Patterns and algorithms in high-throughput sequencing count data: Computational and biological challenges - Softcover
Synopsis
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.
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About the Author
Alessandro 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, where he developed algorithms for Bioinformatics. He currently works on genome sequencing products at Illumina, in Cambridge (UK).
"About this title" may belong to another edition of this title.
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Patterns and algorithms in high-throughput sequencing count data
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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. Seller Inventory # 9783659846229
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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. Seller Inventory # 108469912
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Patterns and algorithms in high-throughput sequencing count data
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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. Seller Inventory # 9783659846229
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