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Reilly, Steven

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Reilly, Steven
Author's Publications: search.filters.isAuthorOfPublication.f592f18c-0c69-4396-8185-3fdb450577d3

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    Direct Characterization of Cis-Regulatory Elements and Functional Dissection of Complex Genetic Associations Using HCR–FlowFISH
    (Springer Science and Business Media LLC, 2021-07-29) Reilly, Steven; Gosai, Sager J.; Gutierrez, Alan; Mackay-Smith, Ava; Ulirsch, Jacob; Kanai, Masahiro; Mouri, Kousuke; Berenzy, Daniel; Kales, Susan; Butler, Gina M.; Gladden-Young, Adrianne; Bhuiyan, Redwan M.; Stitzel, Michael L.; Finucane, Hilary K.; Sabeti, Pardis; Tewhey, Ryan
    Abstract: Effective interpretation of genome function and genetic variation requires a shift from epigenetic mapping of cis-regulatory elements (CREs) to characterization of endogenous function. We developed HCR-FlowFISH, a broadly applicable approach to characterize CRISPR-perturbed CREs via accurate quantification of native transcripts, alongside CASA (CRISPR Activity Screen Analysis), a hierarchical Bayesian model to quantify CRE activity. Across >325,000 perturbations, we provide evidence that CREs can regulate multiple genes, skip over the nearest gene, and can display activating and/or silencing effects. At the cholesterol-level associated FADS locus, we combine endogenous screens with reporter assays to exhaustively characterize multiple genome-wide association signals, functionally nominating causal variants and identifying their target genes.
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    Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows
    (Springer Science and Business Media LLC, 2021-12-14) Lagerborg, Kim A; Normandin, Erica; Bauer, Matthew; Adams, Gordon; Figueroa, Katherine; Loreth, Christine; Gladden-Young, Adrianne; Shaw, Bennett; Pearlman, Leah; Berenzy, Daniel; Dewey, Hannah; Kales, Susan; Dobbins, Sabrina; Seiguer Shenoy, Erica; Hooper, David; Pierce, Virginia; Zachary, Kimon; Park, Daniel; Macinnis, Bronwyn; Tewhey, Ryan; Lemieux, Jacob; Sabeti, Pardis; Reilly, Steven; Siddle, Katherine
    The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will only increase with expanding global production of sequences by diverse laboratories for epidemiological and clinical interpretation, as well in genomic surveillance in future outbreaks. We present SDSI+AmpSeq, an approach which uses synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination through the sequencing workflow. Applying SDSIs to the ARTIC Consortium’s amplicon design, we demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and across thousands of diagnostic samples at multiple laboratories. We establish that SDSI+AmpSeq provides increased confidence in genomic data by detecting and in some cases correcting for relatively common, yet previously unobserved modes of error without impacting genome recovery.