Download or read book Computational Methods for SNPs and Haplotype Inference written by Sorin Istrail and published by Springer Science & Business Media. This book was released on 2004-03-12 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-proceedings of the DIMACS/RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotype Inference held in Piscataway, NJ, USA, in November 2002. The book presents ten revised full papers as well as abstracts of the remaining workshop papers. All relevant current issues in computational methods for SNP and haplotype analysis and their applications to disease associations are addressed.

Download or read book Computational Methods for SNPs and Haplotype Inference written by Sorin Istrail and published by Springer. This book was released on 2004-03-12 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-proceedings of the DIMACS/RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotype Inference held in Piscataway, NJ, USA, in November 2002. The book presents ten revised full papers as well as abstracts of the remaining workshop papers. All relevant current issues in computational methods for SNP and haplotype analysis and their applications to disease associations are addressed.

Download or read book Computational Methods for SNPs and Haplotype Inference written by Sorin Istrail and published by Springer. This book was released on 2014-03-12 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-proceedings of the DIMACS/RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotype Inference held in Piscataway, NJ, USA, in November 2002. The book presents ten revised full papers as well as abstracts of the remaining workshop papers. All relevant current issues in computational methods for SNP and haplotype analysis and their applications to disease associations are addressed.

Download or read book Computational Methods for Haplotype Inference with Application to Haplotype Block Characterization in Cattle written by Rafael Villa Angulo and published by . This book was released on 2009 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic haplotype analysis is important in the identification of DNA variations relevant to several common and complex human diseases, and for the identification of Quantitative Trait Loci genes in animal models. Haplotype analysis is now considered one of the most promising methods for studying gene-disease and gene-phenotype association studies. In this dissertation, we address the problem of haplotype inference from cattle genotypes, which has significant differences with human genotype data. Using data derived by the International Bovine HapMap Consortium, we provide the first high-resolution haplotype block characterization in the cattle genome. In addition, a new genetic algorithm method for haplotype inference in large and complex pedigrees was developed. Novel results indicate that cattle and humans share high similarity in linkage disequilibrium and haplotype block structure in the scale of 1-100 kb. Effective populations size estimated from linkage disequilibrium reflects the period of domestication ~12,000 years ago, and the current bottleneck in breeds during the last ~700 years. Analysis of haplotype block density correlation, block boundary discordances, and haplotype sharing show clear differentiation between indicus, African, and composite breed subgroups, but not between dairy and beef subgoups. Our results support the hypothesis that historic geographic ancestry plays a stronger role in explaining genotypic variation, and haplotype block structure in cattle, than does the more recent selection into breeds with specific agriculture function. Another significant contribution from this dissertation is the development of new method for haplotype inference in large and complex cattle pedigrees. A new representation of the search space for valid haplotype configurations was developed, and a genetic algorithm was used to optimize features of the haplotype assignments. The genetic algorithm includes a novel population initialization method, new crossover and mutation operators, and a fitness function that minimizes the inferred recombinations in the pedigree. The new method outperformed the current available methods capable of handling large and complex pedigrees, and has the advantage of being scalable to larger datasets.

Download or read book Algorithms for Computational Genetics Epidemiology written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The most intriguing problems in genetics epidemiology are to predict genetic disease susceptibility and to associate single nucleotide polymorphisms (SNPs) with diseases. In such these studies, it is necessary to resolve the ambiguities in genetic data. The primary obstacle for ambiguity resolution is that the physical methods for separating two haplotypes from an individual genotype (phasing) are too expensive. Although computational haplotype inference is a well-explored problem, high error rates continue to deteriorate association accuracy. Secondly, it is essential to use a small subset of informative SNPs (tag SNPs) accurately representing the rest of the SNPs (tagging). Tagging can achieve budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs. Recent successes in high throughput genotyping technologies drastically increase the length of available SNP sequences. This elevates importance of informative SNP selection for compaction of huge genetic data in order to make feasible fine genotype analysis. Finally, even if complete and accurate data is available, it is unclear if common statistical methods can determine the susceptibility of complex diseases. The dissertation explores above computational problems with a variety of methods, including linear algebra, graph theory, linear programming, and greedy methods. The contributions include (1)significant speed-up of popular phasing tools without compromising their quality, (2)stat-of-the-art tagging tools applied to disease association, and (3)graph-based method for disease tagging and predicting disease susceptibility.

Download or read book Computational Methods for Analyzing Human Genetic Variation written by Vikas Bansal and published by ProQuest. This book was released on 2008 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the post-genomic era, several large-scale studies that set out to characterize genetic diversity in human populations have significantly changed our understanding of the nature and extent of human genetic variation. The International HapMap Project has genotyped over 3 million Single Nucleotide Polymorphisms (SNPs) in 270 humans from four populations. Several individual genomes have recently been sequenced and thousands of genomes will be available in the near future. In this dissertation, we describe computational methods that utilize these datasets to further enhance our knowledge of the fine-scale structure of human genetic variation. These methods employ a variety of computational techniques and are applicable to organisms other than human. Meiotic recombination represents a fundamental mechanism for generating genetic diversity by shuffling of chromosomes. There is great interest in understanding the non-random distribution of recombination events across the human genome. We describe combinatorial methods for counting historical recombination events using population data. We demonstrate that regions with increased density of recombination events correspond to regions identified as recombination hotspots using experimental techniques. In recent years, large scale structural variants such as deletions, insertions, duplications and inversions of DNA segments have been revealed to be much more frequent than previously thought. High-throughput genome-scanning techniques have enabled the discovery of hundreds of such variants but are unable to detect balanced structural changes such as inversions. We describe a statistical method to detect large inversions using whole genome SNP population data. Using the HapMap data, we identify several known and putative inversion polymorphisms. In the final part of this thesis, we tackle the haplotype assembly problem. High-throughput genotyping methods probe SNPs individually and are unable to provide information about haplotypes: the combination of alleles at SNPs on a single chromosome. We describe Markov chain Monte Carlo (MCMC) and combinatorial algorithms for reconstructing the two haplotypes for an individual using whole genome sequence data. These algorithms are based on computing cuts in graphs derived from the sequenced reads. We analyze the convergence properties of the Markov chain underlying our MCMC algorithm. We apply these methods to assemble highly accurate haplotypes for a recently sequenced human.

Download or read book Comparison of Statistical Methods of Haplotype Reconstruction and Logistic Regression for Association Studies written by Karey Shumansky and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigating association between disease and single nucleotide polymorphisms (SNPs) has been an approach for genetic association studies and more recently investigating association between disease and haplotypes has become another accepted method. Haplotypes are physically linked combinations of alleles from a stretch of DNA and can serve to increase power of finding an association due to interactions between inclusive SNPs and the increased area of chromosome that is taken into consideration. Determining haplotypes experimentally or by family studies is a costly and timeinefficient method, so haplotype reconstruction by statistical methods has become an adopted practice. The problem with computational methods is the extra. source of error from ambiguous haplotypes that has to be included in statistical analysis. This paper investigates methods of error management with three different 1ogistic regression packages, two of which are specific to analysis of genetic data. Methods are applied to simulated data and a data set looking for genetic risk factors for non-Hodgkin Lymphoma.

Download or read book Haplotype Inference from Pedigree Data and Population Data written by Xin Li and published by . This book was released on 2010 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: Haplotype is an important representation of human genetic variation and is thus valuable for investigating the genetics behind diseases. However, humans are diploid and in practice, genotype data instead of haplotype data are collected directly. Consequently, there are great demands for efficient and accurate computational methods to reconstruct haplotypes from genotype data. Our project started with the development of a rule-based haplotyping method for pedigree data with tightly linked markers. We formulate Mendelian constraints as a linear system of inheritance variables and solve the linear system using disjoint-set data structures. Our algorithm achieved the lowest time complexity among all existing methods. Comparisons with two popular algorithms showed that this algorithm made 10 to 10^5-fold improvements on a variety of parameter settings. Based on the zero-recombinant haplotype inference, we went on to construct a general framework for haplotyping population and pedigree mixed data that consist of many families with unrelated founders, by combining novel techniques of recombination event detection and maximum likelihood optimization. This method makes it possible to do the genome-wide haplotype inference on pedigree and population mixed data.

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Download or read book Computational Methods for Understanding Genetic Variations from Next Generation Sequencing Data written by Soyeon Ahn (Ph. D.) and published by . This book was released on 2018 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies of human genetic variation reveal critical information about genetic and complex diseases such as cancer, diabetes and heart disease, ultimately leading towards improvements in health and quality of life. Moreover, understanding genetic variations in viral population is of utmost importance to virologists and helps in search for vaccines. Next-generation sequencing technology is capable of acquiring massive amounts of data that can provide insight into the structure of diverse sets of genomic sequences. However, reconstructing heterogeneous sequences is computationally challenging due to the large dimension of the problem and limitations of the sequencing technology.This dissertation is focused on algorithms and analysis for two problems in which we seek to characterize genetic variations: (1) haplotype reconstruction for a single individual, so-called single individual haplotyping (SIH) or haplotype assembly problem, and (2) reconstruction of viral population, the so-called quasispecies reconstruction (QSR) problem. For the SIH problem, we have developed a method that relies on a probabilistic model of the data and employs the sequential Monte Carlo (SMC) algorithm to jointly determine type of variation (i.e., perform genotype calling) and assemble haplotypes. For the QSR problem, we have developed two algorithms. The first algorithm combines agglomerative hierarchical clustering and Bayesian inference to reconstruct quasispecies characterized by low diversity. The second algorithm utilizes tensor factorization framework with successive data removal to reconstruct quasispecies characterized by highly uneven frequencies of its components. Both algorithms outperform existing methods in both benchmarking tests and real data.

Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-09-12 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

Download or read book Research in Computational Molecular Biology written by Satoru Miyano and published by Springer. This book was released on 2005-05-04 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers presented at the 9th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2005), which was held in Cambridge, Massachusetts, on May 14–18, 2005. The RECOMB conference series was started in 1997 by Sorin Istrail, Pavel Pevzner and Michael Waterman. The list of previous meetings is shown below in the s- tion “Previous RECOMB Meetings. ” RECOMB 2005 was hosted by the Broad Institute of MIT and Harvard, and Boston University’s Center for Advanced - nomic Technology, and was excellently organized by the Organizing Committee Co-chairs Jill Mesirov and Simon Kasif. This year, 217 papers were submitted, of which the Program Committee - lected 39 for presentation at the meeting and inclusion in this proceedings. Each submission was refereed by at least three members of the Program Committee. After the completion of the referees’ reports, an extensive Web-based discussion took place for making decisions. From RECOMB 2005, the Steering Committee decided to publish the proceedings as a volume of Lecture Notes in Bioinf- matics (LNBI) for which the founders of RECOMB are also the editors. The prominent volume number LNBI 3500 was assigned to this proceedings. The RECOMB conference series is closely associated with the Journal of Compu- tional Biology which traditionally publishes special issues devoted to presenting full versions of selected conference papers. The RECOMB Program Committee consistedof42members,aslistedonaseparatepage. Iwouldliketothank the RECOMB 2005 Program Committee members for their dedication and hard work.

Download or read book Handbook of Computational Molecular Biology written by Srinivas Aluru and published by CRC Press. This book was released on 2005-12-21 with total page 1108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology of

Download or read book Genome Wide Association Studies and Genomic Prediction written by Cedric Gondro and published by Humana Press. This book was released on 2017-04-30 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.

Download or read book Analysis of Complex Disease Association Studies written by Eleftheria Zeggini and published by Academic Press. This book was released on 2010-11-17 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: According to the National Institute of Health, a genome-wide association study is defined as any study of genetic variation across the entire human genome that is designed to identify genetic associations with observable traits (such as blood pressure or weight), or the presence or absence of a disease or condition. Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease. This burgeoning science merges the principles of statistics and genetics studies to make sense of the vast amounts of information available with the mapping of genomes. In order to make the most of the information available, statistical tools must be tailored and translated for the analytical issues which are original to large-scale association studies. Analysis of Complex Disease Association Studies will provide researchers with advanced biological knowledge who are entering the field of genome-wide association studies with the groundwork to apply statistical analysis tools appropriately and effectively. With the use of consistent examples throughout the work, chapters will provide readers with best practice for getting started (design), analyzing, and interpreting data according to their research interests. Frequently used tests will be highlighted and a critical analysis of the advantages and disadvantage complimented by case studies for each will provide readers with the information they need to make the right choice for their research. Additional tools including links to analysis tools, tutorials, and references will be available electronically to ensure the latest information is available. - Easy access to key information including advantages and disadvantage of tests for particular applications, identification of databases, languages and their capabilities, data management risks, frequently used tests - Extensive list of references including links to tutorial websites - Case studies and Tips and Tricks

Download or read book Biological Sequence Analysis written by Richard Durbin and published by Cambridge University Press. This book was released on 1998-04-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.

Download or read book Pattern Recognition in Computational Molecular Biology written by Mourad Elloumi and published by John Wiley & Sons. This book was released on 2015-12-24 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular data Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.