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Adelman, Karen

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Adelman

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Karen

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Adelman, Karen
Author's Publications: search.filters.isAuthorOfPublication.9a870e40-eb57-4fe0-83e0-bc33d3a5c7c6

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    The kinetics of pre-mRNA splicing in the Drosophila genome and the influence of gene architecture
    (eLife Sciences Publications, Ltd, 2017) Pai, Athma A; Henriques, Telmo; McCue, Kayla; Burkholder, Adam; Adelman, Karen; Burge, Christopher B
    Production of most eukaryotic mRNAs requires splicing of introns from pre-mRNA. The splicing reaction requires definition of splice sites, which are initially recognized in either intron-spanning (‘intron definition’) or exon-spanning (‘exon definition’) pairs. To understand how exon and intron length and splice site recognition mode impact splicing, we measured splicing rates genome-wide in Drosophila, using metabolic labeling/RNA sequencing and new mathematical models to estimate rates. We found that the modal intron length range of 60–70 nt represents a local maximum of splicing rates, but that much longer exon-defined introns are spliced even faster and more accurately. We observed unexpectedly low variation in splicing rates across introns in the same gene, suggesting the presence of gene-level influences, and we identified multiple gene level variables associated with splicing rate. Together our data suggest that developmental and stress response genes may have preferentially evolved exon definition in order to enhance the rate or accuracy of splicing.
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    Transcription start site profiling uncovers divergent transcription and enhancer-associated RNAs in Drosophila melanogaster
    (BioMed Central, 2018) Meers, Michael P.; Adelman, Karen; Duronio, Robert J.; Strahl, Brian D.; McKay, Daniel J.; Matera, A. Gregory
    Background: High-resolution transcription start site (TSS) mapping in D. melanogaster embryos and cell lines has revealed a rich and detailed landscape of both cis- and trans-regulatory elements and factors. However, TSS profiling has not been investigated in an orthogonal in vivo setting. Here, we present a comprehensive dataset that links TSS dynamics with nucleosome occupancy and gene expression in the wandering third instar larva, a developmental stage characterized by large-scale shifts in transcriptional programs in preparation for metamorphosis. Results: The data recapitulate major regulatory classes of TSSs, based on peak width, promoter-proximal polymerase pausing, and cis-regulatory element density. We confirm the paucity of divergent transcription units in D. melanogaster, but also identify notable exceptions. Furthermore, we identify thousands of novel initiation events occurring at unannotated TSSs that can be classified into functional categories by their local density of histone modifications. Interestingly, a sub-class of these unannotated TSSs overlaps with functionally validated enhancer elements, consistent with a regulatory role for “enhancer RNAs” (eRNAs) in defining developmental transcription programs. Conclusions: High-depth TSS mapping is a powerful strategy for identifying and characterizing low-abundance and/or low-stability RNAs. Global analysis of transcription initiation patterns in a developing organism reveals a vast number of novel initiation events that identify potential eRNAs as well as other non-coding transcripts critical for animal development. Electronic supplementary material The online version of this article (10.1186/s12864-018-4510-7) contains supplementary material, which is available to authorized users.
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    ORIO (Online Resource for Integrative Omics): a web-based platform for rapid integration of next generation sequencing data
    (Oxford University Press, 2017) Lavender, Christopher A.; Shapiro, Andrew J.; Burkholder, Adam B.; Bennett, Brian D.; Adelman, Karen; Fargo, David C.
    Abstract Established and emerging next generation sequencing (NGS)-based technologies allow for genome-wide interrogation of diverse biological processes. However, accessibility of NGS data remains a problem, and few user-friendly resources exist for integrative analysis of NGS data from different sources and experimental techniques. Here, we present Online Resource for Integrative Omics (ORIO; https://orio.niehs.nih.gov/), a web-based resource with an intuitive user interface for rapid analysis and integration of NGS data. To use ORIO, the user specifies NGS data of interest along with a list of genomic coordinates. Genomic coordinates may be biologically relevant features from a variety of sources, such as ChIP-seq peaks for a given protein or transcription start sites from known gene models. ORIO first iteratively finds read coverage values at each genomic feature for each NGS dataset. Data are then integrated using clustering-based approaches, giving hierarchical relationships across NGS datasets and separating individual genomic features into groups. In focusing its analysis on read coverage, ORIO makes limited assumptions about the analyzed data; this allows the tool to be applied across data from a variety of experiments and techniques. Results from analysis are presented in dynamic displays alongside user-controlled statistical tests, supporting rapid statistical validation of observed results. We emphasize the versatility of ORIO through diverse examples, ranging from NGS data quality control to characterization of enhancer regions and integration of gene expression information. Easily accessible on a public web server, we anticipate wide use of ORIO in genome-wide investigations by life scientists.