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Full Description
This book provides a single-source reference on the current state of the ribosome profiling method by describing experimental protocols for the quantitative analysis of translation in a variety of model organisms. In addition, the volume presents a detailed overview of the existing software tools and includes detailed description of methods for statistical analysis, data processing, and visualization of ribosome profiling data. Written for the highly successful Methods in Molecular Biology series, 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 practical, Ribosome Profiling: Methods and Protocols aims to provide the type of standardized protocols that have previously been unavailable in an effort to bypass the major barriers to wide use of ribosome profiling-based approaches.
Contents
Monitoring Bacterial Translation Rates Genome-Wide.- Identification of Translation Start Sites in Bacterial Genomes.- Genome-Wide Ribosome Profiling of the Plasmodium falciparum Intraerythrocytic Developmental Cycle.- Performing Ribosome Profiling to Assess Translation in Vegetative and Meiotic Yeast Cells.- Quantitative Comparisons of Translation Activity by Ribosome Profiling with Internal Standards.- Genome-Wide Analysis of Translation in Replicatively Aged Yeast.- Tissue-Specific Ribosome Profiling in Drosophila.- Measuring Organ-Specific Translation Elongation Rate in Mice.- Active Ribosome Profiling with RiboLace: From Bench to Data Analysis.- Poly-A Tailing and Adaptor Ligation Methods for Ribo-Seq Library Construction.- Choice of Ribonucleases for Ribosome Profiling Experiments.- Codon Resolution Analysis of Ribosome Profiling Data.- Assessing Ribosome Distribution Along Transcripts with Polarity Scores and Regression Slope Estimates.- Unsupervised Bayesian Predictionof RNA Translation from Ribosome Profiling Data.- uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast.- Genome-Wide Analysis of Actively Translated Open Reading Frames Using RiboTaper/ORFquant.
