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      Comparative transcriptome analysis between rhesus macaques ( Macaca mulatta) and crab-eating macaques ( M. fascicularis)

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          Abstract

          Understanding gene expression variations between species is pivotal for deciphering the evolutionary diversity in phenotypes. Rhesus macaques ( Macaca mulatta, MMU) and crab-eating macaques ( M. fascicularis, MFA) serve as crucial nonhuman primate biomedical models with different phenotypes. To date, however, large-scale comparative transcriptome research between these two species has not yet been fully explored. Here, we conducted systematic comparisons utilizing newly sequenced RNA-seq data from 84 samples (41 MFA samples and 43 MMU samples) encompassing 14 common tissues. Our findings revealed a small fraction of genes (3.7%) with differential expression between the two species, as well as 36.5% of genes with tissue-specific expression in both macaques. Comparison of gene expression between macaques and humans indicated that 22.6% of orthologous genes displayed differential expression in at least two tissues. Moreover, 19.41% of genes that overlapped with macaque-specific structural variants showed differential expression between humans and macaques. Of these, the FAM220A gene exhibited elevated expression in humans compared to macaques due to lineage-specific duplication. In summary, this study presents a large-scale transcriptomic comparison between MMU and MFA and between macaques and humans. The discovery of gene expression variations not only enhances the biomedical utility of macaque models but also contributes to the wider field of primate genomics.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              The Sequence Alignment/Map format and SAMtools

              Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                Journal
                Zool Res
                Zool Res
                ZR
                Zoological Research
                Science Press (16 Donghuangchenggen Beijie, Beijing 100717, China )
                2095-8137
                18 March 2024
                : 45
                : 2
                : 299-310
                Affiliations
                [1 ] Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science & Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
                [2 ] Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China
                [3 ] University of Chinese Academy of Sciences, Beijing 100049, China
                [4 ] Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Kunming, Yunnan 650201, China
                [5 ] Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200030, China
                [6 ] Zhiyuan College, Shanghai Jiao Tong University, Shanghai 200240, China
                [7 ] Center for Genomic Research, International Institutes of Medicine, Fourth Affiliated Hospital, Zhejiang University, Yiwu, Zhejiang 322000, China
                Author notes
                Article
                zr-45-2-299
                10.24272/j.issn.2095-8137.2023.322
                11017088
                38485500
                7635e5a6-2778-444e-ba8e-bda444a4645a
                Copyright © 2024 Editorial Office of Zoological Research, Kunming Institute of Zoology, Chinese Academy of Sciences.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 October 2023
                : 28 December 2023
                Funding
                This work was supported by the National Natural Science Foundation of China (82021001 and 31825018 to Q.S., 32370658 to Y.M., 82001372 to X.Y.), National Key Research and Development Program of China (2022YFF0710901) and National Science and Technology Innovation 2030 Major Program (2021ZD0200900) to Q.S., and Shanghai Pujiang Program (22PJ1407300) and Shanghai Jiao Tong University 2030 Initiative (WH510363001-7) to Y.M.
                Categories
                Article

                crab-eating macaques,rhesus macaques,comparative transcriptomics,biomedical models,non-human primates,rna-seq,duplicated genes

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