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.

'This book provides a basic overview of computational tools developed in R for carrying out data analyses in genomics. It can be a valuable companion for anyone whowants to utilise the computational tools developed within the Bioconductor and R environments for education and research. This book’s main target audience are students of computational biology to get a first look at the diversity of machine learning methods. Thebook will also servewell biomedical researchers needing a guide to packages that can help them with the analysis of data that they encounter in their work.'- Krzysztof Podgórski, International Statistical Review (2021) doi: 10.1111/insr.12453

• Introduction to Genomics. 2. Introduction to R for Genomic Data Analysis 3. Statistics for Genomics 4. Exploratory Data Analysis with Unsupervised Machine Learning 5. Predictive Modeling with Supervised Machine Learning 6. Operations on Genomic Intervals and Genome Arithmetic 7. Quality Check, Processing and Alignment of High-throughput Sequencing Reads 8. RNA-seq Analysis 9. ChIP-seq analysis 10. DNA methylation analysis using bisulfite sequencing 11. Multi-omics Analysis.

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.

'This book provides a basic overview of computational tools developed in R for carrying out data analyses in genomics. It can be a valuable companion for anyone whowants to utilise the computational tools developed within the Bioconductor and R environments for education and research. This book’s main target audience are students of computational biology to get a first look at the diversity of machine learning methods. Thebook will also servewell biomedical researchers needing a guide to packages that can help them with the analysis of data that they encounter in their work.'- Krzysztof Podgórski, International Statistical Review (2021) doi: 10.1111/insr.12453

• Introduction to Genomics. 2. Introduction to R for Genomic Data Analysis 3. Statistics for Genomics 4. Exploratory Data Analysis with Unsupervised Machine Learning 5. Predictive Modeling with Supervised Machine Learning 6. Operations on Genomic Intervals and Genome Arithmetic 7. Quality Check, Processing and Alignment of High-throughput Sequencing Reads 8. RNA-seq Analysis 9. ChIP-seq analysis 10. DNA methylation analysis using bisulfite sequencing 11. Multi-omics Analysis.