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Computational Methods For Understanding Genetic Variations From Next Generation Sequencing Data
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Book Synopsis Computational Methods for Understanding Genetic Variations from Next Generation Sequencing Data by : Soyeon Ahn (Ph. D.)
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.
Book Synopsis Computational Methods for Next Generation Sequencing Data Analysis by : Ion Mandoiu
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 464 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.
Book Synopsis Computational Methods for the Analysis of Next Generation Sequencing Data by : Wei Wang
Download or read book Computational Methods for the Analysis of Next Generation Sequencing Data written by Wei Wang and published by . This book was released on 2014 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, next generation sequencing (NGS) technology has emerged as a powerful approach and dramatically transformed biomedical research in an unprecedented scale. NGS is expected to replace the traditional hybridization-based microarray technology because of its affordable cost and high digital resolution. Although NGS has significantly extended the ability to study the human genome and to better understand the biology of genomes, the new technology has required profound changes to the data analysis. There is a substantial need for computational methods that allow a convenient analysis of these overwhelmingly high-throughput data sets and address an increasing number of compelling biological questions which are now approachable by NGS technology. This dissertation focuses on the development of computational methods for NGS data analyses. First, two methods are developed and implemented for detecting variants in analysis of individual or pooled DNA sequencing data. SNVer formulates variant calling as a hypothesis testing problem and employs a binomial-binomial model to test the significance of observed allele frequency by taking account of sequencing error. SNVerGUI is a GUI-based desktop tool that is built upon the SNVer model to facilitate the main users of NGS data, such as biologists, geneticists and clinicians who often lack of the programming expertise. Second, collapsing singletons strategy is explored for associating rare variants in a DNA sequencing study. Specifically, a gene-based genome-wide scan based on singleton collapsing is performed to analyze a whole genome sequencing data set, suggesting that collapsing singletons may boost signals for association studies of rare variants in sequencing study. Third, two approaches are proposed to address the 3'UTR switching problem. PolyASeeker is a novel bioinformatics pipeline for identifying polyadenylation cleavage sites from RNA sequencing data, which helps to enhance the knowledge of alternative polyadenylation mechanisms and their roles in gene regulation. A change-point model based on a likelihood ratio test is also proposed to solve such problem in analysis of RNA sequencing data. To date, this is the first method for detecting 3'UTR switching without relying on any prior knowledge of polyadenylation cleavage sites.
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Book Synopsis Computational Methods for Analyzing and Visualizing NGS Data by : Sruthi Chappidi
Download or read book Computational Methods for Analyzing and Visualizing NGS Data written by Sruthi Chappidi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in next-generation sequencing (NGS) technology have enabled the rapid growth and availability of large quantities of DNA and RNA sequences. These sequences from both model and non-model organisms can now be acquired at a low cost. The sequencing of large amounts of genomic and proteomic data empowers scientific achievements, many of which were thought to be impossible, and novel biological applications have been developed to study their genetic contribution to human diseases and evolution. This is especially true for uncovering new insights from comparative genomics to the evolution of the disease. For example, NGS allows researchers to identify all changes between sequences in the sample set, which could be used in a clinical setting for things like early cancer detection. This dissertation describes a set of computational bioinformatic approaches that bridge the gap between the large-scale, high-throughput sequencing data that is available, and the lack of computational tools to make predictions for and assist in evolutionary studies. Specifically, I have focused on developing computational methods that enable analysis and visualization for three distinct research tasks. These tasks focus on NGS data and will range in scope from processed genomic data to raw sequencing data, to viral proteomic data. The first task focused on the visualization of two genomes and the changes required to transform from one sequence into the other, which mimics the evolutionary process that has occurred on these organisms. My contribution to this task is DCJVis. DCJVis is a visualization tool based on a linear-time algorithm that computes the distance between two genomes and visualizes the number and type of genomic operations necessary to transform one genome set into another. The second task focused on developing a software application and efficient algorithmic workflow for analyzing and comparing raw sequence reads of two samples without the need of a reference genome. Most sequence analysis pipelines start with aligning to a known reference. However, this is not an ideal approach as reference genomes are not available for all organisms and alignment inaccuracies can lead to biased results. I developed a reference-free sequence analysis computational tool, NoRef, using k-length substring (k-mer) analysis. I also proposed an efficient k-mer sorting algorithm that decreases execution time by 3-folds compared to traditional sorting methods. Finally, the NoRef workflow outputs the results in the raw sequence read format based on user-selected filters, that can be directly used for downstream analysis. The third task is focused on viral proteomic data analysis and answers the following questions: 1. How many viral genes originate as "stolen host" (human) genes? 2. What viruses most often steal genes from a host (human) and are specific to certain locations within the host? 3. Can we understand the function of the host (human) gene through a viral perspective? To address these questions, I took a computational approach starting with string sequence comparisons and localization prediction using machine learning models to create a comprehensive community data resource that will enable researchers to gain insights into viruses that affect human immunity and diseases.
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 Biological Sequence Analysis by : Richard Durbin
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.
Book Synopsis Computational Methods for Efficient Processing and Analysis of Short-read Next-Generation DNA Sequencing Data by : Praveen Nadukkalam Ravindran
Download or read book Computational Methods for Efficient Processing and Analysis of Short-read Next-Generation DNA Sequencing Data written by Praveen Nadukkalam Ravindran and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: DNA sequencing has transformed the discipline of population genetics, which seeks to assess the level of genetic diversity within species or populations, and infer the geographic and temporal distributions between members of a population. Restriction-site associated DNA sequencing (RADSeq) is a NGS technique, which produce data that consists of relatively short (typically 50 to 300 nucleotide) fragments or "reads" of sequenced DNA and enables large-scale analysis of individuals and populations. In this thesis, we describe computational methods, which use graph-based structures to represent these short reads obtained and to capture the relationships among them. A key challenge in RADSeq analysis is to identify optimal parameter settings for assignment of reads to loci (singular: Locus), which correspond to specific regions in the genome. The parameter sweep is computationally intensive, as the entire analysis needs to be run for each parameter set. We propose a graph-based structure (RADProc), which provides persistence and eliminates redundancy to enable parameter sweeps. For 20 green crab samples and 32 different parameter sets, RADProc took only 2.5 hours while the widely used Stacks software took 78 hours. Another challenge is to identify paralogs, sequences that are highly similar due to recent duplication events, but occur in different regions of the genome and should not to be merged into the same locus. We introduce PMERGE, which identifies paralogs by clustering the catalog locus consensus sequences based on similarity. PMERGE is built on the fact that paralogs may be wrongly merged into a single locus in some but not all samples. PMERGE identified 62%-87% of paralogs in the Atlantic salmon and green crab datasets. Gene flow is the movement of alleles, specific sequence variants at a given locus, between populations and is an important indicator of population mixing that changes genetic diversity within the populations. We use the RADProc graph to infer gene flow among populations using allele frequency differences in exclusively shared alleles in each pair of populations. The method successfully inferred gene flow patterns in simulated datasets and provided insights into reasons for observed hybridization at two locations in a green crab dataset.
Book Synopsis Computational Methods for Solving Next Generation Sequencing Challenges by : Tamer Ali Aldwairi
Download or read book Computational Methods for Solving Next Generation Sequencing Challenges written by Tamer Ali Aldwairi and published by . This book was released on 2014 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study we build solutions to three common challenges in the fields of bioinformatics through utilizing statistical methods and developing computational approaches. First, we address a common problem in genome wide association studies, which is linking genotype features within organisms of the same species to their phenotype characteristics. We specifically studied FHA domain genes in Arabidopsis thaliana distributed within Eurasian regions by clustering those plants that share similar genotype characteristics and comparing that to the regions from which they were taken. Second, we also developed a tool for calculating transposable element density within different regions of a genome. The tool is built to utilize the information provided by other transposable element annotation tools and to provide the user with a number of options for calculating the density for various genomic elements such as genes, piRNA and miRNA or for the whole genome. It also provides a detailed calculation of densities for each family and sub-family of the transposable elements. Finally, we address the problem of mapping multi reads in the genome and their effects on gene expression. To accomplish this, we implemented methods to determine the statistical significance of expression values within the genes utilizing both a unique and multi-read weighting scheme. We believe this approach provides a much more accurate measure of gene expression than existing methods such as discarding multi reads completely or assigning them randomly to a set of best assignments, while also providing a better estimation of the proper mapping locations of ambiguous reads. Overall, the solutions we built in these studies provide researchers with tools and approaches that aid in solving some of the common challenges that arise in the analysis of high throughput sequence data.
Book Synopsis Studying the Evolution of Gene Regulation Using Next-generation Sequencing by : Jing Leng
Download or read book Studying the Evolution of Gene Regulation Using Next-generation Sequencing written by Jing Leng and published by . This book was released on 2014 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Next Generation Sequencing by : Jerzy Kulski
Download or read book Next Generation Sequencing written by Jerzy Kulski and published by BoD – Books on Demand. This book was released on 2016-01-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.
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 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 Analysis of Single Molecule Sequencing Data by : Ehsan Haghshenas
Download or read book Computational Methods for Analysis of Single Molecule Sequencing Data written by Ehsan Haghshenas and published by . This book was released on 2020 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next-generation sequencing (NGS) technologies paved the way to a significant increase in the number of sequenced genomes, both prokaryotic and eukaryotic. This increase provided an opportunity for considerable advancement in genomics and precision medicine. Although NGS technologies have proven their power in many applications such as de novo genome assembly and variation discovery, computational analysis of the data they generate is still far from being perfect. The main limitation of NGS technologies is their short read length relative to the lengths of (common) genomic repeats. Today, newer sequencing technologies (known as single-molecule sequencing or SMS) such as Pacific Biosciences and Oxford Nanopore are producing significantly longer reads, making it theoretically possible to overcome the difficulties imposed by repeat regions. For instance, for the first time, a complete human chromosome was fully assembled using ultra-long reads generated by Oxford Nanopore. Unfortunately, long reads generated by SMS technologies are characterized by a high error rate, which prevents their direct utilization in many of the standard downstream analysis pipelines and poses new computational challenges. This motivates the development of new computational tools specifically designed for SMS long reads. In this thesis, we present three computational methods that are tailored for SMS long reads. First, we present lordFAST, a fast and sensitive tool for mapping noisy long reads to a reference genome. Mapping sequenced reads to their potential genomic origin is the first fundamental step for many computational biology tasks. As an example, in this thesis, we show the success of lordFAST to be employed in structural variation discovery. Next, we present the second tool, CoLoRMap, which tackles the high level of base-level errors in SMS long reads by providing a means to correct them using a complementary set of NGS short reads. This integrative use of SMS and NGS data is known as hybrid technique. Finally, we introduce HASLR, an ultra-fast hybrid assembler that uses reads generated by both technologies to efficiently generate accurate genome assemblies. We demonstrate that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples.
Book Synopsis Next Generation Sequencing and Data Analysis by : Melanie Kappelmann-Fenzl
Download or read book Next Generation Sequencing and Data Analysis written by Melanie Kappelmann-Fenzl and published by Springer Nature. This book was released on 2021-05-04 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides step-by-step protocols and detailed explanations for RNA Sequencing, ChIP-Sequencing and Epigenetic Sequencing applications. The reader learns how to perform Next Generation Sequencing data analysis, how to interpret and visualize the data, and acquires knowledge on the statistical background of the used software tools. Written for biomedical scientists and medical students, this textbook enables the end user to perform and comprehend various Next Generation Sequencing applications and their analytics without prior understanding in bioinformatics or computer sciences.
Book Synopsis A Computational Approach for Diagnostic Long-read Genome Sequencing by : Esko Kautto
Download or read book A Computational Approach for Diagnostic Long-read Genome Sequencing written by Esko Kautto and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our understanding of the human genome has greatly expanded since the completion of the Human Genome Project. Many large-scale landmark studies have since looked at the role genetic alterations play in the predisposition to disease and identified countless disease-causing mutations. While most of genomics-based research has been made possible through the commoditization of massively parallel next-generation sequencing, recent advances in sequencing technologies have allowed long-read single-molecule sequencing to further characterize and identify genetic alterations that were previously challenging to detect through conventional sequencing. In this research, we have used accurate long-read sequencing from Pacific Biosciences to study cancer and non-cancer samples alike to identify and characterize disease-associated genetic alterations. The work has involved the development of computational methods for stream-lining analysis of such data to provide high-confidence structural variant calls. The analysis pipeline and tools have been used to accurately identify causative mutations in pediatric cancer cases, discover an internal tandem duplication in the HOXD13 gene that caused syndactyly in two unrelated families, and to expand the role that activating FGFR1 mutations may play in closed spinal dysraphism.
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.
