This volume is divided in four sections; covering genome wide approaches, techniques for characterize of paRNA structural features are described, selecting pa-RNA, and paRNA therapeutic potential. Chapters describe how siRNAsdirected against paRNAs can be applied in vivo to modulate transcription of important genes controlled by paRNAs. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Promoter Associated RNA: Methods and Protocols aims to demonstrate paRNAs as new class of regulatory molecules, to further investigate and value as tools for fine transcriptional tuning.

This volume is divided in four sections; covering genome wide approaches, techniques for characterize of paRNA structural features are described, selecting pa-RNA, and paRNA therapeutic potential. Chapters describe how siRNAsdirected against paRNAs can be applied in vivo to modulate transcription of important genes controlled by paRNAs. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Promoter Associated RNA: Methods and Protocols aims to demonstrate paRNAs as new class of regulatory molecules, to further investigate and value as tools for fine transcriptional tuning.

This book is devoted to innovative medicine, comprising the proceedings of the Uehara Memorial Foundation Symposium 2014. It remains extremely rare for the findings of basic research to be developed into clinical applications, and it takes a long time for the process to be achieved. The task of advancing the development of basic research into clinical reality lies with translational science, yet the field seems to struggle to find a way to move forward. To create innovative medical technology, many steps need to be taken: development and analysis of optimal animal models of human diseases, elucidation of genomic and epidemiological data, and establishment of “proof of concept”. There is also considerable demand for progress in drug research, new surgical procedures, and new clinical devices and equipment. While the original research target may be rare diseases, it is also important to apply those findings more broadly to common diseases. The book covers a wide range of topics and is organized into three complementary parts. The first part is basic research for innovative medicine, the second is translational research for innovative medicine, and the third is new technology for innovative medicine. This book helps to understand innovative medicine and to make progress in its realization.

RNAs form complexes with proteins and other RNAs. The RNA‐infrastructure represents the spatiotemporal interaction of these proteins and RNAs in a cell‐wide network. RNA Infrastructure and Networks brings together these ideas to illustrate the scope of RNA‐based biology, and how connecting RNA mechanisms is a powerful tool to investigate regulatory pathways. This book is but a taste of the wide range of RNA‐based mechanisms that connect in the RNA infrastructure.

The control of plant gene expression at the transcriptional level is the main subject of this volume. Genetics, molecular biology and gene technology have dramatically improved our knowledge of this event. The functional analysis of promoters and transcription factors provides more and more insights into the molecular anatomy of initiation complexes assembled from RNA polymerase and the multiplicity of helper and control proteins. Formation of specific DNA-protein complexes - activating or repressing transcription - is the crux of developmental or environmental control of gene expression. The book presents an up-to-date, critical overview of this rapidly advancing field.

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.