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      Disentangling the genetic basis of rhizosphere microbiome assembly in tomato

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          Abstract

          Microbiomes play a pivotal role in plant growth and health, but the genetic factors involved in microbiome assembly remain largely elusive. Here, we map the molecular features of the rhizosphere microbiome as quantitative traits of a diverse hybrid population of wild and domesticated tomato. Gene content analysis of prioritized tomato quantitative trait loci suggests a genetic basis for differential recruitment of various rhizobacterial lineages, including a Streptomyces-associated 6.31 Mbp region harboring tomato domestication sweeps and encoding, among others, the iron regulator FIT and the water channel aquaporin SlTIP2.3. Within metagenome-assembled genomes of root-associated Streptomyces and Cellvibrio, we identify bacterial genes involved in metabolism of plant polysaccharides, iron, sulfur, trehalose, and vitamins, whose genetic variation associates with specific tomato QTLs. By integrating ‘microbiomics’ and quantitative plant genetics, we pinpoint putative plant and reciprocal rhizobacterial traits underlying microbiome assembly, thereby providing a first step towards plant-microbiome breeding programs.

          Abstract

          Genetics factors involved in rhizosphere microbiomes assembly remain largely elusive. Here, the authors integrate microbiomics and quantitative plant genetics to reveal genetic loci associated with specific microbes and rhizobacterial traits underlying microbiome assembly in tomato.

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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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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

              SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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                Author and article information

                Contributors
                benoyserman@gmail.com
                j.raaijmakers@nioo.knaw.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 June 2022
                16 June 2022
                2022
                : 13
                : 3228
                Affiliations
                [1 ]GRID grid.418375.c, ISNI 0000 0001 1013 0288, Department of Microbial Ecology, , Netherlands Institute of Ecology, ; Wageningen, The Netherlands
                [2 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Bioinformatics Group, , Wageningen University, ; Wageningen, The Netherlands
                [3 ]GRID grid.5132.5, ISNI 0000 0001 2312 1970, Institute of Biology, , Leiden University, ; Leiden, The Netherlands
                [4 ]GRID grid.418158.1, ISNI 0000 0004 0534 4718, Department of Data Sciences, , Genentech, Inc. South San Francisco, ; South San Francisco, CA USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [6 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Data Sciences Dana Farber Cancer Institute, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [7 ]GRID grid.4818.5, ISNI 0000 0001 0791 5666, Wageningen Seed Lab, Laboratory of Plant Physiology, , Wageningen University, ; Wageningen, The Netherlands
                [8 ]GRID grid.5477.1, ISNI 0000000120346234, Theoretical Biology and Bioinformatics, , Utrecht University, ; Utrecht, The Netherlands
                Author information
                http://orcid.org/0000-0001-9052-2651
                http://orcid.org/0000-0003-0504-6633
                http://orcid.org/0000-0003-4426-0691
                http://orcid.org/0000-0002-3083-954X
                http://orcid.org/0000-0001-6778-6632
                http://orcid.org/0000-0002-7592-2099
                http://orcid.org/0000-0001-8221-7139
                http://orcid.org/0000-0002-1532-6826
                http://orcid.org/0000-0001-5237-1899
                http://orcid.org/0000-0002-4093-0355
                http://orcid.org/0000-0002-0228-169X
                http://orcid.org/0000-0001-5321-2996
                http://orcid.org/0000-0002-2191-2821
                http://orcid.org/0000-0003-1608-6614
                Article
                30849
                10.1038/s41467-022-30849-9
                9203511
                35710629
                f902fa7d-849f-4c1b-9229-0cdd2707cc7a
                © 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
                : 20 December 2021
                : 19 May 2022
                Categories
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                © The Author(s) 2022

                Uncategorized
                microbiome,agricultural genetics,quantitative trait loci,plant genetics
                Uncategorized
                microbiome, agricultural genetics, quantitative trait loci, plant genetics

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