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Alaska has few roads and even fewer trails-only a few hundred miles
of maintained footpaths exist outside the cities-so paddling the
state's thousands of miles of rivers and lakes is the best way to
get off the beaten track. Paddling Alaska describes the best and
most accessible routes-thirty-six classics in all, from downtown
Anchorage to the Matanuska and Susitna Valleys and the Kenai
Peninsula, and from the southern interior north to the Yukon.
Carefully chosen to accommodate most beginning-to-intermediate
paddlers, each route is within easy driving distance of population
centers, providing quick access to wilderness for city residents
and visitors alike. Look inside to find: * Detailed river
descriptions * Maps showing access points and river miles * Level
of difficulty, optimal flows, rapids and other hazards * Gear and
packing recommendations specific to Alaska conditions
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 DescriptionHandling 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 forThis 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.
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