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      Comparative genomics and bioinformatics approaches revealed the role of CC-NBS-LRR genes under multiple stresses in passion fruit

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          Abstract

          Passion fruit is widely cultivated in tropical, subtropical regions of the world. The attack of bacterial and fungal diseases, and environmental factors heavily affect the yield and productivity of the passion fruit. The CC-NBS-LRR (CNL) gene family being a subclass of R-genes protects the plant against the attack of pathogens and plays a major role in effector-triggered immunity (ETI). However, no information is available regarding this gene family in passion fruit. To address the underlying problem a total of 25 and 21 CNL genes have been identified in the genome of purple ( Passiflora edulis Sims.) and yellow ( Passiflora edulis f. flavicarpa) passion fruit respectively. Phylogenetic tree was divided into four groups with PeCNLs present in 3 groups only. Gene structure analysis revealed that number of exons ranged from 1 to 9 with 1 being most common. Most of the PeCNL genes were clustered at the chromosome 3 and underwent strong purifying selection, expanded through segmental (17 gene pairs) and tandem duplications (17 gene pairs). PeCNL genes contained cis-elements involved in plant growth, hormones, and stress response. Transcriptome data indicated that PeCNL3, PeCNL13, and PeCNL14 were found to be differentially expressed under Cucumber mosaic virus and cold stress. Three genes were validated to be multi-stress responsive by applying Random Forest model of machine learning. To comprehend the biological functions of PeCNL proteins, their 3D structure and gene ontology (GO) enrichment analysis were done. Our research analyzed the CNL gene family in passion fruit to understand stress regulation and improve resilience. This study lays the groundwork for future investigations aimed at enhancing the genetic composition of passion fruit to ensure robust growth and productivity in challenging environments.

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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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            MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.

            We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
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                URI : https://loop.frontiersin.org/people/2525502/overviewRole: Role: Role: Role: Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/350282/overviewRole: Role: Role: Role: Role: Role: Role: Role:
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                26 February 2024
                2024
                : 15
                : 1358134
                Affiliations
                [1] 1 Integrative Omics and Molecular Modeling Laboratory , Department of Bioinformatics and Biotechnology , Government College University Faisalabad (GCUF) , Faisalabad, Pakistan
                [2] 2 College of Horticulture , Shanxi Agricultural University , Taigu, Shanxi, China
                [3] 3 Department of Pharmacology and Toxicology , College of Pharmacy , King Saud University , Riyadh, Saudi Arabia
                Author notes

                Edited by: Sajid Shokat, International Atomic Energy Agency, Austria

                Reviewed by: Vikender Kaur, Indian Council of Agricultural Research (ICAR), India

                Parviz Heidari, Shahrood University of Technology, Iran

                *Correspondence: Muhammad Tahir ul Qamar, tahirulqamar@ 123456gcuf.edu.pk
                Article
                1358134
                10.3389/fgene.2024.1358134
                10929019
                38476402
                838daf5e-6dd6-4597-8a0b-a9cba22f159e
                Copyright © 2024 Zia, Sadaqat, Ding, Fatima, 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
                : 19 December 2023
                : 16 February 2024
                Funding
                The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
                Categories
                Genetics
                Original Research
                Custom metadata
                Genomics of Plants and the Phytoecosystem

                Genetics
                passion fruit,cnl,pathogen resistance,gene ontology,expression profiling,machine learning
                Genetics
                passion fruit, cnl, pathogen resistance, gene ontology, expression profiling, machine learning

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