In the genome era, the analysis of gene expression has become a critical requirement in many laboratories. But there has been no comprehensive source of strategic, conceptual, and technical information to guide this often complex task. Transcriptional Regulation in Eukaryotes answers that need. Written by two experienced investigators, Michael Carey and Stephen Smale at the UCLA School of Medicine, and based in part on the Gene Expression course taught at Cold Spring Harbor Laboratory, this book directly addresses all the concerns of a laboratory studying the regulation of a newly isolated gene and the biochemistry of a new transcription factor. This important and unique book is essential reading for anyone pursuing the analysis of gene expression in model systems or disease states.

A much-needed guide through the overwhelming amount of literature in the field. Comprehensive and detailed, this book combines background information with the most recentinsights. It introduces current concepts, emphasizing the transcriptional control of genetic information. Moreover, it links data on the structure of regulatory proteins with basic cellular processes. Both advanced students and experts will find answers to such intriguing questions as: - How are programs of specific gene repertoires activated and controlled? - Which genes drive and control morphogenesis? - Which genes govern tissue-specific tasks? - How do hormones control gene expression in coordinating the activities of different tissues? An abundant number of clearly presented glossary terms facilitates understanding of the biological background. Speacial feature: over 2200 (!) literature references.

"Yet another cell and molecular biology book? At the very least, you would think that if I was going to write a textbook, I should write one in an area that really needs one instead of a subject that already has multiple excellent and definitive books. So, why write this book, then? First, it's a course that I have enjoyed teaching for many years, so I am very familiar with what a student really needs to take away from this class within the time constraints of a semester. Second, because it is a course that many students take, there is a greater opportunity to make an impact on more students' pocketbooks than if I were to start off writing a book for a highly specialized upper- level course. And finally, it was fun to research and write, and can be revised easily for inclusion as part of our next textbook, High School Biology."--Open Textbook Library.

There is now compelling evidence that the complexity of higher organisms correlates with the relative amount of non-coding RNA rather than the number of protein-coding genes. Previously dismissed as “junk DNA”, it is the non-coding regions of the genome that are responsible for regulation, facilitating complex temporal and spatial gene expression through the combinatorial effect of numerous mechanisms and interactions working together to fine-tune gene expression. The major regions involved in regulation of a particular gene are the 5’ and 3’ untranslated regions and introns. In addition, pervasive transcription of complex genomes produces a variety of non-coding transcripts that interact with these regions and contribute to regulation. This book discusses recent insights into the regulatory roles of the untranslated gene regions and non-coding RNAs in the control of complex gene expression, as well as the implications of this in terms of organism complexity and evolution.​

The Principles of Biology sequence (BI 211, 212 and 213) introduces biology as a scientific discipline for students planning to major in biology and other science disciplines. Laboratories and classroom activities introduce techniques used to study biological processes and provide opportunities for students to develop their ability to conduct research.

2.4 Regulation of Transcription by Termination2.4.1 Transcription Attenuation, Promoter Upstream/Associated Transcription, and Pausing of RNApII; 2.4.2 Alternative Polyadenylation and Termination; 2.5 Mechanisms of Termination by Other RNA Polymerases; 2.6 Future Perspectives; Acknowledgments; References; 3: Posttranscriptional Gene Regulation by an Editor: ADAR and its Role in RNA Editing; 3.1 Introduction; 3.2 The RNA Editing Kinship; 3.3 The ADAR Gene Family; 3.4 The Role of RNA in the A-to-I Editing Mechanism; 3.5 Splice Site Alterations.

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.