to observe mutational classes not seen in earlier yeast MA experiments and infer the rate of strongly deleterious mutations from patterns of missing mutations in each mutational class. Our findings both answer outstanding questions in the field, as well as highlight the need for more studies of spontaneous mutation.

          Abstract

          Mutation is the ultimate source of genetic variation. The most direct and unbiased method of studying spontaneous mutations is via mutation accumulation (MA) lines. Until recently, MA experiments were limited by the cost of sequencing and thus provided us with small numbers of mutational events and therefore imprecise estimates of rates and patterns of mutation. We used whole-genome sequencing to identify nearly 1,000 spontaneous mutation events accumulated over ∼311,000 generations in 145 diploid MA lines of the budding yeast Saccharomyces cerevisiae . MA experiments are usually assumed to have negligible levels of selection, but even mild selection will remove strongly deleterious events. We take advantage of such patterns of selection and show that mutation classes such as indels and aneuploidies (especially monosomies) are proportionately much more likely to contribute mutations of large effect. We also provide conservative estimates of indel, aneuploidy, environment-dependent dominant lethal, and recessive lethal mutation rates. To our knowledge, for the first time in yeast MA data, we identified a sufficiently large number of single-nucleotide mutations to measure context-dependent mutation rates and were able to ( i ) confirm strong AT bias of mutation in yeast driven by high rate of mutations from C/G to T/A and ( ii ) detect a higher rate of mutation at C/G nucleotides in two specific contexts consistent with cytosine methylation in S. cerevisiae .

          Related collections

          Most cited references68

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          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
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Mutational heterogeneity in cancer and the search for new cancer genes

              Major international projects are now underway aimed at creating a comprehensive catalog of all genes responsible for the initiation and progression of cancer. These studies involve sequencing of matched tumor–normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here, we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false positive findings that overshadow true driver events. Here, we show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumor-normal pairs and discover extraordinary variation in (i) mutation frequency and spectrum within cancer types, which shed light on mutational processes and disease etiology, and (ii) mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and allow true cancer genes to rise to attention.
                Bookmark

                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc. Natl. Acad. Sci. U.S.A.
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                June 03 2014
                May 20 2014
                June 03 2014
                : 111
                : 22
                Affiliations
                [1 ]Department of Genetics, Stanford University, Stanford, CA 94305-5120;
                [2 ]Department of Biology, Stanford University, Stanford, CA 94305-5020;
                [3 ]Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003; and
                [4 ]Department of Genetics, University of Georgia, Athens, GA 30602-7223
                Article
                10.1073/pnas.1323011111
                4050626
                24847077
                adeea0b0-377d-4355-b552-ec47189459da
                © 2014
                History

                Comments

                Comment on this article