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      Integrated omics and machine learning-assisted profiling of cysteine-rich-receptor-like kinases from three peanut spp . revealed their role in multiple stresses

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          Abstract

          Arachis hypogaea (peanut) is a leading oil and protein-providing crop with a major food source in many countries. It is mostly grown in tropical regions and is largely affected by abiotic and biotic stresses. Cysteine-rich receptor-like kinases (CRKs) is a family of transmembrane proteins that play important roles in regulating stress-signaling and defense mechanisms, enabling plants to tolerate stress conditions. However, almost no information is available regarding this gene family in Arachis hypogaea and its progenitors. This study conducts a pangenome-wide investigation of A. hypogaea and its two progenitors, A. duranensis and A. ipaensis CRK genes ( AhCRKs, AdCRKs, and AiCRKs). The gene structure, conserved motif patterns, phylogenetic history, chromosomal distribution, and duplication were studied in detail, showing the intraspecies structural conservation and evolutionary patterns. Promoter cis-elements, protein–protein interactions, GO enrichment, and miRNA targets were also predicted, showing their potential functional conservation. Their expression in salt and drought stresses was also comprehensively studied. The CRKs identified were divided into three groups, phylogenetically. The expansion of this gene family in peanuts was caused by both types of duplication: tandem and segmental. Furthermore, positive as well as negative selection pressure directed the duplication process. The peanut CRK genes were also enriched in hormones, light, development, and stress-related elements. MicroRNA (miRNA) also targeted the AhCRK genes, which suggests the regulatory association of miRNAs in the expression of these genes. Transcriptome datasets showed that AhCRKs have varying expression levels under different abiotic stress conditions. Furthermore, the multi-stress responsiveness of the AhCRK genes was evaluated using a machine learning-based method, Random Forest (RF) classifier. The 3D structures of AhCRKs were also predicted. Our study can be utilized in developing a detailed understanding of the stress regulatory mechanisms of the CRK gene family in peanuts and its further studies to improve the genetic makeup of peanuts to thrive better under stress conditions.

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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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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              Highly accurate protein structure prediction with AlphaFold

              Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                20 September 2023
                2023
                : 14
                : 1252020
                Affiliations
                [1] 1 Integrative Omics and Molecular Modeling Laboratory , Department of Bioinformatics and Biotechnology , Government College University Faisalabad (GCUF) , Faisalabad, Pakistan
                [2] 2 State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources , Guangxi Key Laboratory of Sugarcane Biology , College of Agriculture , Guangxi University , Nanning, Guangxi, China
                [3] 3 Department of Pharmacology and Toxicology , College of Pharmacy , King Saud University , Riyadh, Saudi Arabia
                Author notes

                Edited by: Diaa Abd El Moneim, Arish University, Egypt

                Reviewed by: Balpreet Kaur Dhatt, Bayer Crop Science, United States

                Parviz Heidari, Shahrood University of Technology, Iran

                *Correspondence: Muhammad Tahir ul Qamar, tahirulqamar@ 123456gcuf.edu.pk
                Article
                1252020
                10.3389/fgene.2023.1252020
                10547876
                96075dcf-dfc4-4264-b843-dad61bdac3a2
                Copyright © 2023 Fatima, Sadaqat, Azeem, Rao, Albekairi, Alshammari and Tahir ul Qamar.

                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
                : 03 July 2023
                : 05 September 2023
                Funding
                Funded by: King Saud University , doi 10.13039/501100002383;
                Categories
                Genetics
                Original Research
                Custom metadata
                Genomics of Plants and the Phytoecosystem

                Genetics
                peanut,pangenome-wide,receptor-like kinases,gene ontology enrichment,abiotic stress,multi-stress-related genes,random forest

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