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16 results on '"Cristian Del Fabbro"'

1. An extensive evaluation of read trimming effects on Illumina NGS data analysis.

Author

Cristian Del Fabbro, Simone Scalabrin, Michele Morgante, and Federico M Giorgi

Subjects
Medicine, Science
Abstract

Next Generation Sequencing is having an extremely strong impact in biological and medical research and diagnostics, with applications ranging from gene expression quantification to genotyping and genome reconstruction. Sequencing data is often provided as raw reads which are processed prior to analysis 1 of the most used preprocessing procedures is read trimming, which aims at removing low quality portions while preserving the longest high quality part of a NGS read. In the current work, we evaluate nine different trimming algorithms in four datasets and three common NGS-based applications (RNA-Seq, SNP calling and genome assembly). Trimming is shown to increase the quality and reliability of the analysis, with concurrent gains in terms of execution time and computational resources needed.

Published
2013
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9. Grapevine field experiments reveal the contribution of genotype, the influence of environment and the effect of their interaction (GxE) on berry transcriptome

Author

Emanuele De Paoli, Michele Morgante, Paola Zuccolotto, Giovanni Battista Tornielli, Gabriele Di Gaspero, Cristian Del Fabbro, D. Grossi, Gabriele Magris, Silvia Dal Santo, Gabriella De Lorenzis, Marianna Fasoli, Mario Pezzotti, Marco Sandri, Sara Zenoni, and Lucio Brancadoro

Subjects
0106 biological sciences, 0301 basic medicine, Genotype, Secondary Metabolism, Plant Science, Berry, Biology, Environment, Genes, Plant, 01 natural sciences, Genome, Vineyard, phenotypic plasticity, GxE, Transcriptome, Grapevine, environment, GxE, phenotypic plasticity, 03 medical and health sciences, Gene Expression Regulation, Plant, Genetics, Vitis, Allele, Gene–environment interaction, Vitis vinifera (grapevine), Gene, Data mining, Gene Expression Profiling, fungi, Gene Expression variation, Genotype x Environment Interaction (GxE), Cell Biology, 030104 developmental biology, Gene Ontology, Phenotype, Fruit, Grapevine, 010606 plant biology & botany
Abstract

Changes in the performance of genotypes in different environments are defined as genotype × environment (G×E) interactions. In grapevine (Vitis vinifera), complex interactions between different genotypes and climate, soil and farming practices yield unique berry qualities. However, the molecular basis of this phenomenon remains unclear. To dissect the basis of grapevine G×E interactions we characterized berry transcriptome plasticity, the genome methylation landscape and within-genotype allelic diversity in two genotypes cultivated in three different environments over two vintages. We identified, through a novel data-mining pipeline, genes with expression profiles that were: unaffected by genotype or environment, genotype-dependent but unaffected by the environment, environmentally-dependent regardless of genotype, and G×E-related. The G×E-related genes showed different degrees of within-cultivar allelic diversity in the two genotypes and were enriched for stress responses, signal transduction and secondary metabolism categories. Our study unraveled the mutual relationships between genotypic and environmental variables during G×E interaction in a woody perennial species, providing a reference model to explore how cultivated fruit crops respond to diverse environments. Also, the pivotal role of vineyard location in determining the performance of different varieties, by enhancing berry quality traits, was unraveled.

Published
2018

10. GAM: Genomic Assemblies Merger. A Graph Based Method to Integrate Different Assemblies

Author

Alberto Policriti, Cristian Del Fabbro, Alberto Casagrande, Simone Scalabrin, Xue-wen Chen, Sun Kim, Casagrande, Alberto, CRISTIAN DEL, Fabbro, Simone, Scalabrin, and Alberto, Policriti

Subjects
assembly, Computer science, business.industry, Sequence assembly, DNA assemblers, Improving genomic assemblies, Data integration, Graph theory, Genomics, computer.software_genre, Data structure, Genome Assembly Sequence, assembly reconciliation, Software, Improving genomic assemblie, Data mining, DNA assembler, business, Heuristics, computer
Abstract

Many software tools are currently available to solve the hard goal of assembling millions of fragments produced in sequencing projects. Such a variety includes packages for long and short reads, generated by classical and next-generation sequencing technologies. Often the result produced by different tools can diverge---sometime significantly---for many reasons: the underlying algorithm, the data structures employed, the heuristics implemented, default parameters, etc. On the ground of the above considerations, we were motivated in developing a methodology which may both guide in a comparison of different assembler's output and improve the overall quality of the genome assembly sequences,by merging the sequences produced by different assembly programs.

Published
2009

11. Sequencing of diverse mandarin, pummelo and orange genomes reveals complex history of admixture during citrus domestication

Author

William G. Farmerie, Chunxian Chen, Marcos A. Machado, Daniel Ramón, Jarrod Chapman, Patrick Wincker, Dominique Brunel, Jerry Jenkins, Juan V. Munoz-Sanz, Mohammed Mohiuddin, Javier Terol, Victoria Ibanez, Simone Scalabrin, Giuseppe Reforgiato, Amparo Herrero-Ortega, Cristian Del Fabbro, Xavier Perrier, Jeremy Schmutz, Simon E. Prochnik, Jérôme Salse, Kamel Jabbari, Uffe Hellsten, Julie Poulain, Daniel S. Rokhsar, Alexandre Lomsadze, Karin M. Fredrikson, Andrea Zuccolo, Francisco R. Tadeo, Leandro H. Estornell, Luis Navarro, Olivier Jaillon, Brian Desany, Frederick G. Gmitter, François Luro, Federica Cattonaro, G Albert Wu, Paul Burns, Francis Quetier, Marco Aurélio Takita, Mark Borodovsky, Manuel Talon, Tim Harkins, Julián Pérez-Pérez, Jane Grimwood, Pablo Aleza, Florent Murat, Mikeal L. Roose, Patrick Ollitrault, Michele Morgante, Karine Labadie, Arnaud Couloux, Sara Pinosio, Juliana Freitas-Astúa, Manuel Ruiz, Chinnappa D. Kodira, United States Department of Energy, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), DOE Joint Genome Institute [Walnut Creek], Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Applied Genomics Institute (IGA), Instituto Valenciano de Investigaciones Agrarias - Institut Valencià d'Investigacions Agraries - Valencian Institute for agricultural Research (IVIA), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Génomique métabolique (UMR 8030), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institute of Applied Genomics [Udine] (IGA), Institute of Applied Genomics, Plant Genetics Institute, Consiglio Nazionale delle Ricerche [Roma] (CNR), Ecology and Evolutionary Biology [Tucson] (EEB), University of Arizona, Department of Energy / Joint Genome Institute (DOE), Los Alamos National Laboratory (LANL), Centro de Genómica - Centre de Genòmica [IVIA], Department of Food Biotechnology, Biopolis, Universitat de València (UV), Etude du Polymorphisme des Génomes Végétaux (EPGV), Institut National de la Recherche Agronomique (INRA), Génétique et Ecophysiologie de la qualité des agrumes (GEQA), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Institut de Génomique d'Evry (IG), Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Dipartimento di Produzione Vegetale e Tecnologie Agrarie, Università degli Studi di Udine - University of Udine [Italie], National Science and Technology Institute of Genomics for Citrus Breeding, Brazil [FAPESP 08/57909-2, CNPq 573848/08-4], Brazilian Agricultural Research Corporation (Embrapa), Embrapa-Monsanto Agreement, Agence Nationale de la Recherche (ANR) [CITRUSSEQ PCS-08-GENO], program ANR Blanc-PAGE [ANR-2011-BSV6-00801], US National Institutes of Health [HG00783], Generalitat Valenciana, Spain [PrometeoII/2013/008], Ministry of Economy and Innovation-Fondo Europeo de Desarrollo Regional (FEDER), Spain [AGL2011-26490], Conselleria de Agricultura, Pesca, Alimentacion y Agua from the Generalitat Valenciana, Ministerio de Economia e Innovacion [PSE-060000-2009-8, IPT-010000-2010-43], Citruseq-Citrusgenn consortium company (Anecoop S. Coop., Eurosemillas S.A.), Citruseq-Citrusgenn consortium company (Fundacion Ruralcaja Valencia), Citruseq-Citrusgenn consortium company (GCM Variedades Vegetales A.I.E.), Citruseq-Citrusgenn consortium company ( Investigacion Citricola Castellon S.A.), Citruseq-Citrusgenn consortium company (Source Citrus Genesis-Special New Fruit Licensing, Ltd.), Florida Citrus Production Research Advisory Council (FCPRAC), Florida Department of Agriculture and Consumer Services [013646], Florida Department of Citrus (FDOC), Citrus Research and Development Foundation [71], Ministero delle Politiche Agricole Alimentari e Forestali, Project Citrustart, Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR), Programma Operativo Nazionale 'Ricerca e Competitivita', Project IT-Citrus Genomics [PON_01623], Office of Science of the US Department of Energy [DE-AC02-05CH11231], Génétique Diversité et Ecophysiologie des Céréales - Clermont Auvergne (GDEC), Institut National de la Recherche Agronomique (INRA)-Université Clermont Auvergne (UCA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Instituto Valenciano de Investigaciones Agrarias, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Centro de Genomica, Valencian Institute of Agrarian Research (IVIA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)

Subjects
Germplasm, Phylogénie, Citrus, [SDV]Life Sciences [q-bio], Plant genetics, Orange (colour), Breeding, Applied Microbiology and Biotechnology, F30 - Génétique et amélioration des plantes, Domestication, Citrus clementina, Conserved Sequence, ComputingMilieux_MISCELLANEOUS, Genome, food and beverages, F70 - Taxonomie végétale et phytogéographie, séquençage du génome, Molecular Medicine, Ploidy, Sequence Analysis, Citrus × sinensis, Genome, Plant, Biotechnology, Citrus sinensis, Crops, Agricultural, Séquence nucléotidique, Evolution, Molecular Sequence Data, Biomedical Engineering, Bioengineering, Agrume, Crops, Biology, Article, Evolution, Molecular, Citrus maxima, Species Specificity, Phylogenetics, Botany, Hybrid, Génie génétique, Agricultural, Génome, Base Sequence, Molecular, Genetic Variation, Taxonomie, Sequence Analysis, DNA, DNA, Plant, next-generation sequencing, Citrus reticulata
Abstract

Cultivated citrus are selections from, or hybrids of, wild progenitor species whose identities and contributions to citrus domestication remain controversial. Here we sequence and compare citrus genomes-a high-quality reference haploid clementine genome and mandarin, pummelo, sweet-orange and sour-orange genomes- and show that cultivated types derive from two progenitor species. Although cultivated pummelos represent selections from one progenitor species, Citrus maxima, cultivated mandarins are introgressions of C. maxima into the ancestral mandarin species Citrus reticulata. The most widely cultivated citrus, sweet orange, is the offspring of previously admixed individuals, but sour orange is an F1 hybrid of pure C. maxima and C. reticulata parents, thus implying that wild mandarins were part of the early breeding germplasm. A Chinese wild 'mandarin' diverges substantially from C. reticulata, thus suggesting the possibility of other unrecognized wild citrus species. Understanding citrus phylogeny through genome analysis clarifies taxonomic relationships and facilitates sequence-directed genetic improvement., 栽培されている柑橘類は、野生祖先種から選択されたもの、またはその交雑種であるが、その祖先種の正体、および柑橘類栽培化に対する寄与には依然として議論がある。今回、柑橘類ゲノム（クレメンタインの高精度標準ハプロイドゲノム、ならびにマンダリン、パメロ、スイートオレンジ、およびサワーオレンジのゲノム）の配列解読および比較を行ったところ、栽培種が2つの祖先種に由来することが明らかにされた。栽培パメロは単一祖先種 Citrus maxima から選択されたことがわかったが、栽培マンダリンは祖先マンダリン種 Citrus reticulata に C. maxima から遺伝子移入が行われたものであった。最も広く栽培されている柑橘類であるスイートオレンジは過去に混種した個体の子孫であるが、サワーオレンジは純粋な C. maxima と C. reticulata とのF1雑種であり、このことは、野生のマンダリンが初期の育種生殖系列の要素であったことを意味している。中国の野生「マンダリン」は C. reticulata とは大きく異なっており、別の未確認の野生柑橘種である可能性が示唆される。ゲノム解析で柑橘類の系統発生を理解することにより、分類学的な関係が解明され、配列本位の遺伝的改良が促進される。

Published
2014
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12. Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species

Author

Dent Earl, Jacob O. Kitzman, Iain MacCallum, James R. Knight, Jacques Corbeil, Elenie Godzaridis, Cristian Del Fabbro, Paul J. Kersey, Martin Hunt, Octávio S. Paulo, Joseph Fass, Isaac Ho, Michael C. Schatz, Erich D. Jarvis, Dominique Lavenier, Simone Scalabrin, Thomas D. Otto, Nicolas Maillet, Siu-Ming Yiu, Timothy I. Shaw, David B. Jaffe, Henry Song, Ruibang Luo, Steve Goldstein, David Haussler, Francisco Pina-Martins, Richard A. Gibbs, Adam M. Phillippy, Michael Bechner, Ganeshkumar Ganapathy, Stephen Richards, Riccardo Vicedomini, Shuangye Yin, François Laviolette, Yingrui Li, T. Roderick Docking, Binghang Liu, Carson Qu, Wen-Chi Chou, Hao Zhang, Nuno A. Fonseca, Dariusz Przybylski, Bruno Vieira, Yue Liu, Matthew D. MacManes, Sébastien Boisvert, Yujian Shi, Jared T. Simpson, Sergey Kazakov, Sergey Koren, Jarrod Chapman, Giles Hall, Paul Baranay, Sante Gnerre, Shiguo Zhou, Rayan Chikhi, Filipe J. Ribeiro, Jason T. Howard, Zhenyu Li, Pavel Fedotov, Jay Shendure, J. Graham Ruby, Joseph B. Hiatt, Benedict Paten, Ian F Korf, David C. Schwartz, Keith Bradnam, Jianying Yuan, Alexey Sergushichev, Jun Wang, Hamidreza Chitsaz, Daniel S. Rokhsar, Inanc Birol, Huaiyang Jiang, Kim C. Worley, Anton Alexandrov, Zemin Ning, Delphine Naquin, Michael Place, Matthias Haimel, Guojie Zhang, Guillaume Chapuis, Fedor Tsarev, Scott J. Emrich, Shaun D. Jackman, Sergey Melnikov, Xiang Qin, Ted Sharpe, Francesco Vezzi, Tak-Wah Lam, Richard Durbin, Genome Center [UC Davis], University of California [Davis] (UC Davis), University of California (UC)-University of California (UC), National Research University of Information Technologies, Mechanics and Optics [St. Petersburg] (ITMO), Computational Biology and Bioinformatics [New Haven], Yale University [New Haven], Laboratory for Molecular and Computational Genomics [Madison], University of Wisconsin-Madison, Genome Sciences Centre [Vancouver] (GSC), British Columbia Cancer Agency, Infectious Diseases Research Center [Québec], Université Laval [Québec] (ULaval), Faculté de médecine de l'Université Laval [Québec] (ULaval), DOE Joint Genome Institute [Walnut Creek], Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Dependability Interoperability and perfOrmance aNalYsiS Of networkS (DIONYSOS), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Department of computer science [Detroit], Wayne State University [Detroit], Institute of Bioinformatics [Athens], University of Georgia [USA], Institute of Aging Research [Boston], Hebrew SeniorLife [Boston], Department of Molecular Medicine [Québec], Institute of Applied Genomics [Udine] (IGA), Institute of Applied Genomics, The Wellcome Trust Sanger Institute [Cambridge], Howard Hughes Medical Institute [Santa Cruz] (HHMI), Howard Hughes Medical Institute (HHMI)-University of California [Santa Cruz] (UC Santa Cruz), Department of Computer Science and Engineering [South Bend], University of Notre Dame [Indiana] (UND), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, CRACS & Inesc TEC [Porto], Universidade do Porto = University of Porto, Medical Center [Durham], Duke University [Durham], Human Genome Sequencing Center [Houston] (HGSC), Baylor College of Medicine (BCM), Baylor University-Baylor University, Broad Institute [Cambridge], Harvard University-Massachusetts Institute of Technology (MIT), Faculty of Medicine, Department of Genome Sciences [Seattle] (GS), University of Washington [Seattle], 454 Life Sciences [Branford], 454 Life Sciences, National Biodefense Analysis and Countermeasures Center [Frederick], U.S. Social Security Administration, Center for Bioinformatics and Computational Biology [Maryland] (CBCB), University of Maryland [College Park], University of Maryland System-University of Maryland System, HKU-BGI Bioinformatics Algorithms and Core Technology Research Laboratory [Hong Kong], The University of Hong Kong (HKU), Invariant Preserving SOlvers (IPSO), Institut de Recherche Mathématique de Rennes (IRMAR), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Computer Science and Software Engineering [Québec], Beijing Genomics Institute [Shenzhen] (BGI), Berkeley California Institute for Quantitative Biosciences [Berkeley], University of California (UC), Computational Biology & Population Genomics Group [Lisboa], Centre for Environmental Biology, New York Genome Center [New York], New York Genome Center, Department of Molecular and Cell Biology, Department of Biochemistry and Biophysics, Howard Hughes Medical Institute (HHMI), Simons Center for Quantitative Biology [Cold Spring Harbor], Cold Spring Harbor Laboratory, Department of Epidemiology and Biostatistics [Athens], University of Georgia [USA]-College of Public Health, Science for Life Laboratory [Solna], Royal Institute of Technology [Stockholm] (KTH ), Department of Mathematics and Computer Science [Udine], Università degli Studi di Udine - University of Udine [Italie], University of California-University of California, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Howard Hughes Medical Institute (HHMI)-University of California [Santa Cruz] (UCSC), Universidade do Porto, Harvard University [Cambridge]-Massachusetts Institute of Technology (MIT), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-AGROCAMPUS OUEST, Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Inria Rennes – Bretagne Atlantique, and University of California

Subjects
[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Computer science, Sequence assembly, GENOMES, Health Informatics, Computational biology, Assessment, COMPASS, Genome, 03 medical and health sciences, 0302 clinical medicine, biology.animal, Quantitative Biology - Genomics, Gene, 030304 developmental biology, Sequence (medicine), Scaffolds, Genomics (q-bio.GN), Whole genome sequencing, 0303 health sciences, Genome assembly, Heterozygosity, biology, N50, Research, Vertebrate, De novo Assembly, Computer Science Applications, Fosmid, FOS: Biological sciences, Scalability, 030217 neurology & neurosurgery
Abstract

Background - The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements, acceptance of newer read technologies) and in their final output (composition of assembled sequence). More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. The Assemblathon competitions are intended to assess current state-of-the-art methods in genome assembly. Results - In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions - Many current genome assemblers produced useful assemblies, containing a significant representation of their genes, regulatory sequences, and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another., Comment: Additional files available at http://korflab.ucdavis.edu/Datasets/Assemblathon/Assemblathon2/Additional_files/ Major changes 1. Accessions for the 3 read data sets have now been included 2. New file: spreadsheet containing details of all Study, Sample, Run, & Experiment identifiers 3. Made miscellaneous changes to address reviewers comments. DOIs added to GigaDB datasets

Published
2013
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13. Comparative study of RNA-seq- and Microarray-derived coexpression networks in Arabidopsis thaliana

Author

Cristian Del Fabbro, Federico M. Giorgi, Francesco Licausi, Giorgi, Federico M., Del Fabbro, Cristian, and Licausi, Francesco

Subjects
Statistics and Probability, Microarray, Arabidopsis, Gene regulatory network, RNA-Seq, Computational biology, Biology, Biochemistry, Transcriptome, Betweenness centrality, Gene Expression Regulation, Plant, Interaction network, ngs, Computational Theory and Mathematic, rna-seq, microarray, Gene Regulatory Networks, Molecular Biology, Oligonucleotide Array Sequence Analysis, Genetics, Sequence Analysis, RNA, Microarray analysis techniques, Gene Expression Profiling, Medicine (all), Computer Science Applications1707 Computer Vision and Pattern Recognition, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Computational Mathematic, DNA microarray
Abstract

Motivation: Coexpression networks are data-derived representations of genes behaving in a similar way across tissues and experimental conditions. They have been used for hypothesis generation and guilt-by-association approaches for inferring functions of previously unknown genes. So far, the main platform for expression data has been DNA microarrays; however, the recent development of RNA-seq allows for higher accuracy and coverage of transcript populations. It is therefore important to assess the potential for biological investigation of coexpression networks derived from this novel technique in a condition-independent dataset. Results: We collected 65 publicly available Illumina RNA-seq high quality Arabidopsis thaliana samples and generated Pearson correlation coexpression networks. These networks were then compared with those derived from analogous microarray data. We show how Variance-Stabilizing Transformed (VST) RNA-seq data samples are the most similar to microarray ones, with respect to inter-sample variation, correlation coefficient distribution and network topological architecture. Microarray networks show a slightly higher score in biology-derived quality assessments such as overlap with the known protein–protein interaction network and edge ontological agreement. Different coexpression network centralities are investigated; in particular, we show how betweenness centrality is generally a positive marker for essential genes in A.thaliana, regardless of the platform originating the data. In the end, we focus on a specific gene network case, showing that although microarray data seem more suited for gene network reverse engineering, RNA-seq offers the great advantage of extending coexpression analyses to the entire transcriptome. Contact: fgiorgi@appliedgenomics.org Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2013

14. ERNE-BS5: Aligning BS-treated sequences by multiple hits on a 5-letters alphabet

Author

Cristian Del Fabbro, Nicola Prezza, Alberto Policriti, Emanuale De Paoli, and Francesco Vezzi

Subjects
Sequence, Iterative and incremental development, Theoretical computer science, Settore INF/01 - Informatica, Computer science, business.industry, Context (language use), Hamming distance, DNA sequencing, short reads, Set (abstract data type), Identification (information), Software, ngs, Alignment, business
Abstract

Cytosine methylation is a DNA modification that has great impact on the regulation of gene expression and important implications for the biology and health of several living beings, including humans. Bisulfite conversion followed by next generation sequencing (BS-seq) of DNA is the gold standard technique used to detect DNA methylation at single-base resolution on a genome scale through the identification of 5-methylcytosine (5-mC). However, by converting unmethylated cytosines into thymines, BS-seq poses computational challenges to read alignment and aggravates the issue of multiple hits due to the ambiguity raised by the reduced sequence complexity. Here we present ERNE-BS5 (Extended Randomized Numerical alignEr - BiSulfite 5), an aligning program developed to efficiently map BS-treated reads against large genomes (e.g., human). To achieve this goal we have implemented three different ideas: (i) we use a 5-letters alphabet for storing methylation information, (ii) we use a weighted context-aware Hamming distance to identify a T coming from an unmethylated C context, and (iii) we use an iterative process to position multiple-hit reads starting from a preliminary map built using single-hit alignments. The map is corrected and extended at each cycle using the alignments added in the previous iteration. ERNE-BS5 is based on a new improved version of the rNA [20] aligning software with a more efficient core. ERNE (Extended Randomized Numerical alignEr) is a short string alignment package whose goal is to provide an all-inclusive set of tools to handle short reads. ERNE comprises: ERNE-MAP, ERNE-DMAP, ERNE-FILTER, ERNE-VISUAL, and, from now on, ERNE-BS5. ERNE is free software and distributed with an Open Source License (GPL V3) and can be downloaded at: http://erne.sourceforge.net

Published
2012

15. The grapevine genome sequence suggests ancestral hexaploidization in major angiosperm phyla

Author

Graziano Pesole, Benjamin Noel, François Artiguenave, Alberto Policriti, Federica Cattonaro, Alessandro Vezzi, Delphine Jublot, Valérie Laucou, Michel Gouyvenoux, Eléonore Durand, Didier Merdinoglu, Cristian Del Fabbro, Giorgio Valle, Isabelle Le Clainche, Michael Alaux, Irena Juman, Erica Mica, Béatrice Segurens, Alberto Casagrande, Claude Scarpelli, Massimo Delledonne, Gabriele Di Gaspero, Jean-Marc Aury, David S. Horner, Marco Moroldo, M. Enrico Pè, Philippe Hugueney, Mario Pezzotti, Michel Caboche, Anne-Françoise Adam-Blondon, Véronique Anthouard, Nicola Vitulo, Alain Lecharny, Alain Billault, Virginie Vico, Olivier Jaillon, Christian Clepet, Sébastien Aubourg, Michele Morgante, Jean Weissenbach, Patrick Wincker, Nathalie Choisne, Aurélie Canaguier, Corinne Dasilva, Edgardo Ugarte, Philippe Chatelet, Clémence Bruyère, Vincent Dumas, Sophie Paillard, Fabrice Legeai, Nicoletta Felice, Simone Scalabrin, G Malacrida, Julie Poulain, Francis Quetier, Claire Jubin, Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Istituto di Genomica Applicata (IGA), Istituto di Genomica Applicata, Dipartimento di Matematica e Informatica - Universita Udine (DIMI), Università degli Studi di Udine - University of Udine [Italie], Unité de recherche en génomique végétale (URGV), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Scienze Agrarie ed Ambientali - Universita Udine (DISA), CRIBI (CRIBI), Università degli Studi di Padova = University of Padua (Unipd), Unité de Recherche Génomique Info (URGI), Institut National de la Recherche Agronomique (INRA), Santé de la vigne et qualité du vin (SVQV), Institut National de la Recherche Agronomique (INRA)-Université de Strasbourg (UNISTRA), Dipartimento di Scienze Biomolecolari e Biotecnologie, Università degli Studi di Milano = University of Milan (UNIMI), Dipartimento di Biochimica e Biologia Molecolare, Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Istituto Tecnologie Biomediche, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Diversité et génomes des plantes cultivées (UMR DGPC), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Développement et amélioration des plantes (UMR DAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS), Dipartimento Scientifico e Tecnologico (DST), Università degli studi di Verona = University of Verona (UNIVR), Dipartimento di Scienze, Tecnologie e Mercati della Vite e del Vino, VIGNA-CRA Initiative (VIGNA-CRA INITIATIVE), Consorzio Interuniversitario Nazionale per la Biologia Molecolare delle Piante, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Universita degli Studi di Padova, Università degli Studi di Milano [Milano] (UNIMI), Università degli studi di Bari Aldo Moro (UNIBA), Consiglio Nazionale delle Ricerche [Roma] (CNR), University of Verona (UNIVR), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA), Università degli studi di Milano [Milano], Università degli studi di Bari, Centre National de la Recherche Scientifique (CNRS)-Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Università degli Studi di Verona, O., Jaillon, J., Aury, B., Noel, A., Policriti, C., Clepet, Casagrande, Alberto, N., Choisne, S., Aubourg, N., Vitulo, C., Jubin, A., Vezzi, F., Legeai, P., Hugueney, C., Dasilva, D., Horner, E., Mica, D., Jublot, J., Poulain, C., Bruyère, A., Billault, B., Seguren, M., Gouyvenoux, E., Ugarte, F., Cattonaro, V., Anthouard, V., Vico, C., DEL FABBRO, M., Alaux, G., DI GASPERO, V., Duma, N., Felice, S., Paillard, I., Juman, M., Moroldo, S., Scalabrin, A., Canaguier, I., LE CLAINCHE, G., Malacrida, E., Durand, G., Pesole, V., Laucou, P., Chatelet, D., Merdinoglu, M., Delledonne, M., Pezzotti, A., Lecharny, C., Scarpelli, F., Artiguenave, M. E., Pè, G., Valle, M., Morgante, M., Caboche, A., ADAM BLONDON, J., Weissenbach, F., Quétier, and P., Wincker

Subjects
0106 biological sciences, genotype, Arabidopsis, POLYPLOIDY, adaptation, 01 natural sciences, Genome, RNA, Transfer, Gene duplication, plant genomics, plant genome evolution, Sequencing, Vitis, vitis vinifera, genome sequencing, 2. Zero hunger, Genetics, Plant evolution, 0303 health sciences, Multidisciplinary, food and beverages, Exons, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Paleopolyploidy, Populus, RNA, Plant, ARABIDOPSIS-THALIANA, DNA-SEQUENCE, GENE, DUPLICATION, MAP, IDENTIFICATION, EVOLUTION, DNA, Intergenic, vine, Genome, Plant, assembly, Bioinformatics, Molecular Sequence Data, Genomics, Biology, Genes, Plant, Monocotyledon, DNA sequencing, Evolution, Molecular, Polyploidy, 03 medical and health sciences, evolution, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, angiosperm, genome, 030304 developmental biology, Whole genome sequencing, fungi, Oryza, Sequence Analysis, DNA, 15. Life on land, chronology, Introns, grapevine, MicroRNAs, polyploidy, Karyotyping, vitis classification, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], 010606 plant biology & botany
Abstract

International audience; The analysis of the first plant genomes provided unexpected evidence for genome duplication events in species that had previously been considered as true diploids on the basis of their genetics. These polyploidization events may have had important consequences in plant evolution, in particular for species radiation and adaptation and for the modulation of functional capacities. Here we report a high-quality draft of the genome sequence of grapevine (Vitis vinifera) obtained from a highly homozygous genotype. The draft sequence of the grapevine genome is the fourth one produced so far for flowering plants, the second for a woody species and the first for a fruit crop (cultivated for both fruit and beverage). Grapevine was selected because of its important place in the cultural heritage of humanity beginning during the Neolithic period. Several large expansions of gene families with roles in aromatic features are observed. The grapevine genome has not undergone recent genome duplication, thus enabling the discovery of ancestral traits and features of the genetic organization of flowering plants. This analysis reveals the contribution of three ancestral genomes to the grapevine haploid content. This ancestral arrangement is common to many dicotyledonous plants but is absent from the genome of rice, which is a monocotyledon. Furthermore, we explain the chronology of previously described whole-genome duplication events in the evolution of flowering plants.

Published
2007
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16. High throughput approaches reveal splicing of primary microRNA transcripts and tissue specific expression of mature microRNAs in Vitis vinifera

Author

Cristian Del Fabbro, Graziano Pesole, Giorgio Valle, Mario Pezzotti, Alberto Policriti, Cesare Casati, Massimo Delledonne, M. Enrico Pè, Viviana Piccolo, David S. Horner, Alberto Ferrarini, Michele Morgante, and E. Mica

Subjects
Genetics, grapevine, microRNA, next generation sequencing, Small RNA, lcsh:QH426-470, lcsh:Biotechnology, Alternative splicing, Computational biology, Biology, Transcriptome, Gene expression profiling, lcsh:Genetics, lcsh:TP248.13-248.65, RNA splicing, DNA microarray, Gene, Biotechnology
Abstract

Background MicroRNAs are short (~21 base) single stranded RNAs that, in plants, are generally coded by specific genes and cleaved specifically from hairpin precursors. MicroRNAs are critical for the regulation of multiple developmental, stress related and other physiological processes in plants. The recent annotation of the genome of the grapevine (Vitis vinifera L.) allowed the identification of many putative conserved microRNA precursors, grouped into multiple gene families. Results Here we use oligonucleotide arrays to provide the first indication that many of these microRNAs show differential expression patterns between tissues and during the maturation of fruit in the grapevine. Furthermore we demonstrate that whole transcriptome sequencing and deep-sequencing of small RNA fractions can be used both to identify which microRNA precursors are expressed in different tissues and to estimate genomic coordinates and patterns of splicing and alternative splicing for many primary miRNA transcripts. Conclusion Our results show that many microRNAs are differentially expressed in different tissues and during fruit maturation in the grapevine. Furthermore, the demonstration that whole transcriptome sequencing can be used to identify candidate splicing events and approximate primary microRNA transcript coordinates represents a significant step towards the large-scale elucidation of mechanisms regulating the expression of microRNAs at the transcriptional and post-transcriptional levels.

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