Browsing by Author "Snellings, Daniel A"

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    Adaptive sequence divergence forged new neurodevelopmental enhancers in humans.
    (Cell, 2022-11) Mangan, Riley J; Alsina, Fernando C; Mosti, Federica; Sotelo-Fonseca, Jesús Emiliano; Snellings, Daniel A; Au, Eric H; Carvalho, Juliana; Sathyan, Laya; Johnson, Graham D; Reddy, Timothy E; Silver, Debra L; Lowe, Craig B
    Searches for the genetic underpinnings of uniquely human traits have focused on human-specific divergence in conserved genomic regions, which reflects adaptive modifications of existing functional elements. However, the study of conserved regions excludes functional elements that descended from previously neutral regions. Here, we demonstrate that the fastest-evolved regions of the human genome, which we term "human ancestor quickly evolved regions" (HAQERs), rapidly diverged in an episodic burst of directional positive selection prior to the human-Neanderthal split, before transitioning to constraint within hominins. HAQERs are enriched for bivalent chromatin states, particularly in gastrointestinal and neurodevelopmental tissues, and genetic variants linked to neurodevelopmental disease. We developed a multiplex, single-cell in vivo enhancer assay to discover that rapid sequence divergence in HAQERs generated hominin-unique enhancers in the developing cerebral cortex. We propose that a lack of pleiotropic constraints and elevated mutation rates poised HAQERs for rapid adaptation and subsequent susceptibility to disease.
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    Gonomics: Uniting high performance and readability for genomics with Go.
    (Bioinformatics (Oxford, England), 2023-08) Au, Eric H; Fauci, Christiana; Luo, Yanting; Mangan, Riley J; Snellings, Daniel A; Shoben, Chelsea R; Weaver, Seth; Simpson, Shae K; Lowe, Craig B
    Many existing software libraries for genomics require researchers to pick between competing considerations: the performance of compiled languages and the accessibility of interpreted languages. Go, a modern compiled language, provides an opportunity to address this conflict. We introduce Gonomics, an open-source collection of command line programs and bioinformatic libraries implemented in Go that unites readability and performance for genomic analyses. Gonomics contains packages to read, write, and manipulate a wide array of file formats (e.g. FASTA, FASTQ, BED, BEDPE, SAM, BAM, and VCF), and can convert and interface between these formats. Furthermore, our modular library structure provides a flexible platform for researchers developing their own software tools to address specific questions. These commands can be combined and incorporated into complex pipelines to meet the growing need for high-performance bioinformatic resources. Gonomics is implemented in the Go programming language. Source code, installation instructions, and documentation are freely available at https://github.com/vertgenlab/gonomics.