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      Functional and molecular dissection of HCMV long non-coding RNAs

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          Abstract

          Small, compact genomes confer a selective advantage to viruses, yet human cytomegalovirus (HCMV) expresses the long non-coding RNAs (lncRNAs); RNA1.2, RNA2.7, RNA4.9, and RNA5.0. Little is known about the function of these lncRNAs in the virus life cycle. Here, we dissected the functional and molecular landscape of HCMV lncRNAs. We found that HCMV lncRNAs occupy ~ 30% and 50–60% of total and poly(A)+viral transcriptome, respectively, throughout virus life cycle. RNA1.2, RNA2.7, and RNA4.9, the three abundantly expressed lncRNAs, appear to be essential in all infection states. Among these three lncRNAs, depletion of RNA2.7 and RNA4.9 results in the greatest defect in maintaining latent reservoir and promoting lytic replication, respectively. Moreover, we delineated the global post-transcriptional nature of HCMV lncRNAs by nanopore direct RNA sequencing and interactome analysis. We revealed that the lncRNAs are modified with N 6-methyladenosine (m 6A) and interact with m 6A readers in all infection states. In-depth analysis demonstrated that m 6A machineries stabilize HCMV lncRNAs, which could account for the overwhelming abundance of viral lncRNAs. Our study lays the groundwork for understanding the viral lncRNA–mediated regulation of host-virus interaction throughout the HCMV life cycle.

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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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
                hyeshik@snu.ac.kr
                ksahn@snu.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                11 November 2022
                11 November 2022
                2022
                : 12
                : 19303
                Affiliations
                [1 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, School of Biological Sciences, , Seoul National University, ; Seoul, 08826 Republic of Korea
                [2 ]GRID grid.410720.0, ISNI 0000 0004 1784 4496, Institute for Basic Science, Center for RNA Research, ; Seoul, 08826 Republic of Korea
                [3 ]GRID grid.31501.36, ISNI 0000 0004 0470 5905, Interdisciplinary Program in Bioinformatics, , Seoul National University, ; Seoul, 08826 Republic of Korea
                [4 ]GRID grid.37172.30, ISNI 0000 0001 2292 0500, Department of Bio and Brain Engineering, , Korea Advanced Institute of Science and Technology (KAIST), ; Daejeon, 34141 Republic of Korea
                Article
                23317
                10.1038/s41598-022-23317-3
                9652368
                36369338
                a78836b7-9081-4761-b167-f9f996c7c95a
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 25 August 2022
                : 29 October 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100010446, Institute for Basic Science;
                Award ID: IBS-R008-D1
                Funded by: FundRef http://dx.doi.org/10.13039/501100014188, Ministry of Science and ICT, South Korea;
                Award ID: NRF-2020R1A2C3011298
                Funded by: FundRef http://dx.doi.org/10.13039/501100004085, Ministry of Education, Science and Technology;
                Award ID: 2021R1C1C1010758
                Funded by: Ministry of Education, Science, and Technology of Korea
                Award ID: 2019R1A6A1A10073437
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                virology,long non-coding rnas,rna modification
                Uncategorized
                virology, long non-coding rnas, rna modification

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