Bio

Professional Education


  • Ph.D., University of Washington, Genome Sciences (2010)
  • B.S., Washington State University, Biotechnology, Summa Cum Laude (2003)
  • B.S., Washington State University, Genetics & Cell Biology, Summa Cum Laude (2003)

Stanford Advisors


Publications

Journal Articles


  • Regulatory analysis of the C. elegans genome with spatiotemporal resolution. Nature Araya, C. L., Kawli, T., Kundaje, A., Jiang, L., Wu, B., Vafeados, D., Terrell, R., Weissdepp, P., Gevirtzman, L., Mace, D., Niu, W., Boyle, A. P., Xie, D., Ma, L., Murray, J. I., Reinke, V., Waterston, R. H., Snyder, M. 2014; 512 (7515): 400-405

    Abstract

    Discovering the structure and dynamics of transcriptional regulatory events in the genome with cellular and temporal resolution is crucial to understanding the regulatory underpinnings of development and disease. We determined the genomic distribution of binding sites for 92 transcription factors and regulatory proteins across multiple stages of Caenorhabditis elegans development by performing 241 ChIP-seq (chromatin immunoprecipitation followed by sequencing) experiments. Integration of regulatory binding and cellular-resolution expression data produced a spatiotemporally resolved metazoan transcription factor binding map. Using this map, we explore developmental regulatory circuits that encode combinatorial logic at the levels of co-binding and co-expression of transcription factors, characterizing the genomic coverage and clustering of regulatory binding, the binding preferences of, and biological processes regulated by, transcription factors, the global transcription factor co-associations and genomic subdomains that suggest shared patterns of regulation, and identifying key transcription factors and transcription factor co-associations for fate specification of individual lineages and cell types.

    View details for DOI 10.1038/nature13497

    View details for PubMedID 25164749

  • Comparative analysis of regulatory information and circuits across distant species. Nature Boyle, A. P., Araya, C. L., Brdlik, C., Cayting, P., Cheng, C., Cheng, Y., Gardner, K., Hillier, L. W., Janette, J., Jiang, L., Kasper, D., Kawli, T., Kheradpour, P., Kundaje, A., Li, J. J., Ma, L., Niu, W., Rehm, E. J., Rozowsky, J., Slattery, M., Spokony, R., Terrell, R., Vafeados, D., Wang, D., Weisdepp, P., Wu, Y., Xie, D., Yan, K., Feingold, E. A., Good, P. J., Pazin, M. J., Huang, H., Bickel, P. J., Brenner, S. E., Reinke, V., Waterston, R. H., Gerstein, M., White, K. P., Kellis, M., Snyder, M. 2014; 512 (7515): 453-456

    Abstract

    Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.

    View details for DOI 10.1038/nature13668

    View details for PubMedID 25164757

  • Quantitative analysis of RNA-protein interactions on a massively parallel array reveals biophysical and evolutionary landscapes. Nature biotechnology Buenrostro, J. D., Araya, C. L., Chircus, L. M., Layton, C. J., Chang, H. Y., Snyder, M. P., Greenleaf, W. J. 2014; 32 (6): 562-568

    Abstract

    RNA-protein interactions drive fundamental biological processes and are targets for molecular engineering, yet quantitative and comprehensive understanding of the sequence determinants of affinity remains limited. Here we repurpose a high-throughput sequencing instrument to quantitatively measure binding and dissociation of a fluorescently labeled protein to >10(7) RNA targets generated on a flow cell surface by in situ transcription and intermolecular tethering of RNA to DNA. Studying the MS2 coat protein, we decompose the binding energy contributions from primary and secondary RNA structure, and observe that differences in affinity are often driven by sequence-specific changes in both association and dissociation rates. By analyzing the biophysical constraints and modeling mutational paths describing the molecular evolution of MS2 from low- to high-affinity hairpins, we quantify widespread molecular epistasis and a long-hypothesized, structure-dependent preference for G:U base pairs over C:A intermediates in evolutionary trajectories. Our results suggest that quantitative analysis of RNA on a massively parallel array (RNA-MaP) provides generalizable insight into the biophysical basis and evolutionary consequences of sequence-function relationships.

    View details for DOI 10.1038/nbt.2880

    View details for PubMedID 24727714

  • Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures. Bioinformatics (Oxford, England) Phanstiel, D. H., Boyle, A. P., Araya, C. L., Snyder, M. P. 2014

    Abstract

    Motivation: Interpretation and communication of genomic data require flexible and quantitative tools to analyze and visualize diverse data types, and yet, a comprehensive tool to display all common genomic data types in publication quality figures does not exist to date. To address this shortcoming, we present Sushi.R, an R/Bioconductor package that allows flexible integration of genomic visualizations into highly customizable, publication-ready, multi-panel figures from common genomic data formats including Browser Extensible Data (BED), bedGraph and Browser Extensible Data Paired-End (BEDPE). Sushi.R is open source and made publicly available through GitHub (https://github.com/dphansti/Sushi) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/Sushi.html).mpsnyder@stanford.edu or dphansti@stanford.edu.

    View details for DOI 10.1093/bioinformatics/btu379

    View details for PubMedID 24903420

  • A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Araya, C. L., Fowler, D. M., Chen, W., Muniez, I., Kelly, J. W., Fields, S. 2012; 109 (42): 16858-16863

    Abstract

    The ability of a protein to carry out a given function results from fundamental physicochemical properties that include the protein's structure, mechanism of action, and thermodynamic stability. Traditional approaches to study these properties have typically required the direct measurement of the property of interest, oftentimes a laborious undertaking. Although protein properties can be probed by mutagenesis, this approach has been limited by its low throughput. Recent technological developments have enabled the rapid quantification of a protein's function, such as binding to a ligand, for numerous variants of that protein. Here, we measure the ability of 47,000 variants of a WW domain to bind to a peptide ligand and use these functional measurements to identify stabilizing mutations without directly assaying stability. Our approach is rooted in the well-established concept that protein function is closely related to stability. Protein function is generally reduced by destabilizing mutations, but this decrease can be rescued by stabilizing mutations. Based on this observation, we introduce partner potentiation, a metric that uses this rescue ability to identify stabilizing mutations, and identify 15 candidate stabilizing mutations in the WW domain. We tested six candidates by thermal denaturation and found two highly stabilizing mutations, one more stabilizing than any previously known mutation. Thus, physicochemical properties such as stability are latent within these large-scale protein functional data and can be revealed by systematic analysis. This approach should allow other protein properties to be discovered.

    View details for DOI 10.1073/pnas.1209751109

    View details for Web of Science ID 000310515800030

    View details for PubMedID 23035249

  • Enrich: software for analysis of protein function by enrichment and depletion of variants BIOINFORMATICS Fowler, D. M., Araya, C. L., Gerard, W., Fields, S. 2011; 27 (24): 3430-3431

    Abstract

    Measuring the consequences of mutation in proteins is critical to understanding their function. These measurements are essential in such applications as protein engineering, drug development, protein design and genome sequence analysis. Recently, high-throughput sequencing has been coupled to assays of protein activity, enabling the analysis of large numbers of mutations in parallel. We present Enrich, a tool for analyzing such deep mutational scanning data. Enrich identifies all unique variants (mutants) of a protein in high-throughput sequencing datasets and can correct for sequencing errors using overlapping paired-end reads. Enrich uses the frequency of each variant before and after selection to calculate an enrichment ratio, which is used to estimate fitness. Enrich provides an interactive interface to guide users. It generates user-accessible output for downstream analyses as well as several visualizations of the effects of mutation on function, thereby allowing the user to rapidly quantify and comprehend sequence-function relationships.Enrich is implemented in Python and is available under a FreeBSD license at http://depts.washington.edu/sfields/software/enrich/. Enrich includes detailed documentation as well as a small example dataset.dfowler@uw.edu; fields@uw.eduSupplementary data is available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btr577

    View details for Web of Science ID 000297860000017

    View details for PubMedID 22006916

  • Deep mutational scanning: assessing protein function on a massive scale TRENDS IN BIOTECHNOLOGY Araya, C. L., Fowler, D. M. 2011; 29 (9): 435-442

    Abstract

    Analysis of protein mutants is an effective means to understand their function. Protein display is an approach that allows large numbers of mutants of a protein to be selected based on their activity, but only a handful with maximal activity have been traditionally identified for subsequent functional analysis. However, the recent application of high-throughput sequencing (HTS) to protein display and selection has enabled simultaneous assessment of the function of hundreds of thousands of mutants that span the activity range from high to low. Such deep mutational scanning approaches are rapid and inexpensive with the potential for broad utility. In this review, we discuss the emergence of deep mutational scanning, the challenges associated with its use and some of its exciting applications.

    View details for DOI 10.1016/j.tibtech.2011.04.003

    View details for Web of Science ID 000294943400003

    View details for PubMedID 21561674

  • Genome-Wide Analysis of Nascent Transcription in Saccharomyces cerevisiae G3: Genes, Genomes, Genetics McKinlay A*, A. F. 2011; 1 (4): 549-558
  • High-resolution mapping of protein sequence-function relationships NATURE METHODS Fowler, D. M., Araya, C. L., Fleishman, S. J., Kellogg, E. H., Stephany, J. J., Baker, D., Fields, S. 2010; 7 (9): 741-U108

    Abstract

    We present a large-scale approach to investigate the functional consequences of sequence variation in a protein. The approach entails the display of hundreds of thousands of protein variants, moderate selection for activity and high-throughput DNA sequencing to quantify the performance of each variant. Using this strategy, we tracked the performance of >600,000 variants of a human WW domain after three and six rounds of selection by phage display for binding to its peptide ligand. Binding properties of these variants defined a high-resolution map of mutational preference across the WW domain; each position had unique features that could not be captured by a few representative mutations. Our approach could be applied to many in vitro or in vivo protein assays, providing a general means for understanding how protein function relates to sequence.

    View details for DOI 10.1038/nmeth.1492

    View details for Web of Science ID 000281429200020

    View details for PubMedID 20711194

  • Whole-genome sequencing of a laboratory-evolved yeast strain BMC GENOMICS Araya, C. L., Payen, C., Dunham, M. J., Fields, S. 2010; 11

    Abstract

    Experimental evolution of microbial populations provides a unique opportunity to study evolutionary adaptation in response to controlled selective pressures. However, until recently it has been difficult to identify the precise genetic changes underlying adaptation at a genome-wide scale. New DNA sequencing technologies now allow the genome of parental and evolved strains of microorganisms to be rapidly determined.We sequenced >93.5% of the genome of a laboratory-evolved strain of the yeast Saccharomyces cerevisiae and its ancestor at >28x depth. Both single nucleotide polymorphisms and copy number amplifications were found, with specific gains over array-based methodologies previously used to analyze these genomes. Applying a segmentation algorithm to quantify structural changes, we determined the approximate genomic boundaries of a 5x gene amplification. These boundaries guided the recovery of breakpoint sequences, which provide insights into the nature of a complex genomic rearrangement.This study suggests that whole-genome sequencing can provide a rapid approach to uncover the genetic basis of evolutionary adaptations, with further applications in the study of laboratory selections and mutagenesis screens. In addition, we show how single-end, short read sequencing data can provide detailed information about structural rearrangements, and generate predictions about the genomic features and processes that underlie genome plasticity.

    View details for DOI 10.1186/1471-2164-11-88

    View details for Web of Science ID 000275291200001

    View details for PubMedID 20128923

  • AceTree: a tool for visual analysis of Caenorhabditis elegans embryogenesis BMC BIOINFORMATICS Boyle, T. J., Bao, Z., Murray, J. I., Araya, C. L., Waterston, R. H. 2006; 7

    Abstract

    The invariant lineage of the nematode Caenorhabditis elegans has potential as a powerful tool for the description of mutant phenotypes and gene expression patterns. We previously described procedures for the imaging and automatic extraction of the cell lineage from C. elegans embryos. That method uses time-lapse confocal imaging of a strain expressing histone-GFP fusions and a software package, StarryNite, processes the thousands of images and produces output files that describe the location and lineage relationship of each nucleus at each time point.We have developed a companion software package, AceTree, which links the images and the annotations using tree representations of the lineage. This facilitates curation and editing of the lineage. AceTree also contains powerful visualization and interpretive tools, such as space filling models and tree-based expression patterning, that can be used to extract biological significance from the data.By pairing a fast lineaging program written in C with a user interface program written in Java we have produced a powerful software suite for exploring embryonic development.

    View details for DOI 10.1186/1471-2105-7-275

    View details for Web of Science ID 000238984400001

    View details for PubMedID 16740163

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