2009 — 2016 |
Oxford, Geoffrey Eisen, Michael (co-PI) [⬀] Patel, Nipam (co-PI) [⬀] Gillespie, Rosemary [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Genomics of Repeatedly Evolving Color Diversity in the Polymorphic Hawaiian Happy Face Spider @ University of California-Berkeley
A major challenge in evolutionary biology is to understand the molecular basis of diversification. This project applies recent advances in comparative genomics, including genome-wide sequence scanning, linkage mapping, and candidate genes, to address the evolution of diverse color patterns in the exuberantly patterned Hawaiian happy face spider. The happy face spider has a balanced genetic color polymorphism for which the mode of inheritance differs between islands in the Hawaiian chain. Accordingly, despite similar sets and frequencies of color forms across islands, the color diversity has arisen independently on different islands. This research will identify the genomic basis for the differences between islands, and hence the mechanism through which color diversity has been recreated. The research will provide insights into the molecular origins of diversity during evolutionary history and also will produce the first genome sequence and expressed gene data for any spider. The visible and genetically controlled color polymorphism of spiders provides a compelling context for teaching complex concepts in genetics and molecular evolution. Parallel systems to that of the Hawaiian happy face spider occur elsewhere (including California) and will be used to support the development of teaching tools using local spiders in collaboration with an on-going science education project.
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0.915 |
2009 — 2012 |
Eisen, Michael Bruce [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Modeling the Evolution and Function of Drosophila Cis-Regulatory Modules @ University of California Berkeley
Project Abstract The project seeks to understand the function of the cis-regulatory sequences that control animal development, using the embryo of the pomace fly Drosophila melanogaster as a model system. Given the importance of gene regulation in virtually every facet of biology, including many human diseases, it is remarkable how poorly we understand the relationship between the base sequence of the genomic regions that control transcription and their function. Despite a growing catalog of well-characterized regulatory sequences from numerous species, especially D. melanogaster, we still can not reliably recognize regulatory sequences in DNA, determine the expression pattern of a gene from the sequences that surround it, predict the consequences of variation in regulatory sequences, or design regulatory sequences de novo to produce a desired pattern of expression. The early D. melanogaster embryo has long been a model for the study of transcriptional regulation. During the first several hours of its development, the D. melanogaster embryo transforms a small number of crude spatial cues left behind by its mother into intricate spatial patterns of expression of thousands of genes that establish the body plan and tissue identities of the embryo, larvae and adult fly. Technological advances in the last decade have enabled the generation of an increasingly detailed portrait of this regulatory network and the molecular events that underlie it, as well as genome sequences of D. melanogaster and many of its genetic variants and sister species. The central premise of this proposal is that we can infer from these data the molecular logic of gene regulation. In particular, we propose to model three aspects of this system: 1) the manner in which regulatory information is distributed across the D. melanogaster genome, 2) constraints on the evolution of regulatory sequences, and 3) the detailed relationship between DNA sequence and gene expression. Each of these models will reveal not only details of the D. melanogaster regulatory network, but will also illuminate the biophysical and logical principles that unite gene regulation in all animals, including humans.
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1 |
2012 — 2013 |
Eisen, Michael Bruce (co-PI) [⬀] Pachter, Lior S [⬀] |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Association Mapping Without Genotyping @ University of California Berkeley
DESCRIPTION (provided by applicant): Genome wide association studies aim to link genetic markers to phenotypes. The markers assessed are usually single nucleotide polymorphisms (SNPs) that are first identified by large-scale surveys of human genome variation, and later typed in individuals using microarrays. The advent of low-cost high-throughput sequencing allows for the direct sequencing of individual genomes, and opens up the possibility of finding other genomic features, such as insertions or deletions, associated with phenotypes. The complete sequencing of individual genomes also allows for the examination of rare variants and their potential contributions. However the sequences obtained by high-throughput sequencing are short, and it is not currently possible to assemble complete whole genomes directly from them. Individual variant detection is therefore based on first mapping the sequenced reads to a reference genome that serves as a scaffold. There is inevitably a loss of information in such a mapping: some reads do not map due to errors, or because the reference sequence is incomplete. Even when reads can be mapped, the identification of variants is complicated by errors. We propose a novel approach to association mapping by high-throughput sequencing, via the direct comparison of short subsequences (k-mers) extracted from the reads. Such an approach has the advantage of avoiding the need for genome assembly or mapping, and therefore utilizes information that completely represents the underlying genome. The challenge of such an approach is twofold: first, many tests need to be performed, possibly reducing the power to detect association. Second, even if associations are found, they are identified only via short subsequences from the genome; the determination of the underlying genome sequences responsible for the differences is still required. In this proposal we address both of these problems. First, we will demonstrate that association mapping directly from sequenced reads is feasible with sufficient depth of sequencing, to an extent that is already cost effective for short genomes, and that will soon be affordable for larger genomes. Our approach will be based on the large-scale application of multiple hypotheses testing theory. Second, we explain how distinguishing k- mers can be used to extract reads that can then be locally assembled to reveal genomic regions associated with phenotype, thereby avoiding the need for a global genome assembly. A key to our methods is the notion of statistical sequence assembly. This is a formulation of genome assembly based on evaluating the likelihood of proposed assemblies according to a probabilistic model of the random fragmentation used to create libraries, and the subsequent random sequencing of them. Our proposal therefore offers a novel approach to association mapping that circumvents inherent limitations of current approaches by directly assessing differences between cases and controls from sequence data. While we focus on the case of binary traits in this exploratory/developmental grant, our approach should be applicable to quantitative traits as well. PUBLIC HEALTH RELEVANCE: We propose a novel approach to association mapping based on a direct comparison of k-mer counts obtained from reads. This avoids the need for whole genome assembly or read mapping. Instead, genomic regions associated with phenotypes are be identified by local assembly of read containing significantly different k-mers.
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1 |
2018 — 2021 |
Eisen, Michael Bruce (co-PI) [⬀] Rokhsar, Daniel Soleyman [⬀] |
T32Activity Code Description: To enable institutions to make National Research Service Awards to individuals selected by them for predoctoral and postdoctoral research training in specified shortage areas. |
Genomics @ University of California Berkeley
Project Summary/Abstract The GTP provides graduate and postgraduate training and research opportunities at the University of California, Berkeley, emphasizing the cross-disciplinary nature of this rapidly advancing field. Accordingly, the 43 training faculty and proposed trainees are drawn from diverse departments and graduate groups, and is associated with a campus-wide Designated Emphasis that formalizes the requirements for a broad education in computational biology and genomics. The program has three principal thrusts: population and evolutionary genomics, functional genomics, and computational and statistical methods. Trainees will take advantage of a rich training environment of seminars, retreats, and group meetings as well as a diverse set of formal course offerings that range from introductory to advanced methods in genomic biology. Research training will typically begin by the end of the second year, following an introductory period of laboratory rotations, coursework, and preliminary examinations. Progress of the trainees is evaluated by annual thesis reviews and regular meetings with mentors. The Program will train an average of 10 predoctoral students per year in genomics and computational biology.
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1 |