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The Development Of Computational Methods For Large Scale Comparisons And Analyses Of Genome Evolution
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Book Synopsis The Development of Computational Methods for Large-scale Comparisons and Analyses of Genome Evolution by : Stephen Paul Moss
Download or read book The Development of Computational Methods for Large-scale Comparisons and Analyses of Genome Evolution written by Stephen Paul Moss and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sequence — Evolution — Function by : Eugene V. Koonin
Download or read book Sequence — Evolution — Function written by Eugene V. Koonin and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis. Sequence - Evolution - Function should help bridge the "digital divide" between biologists and computer scientists, allowing biologists to better grasp the peculiarities of the emerging field of Genome Biology and to learn how to benefit from the enormous amount of sequence data available in the public databases. The book is non-technical with respect to the computer methods for genome analysis and discusses these methods from the user's viewpoint, without addressing mathematical and algorithmic details. Prior practical familiarity with the basic methods for sequence analysis is a major advantage, but a reader without such experience will be able to use the book as an introduction to these methods. This book is perfect for introductory level courses in computational methods for comparative and functional genomics.
Book Synopsis Evolutionary Genomics by : Maria Anisimova
Download or read book Evolutionary Genomics written by Maria Anisimova and published by Humana Press. This book was released on 2012-03-08 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies. Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight. Volume 2 begins with phylogenomics and continues with in-depth coverage of natural selection, recombination, and genomic innovation. The remaining chapters treat topics of more recent interest, including population genomics, -omics studies, and computational issues related to the handling of large-scale genomic data. Written in the highly successful Methods in Molecular BiologyTM series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses. Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.
Book Synopsis Computational Methods and Analyses in Comparative Genomics and Epigenomics by : Qian Peng
Download or read book Computational Methods and Analyses in Comparative Genomics and Epigenomics written by Qian Peng and published by . This book was released on 2012 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: As biological problems are becoming more complex and data growing at a rate much faster than that of computer hardware, new and faster algorithms are required. This dissertation investigates computational problems arising in two of the fields : comparative genomics and epigenomics, and employs a variety of computational techniques to address the problemsOne fundamental question in the studies of chromosome evolution is whether the rearrangement breakpoints are happening at random positions or along certain hotspots. We investigate the breakpoint reuse phenomenon, and show the analyses that support the more recently proposed fragile breakage model as opposed to the conventional random breakage models for chromosome evolution. The identification of syntenic regions between chromosomes forms the basis for studies of genome architectures, comparative genomics, and evolutionary genomics. The previous synteny block reconstruction algorithms could not be scaled to a large number of mammalian genomes being sequenced; neither did they address the issue of generating non-overlapping synteny blocks suitable for analyzing rearrangements and evolutionary history of large-scale duplications prevalent in plant genomes. We present a new unified synteny block generation algorithm based on A-Bruijn graph framework that overcomes these shortcomings. In the epigenome sequencing, a sample may contain a mixture of epigenomes and there is a need to resolve the distinct methylation patterns from the mixture. Many sequencing applications, such as haplotype inference for diploid or polyploid genomes, and metagenomic sequencing, share the similar objective : to infer a set of distinct assemblies from reads that are sequenced from a heterogeneous sample and subsequently aligned to a reference genome. We model the problem from both a combinatorial and a statistical angles. First, we describe a theoretical framework. A linear-time algorithm is then given to resolve a minimum number of assemblies that are consistent with all reads, substantially improving on previous algorithms. An efficient algorithm is also described to determine a set of assemblies that is consistent with a maximum subset of the reads, a previously untreated problem. We then prove that allowing nested reads or permitting mismatches between reads and their assemblies renders these problems NP-hard. Second, we describe a mixture model-based approach, and applied the model for the detection of allele-specific methylations.
Book Synopsis Evolutionary Genomics by : Maria Anisimova
Download or read book Evolutionary Genomics written by Maria Anisimova and published by Humana Press. This book was released on 2012-03-13 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies. Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight. Volume 1 includes a helpful introductory section of bioinformatician primers followed by detailed chapters detailing genomic data assembly, alignment, and homology inference as well as insights into genome evolution from statistical analyses. Written in the highly successful Methods in Molecular BiologyTM series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses. Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.
Book Synopsis Evolution of Translational Omics by : Institute of Medicine
Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
Book Synopsis Evolutionary Genomics by : Maria Anisimova
Download or read book Evolutionary Genomics written by Maria Anisimova and published by Humana Press. This book was released on 2012-03-08 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies. Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight. Volume 2 begins with phylogenomics and continues with in-depth coverage of natural selection, recombination, and genomic innovation. The remaining chapters treat topics of more recent interest, including population genomics, -omics studies, and computational issues related to the handling of large-scale genomic data. Written in the highly successful Methods in Molecular BiologyTM series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses. Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.
Book Synopsis Bioinformatics of Genome Regulation and Structure II by : Nikolay Kolchanov
Download or read book Bioinformatics of Genome Regulation and Structure II written by Nikolay Kolchanov and published by Springer Science & Business Media. This book was released on 2006-06-15 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last 15 years in development of biology were marked with accumulation of unprecedentedly huge arrays of experimental data. The information was amassed with exclusively high rates due to the advent of highly efficient experimental technologies that provided for high throughput genomic sequencing; of functional genomics technologies allowing investigation of expression dynamics of large groups of genes using expression DNA chips; of proteomics methods giving the possibility to analyze protein compositions of cells, tissues, and organs, assess the dynamics of the cell proteome, and reconstruct the networks of protein-protein interactions; and of metabolomics, in particular, high resolution mass spectrometry study of cell metabolites, and distribution of metabolic fluxes in the cells with a concurrent investigation of the dynamics of thousands metabolites in an individual cell. Analysis, comprehension, and use of the tremendous volumes of experimental data reflecting the intricate processes underlying the functioning of molecular genetic systems are unfeasible in principle without the systems approach and involvement of the state-of-the-art information and computer technologies and efficient mathematical methods for data analysis and simulation of biological systems and processes. The need in solving these problems initiated the birth of a new science— postgenomic bioinformatics or systems biology in silico.
Book Synopsis Computational Methods in Genome Research by : Sándor Suhai
Download or read book Computational Methods in Genome Research written by Sándor Suhai and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of computational methods to solve scientific and pratical problems in genome research created a new interdisciplinary area that transcends boundaries traditionally separating genetics, biology, mathematics, physics, and computer science. Computers have been, of course, intensively used for many year~ in the field of life sciences, even before genome research started, to store and analyze DNA or proteins sequences, to explore and model the three-dimensional structure, the dynamics and the function of biopolymers, to compute genetic linkage or evolutionary processes etc. The rapid development of new molecular and genetic technologies, combined with ambitious goals to explore the structure and function of genomes of higher organisms, has generated, however, not only a huge and burgeoning body of data but also a new class of scientific questions. The nature and complexity of these questions will require, beyond establishing a new kind of alliance between experimental and theoretical disciplines, also the development of new generations both in computer software and hardware technologies, respectively. New theoretical procedures, combined with powerful computational facilities, will substantially extend the horizon of problems that genome research can ·attack with success. Many of us still feel that computational models rationalizing experimental findings in genome research fulfil their promises more slowly than desired. There also is an uncertainity concerning the real position of a 'theoretical genome research' in the network of established disciplines integrating their efforts in this field.
Book Synopsis Computational Methods for Understanding Bacterial and Archaeal Genomes by : Ying Xu
Download or read book Computational Methods for Understanding Bacterial and Archaeal Genomes written by Ying Xu and published by World Scientific. This book was released on 2008 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over 500 prokaryotic genomes have been sequenced to date, and thousands more have been planned for the next few years. While these genomic sequence data provide unprecedented opportunities for biologists to study the world of prokaryotes, they also raise extremely challenging issues such as how to decode the rich information encoded in these genomes. This comprehensive volume includes a collection of cohesively written chapters on prokaryotic genomes, their organization and evolution, the information they encode, and the computational approaches needed to derive such information. A comparative view of bacterial and archaeal genomes, and how information is encoded differently in them, is also presented. Combining theoretical discussions and computational techniques, the book serves as a valuable introductory textbook for graduate-level microbial genomics and informatics courses.
Book Synopsis Protein Function Prediction for Omics Era by : Daisuke Kihara
Download or read book Protein Function Prediction for Omics Era written by Daisuke Kihara and published by Springer Science & Business Media. This book was released on 2011-04-19 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene function annotation has been a central question in molecular biology. The importance of computational function prediction is increasing because more and more large scale biological data, including genome sequences, protein structures, protein-protein interaction data, microarray expression data, and mass spectrometry data, are awaiting biological interpretation. Traditionally when a genome is sequenced, function annotation of genes is done by homology search methods, such as BLAST or FASTA. However, since these methods are developed before the genomics era, conventional use of them is not necessarily most suitable for analyzing a large scale data. Therefore we observe emerging development of computational gene function prediction methods, which are targeted to analyze large scale data, and also those which use such omics data as additional source of function prediction. In this book, we overview this emerging exciting field. The authors have been selected from 1) those who develop novel purely computational methods 2) those who develop function prediction methods which use omics data 3) those who maintain and update data base of function annotation of particular model organisms (E. coli), which are frequently referred
Book Synopsis Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data by : Tanya Ngoc Phung
Download or read book Methods and Models for the Analysis of Genetic Variation Across Species Using Large-scale Genomic Data written by Tanya Ngoc Phung and published by . This book was released on 2018 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding how different evolutionary processes shape genetic variation within and between species is an important question in population genetics. The advent of next generation sequencing has allowed for many theories and hypotheses to be tested explicitly with data. However, questions such as what evolutionary processes affect neutral divergence (DNA differences between species) or genetic variation in different regions of the genome (such as on autosomes versus sex chromosomes) or how many genetic variants contribute to complex traits are still outstanding. In this dissertation, I utilized different large-scale genomic datasets and developed statistical methods to determine the role of natural selection on genetic variation between species, sex-biased evolutionary processes on shaping patterns of genetic variation on the X chromosome and autosomes, and how population history, mutation, and natural selection interact to control complex traits. First, I used genome-wide divergence data between multiple pairs of species ranging in divergence time to show that natural selection has reduced divergence at neutral sites that are linked to those under direct selection. To determine explicitly whether and to what extent linked selection and/or mutagenic recombination could account for the pattern of neutral divergence across the genome, I developed a statistical method and applied it to human-chimp neutral divergence dataset. I showed that a model including both linked selection and mutagenic recombination resulted in the best fit to the empirical data. However, the signal of mutagenic recombination could be coming from biased gene conversion. Comparing genetic diversity between the X chromosome and the autosomes could provide insights into whether and how sex-biased processes have affected genetic variation between different genomic regions. For example, X/A diversity ratio greater than neutral expectation could be due to more X chromosomes than expected and could be a result of mating practices such as polygamy where there are more reproducing females than males. I next utilized whole-genome sequences from dogs and wolves and found that X/A diversity is lower than neutral expectation in both dogs and wolves in ancient time-scales, arguing for evolutionary processes resulting in more males reproducing compared to females. However, within breed dogs, patterns of population differentiation suggest that there have been more reproducing females, highlighting effects from breeding practices such as popular sire effect where one male can father many offspring with multiple females. In medical genetics, a complete understanding of the genetic architecture is essential to unravel the genetic basis of complex traits. While genome wide association studies (GWAS) have discovered thousands of trait-associated variants and thus have furthered our understanding of the genetic architecture, key parameters such as the number of causal variants and the mutational target size are still under-studied. Further, the role of natural selection in shaping the genetic architecture is still not entirely understood. In the last chapter, I developed a computational method called InGeAr to infer the mutational target size and explore the role of natural selection on affecting the variant's effect on the trait. I found that the mutational target size differs from trait to trait and can be large, up to tens of megabases. In addition, purifying selection is coupled with the variant's effect on the trait. I discussed how these results support the omnigenic model of complex traits. In summary, in this dissertation, I utilized different types of large genomic dataset, from genome-wide divergence data to whole genome sequence data to GWAS data to develop models and statistical methods to study how different evolutionary processes have shaped patterns of genetic variation across the genome.
Book Synopsis Statistical and Computational Methods for Analyzing High-Throughput Genomic Data by : Jingyi Li
Download or read book Statistical and Computational Methods for Analyzing High-Throughput Genomic Data written by Jingyi Li and published by . This book was released on 2013 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the burgeoning field of genomics, high-throughput technologies (e.g. microarrays, next-generation sequencing and label-free mass spectrometry) have enabled biologists to perform global analysis on thousands of genes, mRNAs and proteins simultaneously. Extracting useful information from enormous amounts of high-throughput genomic data is an increasingly pressing challenge to statistical and computational science. In this thesis, I will address three problems in which statistical and computational methods were used to analyze high-throughput genomic data to answer important biological questions. The first part of this thesis focuses on addressing an important question in genomics: how to identify and quantify mRNA products of gene transcription (i.e., isoforms) from next-generation mRNA sequencing (RNA-Seq) data? We developed a statistical method called Sparse Linear modeling of RNA-Seq data for Isoform Discovery and abundance Estimation (SLIDE) that employs probabilistic modeling and L1 sparse estimation to answer this ques- tion. SLIDE takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. It is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with existing deterministic isoform assembly algorithms, SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms. Simulation and real data studies show that SLIDE performs as well as or better than major competitors in both isoform discovery and abundance estimation. The second part of this thesis demonstrates the power of simple statistical analysis in correcting biases of system-wide protein abundance estimates and in understanding the rela- tionship between gene transcription and protein abundances. We found that proteome-wide surveys have significantly underestimated protein abundances, which differ greatly from previously published individual measurements. We corrected proteome-wide protein abundance estimates by using individual measurements of 61 housekeeping proteins, and then found that our corrected protein abundance estimates show a higher correlation and a stronger linear relationship with mRNA abundances than do the uncorrected protein data. To estimate the degree to which mRNA expression levels determine protein levels, it is critical to measure the error in protein and mRNA abundance data and to consider all genes, not only those whose protein expression is readily detected. This is a fact that previous proteome-widely surveys ignored. We took two independent approaches to re-estimate the percentage that mRNA levels explain in the variance of protein abundances. While the percentages estimated from the two approaches vary on different sets of genes, all suggest that previous protein-wide surveys have significantly underestimated the importance of transcription. In the third and final part, I will introduce a modENCODE (the Model Organism ENCyclopedia Of DNA Elements) project in which we compared developmental stages, tis- sues and cells (or cell lines) of Drosophila melanogaster and Caenorhabditis elegans, two well-studied model organisms in developmental biology. To understand the similarity of gene expression patterns throughout their development time courses is an interesting and important question in comparative genomics and evolutionary biology. The availability of modENCODE RNA-Seq data for different developmental stages, tissues and cells of the two organisms enables a transcriptome-wide comparison study to address this question. We undertook a comparison of their developmental time courses and tissues/cells, seeking com- monalities in orthologous gene expression. Our approach centers on using stage/tissue/cell- associated orthologous genes to link the two organisms. For every stage/tissue/cell in each organism, its associated genes are selected as the genes capturing specific transcriptional activities: genes highly expressed in that stage/tissue/cell but lowly expressed in a few other stages/tissues/cells. We aligned a pair of D. melanogaster and C. elegans stages/tissues/cells by a hypergeometric test, where the test statistic is the number of orthologous gene pairs associated with both stages/tissues/cells. The test is against the null hypothesis that the two stages/tissues/cells have independent sets of associated genes. We first carried out the alignment approach on pairs of stages/tissues/cells within D. melanogaster and C. elegans respectively, and the alignment results are consistent with previous findings, supporting the validity of this approach. When comparing fly with worm, we unexpectedly observed two parallel collinear alignment patterns between their developmental timecourses and several interesting alignments between their tissues and cells. Our results are the first findings regarding a comprehensive comparison between D. melanogaster and C. elegans time courses, tissues and cells.
Book Synopsis Computational Analysis of Genome Evolution by : Aaron E. Darling
Download or read book Computational Analysis of Genome Evolution written by Aaron E. Darling and published by . This book was released on 2006 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Algorithms for Comparative Genomics by : Khalid Mahmood
Download or read book Computational Algorithms for Comparative Genomics written by Khalid Mahmood and published by . This book was released on 2012 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in high throughput genome sequencing has presented an opportunity to study how species are related, especially, in terms of their evolution and molecular functions. However, the capability to generate genome sequence data outweighs the ability to decipher and translate this data to biological information. Therefore, computational methods play a key role in deciphering large and complex genome data that is essential for bridging the growing gap between genes of known and unknown functions. To this end, computational comparative genomics is an essential task for studying the organization, topology and conservation of genes and strings of genes that lends to a better biological understanding of gene function and annotation. At the core of comparative genomic is the task of identifying gene relationships or matches across genomes. However, large dimensionality of genome data and complex evolutionary artefacts means that gene matching is a non-trivial task and new computational approaches are constantly required to address these issues. This thesis presents new algorithms for gene matching to identify gene relationships across genomes (or complete proteomes). Novel computational methods are presented here that (1) perform comparisons between small related species such as microbial strains, (2) calculate gene matching on large-scale genome data to identify gene orthologs, conserved gene strings and evolutionary rearrangements, (3) calculate complex orthologous relationships such as co-orthologs and (4) calculate rapid large-scale sequence comparisons. The methods described here are applied to a variety of genome comparisons ranging from small microbial strains to large eukarytoes such as human, mouse and rat genomes. The results from these comparisons revealed orthologous and co-orthologous genes, syntenic regions, conserved gene strings and genome rearrangements with high accuracy. Further experiments have also shown the methods described here to be computationally efficient and robust.
Book Synopsis Computational Methods for Analyzing and Detecting Genomic Structural Variation by : Ali Bashir
Download or read book Computational Methods for Analyzing and Detecting Genomic Structural Variation written by Ali Bashir and published by . This book was released on 2009 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding genetic variation has emerged as a key research problem of the post-genomic era. Until recently, the study of large genomic events, or structural variants, was marginal in comparison to smaller events, such as single nucleotide variants/polymorphisms. Technological advancements in sequencing, array design, and primer based assays have made the detection structural variants more cost-effective, reopening the possibility of high-throughput, systematic analysis. Here, we propose algorithms utilizing, detecting, and analyzing these events. Cancer is a largely genomic disease driven by somatic mutation and often characterized by large-scale genome rearrangements. We develop optimization schemes for PCR based diagnostics for detecting genomic lesions in cancer patients. The optimization allows robust detection of highly variable genomic lesions, even in a high background of normal DNA. We propose a subtle change to experimental design that significantly improves the assay without impacting experimental complexity. In a separate study, we present an efficient approach for de novo detection of gene fusion events given paired-end sequencing data. Even in low genomic coverage, ~.6X, with large insert (clone) sizes,>100kb, our method reliably predicts gene fusions. Paired-reads are further applied in reconstructing cancer genome architectures; we focus on local optimizations at complexly amplified or rearranged breakpoints. Large-scale genomic events also play important roles within normal populations and across species. We develop a novel approach that exploits unusual linkage disequilibrium patterns to detect inversion polymorphisms from limited SNP data. For phylogenetic inference, we track the insertion of transposable repeat elements across 28 mammalian species. Our algorithm returns phylogenies highly consistent with other studies and, in some cases, helps resolve points of debate. Lastly, we present a framework for the design of high-throughput sequencing studies directed at transcriptome sequencing, haplotype assembly, and the detection of structural variants. An explicit trade-off is shown between detection and localization of breakpoints for different insert sizes when using paired-reads. We prove that a mix of exactly two insert sizes provides the optimal probability of resolving a breakpoint to a given a resolution. In transcriptome sequencing, we show that it is possible to accurately approximate a sample's underlying gene expression distribution with only 100K reads via a novel correction method.
Book Synopsis Computational Methods for Gene Expression and Genomic Sequence Analysis by : Nam Sy Vo
Download or read book Computational Methods for Gene Expression and Genomic Sequence Analysis written by Nam Sy Vo and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in technologies currently produce more and more cost-effective, high-throughput, and large-scale biological data. As a result, there is an urgent need for developing efficient computational methods for analyzing these massive data. In this dissertation, we introduce methods to address several important issues in gene expression and genomic sequence analysis, two of the most important areas in bioinformatics. Firstly, we introduce a novel approach to predicting patterns of gene response to multiple treatments in case of small sample size. Researchers are increasingly interested in experiments with many treatments such as chemicals compounds or drug doses. However, due to cost, many experiments do not have large enough samples, making it difficult for conventional methods to predict patterns of gene response. Here we introduce an approach which exploited dependencies of pairwise comparisons outcomes and resampling techniques to predict true patterns of gene response in case of insufficient samples. This approach deduced more and better functionally enriched gene clusters than conventional methods. Our approach is therefore useful for multiple-treatment studies which have small sample size or contain highly variantly expressed genes. Secondly, we introduce a novel method for aligning short reads, which are DNA fragments extracted across genomes of individuals, to reference genomes. Results from short read alignment can be used for many studies such as measuring gene expression or detecting genetic variants. Here we introduce a method which employed an iterated randomized algorithm based on FM-index, an efficient data structure for full-text indexing, to align reads to the reference. This method improved alignment performance across a wide range of read lengths and error rates compared to several popular methods, making it a good choice for community to perform short read alignment. Finally, we introduce a novel approach to detecting genetic variants such as SNPs (single nucleotide polymorphisms) or INDELs (insertions/deletions). This study has great significance in a wide range of areas, from bioinformatics and genetic research to medical field. For example, one can predict how genomic changes are related to phenotype in their organism of interest, or associate genetic changes to disease risk or medical treatment efficacy. Here we introduce a method which leveraged known genetic variants existing in well-established databases to improve accuracy of detecting variants. This method had higher accuracy than several state-of-the-art methods in many cases, especially for detecting INDELs. Our method therefore has potential to be useful in research and clinical applications which rely on identifying genetic variants accurately.
