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Bioinformatik-Datenkompetenzen: Reproduzierbare und robuste Forschung mit Open Source für-
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Standort: Austin, Texas, USA
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Artikelmerkmale
- Artikelzustand
- ISBN
- 9781449367374
Über dieses Produkt
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1449367372
ISBN-13
9781449367374
eBay Product ID (ePID)
201595784
Product Key Features
Number of Pages
538 Pages
Language
English
Publication Name
Bioinformatics Data Skills : Reproducible and Robust Research with Open Source Tools
Subject
Programming / Open Source
Publication Year
2015
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Height
1.3 in
Item Weight
32.5 Oz
Item Length
9.2 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2015-458838
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
570.285
Synopsis
This practical book teaches the skills that scientists need for turning large sequencing datasets into reproducible and robust biological findings. Many biologists begin their bioinformatics training by learning languages like Perl and R alongside the Unix command line. But there's a huge gap between knowing a few programming languages and being prepared to analyze large amounts of biological data. Rather than teach bioinformatics as a set of workflows that are likely to change with this rapidly evolving field, this book demsonstrates the practice of bioinformatics through data skills. Rigorous assessment of data quality and of the effectiveness of tools is the foundation of reproducible and robust bioinformatics analysis. Through open source and freely available tools, you'll learn not only how to do bioinformatics, but how to approach problems as a bioinformatician. Go from handling small problems with messy scripts to tackling large problems with clever methods and tools Focus on high-throughput (or "next generation") sequencing data Learn data analysis with modern methods, versus covering older theoretical concepts Understand how to choose and implement the best tool for the job Delve into methods that lead to easier, more reproducible, and robust bioinformatics analysis, 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, 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, Though many biologists begin bioinformatics training by learning Perl and R, but there's a huge gap between solving small problems with messy scripts and analyzing large amounts of biological data. This practical book teaches the data skills you need to turn large sequencing datasets into reproducible and robust biological findings.
LC Classification Number
QH324.2.B838 2015
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