Download or read book Computational Methods to Improve Genome Assembly and Gene Prediction written by David Kelley and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Gene Prediction written by Martin Kollmar and published by Humana Press. This book was released on 2019-05-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume introduces software used for gene prediction with focus on eukaryotic genomes. The chapters in this book describe software and web server usage as applied in common use-cases, and explain ways to simplify re-annotation of long available genome assemblies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary computational requirements, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Gene Prediction: Methods and Protocols is a valuable resource for researchers and research groups working on the assembly and annotation of single species or small groups of species. Chapter 3 is available open access under a CC BY 4.0 license via link.springer.com.

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.

Download or read book Introduction To Computational Metagenomics written by Zhong Wang and published by World Scientific. This book was released on 2022-04-11 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breakthroughs in high-throughput genome sequencing and high-performance computing technologies have empowered scientists to decode many genomes including our own. Now they have a bigger ambition: to fully understand the vast diversity of microbial communities within us and around us, and to exploit their potential for the improvement of our health and environment. In this new field called metagenomics, microbial genomes are sequenced directly from the habitats without lab cultivation. Computational metagenomics, however, faces both a data challenge that deals with tens of tera-bases of sequences and an algorithmic one that deals with the complexity of thousands of species and their interactions.This interdisciplinary book is essential reading for those who are interested in beginning their own journey in computational metagenomics. It is a prism to look through various intricate computational metagenomics problems and unravel their three distinctive aspects: metagenomics, data engineering, and algorithms. Graduate students and advanced undergraduates from genomics science or computer science fields will find that the concepts explained in this book can serve as stepping stones for more advanced topics, while metagenomics practitioners and researchers from similar disciplines may use it to broaden their knowledge or identify new research targets.

Download or read book Methods for Computational Gene Prediction written by William H. Majoros and published by . This book was released on 2007-08-16 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: A self-contained, rigorous text describing models used to identify genes in genomic DNA sequences.

Download or read book Comparative Gene Finding written by Marina Axelson-Fisk and published by Springer. This book was released on 2015-04-13 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a guide to building computational gene finders, and describes the state of the art in computational gene finding methods, with a focus on comparative approaches. Fully updated and expanded, this new edition examines next-generation sequencing (NGS) technology. The book also discusses conditional random fields, enhancing the broad coverage of topics spanning probability theory, statistics, information theory, optimization theory and numerical analysis. Features: introduces the fundamental terms and concepts in the field; discusses algorithms for single-species gene finding, and approaches to pairwise and multiple sequence alignments, then describes how the strengths in both areas can be combined to improve the accuracy of gene finding; explores the gene features most commonly captured by a computational gene model, and explains the basics of parameter training; illustrates how to implement a comparative gene finder; examines NGS techniques and how to build a genome annotation pipeline.

Download or read book Algorithms for Massive Biological Datasets written by Douglas Wesley Bryant and published by . This book was released on 2011 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block, DNA. With this transformation's potential a host of brand-new challenges have emerged, many of which lend themselves to being solved through computational methods. From de novo and reference-guided genome assembly to gene prediction and identification, from genome annotation to gene expression, a multitude of biological questions are being asked and answered using high-throughput sequencing and computational methods. In this thesis we examine topics relating to high-throughput sequencing. Beginning with de novo assembly we outline current state-of-the-art methods for stitching short reads, the output of high-throughput sequencing experiments, into cohesive genomic contigs and scaffolds. Next we present our own de novo assembly software, QSRA, created in an effort to form longer contigs even through areas of low coverage and high error. We then present an application of short-read assembly and mutation analysis in a discussion of single nucleotide polymorphism discovery in hazelnut, followed by a review of de novo gene finding, the act of identifying genes in anonymous stretches of genomic sequence. Next we outline our supersplat software, built to align short reads generated by RNA-seq experiments, which span splice junctions, followed by the presentation of our gumby software, build to construct putative gene models from purely empirical short-read data. Finally we outline current state-of-the-art methods for discovering and quantifying alternative splicing variants from RNA-seq short-read data. High-throughput sequencing has fundamentally changed the way in which we approach biological questions. While an exceptionally powerful tool, high-throughput sequencing analysis demands equally powerful algorithmic techniques. We examine these issues through the lens of computational biology.

Download or read book Computational Exome and Genome Analysis written by Peter N. Robinson and published by CRC Press. This book was released on 2017-09-13 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.

Download or read book Theoretical and 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 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application ofcomputational methods to solve scientific and practical problems in genome research created a new interdisciplinary area that transcends boundaries tradi tionally separating genetics, biology, mathematics, physics, and computer science. Com puters have, of course, been intensively used in the field of life sciences for many years, even before genome research started, to store and analyze DNA or protein sequences; to explore and model the three-dimensional structure, the dynamics, and the function of biopolymers; to compute genetic linkage or evolutionary processes; and more. The rapid development of new molecular and genetic technologies, combined with ambitious goals to explore the structure and function ofgenomes ofhigher organisms, has generated, how ever, not only a huge and exponentially increasing body of data but also a new class of scientific questions. The nature and complexity of these questions will also require, be yond establishing a new kind ofalliance between experimental and theoretical disciplines, the development of new generations both in computer software and hardware technolo gies. New theoretical procedures, combined with powerful computational facilities, will substantially extend the horizon of problems that genome research can attack with suc cess. Many of us still feel that computational models rationalizing experimental findings in genome research fulfill their promises more slowly than desired. There is also an uncer tainty concerning the real position of a "theoretical genome research" in the network of established disciplines integrating their efforts in this field.

Download or read book Genome Analysis Current Procedures and Applications written by Maria S. Poptsova and published by Caister Academic Press. This book was released on 2019-04-28 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there have been tremendous achievements made in DNA sequencing technologies and corresponding innovations in data analysis and bioinformatics that have revolutionized the field of genome analysis.In this book, an impressive array of expert authors highlight and review current advances in genome analysis. This volume provides an invaluable, up-to-date and comprehensive overview of the methods currently employed for next-generation sequencing (NGS) data analysis, highlights their problems and limitations, demonstrates the applications and indicates the developing trends in various fields of genome research. The first part of the book is devoted to the methods and applications that arose from, or were significantly advanced by, NGS technologies: the identification of structural variation from DNA-seq data; whole-transcriptome analysis and discovery of small interfering RNAs (siRNAs) from RNA-seq data; motif finding in promoter regions, enhancer prediction and nucleosome sequence code discovery from ChiP-Seq data; identification of methylation patterns in cancer from MeDIP-seq data; transposon identification in NGS data; metagenomics and metatranscriptomics; NGS of viral communities; and causes and consequences of genome instabilities. The second part is devoted to the field of RNA biology with the last three chapters devoted to computational methods of RNA structure prediction including context-free grammar applications.An essential book for everyone involved in sequence data analysis, next-generation sequencing, high-throughput sequencing, RNA structure prediction, bioinformatics and genome analysis.

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.

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

Download or read book The Pangenome written by Hervé Tettelin and published by Springer Nature. This book was released on 2020-04-30 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book offers the first comprehensive account of the pan-genome concept and its manifold implications. The realization that the genetic repertoire of a biological species always encompasses more than the genome of each individual is one of the earliest examples of big data in biology that opened biology to the unbounded. The study of genetic variation observed within a species challenges existing views and has profound consequences for our understanding of the fundamental mechanisms underpinning bacterial biology and evolution. The underlying rationale extends well beyond the initial prokaryotic focus to all kingdoms of life and evolves into similar concepts for metagenomes, phenomes and epigenomes. The book’s respective chapters address a range of topics, from the serendipitous emergence of the pan-genome concept and its impacts on the fields of microbiology, vaccinology and antimicrobial resistance, to the study of microbial communities, bioinformatic applications and mathematical models that tie in with complex systems and economic theory. Given its scope, the book will appeal to a broad readership interested in population dynamics, evolutionary biology and genomics.

Download or read book Introduction to Computational Genomics written by Nello Cristianini and published by Cambridge University Press. This book was released on 2006-12-14 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.

Download or read book Computational Methods for 3D Genome Analysis written by Ryuichiro Nakato and published by Springer Nature. This book was released on with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Gene Prediction Applying Ontology and Machine Learning Volume III written by Casper Harvey and published by Larsen and Keller Education. This book was released on 2023-09-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.

Download or read book Gene Prediction Applying Ontology and Machine Learning Volume II written by Casper Harvey and published by Larsen and Keller Education. This book was released on 2023-09-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.