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.

13Background: As the number of RNA-seq datasets that become available to explore transcriptome 14 diversity increases, so does the need for easy-to-use comprehensive computational workflows. 15Many available tools facilitate analyses of one of the two major mechanisms of transcriptome 16 diversity, namely, differential expression of isoforms due to alternative splicing, while the second 17 major mechanism -RNA editing due to post-transcriptional changes of individual nucleotides -18 remains under-appreciated. Both these mechanisms play an essential role in physiological and 19 diseases processes, including cancer and neurological disorders. However, elucidation of RNA 20 editing events at transcriptome-wide level requires increasingly complex computational tools, in 21 turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant 22 calling applications on a large scale but lack the manpower and/or computational expertise. 23 24 Results: Here we present an easy-to-use, fully automated, computational pipeline (Automated 25 Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map 26 transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid 27 system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. 28 . The analytical tasks and format conversions are accomplished via a set of automated scripts that 29 enable the user to go from a set of raw data, such as fastq files, to publication-ready results and 30 figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is 31 used to illustrate AIDD's capabilities. 32 33 Conclusions: AIDD pipeline offers a user-friendly interface for comprehensive and reproducible 34 RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, 35 including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such 36 editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA 37 editing enzymes linked with the innate immune system and viral infections. These findings offer 38 insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including 39 those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline 40 enables even a novice user to easily explore common mechanisms of transcriptome diversity, 41 including RNA editing landscapes. 42 43Keywords: high-throughput sequencing, analysis of RNA-seq, transcriptome, editome, RNA editing, isoform, 44 differential expression, sequencing variants, adenosine deaminases acting on RNA (ADAR) 45Background: 47Transcriptome complexity and diversity, including patterns of differential isoform

Parkinson’s Disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s Disease, currently affecting more than 10 million people worldwide and 1.5 times more males than females. The progression of PD results in the loss of function due to neurodegeneration and neuroinflammation. The etiology of PD is multifactorial, including both genetic and environmental origins. Here we explored changes in RNA editing, specifically editing through the actions of the Adenosine Deaminases Acting on RNA (ADARs), in the progression of PD. Analysis of ADAR editing of skeletal muscle transcriptomes from PD patients and controls, including those that engaged in a rehabilitative exercise training program revealed significant differences in ADAR editing patterns based on age, disease status, and following rehabilitative exercise. Further, deleterious editing events in protein coding regions were identified in multiple genes with known associations to PD pathogenesis. Our findings of differential ADAR editing complement findings of changes in transcriptional network identified by a recent Lavin et al. (2020) study and offer insights into dynamic ADAR editing changes associated with PD pathogenesis.