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Barrera, Luis A.

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Barrera

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Luis A.

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Barrera, Luis A.

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2012 - 20154

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Author's Publications: search.filters.isAuthorOfPublication.89e52825-b082-41f7-b045-81998ba5fc4d

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Now showing 1 - 4 of 4
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    Highly parallel assays of tissue-specific enhancers in whole Drosophila embryos
    (2013) Gisselbrecht, Stephen S.; Barrera, Luis A.; Porsch, Martin; Aboukhalil, Anton; Estep, Preston W.; Vedenko, Anastasia; Palagi, Alexandre; Kim, Yongsok; Zhu, Xianmin; Busser, Brian W.; Gamble, Caitlin E.; Hafner, Antonina; Singhania, Aditi; Michelson, Alan M.; Bulyk, Martha
    Transcriptional enhancers are a primary mechanism by which tissue-specific gene expression is achieved. Despite the importance of these regulatory elements in development, responses to environmental stresses, and disease, testing enhancer activity in animals remains tedious, with a minority of enhancers having been characterized. Here, we have developed ‘enhancer-FACS-Seq’ (eFS) technology for highly parallel identification of active, tissue-specific enhancers in Drosophila embryos. Analysis of enhancers identified by eFS to be active in mesodermal tissues revealed enriched DNA binding site motifs of known and putative, novel mesodermal transcription factors (TFs). Naïve Bayes classifiers using TF binding site motifs accurately predicted mesodermal enhancer activity. Application of eFS to other cell types and organisms should accelerate the cataloging of enhancers and understanding how transcriptional regulation is encoded within them.
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    Context influences on TALE–DNA binding revealed by quantitative profiling
    (Nature Pub. Group, 2015) Rogers, Julia; Barrera, Luis A.; Reyon, Deepak; Sander, Jeffry D.; Kellis, Manolis; Joung, J Keith; Bulyk, Martha
    Transcription activator-like effector (TALE) proteins recognize DNA using a seemingly simple DNA-binding code, which makes them attractive for use in genome engineering technologies that require precise targeting. Although this code is used successfully to design TALEs to target specific sequences, off-target binding has been observed and is difficult to predict. Here we explore TALE–DNA interactions comprehensively by quantitatively assaying the DNA-binding specificities of 21 representative TALEs to ∼5,000–20,000 unique DNA sequences per protein using custom-designed protein-binding microarrays (PBMs). We find that protein context features exert significant influences on binding. Thus, the canonical recognition code does not fully capture the complexity of TALE–DNA binding. We used the PBM data to develop a computational model, Specificity Inference For TAL-Effector Design (SIFTED), to predict the DNA-binding specificity of any TALE. We provide SIFTED as a publicly available web tool that predicts potential genomic off-target sites for improved TALE design.
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    Towards a Systematic Approach for Characterizing Regulatory Variation
    (2015-11-03) Barrera, Luis A.; Gewurz, Benjamin E.; Kharchenko, Peter V.; Sunyaev, Shamil R.; Hogle, James M.
    A growing body of evidence suggests that genetic variants that alter gene expression are responsible for many phenotypic differences across individuals, particularly for the risk of developing common diseases. However, the molecular mechanisms that underlie the vast majority of associations between genetic variants and their phenotypes remain unknown. An important limiting factor is that genetic variants remain difficult to interpret, particularly in noncoding sequences. Developing truly systematic approaches for characterizing regulatory variants will require: (a) improved annotations for the genomic sequences that control gene expression, (b) a more complete understanding of the molecular mechanisms through which genetic variants, both coding and noncoding, can affect gene expression, and (c) better experimental tools for testing hypotheses about regulatory variants. In this dissertation, I present conceptual and methodological advances that directly contribute to each of these goals. A recurring theme in all of these developments is the statistical modeling of protein-DNA interactions and its integration with other data types. First, I describe enhancer-FACS-Seq, a high-throughput experimental approach for screening candidate enhancer sequences to test for in vivo, tissue-specific activity. Second, I present an integrative computational analysis of the in vivo binding of NF-kappaB, a key regulator of the immune system, yielding new insights into how genetic variants can affect NF-kappaB binding. Next, I describe the first comprehensive survey of coding variation in human transcription factors and what it reveals about additional sources of genetic variation that can affect gene expression. Finally, I present SIFTED, a statistical framework and web tool for the optimal design of TAL effectors, which have been used successfully in genome editing and can thus be used to test hypotheses about regulatory variants. Together, these developments help fulfill key needs in the quest to understand the molecular basis of human phenotypic variation.
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    EpiFire: An Open Source C++ Library and Application for Contact Network Epidemiology
    (BioMed Central, 2012) Hladish, Thomas; Melamud, Eugene; Barrera, Luis A.; Galvani, Alison; Meyers, Lauren Ancel
    Background: Contact network models have become increasingly common in epidemiology, but we lack a flexible programming framework for the generation and analysis of epidemiological contact networks and for the simulation of disease transmission through such networks. Results: Here we present EpiFire, an applications programming interface and graphical user interface implemented in C++, which includes a fast and efficient library for generating, analyzing and manipulating networks. Network-based percolation and chain-binomial simulations of susceptible-infected-recovered disease transmission, as well as traditional non-network mass-action simulations, can be performed using EpiFire. Conclusions: EpiFire provides an open-source programming interface for the rapid development of network models with a focus in contact network epidemiology. EpiFire also provides a point-and-click interface for generating networks, conducting epidemic simulations, and creating figures. This interface is particularly useful as a pedagogical tool.