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Genome

About: Genome is a research topic. Over the lifetime, 74231 publications have been published within this topic receiving 3819713 citations.


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Journal ArticleDOI
TL;DR: Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity.
Abstract: The emergence of new sequencing technologies has facilitated the use of bacterial whole genome alignments for evolutionary studies and outbreak analyses. These datasets, of increasing size, often include examples of multiple different mechanisms of horizontal sequence transfer resulting in substantial alterations to prokaryotic chromosomes. The impact of these processes demands rapid and flexible approaches able to account for recombination when reconstructing isolates' recent diversification. Gubbins is an iterative algorithm that uses spatial scanning statistics to identify loci containing elevated densities of base substitutions suggestive of horizontal sequence transfer while concurrently constructing a maximum likelihood phylogeny based on the putative point mutations outside these regions of high sequence diversity. Simulations demonstrate the algorithm generates highly accurate reconstructions under realistically parameterized models of bacterial evolution, and achieves convergence in only a few hours on alignments of hundreds of bacterial genome sequences. Gubbins is appropriate for reconstructing the recent evolutionary history of a variety of haploid genotype alignments, as it makes no assumptions about the underlying mechanism of recombination. The software is freely available for download at github.com/sanger-pathogens/Gubbins, implemented in Python and C and supported on Linux and Mac OS X.

1,608 citations

Journal ArticleDOI
01 Jul 2004-Nature
TL;DR: Analysis of chromosome maps and genome redundancies reveal that the different yeast lineages have evolved through a marked interplay between several distinct molecular mechanisms, including tandem gene repeat formation, segmental duplication, a massive genome duplication and extensive gene loss.
Abstract: Identifying the mechanisms of eukaryotic genome evolution by comparative genomics is often complicated by the multiplicity of events that have taken place throughout the history of individual lineages, leaving only distorted and superimposed traces in the genome of each living organism. The hemiascomycete yeasts, with their compact genomes, similar lifestyle and distinct sexual and physiological properties, provide a unique opportunity to explore such mechanisms. We present here the complete, assembled genome sequences of four yeast species, selected to represent a broad evolutionary range within a single eukaryotic phylum, that after analysis proved to be molecularly as diverse as the entire phylum of chordates. A total of approximately 24,200 novel genes were identified, the translation products of which were classified together with Saccharomyces cerevisiae proteins into about 4,700 families, forming the basis for interspecific comparisons. Analysis of chromosome maps and genome redundancies reveal that the different yeast lineages have evolved through a marked interplay between several distinct molecular mechanisms, including tandem gene repeat formation, segmental duplication, a massive genome duplication and extensive gene loss.

1,604 citations

Journal ArticleDOI
John P. Vogel1, David F. Garvin2, Todd C. Mockler2, Jeremy Schmutz, Daniel S. Rokhsar3, Michael W. Bevan4, Kerrie Barry5, Susan Lucas5, Miranda Harmon-Smith5, Kathleen Lail5, Hope Tice5, Jane Grimwood, Neil McKenzie4, Naxin Huo6, Yong Q. Gu6, Gerard R. Lazo6, Olin D. Anderson6, Frank M. You7, Ming-Cheng Luo7, Jan Dvorak7, Jonathan M. Wright4, Melanie Febrer4, Dominika Idziak8, Robert Hasterok8, Erika Lindquist5, Mei Wang5, Samuel E. Fox2, Henry D. Priest2, Sergei A. Filichkin2, Scott A. Givan2, Douglas W. Bryant2, Jeff H. Chang2, Haiyan Wu9, Wei Wu10, An-Ping Hsia10, Patrick S. Schnable9, Anantharaman Kalyanaraman11, Brad Barbazuk12, Todd P. Michael, Samuel P. Hazen13, Jennifer N. Bragg6, Debbie Laudencia-Chingcuanco6, Yiqun Weng14, Georg Haberer, Manuel Spannagl, Klaus F. X. Mayer, Thomas Rattei15, Therese Mitros3, Sang-Jik Lee16, Jocelyn K. C. Rose16, Lukas A. Mueller16, Thomas L. York16, Thomas Wicker17, Jan P. Buchmann17, Jaakko Tanskanen18, Alan H. Schulman18, Heidrun Gundlach, Michael W. Bevan4, Antonio Costa de Oliveira19, Luciano da C. Maia19, William R. Belknap6, Ning Jiang, Jinsheng Lai9, Liucun Zhu20, Jianxin Ma20, Cheng Sun21, Ellen J. Pritham21, Jérôme Salse, Florent Murat, Michael Abrouk, Rémy Bruggmann, Joachim Messing, Noah Fahlgren2, Christopher M. Sullivan2, James C. Carrington2, Elisabeth J. Chapman, Greg D. May22, Jixian Zhai23, Matthias Ganssmann23, Sai Guna Ranjan Gurazada23, Marcelo A German23, Blake C. Meyers23, Pamela J. Green23, Ludmila Tyler3, Jiajie Wu7, James A. Thomson6, Shan Chen13, Henrik Vibe Scheller24, Jesper Harholt25, Peter Ulvskov25, Jeffrey A. Kimbrel2, Laura E. Bartley24, Peijian Cao24, Ki-Hong Jung26, Manoj Sharma24, Miguel E. Vega-Sánchez24, Pamela C. Ronald24, Chris Dardick6, Stefanie De Bodt27, Wim Verelst27, Dirk Inzé27, Maren Heese28, Arp Schnittger28, Xiaohan Yang29, Udaya C. Kalluri29, Gerald A. Tuskan29, Zhihua Hua14, Richard D. Vierstra14, Yu Cui9, Shuhong Ouyang9, Qixin Sun9, Zhiyong Liu9, Alper Yilmaz30, Erich Grotewold30, Richard Sibout31, Kian Hématy31, Grégory Mouille31, Herman Höfte31, Todd P. Michael, Jérôme Pelloux32, Devin O'Connor3, James C. Schnable3, Scott C. Rowe3, Frank G. Harmon3, Cynthia L. Cass33, John C. Sedbrook33, Mary E. Byrne4, Sean Walsh4, Janet Higgins4, Pinghua Li16, Thomas P. Brutnell16, Turgay Unver34, Hikmet Budak34, Harry Belcram, Mathieu Charles, Boulos Chalhoub, Ivan Baxter35 
11 Feb 2010-Nature
TL;DR: The high-quality genome sequence will help Brachypodium reach its potential as an important model system for developing new energy and food crops and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat.
Abstract: Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops.

1,603 citations

Journal ArticleDOI
Peter J. Campbell1, Gad Getz2, Jan O. Korbel3, Joshua M. Stuart4  +1329 moreInstitutions (238)
06 Feb 2020-Nature
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18.

1,600 citations

Journal ArticleDOI
12 Nov 1998-Nature
TL;DR: The complete genome sequence of the obligate intracellular parasite Rickettsia prowazekii, the causative agent of epidemic typhus, is described, which contains 834 protein-coding genes and is more closely related to mitochondria than is any other microbe studied so far.
Abstract: We describe here the complete genome sequence (1,111,523 base pairs) of the obligate intracellular parasite Rickettsia prowazekii, the causative agent of epidemic typhus. This genome contains 834 p ...

1,599 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20242
20237,313
202214,209
20214,955
20205,080
20194,839