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      Epitranscriptome analysis of NAD-capped RNA by spike-in-based normalization and prediction of chronological age

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          Summary

          Nicotinamide adenine dinucleotide (NAD) can be used as an initiating nucleotide in RNA transcription to produce NAD-capped RNA (NAD-RNA). RNA modification by NAD that links metabolite with expressed transcript is a poorly studied epitranscriptomic modification. Current NAD-RNA profiling methods involve multi-steps of chemo-enzymatic labeling and affinity-based enrichment, thus presenting a critical analytical challenge to remove unwanted variations, particularly batch effects. Here, we propose a computational framework, enONE, to remove unwanted variations. We demonstrate that designed spike-in RNA, together with modular normalization procedures and evaluation metrics, can mitigate technical noise, empowering quantitative and comparative assessment of NAD-RNA across different datasets. Using enONE and a human aging cohort, we reveal age-associated features of NAD-capping and further develop an accurate RNA-based aging clock that combines signatures from both transcriptome and NAD-modified epitranscriptome. enONE facilitates the discovery of NAD-RNA responsive to physiological changes, laying an important foundation for functional investigations into this modification.

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          Highlights

          • enONE, a computational framework of NAD-RNA, reduces technical noise using spike-in

          • Human peripheral blood cells contain NAD-RNA and tend to increase with age

          • RNA-based aging clock, integrating gene expression with NAD-RNA, can predict age

          • NAD-capped RNA from circulating blood can be developed as potential biomarkers

          Abstract

          Computational bioinformatics; Sequence analysis; Transcriptomics; Methodology in biological sciences

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          Most cited references54

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                22 November 2023
                15 December 2023
                22 November 2023
                : 26
                : 12
                : 108558
                Affiliations
                [1 ]Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 100 Hai Ke Road, Pudong, Shanghai 201210, China
                [2 ]University of Chinese Academy of Sciences, Beijing 100049, China
                [3 ]Department of Vascular and Endovascular Surgery, Chang Zheng Hospital, Naval Medical University, Shanghai 200003, China
                [4 ]National Clinical Research Center for Aging and Medicine, Huashan Hospital, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, 131 Dong An Road, Shanghai 200032, China
                [5 ]Metalife Biotechnology, 1000 Zhen Chen Road, Baoshan, Shanghai 200444, China
                [6 ]Singlera Genomics, 500 Fu Rong Hua Road, Pudong, Shanghai 201204, China
                [7 ]Shanghai Key Laboratory of Aging Studies, 100 Hai Ke Road, Pudong, Shanghai 201210, China
                Author notes
                []Corresponding author czhao@ 123456fudan.edu.cn
                [∗∗ ]Corresponding author liunan@ 123456sioc.ac.cn
                [∗∗∗ ]Corresponding author qulefeng@ 123456smmu.edu.cn
                [8]

                These authors contributed equally

                [9]

                Lead contact

                Article
                S2589-0042(23)02635-4 108558
                10.1016/j.isci.2023.108558
                10716591
                38094247
                601cceba-1fd0-4efa-86bd-9cf69a07d0e0
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 1 August 2023
                : 14 October 2023
                : 20 November 2023
                Categories
                Article

                computational bioinformatics,sequence analysis,transcriptomics,methodology in biological sciences

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