Computational Genomics with R

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology.

After reading:

• You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages.
• You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data.
• You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation.
• You will know the basics of processing and quality checking high-throughput sequencing data.
• You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites.
• You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization.
• You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq.
• You will know basic techniques for integrating and interpreting multi-omics datasets.

Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

《使用 R 进行计算基因组学》为基因组数据分析的初学者提供了一个起点，同时也指导更高级的从业者使用复杂的基因组数据分析技术。本书涵盖了从 R 编程到机器学习和统计学，再到最新的基因组数据分析技术的主题。本书提供易于理解的信息和解释，始终将基因组学背景作为背景。书中还包含了在 R 中实际且有文档支持的示例，读者可以通过重用所呈现的代码来分析他们的数据。由于计算基因组学是一门跨学科的领域，因此对于具有不同背景的人来说，需要不同的起点。例如，生物学家可能会跳过基本基因组生物学的部分，直接从 R 编程开始，而计算机科学家可能希望从基因组生物学开始。

阅读本书后，您将会：
- 掌握 R 的基础知识，并能够深入研究 R 在计算基因组学中的专业应用，如使用 Bioconductor 包。
- 熟悉统计学、有监督和无监督学习技术，这些技术在数据建模和高维数据的探索性分析中非常重要。
- 理解基因组区间及其上的操作，这些操作用于诸如对齐读数计数和基因组特征注释等任务。
- 了解处理和质量检查高通量测序数据的基础知识。
- 能够进行序列分析，例如计算基因组部分的 GC 含量或查找转录因子结合位点。
- 了解基因组学中使用的可视化技术，如热图、元基因图和基因组轨迹可视化。
- 熟悉不同高通量测序数据集的分析，如 RNA-seq、ChIP-seq 和 BS-seq。
- 掌握整合和解释多组学数据集的基本技术。

Altuna Akalin 是柏林医学系统生物学研究所 Max Delbrück Center 的团队负责人，也是生物信息学和组学数据科学平台的负责人。自 2002 年以来，他一直在开发用于分析和整合大规模基因组学数据集的计算方法。他在这个领域发表了大量的研究成果。本书的框架源于他自 2015 年以来组织和教授的年度计算基因组学课程。

Dr. Altuna Akalin is a bioinformatics scientist and the head of Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center in Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. His interest is in using machine learning and statistics to uncover patterns related to important biological variables such as disease state and type. He has lived in the USA, Norway, Turkey, Japan, and Switzerland in order to pursue research work and education related to computational genomics.

Dr. Altuna Akalin是一位生物信息學科學家，也是柏林醫學系統生物學研究所Max Delbrück Center的生物信息學和Omics數據科學平台的負責人。自2002年以來，他一直在開發用於分析和整合大規模基因組學數據集的計算方法。他的興趣在於使用機器學習和統計方法來揭示與重要生物變量（如疾病狀態和類型）相關的模式。為了從事與計算基因組學相關的研究工作和教育，他曾在美國、挪威、土耳其、日本和瑞士居住過。