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      Genomic Analyses Reveal Association of ASIP with a Recurrently evolving Adaptive Color Pattern in Frogs

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          Abstract

          Traits shared among distantly related lineages are indicators of common evolutionary constraints, at the ecological, physiological, or molecular level. Here, we show that the vertebral stripe, a cryptic color pattern, has evolved hundreds of times in the evolutionary history of anurans (frogs and toads) and is favored in terrestrial habitats. Using a genome-wide association study, we demonstrate that variation near the Agouti signaling protein gene ( ASIP) is responsible for the different vertebral stripe phenotypes in the African grass frog Ptychadena robeensis. RNAseq and real-time quantitative PCR revealed that differential expression of the gene and an adjacent long non-coding RNA is linked to patterning in this species. Surprisingly, and although the stripe phenotypes are shared with closely related species, we found that the P. robeensis alleles are private to the species and unlikely to evolve under long-term balancing selection, thus indicating that the vertebral stripe phenotypes result from parallel evolution within the group. Our findings demonstrate that this cryptic color pattern evolved rapidly and recurrently in terrestrial anurans, and therefore constitutes an ideal system to study repeated evolution.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              NIH Image to ImageJ: 25 years of image analysis

              For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press (US )
                0737-4038
                1537-1719
                November 2022
                01 November 2022
                01 November 2022
                : 39
                : 11
                : msac235
                Affiliations
                Division of Science, New York University Abu Dhabi , Saadiyat Island, Abu Dhabi, United Arab Emirates
                Division of Science, New York University Abu Dhabi , Saadiyat Island, Abu Dhabi, United Arab Emirates
                Division of Science, New York University Abu Dhabi , Saadiyat Island, Abu Dhabi, United Arab Emirates
                School of Biological Sciences, University of Portsmouth , Portsmouth, United Kingdom
                Division of Science, New York University Abu Dhabi , Saadiyat Island, Abu Dhabi, United Arab Emirates
                Author notes
                Author information
                https://orcid.org/0000-0002-8939-3773
                https://orcid.org/0000-0002-8760-1284
                Article
                msac235
                10.1093/molbev/msac235
                9667977
                36317199
                b2bf53f6-de96-4318-a986-c97f34eee5e1
                © The Author(s) 2022. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 14
                Funding
                Funded by: NYUAD, doi 10.13039/100012025;
                Award ID: AD180
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                repeated evolution,adaptation,agouti signaling protein,color polymorphism,anura
                Molecular biology
                repeated evolution, adaptation, agouti signaling protein, color polymorphism, anura

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