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Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools
86% of respondents would recommend this to a friend
NIO 2147
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What Stands Out
Detalles de producto
- Teaches the skills needed to turn large sequencing datasets into reproducible and robust biological findings
- Emphasizes the practice of bioinformatics through data skills rather than teaching it as a set of workflows
- Focuses on rigorous assessment of data quality and tool effectiveness for reproducible and robust bioinformatics analysis
- Covers high-throughput sequencing data analysis using modern methods
- Provides guidance on choosing and implementing the best tools for bioinformatics tasks
- Offers methods for achieving easier, more reproducible, and robust bioinformatics analysis
| Publisher | O'Reilly Media |
| Publication date | August 18, 2015 |
| Edition | 1st |
| Language | English |
| Print length | 536 pages |
| ISBN-10 | 1449367372 |
| ISBN-13 | 978-1449367374 |
| Item Weight | 2.05 pounds (930 grams) |
| Dimensions | 7 x 1.25 x 9 inches (17.8 x 3.2 x 22.9 cm) |
Who Should Buy?
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Aspiring Bioinformaticians
Ideal for newcomers seeking practical skills in bioinformatics and data handling with open-source tools.
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Researchers in Biology
Perfect for biologists who want to enhance their research reproducibility and data analysis skills using bioinformatics.
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Educators and Instructors
Useful for teaching bioinformatics concepts and methodologies, providing structured guidance for curriculum development.
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Complete Beginners
Not suitable for individuals with no prior programming or data analysis knowledge, as it assumes some familiarity.
DESCRIPCIÓN DEL PRODUCTO
Preguntas y respuestas de los clientes
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Pregunta:
What is the focus of this book?
Respuesta: The book focuses on teaching practical skills for analyzing large sequencing datasets in the field of bioinformatics. -
Pregunta:
What kind of data does it cover?
Respuesta: It specifically focuses on high-throughput (or next generation) sequencing data. -
Pregunta:
What will I learn from this book?
Respuesta: You will learn how to choose and implement the best tools for bioinformatics analysis, as well as modern data analysis methods. -
Pregunta:
Is this book suitable for beginners?
Respuesta: The book may be a bit dense for beginners, but it provides valuable knowledge and is a good reference. -
Pregunta:
Is the book applicable to real-world work?
Respuesta: Yes, the book covers Unix tools and remote machines, which are directly applicable to real-world work. -
Pregunta:
Does the book emphasize reproducible research?
Respuesta: Yes, the book emphasizes reproducible research and provides good data analysis practices.
Software Development Editorial Review
Bioinformatics Data Skills is highly recommended for scientists with a background in biology looking to get into data analysis. It is a well-written and easy-to-follow book that introduces all the skills needed to work on state-of-the-art NGS data. The author's experience in interacting with beginners is evident in his step-by-step approach. The book covers a wide range of topics, with a good balance of depth. The chapters on Unix tools and remote machines are directly applicable to real-world work. The book emphasizes reproducible research and provides practical knowledge and good data analysis practices. It may be a bit dense for beginners but serves as a valuable reference.
Customer Reviews & Ratings
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5 estrella
77%
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4 estrella
15%
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3 estrella
3%
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2 estrella
3%
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1 estrella
2%
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ventajas
- Readable and concise
- Step-by-step approach
- Covers a wide range of topics
- Applicable to real-world work
- Emphasizes reproducible research
Contras
- Middle third of the book might not be relevant for some readers
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NIO 2147
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características y beneficios
- Learn practical skills for turning large sequencing datasets into reproducible and robust biological findings
- Focus on high-throughput sequencing data
- Learn data analysis with modern methods
- 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
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