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

Abstract

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.

Alterations in RNA editing in skeletal muscle following exercise training in individuals with Parkinson’s disease

“…Ultimately, while mutations in specific candidate genes have been suggested as culprits in PD pathogenesis, no monogenic targets have been definitively identified, making it more likely that a variety of genetic and environmental influences are at play in the manifestation of the neuroinflammation indicative of PD. ADAR editing, a known factor in the pathology of multiple neurodegenerative and psychiatric disorders [46,88,106], must be acknowledged as a potential role-player in PD pathology. Further research must strive to understand how dynamic changes in ADAR editing may function as a causal agent in PD progression or whether RNA editing dysregulation is simply an outcome of the neuroinflammation and neurodegeneration present in the disease.…”

“…The Automated Isoform Diversity Detector (AIDD) pipeline [88] was used to infer ADAR editing events. Briefly, fastq files of individual patients were trimmed and aligned to the chosen human reference (GRCh37) using HISAT2 [89].…”

“…Once aligned, the transcriptome was assembled using StringTie [90], to estimate levels of individual gene expression as Transcripts Per Kilobase Million (TPM). ADAR editing events were inferred using GATK haplotype caller [91] following the best practices, as described in Plonski et al [88]. VCF files are available at https://doi.org/10.5281/zenodo.7971793.…”

The role of ADAR editing and nonsense-mediated decay in Parkinson’s Disease

“…To create a standardized editing index per sample, we scaled the number of putative ADAR edits by 1,000,000 prior to the calculation of the indices. Variant calling was performed with GATK [73] using best practices described by Plonski et al [69] through which only variants for sites with MAPQ ≥ 40 are called.…”

Reovirus infection induces transcriptome-wide unique A-to-I editing changes in the murine fibroblasts

“…The Automated Isoform Diversity Detector (AIDD) pipeline (Plonski et al, 2020) was used to map the reads, infer ADAR expression, and predict ADAR editing sites (Supplemental_Table_1). DESeq2 R package (Love et al, 2014) was used for differential expression analysis of ADAR genes and transcripts.…”