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      A comparative analysis of ADAR mutant mice reveals site-specific regulation of RNA editing

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          Abstract

          Adenosine-to-inosine RNA editing is an essential post-transcriptional modification catalyzed by adenosine deaminase acting on RNA (ADAR)1 and ADAR2 in mammals. For numerous sites in coding sequences (CDS) and microRNAs, editing is highly conserved and has significant biological consequences, for example, by altering amino acid residues and target recognition. However, no comprehensive and quantitative studies have been undertaken to determine how specific ADARs contribute to conserved sites in vivo. Here, we amplified each RNA region with editing site(s) separately and combined these for deep sequencing. Then, we compared the editing ratios of all sites that were conserved in CDS and microRNAs in the cerebral cortex and spleen of wild-type mice, Adar1 E861A/E861AIfih −/− mice expressing inactive ADAR1 (Adar1 KI) and Adar2 −/−Gria2 R/R (Adar2 KO) mice. We found that most of the sites showed a preference for one ADAR. In contrast, some sites, such as miR-3099-3p, showed no ADAR preference. In addition, we found that the editing ratio for several sites, such as DACT3 R/G, was up-regulated in either Adar mutant mouse strain, whereas a coordinated interplay between ADAR1 and ADAR2 was required for the efficient editing of specific sites, such as the 5-HT 2CR B site. We further created double mutant Adar1 KI Adar2 KO mice and observed viable and fertile animals with the complete absence of editing, demonstrating that ADAR1 and ADAR2 are the sole enzymes responsible for all editing sites in vivo. Collectively, these findings indicate that editing is regulated in a site-specific manner by the different interplay between ADAR1 and ADAR2.

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          HISAT: a fast spliced aligner with low memory requirements.

          HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Journal
                RNA
                RNA
                RNA
                RNA
                Cold Spring Harbor Laboratory Press
                1355-8382
                1469-9001
                April 2020
                : 26
                : 4
                : 454-469
                Affiliations
                Department of RNA Biology and Neuroscience, Graduate School of Medicine, Osaka University, Suita, Osaka 565-0871, Japan
                Author notes
                Author information
                http://orcid.org/0000-0002-2629-9405
                Article
                9509184 RA
                10.1261/rna.072728.119
                7075269
                31941663
                bea08fc9-56e8-4a7e-b51c-9f75b2d94d15
                © 2020 Costa Cruz et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society

                This article is distributed exclusively by the RNA Society for the first 12 months after the full-issue publication date (see http://rnajournal.cshlp.org/site/misc/terms.xhtml). After 12 months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 26 July 2019
                : 9 January 2020
                Page count
                Pages: 16
                Funding
                Funded by: KAKENHI
                Award ID: 17K19352
                Award ID: 19K22580
                Award ID: 18K15186
                Award ID: 15K19126
                Award ID: 18K11526
                Award ID: 15K00401
                Funded by: Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan
                Funded by: SENSHIN Medical Research Foundation , open-funder-registry 10.13039/501100008667;
                Funded by: Mochida Memorial Foundation for Medical and Pharmaceutical Research , open-funder-registry 10.13039/501100005865;
                Funded by: Nagao Memorial Fund
                Funded by: Takeda Science Foundation , open-funder-registry 10.13039/100007449;
                Funded by: MEXT scholarship
                Categories
                Article

                adar1,adar2,coding sequence,microrna,post-transcriptional modification

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