Historical Introduction The Marfan Syndrome: From Clinical Delineation to Mutational Characterization, a Semiautobiographic Account VictorA. McKusick l n 1876, E. Williams, an ophthalmologistin Cincinnati, Ohio, described ectopia lentis in a brother and sister who were exceptionally tall and had been loosejointed from birth. I Although there is a Williams syndrome that has aortic manifestations (supravalvar aortic stenosis), the name Williams was never associated with the disorder we now call Marfan syndrome. The reason is clear: Williamswas geographically removed from the leading medical centers and published in the Transactions of the American Ophthalmological Society; surely his report attracted little attention and the non-ocular features were not emphasized. 2 The case report that brought the disorder to attention was provided by a prominent Pari- sian professor of pediatrics, Antoine Bernard-Jean Marfan (1858-1942), who did much to establish pediatrics as a specialty in France and elsewhere. He was the author of widely read textbooks and monographson pediatrictopics and waseditor of Le Nourrisson for a great many years. In addition to the syndromeunder discussion here, his name is often attached to "Marfan's law" (that immunity to pulmonary phthisis is conferred by the healing of a local tuberculous 3 lesion) and Marfan's subxiphoid approach for aspiratingfluid from the pericardial sac. (Please pardon my use of the possessive form of the eponym in these two instances!) Pictures of Marfan (Fig.

Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.

Introduction to Bio-Ontologies explores the computational background of ontologies. Emphasizing computational and algorithmic issues surrounding bio-ontologies, this self-contained text helps readers understand ontological algorithms and their applications. The first part of the book defines ontology and bio-ontologies. It also explains the importance of mathematical logic for understanding concepts of inference in bio-ontologies, discusses the probability and statistics topics necessary for understanding ontology algorithms, and describes ontology languages, including OBO (the preeminent language for bio-ontologies), RDF, RDFS, and OWL. The second part covers significant bio-ontologies and their applications. The book presents the Gene Ontology; upper-level ontologies, such as the Basic Formal Ontology and the Relation Ontology; and current bio-ontologies, including several anatomy ontologies, Chemical Entities of Biological Interest, Sequence Ontology, Mammalian Phenotype Ontology, and Human Phenotype Ontology. The third part of the text introduces the major graph-based algorithms for bio-ontologies. The authors discuss how these algorithms are used in overrepresentation analysis, model-based procedures, semantic similarity analysis, and Bayesian networks for molecular biology and biomedical applications. With a focus on computational reasoning topics, the final part describes the ontology languages of the Semantic Web and their applications for inference. It covers the formal semantics of RDF and RDFS, OWL inference rules, a key inference algorithm, the SPARQL query language, and the state of the art for querying OWL ontologies. Web ResourceSoftware and data designed to complement material in the text are available on the book's website: http://bio-ontologies-book.org The site provides the R Robo package developed for the book, along with a compressed archive of data and ontology files used in some of the exercises. It also offers teaching/presentation slides and links to other relevant websites. This book provides readers with the foundation to use ontologies as a starting point for new bioinformatics research projects or to support current molecular genetics research projects. By supplying a self-contained introduction to OBO ontologies and the Semantic Web, it bridges the gap between both fields and helps readers see what each can contribute to the analysis and understanding of biomedical data.

Introduction to Bio-Ontologies explores the computational background of ontologies. Emphasizing computational and algorithmic issues surrounding bio-ontologies, this self-contained text helps readers understand ontological algorithms and their applications.

The first part of the book defines ontology and bio-ontologies. It also explains the importance of mathematical logic for understanding concepts of inference in bio-ontologies, discusses the probability and statistics topics necessary for understanding ontology algorithms, and describes ontology languages, including OBO (the preeminent language for bio-ontologies), RDF, RDFS, and OWL.

The second part covers significant bio-ontologies and their applications. The book presents the Gene Ontology; upper-level ontologies, such as the Basic Formal Ontology and the Relation Ontology; and current bio-ontologies, including several anatomy ontologies, Chemical Entities of Biological Interest, Sequence Ontology, Mammalian Phenotype Ontology, and Human Phenotype Ontology.

The third part of the text introduces the major graph-based algorithms for bio-ontologies. The authors discuss how these algorithms are used in overrepresentation analysis, model-based procedures, semantic similarity analysis, and Bayesian networks for molecular biology and biomedical applications.

With a focus on computational reasoning topics, the final part describes the ontology languages of the Semantic Web and their applications for inference. It covers the formal semantics of RDF and RDFS, OWL inference rules, a key inference algorithm, the SPARQL query language, and the state of the art for querying OWL ontologies.

Web Resource
Software and data designed to complement material in the text are available on the book s website: http: //bio-ontologies-book.org The site provides the R Robo package developed for the book, along with a compressed archive of data and ontology files used in some of the exercises. It also offers teaching/presentation slides and links to other relevant websites.

This book provides readers with the foundation to use ontologies as a starting point for new bioinformatics research projects or to support current molecular genetics research projects. By supplying a self-contained introduction to OBO ontologies and the Semantic Web, it bridges the gap between both fields and helps readers see what each can contribute to the analysis and understanding of biomedical data.

Historical Introduction The Marfan Syndrome: From Clinical Delineation to Mutational Characterization, a Semiautobiographic Account VictorA. McKusick l n 1876, E. Williams, an ophthalmologistin Cincinnati, Ohio, described ectopia lentis in a brother and sister who were exceptionally tall and had been loosejointed from birth. I Although there is a Williams syndrome that has aortic manifestations (supravalvar aortic stenosis), the name Williams was never associated with the disorder we now call Marfan syndrome. The reason is clear: Williamswas geographically removed from the leading medical centers and published in the Transactions of the American Ophthalmological Society; surely his report attracted little attention and the non-ocular features were not emphasized. 2 The case report that brought the disorder to attention was provided by a prominent Pari- sian professor of pediatrics, Antoine Bernard-Jean Marfan (1858-1942), who did much to establish pediatrics as a specialty in France and elsewhere. He was the author of widely read textbooks and monographson pediatrictopics and waseditor of Le Nourrisson for a great many years. In addition to the syndromeunder discussion here, his name is often attached to "Marfan's law" (that immunity to pulmonary phthisis is conferred by the healing of a local tuberculous 3 lesion) and Marfan's subxiphoid approach for aspiratingfluid from the pericardial sac. (Please pardon my use of the possessive form of the eponym in these two instances!) Pictures of Marfan (Fig.

Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.