next generation sequencing data (144 Ergebnisse)

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Taschenbuch. Zustand: Neu. Neuware -The 14 contributed chapters in this book survey the most recent developments in high-performance algorithms for NGS data, offering fundamental insights and technical information specifically on indexing, compression and storage; error correction; alignment; and assembly.The book will be of value to researchers, practitioners and students engaged with bioinformatics, computer science, mathematics, statistics and life sciences.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 372 pp. Englisch.

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Taschenbuch. Zustand: Neu. Neuware -Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine.About the editors:Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics.Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.

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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - First insights into the genome of Cucurbita pepo are presented in this thesis. 15.67 percent of the 2,349,855 sequence reads generated with Roche s 454 Pryosequencer received a classification in preprocessing. Of the remaining sequence reads 38.82% were classified as repeats, 11.08 percent as gene coding region, 7.08 percent as chloroplast and 5.39 percent as mitochondrial sequences. A comparison with the genome of cucumber reinforces this distribution. Moreover, this thesis describes an automated analysis pipeline for genome analyses. It is equipped with a set of newly developed components to perform whole genome analyses with next generation sequencing datasets, to store the obtained results into a database and to visualize them on a web interface. Comparisons with state of the art analysis pipelines show that its visualization capabilities and automatic input checker are rare but very helpful features.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Regulation of gene transcription plays a major role in mediating cellular responses and physiological behavior in all known organisms. The finding that similar genes are often regulated in a similar manner (co-regulated or 'co-expressed') has directed several 'guilt-by-association' approaches in order to reverse-engineer the cellular transcriptional networks using gene expression data as a compass. This kind of studies has been considerably assisted in the recent years by the development of high-throughput transcript measurement platforms, specifically gene microarrays and next-generation sequencing. In this work, several approaches are described for improving the extraction and interpretation of the information contained in plant transcriptional data, obtained from microarrays or through next generation sequencing. Applications of these theoretical approaches, ranging from Pearson correlation to LASSO and hybrid methods, are described for the reverse engineering of tuber growth transcriptional networks, for the prediction of essential genes and in the task of defining the full transcriptome of the extremophile plant Thellungiella salsuginea.