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      Genome-wide association study identifies a gene responsible for temperature-dependent rice germination

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          Abstract

          Environment is an important determinant of agricultural productivity; therefore, crops have been bred with traits adapted to their environment. It is assumed that the physiology of seed germination is optimised for various climatic conditions. Here, to understand the genetic basis underlying seed germination, we conduct a genome-wide association study considering genotype-by-environment interactions on the germination rate of Japanese rice cultivars under different temperature conditions. We find that a 4 bp InDel in one of the 14-3-3 family genes, GF14h, preferentially changes the germination rate of rice under optimum temperature conditions. The GF14h protein constitutes a transcriptional regulatory module with a bZIP-type transcription factor, OREB1, and a florigen-like protein, MOTHER OF FT AND TFL 2, to control the germination rate by regulating abscisic acid (ABA)-responsive genes. The GF14h loss-of-function allele enhances ABA signalling and reduces the germination rate. This allele is found in rice varieties grown in the northern area and in modern cultivars of Japan and China, suggesting that it contributes to the geographical adaptation of rice. This study demonstrates the complicated molecular system involved in the regulation of seed germination in response to temperature, which has allowed rice to be grown in various geographical locations.

          Abstract

          Physiology of seed germination has been optimized for various climatic conditions. Here, the authors report a 14-3-3 protein encoding gene GF14h is responsible for temperature-dependent rice germination by regulating ABA-responsive genes and contributes to the geographical adaptation of rice.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              popart: full-feature software for haplotype network construction

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                Author and article information

                Contributors
                hyoshida@agri.fukushima-u.ac.jp
                matsuoka@agri.fukushima-u.ac.jp
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                29 September 2022
                29 September 2022
                2022
                : 13
                : 5665
                Affiliations
                [1 ]GRID grid.443549.b, ISNI 0000 0001 0603 1148, Institute of Fermentation Sciences, , Fukushima University, ; Fukushima, 960-1248 Japan
                [2 ]GRID grid.27476.30, ISNI 0000 0001 0943 978X, Bioscience and Biotechnology Center, , Nagoya University, ; Aichi, 464-8601 Japan
                [3 ]GRID grid.509456.b, Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, ; Tokyo, 103-0027 Japan
                [4 ]GRID grid.411764.1, ISNI 0000 0001 2106 7990, Graduate School of Agriculture, Meiji University 1-1-1 Higashi-Mita, Tama-ku, ; Kawasaki, Kanagawa 214-8571 Japan
                Author information
                http://orcid.org/0000-0002-7016-6108
                http://orcid.org/0000-0002-8167-7886
                Article
                33318
                10.1038/s41467-022-33318-5
                9523024
                36175401
                cc1b516c-ad2d-4cb6-ac20-4929304fe9cc
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 August 2021
                : 13 September 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: 17J09723
                Award ID: 21K15120
                Award ID: 16H06468
                Award ID: 22H02294
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002770, Cabinet Office, Government of Japan;
                Award ID: JPJ009237
                Award ID: JPJ009237
                Award ID: JPJ009237
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                agricultural genetics,plant breeding,plant genetics,plant domestication
                Uncategorized
                agricultural genetics, plant breeding, plant genetics, plant domestication

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