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      Differential RNA Editing and Intron Splicing in Soybean Mitochondria during Nodulation

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          Abstract

          Nitrogen fixation in soybean consumes a tremendous amount of energy, leading to substantial differences in energy metabolism and mitochondrial activities between nodules and uninoculated roots. While C-to-U RNA editing and intron splicing of mitochondrial transcripts are common in plant species, their roles in relation to nodule functions are still elusive. In this study, we performed RNA-seq to compare transcript profiles and RNA editing of mitochondrial genes in soybean nodules and roots. A total of 631 RNA editing sites were identified on mitochondrial transcripts, with 12% or 74 sites differentially edited among the transcripts isolated from nodules, stripped roots, and uninoculated roots. Eight out of these 74 differentially edited sites are located on the matR transcript, of which the degrees of RNA editing were the highest in the nodule sample. The degree of mitochondrial intron splicing was also examined. The splicing efficiencies of several introns in nodules and stripped roots were higher than in uninoculated roots. These include nad1 introns 2/3/4, nad4 intron 3, nad5 introns 2/3, cox2 intron 1, and ccmFc intron 1. A greater splicing efficiency of nad4 intron 1, a higher NAD4 protein abundance, and a reduction in supercomplex I + III 2 were also observed in nodules, although the causal relationship between these observations requires further investigation.

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          Most cited references60

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

            Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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              A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

              Heng Li (2011)
              Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                09 December 2020
                December 2020
                : 21
                : 24
                : 9378
                Affiliations
                [1 ]School of Biological Sciences, University of Hong Kong, Pokfulam, Hong Kong, China; yzsun@ 123456connect.hku.hk (Y.S.); JodieXu85@ 123456hotmail.com (Z.X.); mikchankc@ 123456gmail.com (K.C.C.); zjiaer@ 123456gmail.com (J.Y.Z.)
                [2 ]Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong, China; feixue1039@ 123456gmail.com (M.X.); kejing68164614@ 123456gmail.com (K.F.); johannawh.wong@ 123456gmail.com (J.W.-B.)
                [3 ]School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong, China
                Author notes
                [* ]Correspondence: honming@ 123456cuhk.edu.hk (H.-M.L.); bllim@ 123456hku.hk (B.L.L.); Tel.: +852-3943-6336 (H.-M.L.); +852-2299-0826 (B.L.L.); Fax: +852-2603-6382 (H.-M.L.); +852-2559-9114 (B.L.L.)
                Author information
                https://orcid.org/0000-0002-5652-5039
                https://orcid.org/0000-0002-6673-8740
                https://orcid.org/0000-0002-2720-2353
                Article
                ijms-21-09378
                10.3390/ijms21249378
                7764374
                33317061
                3fb3e076-bac7-45db-8574-4d7ad615415d
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 08 November 2020
                : 07 December 2020
                Categories
                Article

                Molecular biology
                complex i,intron splicing,maturase,ndh,rna editing,mitochondria
                Molecular biology
                complex i, intron splicing, maturase, ndh, rna editing, mitochondria

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