Bioinformatics Data Skills: Reproducible and Robust Research with Open Source Tools (Paperback)
暫譯: 生物資訊數據技能:使用開源工具進行可重複和穩健的研究 (平裝本)

Vince Buffalo

  • 出版商: O'Reilly
  • 出版日期: 2015-08-18
  • 定價: $1,950
  • 售價: 8.8$1,716
  • 語言: 英文
  • 頁數: 538
  • 裝訂: Paperback
  • ISBN: 1449367372
  • ISBN-13: 9781449367374
  • 相關分類: 生物資訊 Bioinformatics
  • 立即出貨 (庫存 < 3)

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商品描述

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 scripting languages like Python 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

商品描述(中文翻譯)

這本實用的書籍教授科學家將大型測序數據集轉化為可重複和穩健的生物學發現所需的技能。許多生物學家在學習生物信息學時,會先學習像 Python 和 R 這樣的腳本語言,並熟悉 Unix 命令行。然而,了解幾種程式語言與準備分析大量生物數據之間存在著巨大的差距。

這本書並不是將生物信息學作為一組隨著這個快速發展的領域而可能改變的工作流程來教授,而是通過數據技能來展示生物信息學的實踐。對數據質量和工具有效性的嚴格評估是可重複和穩健的生物信息學分析的基礎。通過開源和免費的工具,您將學習如何進行生物信息學分析,以及如何以生物信息學家的身份來解決問題。

- 從處理小問題和雜亂的腳本,轉向使用巧妙的方法和工具來解決大型問題
- 專注於高通量(或稱「下一代」)測序數據
- 學習使用現代方法進行數據分析,而不是涵蓋舊的理論概念
- 理解如何選擇和實施最適合工作的工具
- 深入探討導致更簡單、更可重複和穩健的生物信息學分析的方法