Patterns and algorithms in high-throughput sequencing count data : Computational and biological challenges

Alessandro Mammana

ISBN 10: 3659846228 / ISBN 13: 9783659846229

Published by Scholars' Press, 2017

About this Item

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.

Seller Inventory # 9783659846229

Bibliographic Details

• Title: Patterns and algorithms in high-throughput sequencing count data : Computational and biological challenges
• Publisher: Scholars' Press
• Publication Date: 2017
• Language: English
• ISBN 10: 3659846228
• ISBN 13: 9783659846229
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 227 grams

About this title

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.

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).