scholarly journals RESIC: A tool for comprehensive adenosine to inosine RNA Editing Site Identification and Classification

2021 ◽  
Author(s):  
Dean Light ◽  
Roni Haas ◽  
Mahmoud Yazbak ◽  
Tal Elfand ◽  
Tal Blau ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Rna Editing ◽  
Editing Site ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Double Stranded Rna ◽  
Adenosine Deaminases ◽  
Genomic Regions ◽  
Site Identification

AbstractAdenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here we present RESIC, an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides 1. The detection of editing sites in both repetitive and non-repetitive genomic regions; 2. The identification of hyper-edited regions; 3. Optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable.

scholarly journals RESIC: A Tool for Comprehensive Adenosine to Inosine RNA Editing Site Identification and Classification

2021 ◽  
Vol 12 ◽  
Author(s):  
Dean Light ◽  
Roni Haas ◽  
Mahmoud Yazbak ◽  
Tal Elfand ◽  
Tal Blau ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Rna Editing ◽  
Editing Site ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Double Stranded Rna ◽  
Adenosine Deaminases ◽  
Genomic Regions ◽  
Site Identification

Adenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here, we present RNA Editing Sites Identification and Classification (RESIC), an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides (1) the detection of editing sites in both repetitive and non-repetitive genomic regions; (2) the identification of hyper-edited regions; and (3) optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable.

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

scholarly journals Developmental atlas of the RNA editome in Sus scrofa skeletal muscle

DNA Research ◽  
2019 ◽  
Vol 26 (3) ◽  
pp. 261-272 ◽  
Author(s):  
Yalan Yang ◽  
Min Zhu ◽  
Xinhao Fan ◽  
Yilong Yao ◽  
Junyu Yan ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Rna Editing ◽  
Sus Scrofa ◽  
Muscle Development ◽  
Rna Seq ◽  
Sequencing Data ◽  
Adenosine Deaminases ◽  
Bisulphite Sequencing ◽  
Highly Correlated ◽  
Gain Loss

AbstractAdenosine-to-inosine (A-to-I) RNA editing meditated by adenosine deaminases acting on RNA (ADARs) enzymes is a widespread post-transcriptional event in mammals. However, A-to-I editing in skeletal muscle remains poorly understood. By integrating strand-specific RNA-seq, whole genome bisulphite sequencing, and genome sequencing data, we comprehensively profiled the A-to-I editome in developing skeletal muscles across 27 prenatal and postnatal stages in pig, an important farm animal and biomedical model. We detected 198,892 A-to-I editing sites and found that they occurred more frequently at prenatal stages and showed low conservation among pig, human, and mouse. Both the editing level and frequency decreased during development and were positively correlated with ADAR enzymes expression. The hyper-edited genes were functionally related to the cell cycle and cell division. A co-editing module associated with myogenesis was identified. The developmentally differential editing sites were functionally enriched in genes associated with muscle development, their editing levels were highly correlated with expression of their host mRNAs, and they potentially influenced the gain/loss of miRNA binding sites. Finally, we developed a database to visualize the Sus scrofa RNA editome. Our study presents the first profile of the dynamic A-to-I editome in developing animal skeletal muscle and provides evidences that RNA editing is a vital regulator of myogenesis.

scholarly journals Identifying RNA editing sites using RNA sequencing data alone

2013 ◽  
Vol 10 (2) ◽  
pp. 128-132 ◽  
Author(s):  
Gokul Ramaswami ◽  
Rui Zhang ◽  
Robert Piskol ◽  
Liam P Keegan ◽  
Patricia Deng ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Rna Editing ◽  
Sequencing Data

scholarly journals SNV identification from single-cell RNA sequencing data

2019 ◽  
Vol 28 (21) ◽  
pp. 3569-3583 ◽  
Author(s):  
Patricia M Schnepp ◽  
Mengjie Chen ◽  
Evan T Keller ◽  
Xiang Zhou
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Specific Gene ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Cell Rna Sequencing ◽  
Sequencing Studies ◽  
Genomic Regions

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

Abstract 5592: Molecular classification of triple negative breast cancer via RNA-sequencing data

2014 ◽  
Author(s):  
Kevin J. Thompson ◽  
Xiaojia Tang ◽  
Zhifu Sun ◽  
Jason P. Sinnwell ◽  
Hugues Sicotte ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Rna Sequencing ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Molecular Classification ◽  
Sequencing Data

scholarly journals A-to-I RNA Editing Affects lncRNAs Expression after Heat Shock

Genes ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 627 ◽  
Author(s):  
Roni Haas ◽  
Nabeel Ganem ◽  
Ayya Keshet ◽  
Angela Orlov ◽  
Alla Fishman ◽  
...  
Keyword(s):  
Heat Shock ◽  
Rna Editing ◽  
Rna Structures ◽  
Wild Type ◽  
Double Stranded Rna ◽  
Adenosine Deaminases ◽  
Heat Shock Stress ◽  
Non Coding Rnas ◽  
Upregulated Genes ◽  
Shock Stress

Adenosine to inosine (A-to-I) RNA editing is a highly conserved regulatory process carried out by adenosine-deaminases (ADARs) on double-stranded RNA (dsRNAs). Although a considerable fraction of the transcriptome is edited, the function of most editing sites is unknown. Previous studies indicate changes in A-to-I RNA editing frequencies following exposure to several stress types. However, the overall effect of stress on the expression of ADAR targets is not fully understood. Here, we performed high-throughput RNA sequencing of wild-type and ADAR mutant Caenorhabditis elegans worms after heat-shock to analyze the effect of heat-shock stress on the expression pattern of genes. We found that ADAR regulation following heat-shock does not directly involve heat-shock related genes. Our analysis also revealed that long non-coding RNAs (lncRNAs) and pseudogenes, which have a tendency for secondary RNA structures, are enriched among upregulated genes following heat-shock in ADAR mutant worms. The same group of genes is downregulated in ADAR mutant worms under permissive conditions, which is likely, considering that A-to-I editing protects endogenous dsRNA from RNA-interference (RNAi). Therefore, temperature increases may destabilize dsRNA structures and protect them from RNAi degradation, despite the lack of ADAR function. These findings shed new light on the dynamics of gene expression under heat-shock in relation to ADAR function.

scholarly journals Correction: Detection and classification of peaks in 5' cap RNA sequencing data

BMC Genomics ◽  
2013 ◽  
Vol 14 (1) ◽  
pp. 767
Author(s):  
Dario Strbenac ◽  
Nicola J Armstrong ◽  
Jean YH Yang
Keyword(s):  
Rna Sequencing ◽  
Sequencing Data

scholarly journals Cellular and genetic drivers of RNA editing variation in the human brain

2021 ◽  
Author(s):  
Ryn Cuddleston ◽  
Junhao Li ◽  
Xuanjia Fan ◽  
Alexey Kozenkov ◽  
Matthew Lalli ◽  
...  
Keyword(s):  
Human Brain ◽  
Rna Sequencing ◽  
Rna Editing ◽  
Genetic Regulation ◽  
Cell Types ◽  
Brain Regions ◽  
Brain Cell ◽  
Cell Type ◽  
Sequencing Data ◽  
Regulatory Effects

Posttranscriptional adenosine-to-inosine modifications amplify the functionality of RNA molecules in the brain, yet the cellular and genetic regulation of RNA editing is poorly described. We quantified base-specific RNA editing across three major cell populations from the human prefrontal cortex: glutamatergic neurons, medial ganglionic eminence GABAergic neurons, and oligodendrocytes. We found more selective editing and RNA hyper-editing in neurons relative to oligodendrocytes. The pattern of RNA editing was highly cell type-specific, with 189,229 cell type-associated sites. The cellular specificity for thousands of sites was confirmed by single nucleus RNA-sequencing. Importantly, cell type-associated sites were enriched in GTEx RNA-sequencing data, edited ~twentyfold higher than all other sites, and variation in RNA editing was predominantly explained by neuronal proportions in bulk brain tissue. Finally, we discovered 661,791 cis-editing quantitative trait loci across thirteen brain regions, including hundreds with cell type-associated features. These data reveal an expansive repertoire of highly regulated RNA editing sites across human brain cell types and provide a resolved atlas linking cell types to editing variation and genetic regulatory effects.

scholarly journals RNA editing alterations in a multi-ethnic Alzheimer disease cohort converge on immune and endocytic molecular pathways

2019 ◽  
Vol 28 (18) ◽  
pp. 3053-3061 ◽  
Author(s):  
Olivia K Gardner ◽  
Lily Wang ◽  
Derek Van Booven ◽  
Patrice L Whitehead ◽  
Kara L Hamilton-Nelson ◽  
...  
Keyword(s):  
Alzheimer Disease ◽  
Rna Sequencing ◽  
Rna Editing ◽  
Neuronal Development ◽  
Molecular Pathways ◽  
Sequencing Data ◽  
Transcriptional Regulatory ◽  
Blood Specimens ◽  
Whole Transcriptome ◽  
Hispanic White

AbstractLittle is known about the post-transcriptional mechanisms that modulate the genetic effects in the molecular pathways underlying Alzheimer disease (AD), and even less is known about how these changes might differ across diverse populations. RNA editing, the process that alters individual bases of RNA, may contribute to AD pathogenesis due to its roles in neuronal development and immune regulation. Here, we pursued one of the first transcriptome-wide RNA editing studies in AD by examining RNA sequencing data from individuals of both African-American (AA) and non-Hispanic White (NHW) ethnicities. Whole transcriptome RNA sequencing and RNA editing analysis were performed on peripheral blood specimens from 216 AD cases (105 AA, 111 NHW) and 212 gender matched controls (105 AA, 107 NHW). 449 positions in 254 genes and 723 positions in 371 genes were differentially edited in AA and NHW, respectively. While most differentially edited sites localized to different genes in AA and NHW populations, these events converged on the same pathways across both ethnicities, especially endocytic and inflammatory response pathways. Furthermore, these differentially edited sites were preferentially predicted to disrupt miRNA binding and induce nonsynonymous coding changes in genes previously associated with AD in molecular studies, including PAFAH1B2 and HNRNPA1. These findings suggest RNA editing is an important post-transcriptional regulatory program in AD pathogenesis.

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