Computer scientists have increasingly been enlisted as "bioinformaticians" to assist molecular biologists in their research. This book is a practical introduction to bioinformatics for these computer scientists. The chapters are in-depth discussions by expert bioinformaticians on both general techniques and specific approaches to a range of selected bioinformatics problems. The book is organized into clusters of chapters on the following topics: - Overview of modern molecular biology and a broad spectrum of techniques from computer science -- data mining, machine learning, mathematical modeling, sequence alignment, data integration, workflow development, etc. - In-depth discussion of computational recognition of functional and regulatory sites in DNA sequences. - Incisive discussion of computational prediction of secondary structure of RNA sequences. - Overview of computational prediction of protein cellular localization, and selected discussions of inference of protein function. - Overview of methods for discovering protein-protein interactions. - Detailed discussion of approaches to gene expression analysis for the diagnosis of diseases, the treatment of diseases, and the understanding of gene functions. - Case studies on analysis of phylogenies, functional annotation of proteins, construction of purposebuilt integrated biological databases, and development of workflows underlying the large-scale-effort gene discovery. - Written in a practical, in-depth tutorial style - Covers a broad range of bioinformatics topics and of techniques used in bioinformatics - Comprehensive overviews of the development of various approaches in a number of selectedtopics - In-depth exposition of a number of important topics - Contributions by prominent researchers: Vladimir Bajic, Ming Li, Kenta Nakai, Limsoon Wong, Cathy Wu, etc. - Extensive, integrated references to background liter
Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.
The Institute for Mathematical Sciences at the National University of Singapore organized a program on OC Post-Genome Knowledge DiscoveryOCO from January to June 2002. The program focused on the computational and statistical analysis of sequences and genetics, and the mathematical modeling of complex biological interactions, which are critical to the accurate annotation of genomic sequences, the study of the interplay between genes and proteins, and the study of the genetic variability of species. As part of the program, tutorials for graduate students and newcomers to this transdisciplinary area of research were given by experts in these fields. This important volume collects the expanded notes of some of the tutorials that were given during the program. The topics include comparison and alignment of biological sequences, modeling and analysis of biological pathways, data mining and knowledge discovery from biological and clinical data. Contents: Dynamic Programming Strategies for Analyzing Biomolecular Sequences (K-M Chao); The Representation, Comparison, and Prediction of Protein Pathways (J Tillinghast et al.); Gene Network Inference and Biopathway Modeling (S Miyano); Data Mining Techniques (M J Zaki & L Wong). Readership: Graduate students and researchers in knowledge discovery, statistical learning, computer algorithms, computer simulations, molecular biology and genomics who are interested in bioinformatics.
Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.
Computer scientists have increasingly been enlisted as OC bioinformaticiansOCO to assist molecular biologists in their research. This book is a practical introduction to bioinformatics for these computer scientists. The chapters are in-depth discussions by expert bioinformaticians on both general techniques and specific approaches to a range of selected bioinformatics problems. The book is organized into clusters of chapters on the following topics: . OCo Overview of modern molecular biology and a broad spectrum of techniques from computer science OCo data mining, machine learning, mathematical modeling, sequence alignment, data integration, workflow development, etc. OCo In-depth discussion of computational recognition of functional and regulatory sites in DNA sequences. OCo Incisive discussion of computational prediction of secondary structure of RNA sequences. OCo Overview of computational prediction of protein cellular localization, and selected discussions of inference of protein function. OCo Overview of methods for discovering proteinOCoprotein interactions. OCo Detailed discussion of approaches to gene expression analysis for the diagnosis of diseases, the treatment of diseases, and the understanding of gene functions. OCo Case studies on analysis of phylogenies, functional annotation of proteins, construction of purpose-built integrated biological databases, and development of workflows underlying the large-scale-effort gene discovery. Sample Chapter(s). Chapter 4: Techniques for Recognition of Translation Initiation Sites (385 KB). Chapter 10: Homology Search Methods (483 KB). Contents: Molecular Biology for the Practical Bioinformatician; Strategy and Planning of Bioinformatics Experiments; Data Mining Techniques for the Practical Bioinformatician; Techniques for Recognition of Translation Initiation Sites; How Neural Networks Find Promoters Using Recognition of Micro-Structural Promoter Components; Neural-Statistical Model of TATA-Box Motifs in Eukaryotes; Tuning the Dragon Promoter Finder System for Human Promoter Recognition; RNA Secondary Structure Prediction; Protein Localization Prediction; Homology Search Methods; Analysis of Phylogeny: A Case Study on Saururaceae; Functional Annotation and Protein Families: From Theory to Practice; Discovering ProteinOCoProtein Interactions; Techniques for Analysis of Gene Expression; Genome-Wide cDNA Oligo Probe Design and Its Applications in i>Schizosaccharomyces Pombe; Mining New Motifs from cDNA Sequence Data; Technologies for Biological Data Integration; Construction of Biological Databases: A Case Study on the Protein Phosphatase DataBase (PPDB); A Family Classification Approach to Functional Annotation of Proteins; Informatics for Efficient EST-Based Gene Discovery in Normalized and Subtracted cDNA Libraries. Readership: Computer scientists planning to be a bioinformatician; computer science undergraduates in their sophomore and/or senior years.
This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006, held in Hong Kong, within the scope of the 18th International Conference on Pattern Recognition, ICPR 2006. The book presents 19 revised full papers, covering all topics of the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data. Includes an introduction to Pattern Recognition in Bioinformatics.
This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006, held in Hong Kong, within the scope of the 18th International Conference on Pattern Recognition, ICPR 2006. The book presents 19 revised full papers, covering all topics of the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data. Includes an introduction to Pattern Recognition in Bioinformatics.
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