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      Quantitative trait loci mapping reveals important genomic regions controlling root architecture and shoot biomass under nitrogen, phosphorus, and potassium stress in rapeseed ( Brassica napus L.)

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          Abstract

          Plants rely on root systems for nutrient uptake from soils. Marker-assisted selection helps breeders to select desirable root traits for effective nutrient uptake. Here, 12 root and biomass traits were investigated at the seedling stage under low nitrogen (LN), low phosphorus (LP), and low potassium (LK) conditions, respectively, in a recombinant inbred line (RIL) population, which was generated from Brassica napus L. Zhongshuang11 and 4D122 with significant differences in root traits and nutrient efficiency. Significant differences for all the investigated traits were observed among RILs, with high heritabilities (0.43–0.74) and high correlations between the different treatments. Quantitative trait loci (QTL) mapping identified 57, 27, and 36 loci, explaining 4.1–10.9, 4.6–10.8, and 4.9–17.4% phenotypic variances under LN, LP, and LK, respectively. Through QTL-meta analysis, these loci were integrated into 18 significant QTL clusters. Four major QTL clusters involved 25 QTLs that could be repeatedly detected and explained more than 10% phenotypic variances, including two NPK-common and two specific QTL clusters (K and NK-specific), indicating their critical role in cooperative nutrients uptake of N, P, and K. Moreover, 264 genes within the four major QTL clusters having high expressions in roots and SNP/InDel variations between two parents were identified as potential candidate genes. Thirty-eight of them have been reported to be associated with root growth and development and/or nutrient stress tolerance. These key loci and candidate genes lay the foundation for deeper dissection of the NPK starvation response mechanisms in B. napus.

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          The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

          Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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            Empirical threshold values for quantitative trait mapping.

            The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.
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              Protein-Protein Interaction Detection: Methods and Analysis

              Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.
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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
                12 September 2022
                2022
                : 13
                : 994666
                Affiliations
                [1] 1Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Ministry of Agriculture , Wuhan, China
                [2] 2Hubei Hongshan Laboratory , Wuhan, China
                Author notes

                Edited by: Sajid Fiaz, The University of Haripur, Pakistan

                Reviewed by: Fang Hui, Nantong University, China; Nian Wang, Huazhong Agricultural University, China; Hong An, University of Missouri, United States; Hongbo Chao, Zhengzhou University, China; Chengming Sun, Jiangsu Academy of Agricultural Sciences (JAAS), China; Kunjiang Yu, Guizhou University, China

                *Correspondence: Hanzhong Wang, wanghz@ 123456oilcrops.cn

                These authors have contributed equally to this work

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

                Article
                10.3389/fpls.2022.994666
                9511887
                7d2253d6-cbac-43c0-baeb-d525e09bc9f0
                Copyright © 2022 Ahmad, Ibrahim, Tian, Kuang, Wang, Wang and Dun.

                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 July 2022
                : 15 August 2022
                Page count
                Figures: 5, Tables: 4, Equations: 0, References: 101, Pages: 16, Words: 10697
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Funded by: Agricultural Science and Technology Innovation Program, doi 10.13039/501100012421;
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                brassica napus l.,nutrient uptake,candidate genes,major qtl,qtl mapping
                Plant science & Botany
                brassica napus l., nutrient uptake, candidate genes, major qtl, qtl mapping

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