Algorithms For Structural Variation Discovery And Protein Protein Interaction Prediction
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Algorithms for Structural Variation Discovery and Protein-protein Interaction Prediction
This thesis has two main parts. In the first part, we will give an introduction on human genomic sequences, next-generation sequencing technologies, the structural differences among genomes of different individuals, and the 1000 Genomes Project. We will then discuss the problems of finding novel sequence insertions and mobile element insertions (e.g. Alu elements) in sequenced genomes. To identify those genomic variations with much higher accuracy than what is currently possible, we propose to move from the current model of (1) detecting genomic variations in individual nextgeneration sequenced (NGS) donor genomes independently, and (2) checking whether two or more donor genomes, indeed, agree or disagree on the variationswe will call this model the independent structural variation detection and merging (ISV&M) framework. As an alternative, we propose a new model in which genomic variation is detected among multiple genomes simultaneously. The second part of the thesis focuses on a different project which is concerned with gene tree alignment. The aim is to present the first efficient approach to the problem of determining the interaction partners among protein/domain families. This is a hard computational problem, in particular in the presence of paralogous proteins. We devise a deterministic algorithm which directly maximizes the similarity between two leaf labeled trees with edge lengths, obtaining a score optimal alignment of the two trees in question.
Algorithms for Next-Generation Sequencing
Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before – as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data? Algorithms for Next-Generation Sequencing is an invaluable tool for students and researchers in bioinformatics and computational biology, biologists seeking to process and manage the data generated by next-generation sequencing, and as a textbook or a self-study resource. In addition to offering an in-depth description of the algorithms for processing sequencing data, it also presents useful case studies describing the applications of this technology.
Computational Methods for Rational Drug Design
Comprehensive resource covering computational tools and techniques for the development of cost-effective drugs to combat diseases, with specific disease examples Computational Methods for Rational Drug Design covers the tools and techniques of drug design with applications to the discovery of small molecule-based therapeutics, detailing methodologies and practical applications and addressing the challenges of techniques like AI/ML and drug design for unknown receptor structures. Divided into 23 chapters, the contributors address various cutting-edge areas of therapeutic importance such as neurodegenerative disorders, cancer, multi-drug resistant bacterial infections, inflammatory diseases, and viral infections. Edited by a highly qualified academic with significant research contributions to the field, Computational Methods for Rational Drug Design explores topics including: Computer-assisted methods and tools for structure- and ligand-based drug design, virtual screening and lead discovery, and ADMET and physicochemical assessments In silico and pharmacophore modeling, fragment-based design, de novo drug design and scaffold hopping, network-based methods and drug discovery Rational design of natural products, peptides, enzyme inhibitors, drugs for neurodegenerative disorders, anti-inflammatory therapeutics, antibacterials for multi-drug resistant infections, and antiviral and anticancer therapeutics Protac and protide strategies in drug design, intrinsically disordered proteins (IDPs) in drug discovery and lung cancer treatment through ALK receptor-targeted drug metabolism and pharmacokinetics Helping readers seamlessly navigate the challenges of drug design, Computational Methods for Rational Drug Design is an essential reference for pharmaceutical and medicinal chemists, biochemists, pharmacologists, and phytochemists, along with molecular modeling and computational drug discovery professionals.