Next Steps For Functional Genomics

One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.

One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.

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.

Reflecting developments in genome editing, this third edition volume fully updates a collection of key techniques for the study of functional genomics. The book is broken up into sections on bioinformatics, DNA, RNA, and protein analysis, as well as a closing section entitled “From Genotype to Phenotype.” 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. Updated and authoritative, Functional Genomics: Methods and Protocols, Third Edition seeks to aid scientists in establishing or extending technologies and techniques in their laboratories.

This volume provides a collection of protocols for the study of DNA-DNA contact maps, replication profiles, transcription rates, RNA secondary structures, protein-RNA interactions, ribosome profiling and quantitative proteomes and metabolomes. Written for the 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, Yeast Functional Genomics: Methods and Protocols aims to ensure successful results in the further study of this vital field.

This collection of robust, readily reproducible methods for microarray-based studies includes expert guidance in the optimal data analysis and informatics. On the methods side are proven techniques for monitoring subcellular RNA localization en masse, for mapping chromosomes at the resolution of a single gene, and for surveying the steady-state genome-wide distribution of DNA binding proteins in vivo. For those workers dealing with massive data sets, the book discusses the methodological aspects of data analysis and informatics in the design of microarray experiments, the choice of test statistic, and the assessment of observational significance, data reduction, and clustering.

With the complete genomes of many organisms now available, and the first draft of the human genome imminent, there is an increasing demand from researchers in a range of disciplines for techniques that will allow them to utilize these resources in their own research. This book provides a comprehensive treatment of the range of methods available for gene and protein expression profiling in a variety of systems. These include large-scale methods, such as cDNA microarrays, serial analysis of gene expression (SAGE) and proteomics, and also methods suitable for non-specialist laboratories, such as differential display and suppression subtraction hybridization. In all cases the chapters have been written by the developers of these methods or experienced users and include detailed protocols to facilitate the introduction of these methods to the readers' laboratories.

Functional genomics is a young discipline whose origin can be traced back to the late 1980s and early 1990s, when molecular tools became available to determine the cellular functions of genes. Today, functional genomics is p- ceived as the analysis, often large-scale, that bridges the structure and organi- tion of genomes and the assessment of gene function. The completion in 2000 of the genome sequence of Arabidopsis thaliana has created a number of new and exciting challenges in plant functional genomics. The immediate task for the plant biology community is to establish the functions of the approximately 25,000 genes present in this model plant. One major issue that will remain even after this formidable task is c- pleted is establishing to what degree our understanding of the genome of one model organism, such as the dicot Arabidopsis, provides insight into the or- nization and function of genes in other plants. The genome sequence of rice, completed in 2002 as a result of the synergistic interaction of the private and public sectors, promises to significantly enrich our knowledge of the general organization of plant genomes. However, the tools available to investigate gene function in rice are lagging behind those offered by other model plant systems. Approaches available to investigate gene function become even more limited for plants other than the model systems of Arabidopsis, rice, and maize.