With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets.
Author: Vince Buffalo
Publisher: "O'Reilly Media, Inc."
ISBN: 9781449367510
Category: Computers
Page: 538
View: 617
Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings. With this practical guide, you’ll learn how to use freely available open source tools to extract meaning from large complex biological data sets. At no other point in human history has our ability to understand life’s complexities been so dependent on our skills to work with and analyze data. This intermediate-level book teaches the general computational and data skills you need to analyze biological data. If you have experience with a scripting language like Python, you’re ready to get started. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Process bioinformatics data with powerful Unix pipelines and data tools Learn how to use exploratory data analysis techniques in the R language Use efficient methods to work with genomic range data and range operations Work with common genomics data file formats like FASTA, FASTQ, SAM, and BAM Manage your bioinformatics project with the Git version control system Tackle tedious data processing tasks with with Bash scripts and Makefiles
Offers a structured approach to biological data and the computer tools needed to analyze it, covering UNIX, databases, computation, Perl, data mining, data visualization, and tailoring software to suit specific research needs.
Author: Cynthia Gibas
Publisher: "O'Reilly Media, Inc."
ISBN: 1565926641
Category: Computers
Page: 427
View: 680
Offers a structured approach to biological data and the computer tools needed to analyze it, covering UNIX, databases, computation, Perl, data mining, data visualization, and tailoring software to suit specific research needs.
The book explores many significant topics of Big Data analyses in an easily understandable format. It describes popular tools and software for Big Data analyses and explains next-generation DNA sequencing data analyses.
Author: Shui Qing Ye
Publisher: Chapman & Hall/CRC
ISBN: 1498724523
Category: Mathematics
Page: 276
View: 770
Big Data offers both unprecedented opportunities and overwhelming challenges. This book is intended to provide biologists, biomedical scientists, bioinformaticians and computer data analysts a pragmatic overview of the field as it applies to bioinformatics and biomedical science, allowing enthusiastic readers to more quickly, easily and effectively harness the power of Big Data in their ground-breaking biological discoveries, translational medical researches and personalized genomic medicine. It covers common tools and software, next generation sequencing data analysis and Big Data analysis pipelines.
Structuring its presentation around four main areas of study, this book covers the skills vital to the day-to-day activities of today’s bioinformatician.
Author: Michael Moorhouse
Publisher: John Wiley & Sons
ISBN: 9780470026458
Category: Medical
Page: 506
View: 277
Bioinformatics, Biocomputing and Perl presents a modern introduction to bioinformatics computing skills and practice. Structuring its presentation around four main areas of study, this book covers the skills vital to the day-to-day activities of today’s bioinformatician. Each chapter contains a series of maxims designed to highlight key points and there are exercises to supplement and cement the introduced material. Working with Perl presents an extended tutorial introduction to programming through Perl, the premier programming technology of the bioinformatics community. Even though no previous programming experience is assumed, completing the tutorial equips the reader with the ability to produce powerful custom programs with ease. Working with Data applies the programming skills acquired to processing a variety of bioinformatics data. In addition to advice on working with important data stores such as the Protein DataBank, SWISS-PROT, EMBL and the GenBank, considerable discussion is devoted to using bioinformatics data to populate relational database systems. The popular MySQL database is used in all examples. Working with the Web presents a discussion of the Web-based technologies that allow the bioinformatics researcher to publish both data and applications on the Internet. Working with Applications shifts gear from creating custom programs to using them. The tools described include Clustal-W, EMBOSS, STRIDE, BLAST and Xmgrace. An introduction to the important Bioperl Project concludes this chapter and rounds off the book.
This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and ...
Author: Dan MacLean
Publisher: Packt Publishing Ltd
ISBN: 9781789955590
Category: Computers
Page: 316
View: 903
Over 60 recipes to model and handle real-life biological data using modern libraries from the R ecosystem Key Features Apply modern R packages to handle biological data using real-world examples Represent biological data with advanced visualizations suitable for research and publications Handle real-world problems in bioinformatics such as next-generation sequencing, metagenomics, and automating analyses Book Description Handling biological data effectively requires an in-depth knowledge of machine learning techniques and computational skills, along with an understanding of how to use tools such as edgeR and DESeq. With the R Bioinformatics Cookbook, you’ll explore all this and more, tackling common and not-so-common challenges in the bioinformatics domain using real-world examples. This book will use a recipe-based approach to show you how to perform practical research and analysis in computational biology with R. You will learn how to effectively analyze your data with the latest tools in Bioconductor, ggplot, and tidyverse. The book will guide you through the essential tools in Bioconductor to help you understand and carry out protocols in RNAseq, phylogenetics, genomics, and sequence analysis. As you progress, you will get up to speed with how machine learning techniques can be used in the bioinformatics domain. You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse. By the end of this book, you’ll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data. What you will learn Employ Bioconductor to determine differential expressions in RNAseq data Run SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and Indels Use ggplot to create and annotate a range of visualizations Query external databases with Ensembl to find functional genomics information Execute large-scale multiple sequence alignment with DECIPHER to perform comparative genomics Use d3.js and Plotly to create dynamic and interactive web graphics Use k-nearest neighbors, support vector machines and random forests to find groups and classify data Who this book is for This book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. Working knowledge of R programming language and basic knowledge of bioinformatics are prerequisites.
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills.
Author: Ju Han Kim
Publisher: Springer
ISBN: 9789811319426
Category: Science
Page: 367
View: 626
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
... Appreciate the value of bioinformatic data mining to develop hypotheses for
further research Suggestions for Using the Project This chapter includes a Web
Exploration Project but no Guided Programming Project. Its intent is to develop skills ...
Author: Caroline St. Clair
Publisher: Jones & Bartlett Publishers
ISBN: 9781284023466
Category: Computers
Page: 360
View: 179
Thoroughly revised and updated, Exploring Bioinformatics: A Project-Based Approach, Second Edition is intended for an introductory course in bioinformatics at the undergraduate level. Through hands-on projects, students are introduced to current biological problems and then explore and develop bioinformatic solutions to these issues. Each chapter presents a key problem, provides basic biological concepts, introduces computational techniques to address the problem, and guides students through the use of existing web-based tools and software solutions. This progression prepares students to tackle the On-Your-Own Project, where they develop their own software solutions. Topics such as antibiotic resistance, genetic disease, and genome sequencing provide context and relevance to capture student interest.
The algorithms described in this paper have been implemented and tested over
simple relational data source and integrated schemas. We are currently
deploying them as part of a broader bioinformatics data warehousing project (
BIOMAP).
Author: Mike Jackson
Publisher: Springer Science & Business Media
ISBN: 9783540269731
Category: Computers
Page: 184
View: 154
This book constitutes the refereed proceedings of the 22nd British National Conference on Databases, BNCOD 22, held in Sunderland, UK in July 2005. The 16 revised full papers presented together with an invited paper and the abstract of an invited talk were carefully reviewed and selected from 66 submissions. The papers are organized in topical sections on spatio-temporal databases, data integration and information retrieval, XML, and applied information management.
Discusses the ties between biology and computer science, how to program, basic PERL programming concepts, and how to use PERL to perform tasks including analyzing genetic codes.
Author: James Tisdall
Publisher: "O'Reilly Media, Inc."
ISBN: 9780596000806
Category: Computers
Page: 368
View: 662
Discusses the ties between biology and computer science, how to program, basic PERL programming concepts, and how to use PERL to perform tasks including analyzing genetic codes.
This book aims to assist research scientists in choosing the most applicable database or bioinformatics tools to aid and promote their research in plant biotechnology.
Author: David Edwards
Publisher: Springer Science & Business Media
ISBN: 9781588296535
Category: Science
Page: 552
View: 210
This book aims to assist research scientists in choosing the most applicable database or bioinformatics tools to aid and promote their research in plant biotechnology. Chapters include practical examples and highlight common problems encountered in bioinformatics analysis. Further chapters are aimed at researchers developing bioinformatics databases and tools, detailing commonly applied database formats and biology-focused scripting languages.
The skills required to apply computational analysis to target research on a wide range of applications that include identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine and higher crop ...
Author: Low Lloyd Wai Yee
Publisher: #N/A
ISBN: 9789813144767
Category: Computers
Page: 252
View: 483
Rapid technological developments have led to increasingly efficient sequencing approaches. Next Generation Sequencing (NGS) is increasingly common and has become cost-effective, generating an explosion of sequenced data that need to be analyzed. The skills required to apply computational analysis to target research on a wide range of applications that include identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine and higher crop yields in agriculture are highly sought after. This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform essential analyses from raw sequenced data to answering important biological questions. It is an excellent hands-on material for teachers who conduct courses in bioinformatics and as a reference material for professionals. The chapters are written to be standalone recipes making it suitable for readers who wish to self-learn selected topics. Readers will gain skills necessary to work on sequenced data from NGS platforms and hence making themselves more attractive to employers who need skilled bioinformaticians to handle the deluge of data.
Archiving of bioinformatics data was originally carried out by individual research
groups motivated by an interest in the associated science. As the requirements
for equipment and personnel grew—and the nature of the skills required ...
Author: Arthur Lesk
Publisher: Oxford University Press
ISBN: 9780199651566
Category: Science
Page: 371
View: 394
Lesk provides an accessible and thorough introduction to a subject which is becoming a fundamental part of biological science today. The text generates an understanding of the biological background of bioinformatics.
This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features.
Author: Zheng Rong Yang
Publisher: World Scientific
ISBN: 9789814287319
Category: Computers
Page: 322
View: 357
This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research. Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes. An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.
R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand exper
Author: Robert Gentleman
Publisher: CRC Press
ISBN: 1420063685
Category: Mathematics
Page: 328
View: 826
Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems. Drawing on the author’s first-hand experiences as an expert in R, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code. With numerous examples and exercises, this practical guide focuses on developing R programming skills in order to tackle problems encountered in bioinformatics and computational biology.
When used in conjunction with the appropriate visualization tools , data mining
allows the researcher to use her highly advanced pattern - recognition skills and
knowledge of molecular biology to determine which results warrant further study .
Author: Bryan P. Bergeron
Publisher: Prentice Hall Professional
ISBN: 0131008250
Category: Computers
Page: 439
View: 852
Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.
The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field.
Author: Miguel Rocha
Publisher: Academic Press
ISBN: 9780128125212
Category: Technology & Engineering
Page: 400
View: 405
Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications. Presents an ideal text for bioinformatics students with little to no knowledge of computer programming Based on over 12 years of pedagogical materials used by the authors in their own classrooms Features a companion website with downloadable codes and runnable examples (such as using Jupyter Notebooks) and exercises relating to the book
Schema Fragment I consider a schema fragment as a partial data model that
contains only one topic area . ... transcriptome clustering and sequence
comparison 3 ) Human Gene Indexing and Genome annotation ; 4 ) Bioinformatics data extraction , and integration ... data analysts with successful
design solutions on selected topics , through which they develop their own skills
in genomic data modeling .
This is a handbook of methods and protocols for biologists.
Author: Wang Ziling
Publisher: World Scientific
ISBN: 9781848169265
Category: Computers
Page: 304
View: 241
This is a handbook of methods and protocols for biologists. It aimed at undergraduate, graduate students and researchers originally trained in biological or medical sciences who need to know how to access the data archives of genomes, proteins, metabolites, gene expression profiles and the questions these data and tools can answer. For each chapter, the conceptual and experimental background is provided, together with specific guidelines for handling raw data, including preprocessing and analysis. The content is structured into three parts. Part one introduces basic knowledge about popular bioinformatics tools, databases and web resources. Part two presents examples of omics bioinformatics applications. Part three provides basic statistical analysis skills and programming skills needed to handle and analyze omics datasets.
