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      Underwater CAM photosynthesis elucidated by Isoetes genome

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          Abstract

          To conserve water in arid environments, numerous plant lineages have independently evolved Crassulacean Acid Metabolism (CAM). Interestingly, Isoetes, an aquatic lycophyte, can also perform CAM as an adaptation to low CO 2 availability underwater. However, little is known about the evolution of CAM in aquatic plants and the lack of genomic data has hindered comparison between aquatic and terrestrial CAM. Here, we investigate underwater CAM in Isoetes taiwanensis by generating a high-quality genome assembly and RNA-seq time course. Despite broad similarities between CAM in Isoetes and terrestrial angiosperms, we identify several key differences. Notably, Isoetes may have recruited the lesser-known ‘bacterial-type’ PEPC, along with the ‘plant-type’ exclusively used in other CAM and C4 plants for carboxylation of PEP. Furthermore, we find that circadian control of key CAM pathway genes has diverged considerably in Isoetes relative to flowering plants. This suggests the existence of more evolutionary paths to CAM than previously recognized.

          Abstract

          Despite extensive characterization of crassulacean acid metabolism (CAM) in terrestrial angiosperms, little attention has been given to aquatics and early diverging land plants. Here, the authors assemble the genome of Isoetes taiwanensis and investigate the genetic factors driving CAM in this aquatic lycophyte.

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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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                tmichael@salk.edu
                fl329@cornell.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 November 2021
                3 November 2021
                2021
                : 12
                : 6348
                Affiliations
                [1 ]GRID grid.5386.8, ISNI 000000041936877X, Plant Biology Section, School of Integrative Plant Science, , Cornell University, ; Ithaca, NY USA
                [2 ]GRID grid.5386.8, ISNI 000000041936877X, Boyce Thompson Institute, ; Ithaca, NY USA
                [3 ]GRID grid.38348.34, ISNI 0000 0004 0532 0580, Institute of Molecular & Cellular Biology, , National Tsing Hua University, ; Hsinchu, Taiwan
                [4 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Department of Applied Bioinformatics, Institute for Microbiology and Genetics, , University of Goettingen, ; Goettingen, Germany
                [5 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Campus Institute Data Science, , University of Goettingen, ; Goettingen, Germany
                [6 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Department of Applied Bioinformatics, Goettingen Center for Molecular Biosciences, , University of Goettingen, ; Goettingen, Germany
                [7 ]GRID grid.410768.c, ISNI 0000 0000 9220 4043, Taiwan Forestry Research Institute, ; Taipei, Taiwan
                [8 ]GRID grid.89336.37, ISNI 0000 0004 1936 9924, Department of Integrative Biology, , University of Texas at Austin, ; Austin, TX USA
                [9 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, Department of Ecology and Evolutionary Biology, , University of Arizona, ; Tucson, AZ USA
                [10 ]GRID grid.250671.7, ISNI 0000 0001 0662 7144, The Molecular and Cellular Biology Laboratory, , The Salk Institute for Biological Studies, ; La Jolla, CA USA
                Author information
                http://orcid.org/0000-0002-3388-3757
                http://orcid.org/0000-0001-7649-2388
                http://orcid.org/0000-0002-5267-8935
                http://orcid.org/0000-0003-3507-5195
                http://orcid.org/0000-0001-6894-9616
                http://orcid.org/0000-0001-7173-1319
                http://orcid.org/0000-0001-7916-7791
                http://orcid.org/0000-0001-6272-2875
                http://orcid.org/0000-0002-0076-0152
                Article
                26644
                10.1038/s41467-021-26644-7
                8566536
                34732722
                46e0526b-5271-4385-bc20-c69f292b7371
                © The Author(s) 2021

                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
                : 22 June 2021
                : 12 October 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                evolutionary genetics,photosynthesis,genome evolution,genome
                Uncategorized
                evolutionary genetics, photosynthesis, genome evolution, genome

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