Patterns Algorithms High Throughput Sequencing by Mammana Alessandro (10 results)

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Taschenbuch. Condition: Neu. Patterns and algorithms in high-throughput sequencing count data | Computational and biological challenges | Alessandro Mammana | Taschenbuch | 140 S. | Englisch | 2017 | Scholars' Press | EAN 9783659846229 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Proteins interacting with the genome, such as histones and transcription factors, play a major role in the regulation of gene expression. Experimental techniques such as ChIP-seq provide a new type of digital sequences that quantify the presence of a protein along the genome. These are count signals: sequences as long as the genetic code, but with the natural numbers as an alphabet. The computational analysis of these sequences is challenging, as the biological patterns are complex and the datasets are large. This thesis presents 3 efficient algorithms for pattern detection problems in count signals. The first infers the genomic locations of positioned nucleosomes by using an appropriate wavelet and by integrating measurements from multiple ChIP-seq experiments. The second characterizes the regulatory processes acting on the chromatin and is based on an accurate probabilistic model for read counts. The third detects transcription factor binding sites from ChIP-exo data by simultaneously modelling the sequence and the read counts associated to a binding event. Overall, the thesis presents a general computational framework that is likely to be important for future challenges. 140 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mammana AlessandroAlessandro Mammana graduated in Computer Science at the University of Padua and at the Scuola Galileiana di Studi Superiori. He obtained his doctoral degree at the Max Planck Institute for Molecular Genetics, Berlin.

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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Proteins interacting with the genome, such as histones and transcription factors, play a major role in the regulation of gene expression. Experimental techniques such as ChIP-seq provide a new type of digital sequences that quantify the presence of a protein along the genome. These are count signals: sequences as long as the genetic code, but with the natural numbers as an alphabet. The computational analysis of these sequences is challenging, as the biological patterns are complex and the datasets are large. This thesis presents 3 efficient algorithms for pattern detection problems in count signals. The first infers the genomic locations of positioned nucleosomes by using an appropriate wavelet and by integrating measurements from multiple ChIP-seq experiments. The second characterizes the regulatory processes acting on the chromatin and is based on an accurate probabilistic model for read counts. The third detects transcription factor binding sites from ChIP-exo data by simultaneously modelling the sequence and the read counts associated to a binding event. Overall, the thesis presents a general computational framework that is likely to be important for future challenges.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 140 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Proteins interacting with the genome, such as histones and transcription factors, play a major role in the regulation of gene expression. Experimental techniques such as ChIP-seq provide a new type of digital sequences that quantify the presence of a protein along the genome. These are count signals: sequences as long as the genetic code, but with the natural numbers as an alphabet. The computational analysis of these sequences is challenging, as the biological patterns are complex and the datasets are large. This thesis presents 3 efficient algorithms for pattern detection problems in count signals. The first infers the genomic locations of positioned nucleosomes by using an appropriate wavelet and by integrating measurements from multiple ChIP-seq experiments. The second characterizes the regulatory processes acting on the chromatin and is based on an accurate probabilistic model for read counts. The third detects transcription factor binding sites from ChIP-exo data by simultaneously modelling the sequence and the read counts associated to a binding event. Overall, the thesis presents a general computational framework that is likely to be important for future challenges.