(PDF) Computational Biology: A Practical Introduction to BioData Processing and Analysis with Linux, MySQL, and R Free
This greatly expanded 2nd edition provides a practical introduction to - data processing with Linux tools and the programming languages AWK and Perl- data management with the relational database system MySQL, and- data analysis and visualization with the statistical computing environment R for students and practitioners in the life sciences. Although written for beginners, experienced researchers in areas involving bioinformatics and computational biology may benefit from numerous tips and tricks that help to process, filter and format large datasets. Learning by doing is the basic concept of this book. Worked examples illustrate how to employ data processing and analysis techniques, e.g. fo
This greatly expanded 2nd edition provides a practical introduction to - data processing with Linux tools and the programming languages AWK and Perl- data management with the relational database system MySQL, and- data analysis and visualization with the statistical computing environment R for students and practitioners in the life sciences. Although written for beginners, experienced researchers in areas involving bioinformatics and computational biology may benefit from numerous tips and tricks that help to process, filter and format large datasets. Learning by doing is the basic concept of this book. Worked examples illustrate how to employ data processing and analysis techniques, e.g. fo
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(PDF) Computational Biology: A Practical Introduction to
BioData Processing and Analysis with Linux, MySQL, and R
Free
(PDF) Computational Biology: A Practical Introduction to BioData Processing
and Analysis with Linux, MySQL, and R Free
Description :
This greatly expanded 2nd edition provides a practical introduction to - data
processing with Linux tools and the programming languages AWK and Perldata
management with the relational database system MySQL, and- data
analysis and visualization with the statistical computing environment R for
students and practitioners in the life sciences. Although written for beginners,
experienced researchers in areas involving bioinformatics and computational
biology may benefit from numerous tips and tricks that help to process, filter
and format large datasets. Learning by doing is the basic concept of this book.
Worked examples illustrate how to employ data processing and analysis
techniques, e.g. for - finding proteins potentially causing pathogenicity in
bacteria, - supporting the significance of BLAST with homology modeling, ordetecting
candidate proteins that may be redox-regulated, on the basis of their
structure.All the software tools and datasets used are freely available. One
section is devoted to explaining setup and maintenance of Linux as an
operating system independent virtual machine. The author's experiences and
knowledge gained from working and teaching in both academia and industry
constitute the foundation for this practical approach.
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