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      Origins of lineage‐specific elements via gene duplication, relocation, and regional rearrangement in Neurospora crassa

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          Abstract

          The origin of new genes has long been a central interest of evolutionary biologists. However, their novelty means that they evade reconstruction by the classical tools of evolutionary modelling. This evasion of deep ancestral investigation necessitates intensive study of model species within well‐sampled, recently diversified, clades. One such clade is the model genus Neurospora, members of which lack recent gene duplications. Several Neurospora species are comprehensively characterized organisms apt for studying the evolution of lineage‐specific genes (LSGs). Using gene synteny, we documented that 78% of Neurospora LSG clusters are located adjacent to the telomeres featuring extensive tracts of non‐coding DNA and duplicated genes. Here, we report several instances of LSGs that are likely from regional rearrangements and potentially from gene rebirth. To broadly investigate the functions of LSGs, we assembled transcriptomics data from 68 experimental data points and identified co‐regulatory modules using Weighted Gene Correlation Network Analysis, revealing that LSGs are widely but peripherally involved in known regulatory machinery for diverse functions. The ancestral status of the LSG mas‐1, a gene with roles in cell‐wall integrity and cellular sensitivity to antifungal toxins, was investigated in detail alongside its genomic neighbours, indicating that it arose from an ancient lysophospholipase precursor that is ubiquitous in lineages of the Sordariomycetes. Our discoveries illuminate a “rummage region” in the N. crassa genome that enables the formation of new genes and functions to arise via gene duplication and relocation, followed by fast mutation and recombination facilitated by sequence repeats and unconstrained non‐coding sequences.

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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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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Contributors
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                Journal
                Molecular Ecology
                Molecular Ecology
                Wiley
                0962-1083
                1365-294X
                October 16 2023
                Affiliations
                [1 ] Department of Biostatistics Yale School of Public Health New Haven Connecticut USA
                [2 ] College of Biological Sciences University of California, Davis Davis California USA
                [3 ] Yale Center for Genomic Analysis New Haven Connecticut USA
                [4 ] Institute of Microbiology Chinese Academy of Sciences Beijing China
                [5 ] Department of Plant Pathology and Microbiology, The Robert H. Smith Faculty of Agriculture, Food and Environment The Hebrew University of Jerusalem Rehovot Israel
                [6 ] Department of Ecology and Evolutionary Biology, Program in Microbiology, and Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut USA
                Article
                10.1111/mec.17168
                9c28cc3f-7a8c-440a-979e-4d14953e94c1
                © 2023

                http://creativecommons.org/licenses/by-nc/4.0/

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