Cytokinetic furrowing and abscission dynamics during brain development revealed by live imaging

McNeely, Katrina C.; Little, Jessica Neville; Dwyer, Noelle D.

doi:10.1101/529164

Cited by 1 publication

(1 citation statement)

References 61 publications

(69 reference statements)

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“…ZIKV infected K048 cells showed likely editing events at multiple sites, including at two ion channel receptors (namely, GRIA3 and GRIN3B). Other ZIKV-induced editing events were detected at IGFBP7, KIF20B and SRP9 genes, responsible for controlling cellular metabolism, vesicular transport, and proper protein storage and transport respectively [63][64][65][66]. There was also an increased editing detected at the ATXN7 gene that is implicated in degenerative ataxia [67].…”

Section: Mapping Adar Expression and Editing Landscapesmentioning

confidence: 99%

Automated Isoform Diversity Detector (AIDD): a pipeline for investigating transcriptome diversity of RNA-seq data

et al. 2020

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Background As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism—RNA editing due to post-transcriptional changes of individual nucleotides—remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise. Results Here we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities. Conclusions AIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.

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