Author: Diem Trang Pham
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
DNA sequencing technologies have transformed genomics, allowing for the decoding of genetic information stored within an organism's DNA. Recent advances in Next-Generation Sequencing technologies (NGS) provide great opportunities to obtain, analyze, and understand genetic information of many species. Using NGS, researchers can obtain millions fragments of DNA, known as \textit{reads}, from different environments. Metagenomics, a study of DNA from environmental samples, explores microbial taxonomy and function. Analyzing metagenomic data is an important problem as recent studies shows the relation of microbial composition to certain diseases or disorders of human health. This task can be computationally expensive since microbial communities usually consist of hundreds to thousands of environmental microbial species. In this dissertation, we introduce methods to profile and identify bacteria in metagenomic samples, two of the most fundamental tasks in metagenomic analysis. \textit{Firstly}, we introduce an efficient alignment-free approach to estimate abundances of microbial genomes in metagenomic samples. The approach is based on solving linear and quadratic programs, which are represented by Genome-Specific Markers. We compared our method against popular alignment-free and alignment-based methods. Without contamination, our method was more accurate than other alignment-free methods while being much faster than a alignment-based method. In more realistic settings where samples were contaminated with human DNA, our method was the most accurate method in predicting abundance at varying levels of contamination. We achieve higher accuracy than both alignment-free and alignment-based methods. \textit{Secondly}, we introduce a new method for representing bacteria in a microbial community using genomic signatures of those bacteria. With respect to the microbial community, the genomic signatures of each bacterium are unique to that bacterium; they do not exist in other bacteria in the community. Further, since the genomic signatures of a bacterium are much smaller than its genome size, the approach allows for a compressed representation of the microbial community. This approach uses a modified Bloom filter to store short k-mers with hash values that are unique to each bacterium. We show that most bacteria in many microbiomes can be represented uniquely using the proposed genomic signatures. This approach paves the way toward new methods for classifying bacteria in metagenomic samples. \textit{Finally}, we introduce a new method which designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. This method utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. The method was evaluated and compared with several well-established tools across various datasets. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in the field. The study lays the groundwork for future improvements in computational efficiency and the expansion of microbial databases.
Author: Suparna Mitra
Publisher: Springer Nature
ISBN: 1071630725
Category : Science
Languages : en
Pages : 443
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Book Description
This volume describes different sequencing methods, pipelines and tools for metagenome data analyses. Chapters guide readers through quality control of raw sequence data, metagenomics databases for bacterial annotations such Greengenes, SILVA, RDP and GTDB, guide to 16S rRNA microbiome analysis and pipelines such as mothur, DADA2, QIIME2 , whole genome shotgun metagenomics data analyses pipeline using MEGAN and DIAMOND, web service such as PATRIC, RDP, mothur, Kaiju, PhyloPythiaS, MG-RAST, WebMGA, MicrobiomeAnalyst, WHAM!, METAGENassist and MGnify: EBI-Metagenomics, MG-RAST Metagenomics Analysis. Then the chapters inform the readers regarding Third-generation sequencing (TGS) approaches as MinION sequencing and teaches use of Ubuntu Linux Virtual Machine configuration, clinical and environmental resistomes, use of FISH techniques and designing FISH probes, protocols for viral metagenomics, and comprehensive guideline for microbiome analysis using most used R packages. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Metagenomic Data Analysis: Methods and Protocols aims to be comprehensive guide for researchers to specialize in the metagenomics field.
Author: Trevor C. Charles
Publisher: Springer
ISBN: 3319615106
Category : Science
Languages : en
Pages : 256
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Book Description
In this book, the latest tools available for functional metagenomics research are described.This research enables scientists to directly access the genomes from diverse microbial genomes at one time and study these “metagenomes”. Using the modern tools of genome sequencing and cloning, researchers have now been able to harness this astounding metagenomic diversity to understand and exploit the diverse functions of microorganisms. Leading scientists from around the world demonstrate how these approaches have been applied in many different settings, including aquatic and terrestrial habitats, microbiomes, and many more environments. This is a highly informative and carefully presented book, providing microbiologists with a summary of the latest functional metagenomics literature on all specific habitats.
Author: Mourad Elloumi
Publisher: Springer
ISBN: 3319598260
Category : Computers
Languages : en
Pages : 356
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Book Description
The 14 contributed chapters in this book survey the most recent developments in high-performance algorithms for NGS data, offering fundamental insights and technical information specifically on indexing, compression and storage; error correction; alignment; and assembly. The book will be of value to researchers, practitioners and students engaged with bioinformatics, computer science, mathematics, statistics and life sciences.
Author: Ruichen Rong
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 95
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Book Description
Metagenomics is the study of the totality of the complete genetic elements discovered from a defined environment. Different from traditional microbiology study, which only analyzes a small percent of microbes that could survive in laboratory, metagenomics allows researchers to get entire genetic information from all the samples in the communities. So metagenomics enables understanding of the target environments and the hidden relationships between bacteria and diseases. In order to efficiently analyze the metagenomics data, cutting-edge technologies for analyzing the relationships among microbes and communities are required. To overcome the challenges brought by rapid growth in metagenomics datasets, advances in novel methodologies for interpreting metagenomics data are clearly needed. The first two chapters of this dissertation summarize and compare the widely-used methods in metagenomics and integrate these methods into pipelines. Properly analyzing metagenomics data requires a variety of bioinformatcis and statistical approaches to deal with different situations. The raw reads from sequencing centers need to be processed and denoised by several steps and then be further interpreted by ecological and statistical analysis. So understanding these algorithms and combining different approaches could potentially reduce the influence of noises and biases at different steps. And an efficient and accurate pipeline is important to robustly decipher the differences and functionality of bacteria in communities. Traditional statistical analysis and machine learning algorithms have their limitations on analyzing metagenomics data. Thus, rest three chapters describe a new phylogeny based machine learning and feature selection algorithm to overcome these problems. The new method outperforms traditional algorithms and can provide more robust candidate microbes for further analysis. With the frowing sample size, deep neural network could potentially describe more complicated characteristic of data and thus improve model accuracy. So a deep learning framework is designed on top of the shallow learning algorithm stated above in order to further improve the prediction and selection accuracy. The present dissertation work provides a powerful tool that utilizes machine learning techniques to identify signature bacteria and key information from huge amount of metagenomics data.
Author: Ion Mandoiu
Publisher: John Wiley & Sons
ISBN: 1119272165
Category : Computers
Languages : en
Pages : 464
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Book Description
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.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309106761
Category : Science
Languages : en
Pages : 170
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Book Description
Although we can't usually see them, microbes are essential for every part of human life-indeed all life on Earth. The emerging field of metagenomics offers a new way of exploring the microbial world that will transform modern microbiology and lead to practical applications in medicine, agriculture, alternative energy, environmental remediation, and many others areas. Metagenomics allows researchers to look at the genomes of all of the microbes in an environment at once, providing a "meta" view of the whole microbial community and the complex interactions within it. It's a quantum leap beyond traditional research techniques that rely on studying-one at a time-the few microbes that can be grown in the laboratory. At the request of the National Science Foundation, five Institutes of the National Institutes of Health, and the Department of Energy, the National Research Council organized a committee to address the current state of metagenomics and identify obstacles current researchers are facing in order to determine how to best support the field and encourage its success. The New Science of Metagenomics recommends the establishment of a "Global Metagenomics Initiative" comprising a small number of large-scale metagenomics projects as well as many medium- and small-scale projects to advance the technology and develop the standard practices needed to advance the field. The report also addresses database needs, methodological challenges, and the importance of interdisciplinary collaboration in supporting this new field.
Author: Wael N. Hozzein
Publisher: BoD – Books on Demand
ISBN: 1838800557
Category : Science
Languages : en
Pages : 164
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Book Description
This book is for the students starting their research projects in the field of metagenomics, for researchers interested in the new developments and applications in this field; and for teachers involved in teaching this subject. The book is divided into three sections as indicated from its title, namely; the basics of metagenomics, metagenomic analysis, and applications of metagenomics. It covers the basics of metagenomics from its history and background, to the analysis of metagenomic data as well as its recent applications in different fields. The book contains excellent texts at both the introductory and advanced levels, that describe the latest metagenomic approaches and applications, from sampling to data analysis for taxonomic, environmental, and medical studies. Finally, the publication of this book was an interesting journey for me and I hope the readers will enjoy reading it.
Author: Joao Carlos Setubal
Publisher: Frontiers Media SA
ISBN: 2889664376
Category : Science
Languages : en
Pages : 170
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Book Description
Author: Santosh S. Vempala
Publisher: American Mathematical Soc.
ISBN: 0821837931
Category : Mathematics
Languages : en
Pages : 120
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Book Description
Random projection is a simple geometric technique for reducing the dimensionality of a set of points in Euclidean space while preserving pairwise distances approximately. The technique plays a key role in several breakthrough developments in the field of algorithms. In other cases, it provides elegant alternative proofs. The book begins with an elementary description of the technique and its basic properties. Then it develops the method in the context of applications, which are divided into three groups. The first group consists of combinatorial optimization problems such as maxcut, graph coloring, minimum multicut, graph bandwidth and VLSI layout. Presented in this context is the theory of Euclidean embeddings of graphs. The next group is machine learning problems, specifically, learning intersections of halfspaces and learning large margin hypotheses. The projection method is further refined for the latter application. The last set consists of problems inspired by information retrieval, namely, nearest neighbor search, geometric clustering and efficient low-rank approximation. Motivated by the first two applications, an extension of random projection to the hypercube is developed here. Throughout the book, random projection is used as a way to understand, simplify and connect progress on these important and seemingly unrelated problems. The book is suitable for graduate students and research mathematicians interested in computational geometry.
