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      Mining for New Sources of Resistance to Powdery Mildew in Genetic Resources of Winter Wheat

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          Abstract

          Genetic pathogen control is an economical and sustainable alternative to the use of chemicals. In order to breed resistant varieties, information about potentially unused genetic resistance mechanisms is of high value. We phenotyped 8,316 genotypes of the winter wheat collection of the German Federal ex situ gene bank for Agricultural and Horticultural Crops, Germany, for resistance to powdery mildew (PM), Blumeria graminis f. sp. tritici, one of the most important biotrophic pathogens in wheat. To achieve this, we used a semi-automatic phenotyping facility to perform high-throughput detached leaf assays. This data set, combined with genotyping-by-sequencing (GBS) marker data, was used to perform a genome-wide association study (GWAS). Alleles of significantly associated markers were compared with SNP profiles of 171 widely grown wheat varieties in Germany to identify currently unexploited resistance conferring genes. We also used the Chinese Spring reference genome annotation and various domain prediction algorithms to perform a domain enrichment analysis and produced a list of candidate genes for further investigation. We identified 51 significantly associated regions. In most of these, the susceptible allele was fixed in the tested commonly grown wheat varieties. Eleven of these were located on chromosomes for which no resistance conferring genes have been previously reported. In addition to enrichment of leucine-rich repeats (LRR), we saw enrichment of several domain types so far not reported as relevant to PM resistance, thus, indicating potentially novel candidate genes for the disease resistance research and prebreeding in wheat.

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          Pfam: The protein families database in 2021

          Abstract The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.
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            MissForest--non-parametric missing value imputation for mixed-type data.

            Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a non-parametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest, we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple datasets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in datasets including different types of variables. In our comparative study, missForest outperforms other methods of imputation especially in data settings where complex interactions and non-linear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data. The package missForest is freely available from http://stat.ethz.ch/CRAN/. stekhoven@stat.math.ethz.ch; buhlmann@stat.math.ethz.ch
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              Shifting the limits in wheat research and breeding using a fully annotated reference genome

              An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage-related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                01 March 2022
                2022
                : 13
                : 836723
                Affiliations
                [1] 1Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) , Seeland, Germany
                [2] 2German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , Leipzig, Germany
                [3] 3Center for Integrated Breeding Research (CiBreed), Georg-August-University , Göttingen, Germany
                Author notes

                Edited by: Christina Cowger, Agricultural Research Service, USDA, United States

                Reviewed by: Raj K. Pasam, AgriBio, La Trobe University, Australia; Hongjie Li, Institute of Crop Sciences (CAAS), China

                *Correspondence: Albert W. Schulthess schulthess@ 123456ipk-gatersleben.de

                This article was submitted to Plant Pathogen Interactions, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.836723
                8922026
                35300015
                1ec618d9-d6b4-43b9-af97-04b872c832f0
                Copyright © 2022 Hinterberger, Douchkov, Lück, Kale, Mascher, Stein, Reif and Schulthess.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 December 2021
                : 31 January 2022
                Page count
                Figures: 5, Tables: 4, Equations: 4, References: 136, Pages: 16, Words: 12697
                Funding
                Funded by: Bundesministerium für Bildung und Forschung, doi 10.13039/501100002347;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                genome-wide association mapping,powdery mildew,wheat,resistance,candidate genes,detached leaf assay,phenotyping,microphenomics

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