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      The identification of the Rosa S-locus provides new insights into the breeding and wild origins of continuous-flowering roses

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          Abstract

          This study aims to: (i) identify the Rosa S-locus controlling self-incompatibility (SI); (ii) test the genetic linkage of the S-locus with other loci controlling important ornamental traits, such as the continuous-flowering (CF) characteristic; (iii) identify the S-alleles ( S C ) of old Chinese CF cultivars (e.g, Old Blush, Slater’s Crimson China) and examine the changes in the frequency of cultivars with Sc through the history of breeding; (iv) identify wild species carrying the Sc-alleles to infer wild origins of CF cultivars. We identified a new S- RNase ( S C2 ) of Rosa chinensis in a contig from a genome database that has not been integrated into one of the seven chromosomes yet. Genetic mapping indicated that S C2 is allelic to the previously-identified S-RNase ( S C1 ) in chromosome 3. Pollination experiments with half-compatible pairs of roses confirmed that they are the pistil-determinant of SI. The segregation analysis of an F 1 -population indicated genetic linkage between the S-locus and the floral repressor gene KSN. The non-functional allele ksn is responsible for the CF characteristic. A total of five S-alleles ( S C1–5 ) were identified from old CF cultivars. The frequency of cultivars with S C dramatically increased after the introgression of ksn from Chinese to European cultivars and remains high (80%) in modern cultivars, suggesting that S-genotyping is helpful for effective breeding. Wild individuals carrying S C were found in Rosa multiflora ( S C1 ), Rosa chinensis var . spontanea ( S C3 ), and Rosa gigantea ( S C2 , S C4 ), supporting the hypothesis of hybrid origins of CF cultivars and providing a new evidence for the involvement of Rosa multiflora.

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          FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

          Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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            FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix

            Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N 2) space and O(N 2 L) time, but FastTree requires just O(NLa + N ) memory and O(N log (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
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              How to usefully compare homologous plant genes and chromosomes as DNA sequences.

              There are four sequenced and publicly available plant genomes to date. With many more slated for completion, one challenge will be to use comparative genomic methods to detect novel evolutionary patterns in plant genomes. This research requires sequence alignment algorithms to detect regions of similarity within and among genomes. However, different alignment algorithms are optimized for identifying different types of homologous sequences. This review focuses on plant genome evolution and provides a tutorial for using several sequence alignment algorithms and visualization tools to detect useful patterns of conservation: conserved non-coding sequences, false positive noise, subfunctionalization, synteny, annotation errors, inversions and local duplications. Our tutorial encourages the reader to experiment online with the reviewed tools as a companion to the text.
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                Author and article information

                Contributors
                Journal
                Hortic Res
                Hortic Res
                hr
                Horticulture Research
                Oxford University Press
                2662-6810
                2052-7276
                2022
                28 February 2022
                28 February 2022
                : 9
                : uhac155
                Affiliations
                Department of Environmental Engineering, Osaka Institute of Technology , Japan
                Gifu International Academy of Horticulture , Japan
                Gifu World Rose Garden , Japan
                Graduate School of Bioagricultural Sciences, Nagoya University , Japan
                Graduate School of Bioagricultural Sciences, Nagoya University , Japan
                College of Bioscience and Biotechnology, Chubu University , Japan
                Graduate School of Bioagricultural Sciences, Nagoya University , Japan
                College of Life Sciences, Sichuan University , China
                Jiangsu Provincial Department of Agriculture and Rural Affairs , China
                Agricultural University of Nanjing , China
                Univ Angers , INRAE, Institut Agro, IRHS, SFR QUASAV, F-49000 Angers, France
                Univ Angers , INRAE, Institut Agro, IRHS, SFR QUASAV, F-49000 Angers, France
                Leibniz Universität , Hannover, Germany
                Leibniz Universität , Hannover, Germany
                Author notes
                Corresponding author. E-mail: koji.kawamura@ 123456oit.ac.jp
                Article
                uhac155
                10.1093/hr/uhac155
                9527601
                36196069
                56cc146c-d521-415f-b1d1-0bebee8bc9ab
                © The Author(s) 2022. Published by Oxford University Press on behalf of Nanjing Agricultural University

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 October 2022
                : 3 July 2022
                : 01 October 2022
                Page count
                Pages: 15
                Categories
                AcademicSubjects/SCI01210
                AcademicSubjects/SCI01140
                Article

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