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    ABSTRACT: Thousands of long noncoding RNAs (lncRNAs) have been discovered, but their functional characterization has been slowed by a limited set of research tools. Here we review emerging RNA-centric methods to interrogate the intrinsic structure of lncRNAs as well as their genomic localization and biochemical partners. Understanding these technologies, including their advantages and caveats, and developing them in the future will be essential to progress from description to comprehension of the myriad roles of lncRNAs.
    Nature Structural & Molecular Biology 01/2015; 22(1):29-35. · 11.63 Impact Factor
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    ABSTRACT: Gene expression is a fundamental process that underlies development, homeostasis, and behavior of organisms. The fact that it relies on nucleic acid intermediates, which can specifically interact with complementary probes, provides an excellent opportunity for studying the multiple steps—transcription, RNA processing, transport, translation, degradation, and so forth—through which gene function manifests. Over the past three decades, the toolbox of nucleic acid science has expanded tremendously, making high-precision in situ detection of DNA and RNA possible. This has revealed that many—probably the vast majority of—transcripts are distributed within the cytoplasm or the nucleus in a nonrandom fashion. With the development of microscopy techniques we have learned not only about the qualitative localization of these molecules but also about their absolute numbers with great precision. Single-molecule techniques for nucleic acid detection have been transforming our views of biology with elementary power: cells are not average members of their population but are highly distinct individuals with greatly and suddenly changing gene expression, and this behavior of theirs can be measured, modeled, and thus predicted and, finally, comprehended.For further resources related to this article, please visit the WIREs website.Conflict of interest: The authors have declared no conflicts of interest for this article.
    Wiley Interdisciplinary Reviews: Developmental Biology. 01/2015;
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    ABSTRACT: We present single-cell clustering using bifurcation analysis (SCUBA), a novel computational method for extracting lineage relationships from single-cell gene expression data and modeling the dynamic changes associated with cell differentiation. SCUBA draws techniques from nonlinear dynamics and stochastic differential equation theories, providing a systematic framework for modeling complex processes involving multilineage specifications. By applying SCUBA to analyze two complementary, publicly available datasets we successfully reconstructed the cellular hierarchy during early development of mouse embryos, modeled the dynamic changes in gene expression patterns, and predicted the effects of perturbing key transcriptional regulators on inducing lineage biases. The results were robust with respect to experimental platform differences between RT-PCR and RNA sequencing. We selectively tested our predictions in Nanog mutants and found good agreement between SCUBA predictions and the experimental data. We further extended the utility of SCUBA by developing a method to reconstruct missing temporal-order information from a typical single-cell dataset. Analysis of a hematopoietic dataset suggests that our method is effective for reconstructing gene expression dynamics during human B-cell development. In summary, SCUBA provides a useful single-cell data analysis tool that is well-suited for the investigation of developmental processes.
    Proceedings of the National Academy of Sciences 12/2014; · 9.81 Impact Factor