Focus Computational Biology Series – serie

Chromatin immunoprecipitation sequencing (ChIP-seq), which maps the genome-wide localization patterns of transcription factors and epigenetic marks, is among the most widely used methods in molecular biology. Practical Guide to ChIP-seq Data Analysis will guide readers through the steps of ChIP-seq analysis: from quality control, through peak calling, to downstream analyses. It will help experimental biologists to design their ChIP-seq experiments with the analysis in mind, and to perform the basic analysis steps themselves. It also aims to support bioinformaticians to understand how the data is generated, what the sources of biases are, and which methods are appropriate for different analyses.

Systems-Level Modelling of Microbial Communities: Theory and Practice introduces various aspects of modelling microbial communities and presents a detailed overview of the computational methods which have been developed in this area. This book is aimed at researchers in the field of computational/systems biology as well as biologists/experimentalists studying microbial communities, who are keen on embracing the concepts of computational modelling. The primary focus of this book is on methods for modelling interactions between micro-organisms in a community, with special emphasis on constraint-based and network-based modelling techniques. A brief overview of population- and agent-based modelling is also presented. Lastly, it covers the experimental methods to understand microbial communities, and provides an outlook on how the field may evolve in the coming years.

Chromatin immunoprecipitation sequencing (ChIP-seq), which maps the genome-wide localization patterns of transcription factors and epigenetic marks, is among the most widely used methods in molecular biology. Practical Guide to ChIP-seq Data Analysis will guide readers through the steps of ChIP-seq analysis: from quality control, through peak calling, to downstream analyses. It will help experimental biologists to design their ChIP-seq experiments with the analysis in mind, and to perform the basic analysis steps themselves. It also aims to support bioinformaticians to understand how the data is generated, what the sources of biases are, and which methods are appropriate for different analyses.