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      Genome resources for three modern cotton lines guide future breeding efforts

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      1 , 2 , , 1 , 2 , 1 , 3 , 1 , 1 , 4 , 4 , 2 , 2 , 2 , 5 , 5 , 6 , 7 , 8 , 9 , 16 , 10 , 6 , 11 , 11 , 1 , 1 , 1 , 12 , 5 , 13 , 14 , 4 , 1 , 3 , 15 , 1 , 2 ,
      Nature Plants
      Nature Publishing Group UK
      Plant breeding, Plant genetics

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          Abstract

          Cotton ( Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (‘UGA230’, ‘UA48’ and ‘CSX8308’) and updating the ‘TM-1’ cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from ‘Pima’ cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

          Abstract

          Analyses of three newly sequenced modern cultivar cotton genomes revealed sequence and structural variation alongside traces of ancient and ongoing introgressions. Moreover, transcriptome analysis pointed at unique fibre quality traits of cultivars.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Contributors
                asreedasyam@hudsonalpha.org
                jschmutz@hudsonalpha.org
                Journal
                Nat Plants
                Nat Plants
                Nature Plants
                Nature Publishing Group UK (London )
                2055-0278
                30 May 2024
                30 May 2024
                2024
                : 10
                : 6
                : 1039-1051
                Affiliations
                [1 ]Genome Sequencing Center, HudsonAlpha Institute for Biotechnology, ( https://ror.org/04nz0wq19) Huntsville, AL USA
                [2 ]DOE Joint Genome Institute, ( https://ror.org/04xm1d337) Berkeley, CA USA
                [3 ]Department of Crop and Soil Sciences and Institute of Plant Breeding, Genetics, and Genomics, University of Georgia, ( https://ror.org/02bjhwk41) Tifton, GA USA
                [4 ]Department of Plant and Environmental Sciences, Clemson University, ( https://ror.org/037s24f05) Clemson, SC USA
                [5 ]Department of Molecular Biosciences, The University of Texas at Austin, ( https://ror.org/00hj54h04) Austin, TX USA
                [6 ]School of Plant Sciences, University of Arizona, ( https://ror.org/03m2x1q45) Tucson, AZ USA
                [7 ]GRID grid.508985.9, USDA-ARS, , Coastal Plains Soil Water and Plant Research Center, ; Florence, SC USA
                [8 ]Department of Crop, Soil and Environmental Sciences, Auburn University, ( https://ror.org/02v80fc35) Auburn, AL USA
                [9 ]GRID grid.264756.4, ISNI 0000 0004 4687 2082, Texas A&M AgriLife Research, ; Lubbock, TX USA
                [10 ]GRID grid.508985.9, USDA-ARS, , Crop Genetics Research Unit, ; Stoneville, MS USA
                [11 ]USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS USA
                [12 ]GRID grid.512846.c, ISNI 0000 0004 0616 2502, USDA-ARS, , Crop Germplasm Research Unit, ; College Station, TX USA
                [13 ]GRID grid.411017.2, ISNI 0000 0001 2151 0999, Northeast Research and Extension Center (NEREC), , University of Arkansas, ; Keiser, AR USA
                [14 ]CSIRO Agriculture and Food Cotton Research Unit, Narrabri, New South Wales Australia
                [15 ]GRID grid.453294.d, ISNI 0000 0004 0386 404X, Agriculture and Environmental Research Cotton Incorporated, ; Cary, NC USA
                [16 ]Present Address: Pee Dee Research and Education Center, Clemson University, ( https://ror.org/037s24f05) Florence, SC USA
                Author information
                http://orcid.org/0000-0001-7336-7012
                http://orcid.org/0000-0002-8938-1166
                http://orcid.org/0000-0002-3837-6121
                http://orcid.org/0000-0002-3342-0381
                http://orcid.org/0000-0002-7943-3997
                http://orcid.org/0000-0002-0109-5174
                http://orcid.org/0009-0001-0776-5917
                http://orcid.org/0000-0002-4336-8994
                http://orcid.org/0000-0001-6287-2697
                http://orcid.org/0000-0001-9523-259X
                http://orcid.org/0000-0001-9401-4494
                http://orcid.org/0000-0001-8895-5757
                http://orcid.org/0000-0003-0285-2189
                http://orcid.org/0000-0002-8292-2388
                http://orcid.org/0000-0002-3586-8411
                http://orcid.org/0000-0002-3907-8265
                http://orcid.org/0009-0005-6155-6218
                http://orcid.org/0000-0003-0978-4764
                http://orcid.org/0000-0001-5006-8036
                http://orcid.org/0000-0002-2780-4274
                http://orcid.org/0000-0002-8356-8325
                http://orcid.org/0000-0001-8391-004X
                http://orcid.org/0000-0001-8062-9172
                Article
                1713
                10.1038/s41477-024-01713-z
                11208153
                38816498
                ea1110de-a8e1-402a-bbb5-e05fba52ba50
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 October 2023
                : 27 April 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100006481, Cotton Incorporated (Cotton Inc.);
                Award ID: 18-753
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100005825, United States Department of Agriculture | National Institute of Food and Agriculture (NIFA);
                Funded by: NSF-PGRP (2102120) Cotton Incorporated (20-720)
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: IOS1739092
                Award ID: IOS1444552
                Award ID: IOS1739092
                Award Recipient :
                Funded by: Cotton Incorporated (20-799)
                Funded by: Cotton Incorporated (19-860)
                Funded by: Cotton Incorporated (18-753)
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                © Springer Nature Limited 2024

                plant breeding,plant genetics
                plant breeding, plant genetics

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