Prazsák István, Csabai Zsolt, Torma Gábor, Papp Henrietta, Földes Fanni, Kemenesi Gábor, Jakab Ferenc, Gulyás Gábor, Fülöp Ádám, Megyeri Klára, Dénes Béla, Boldogkői Zsolt, Tombácz Dóra
Department of Medical Biology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
National Laboratory of Virology, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.
Data Brief. 2022 Jun 18;43:108386. doi: 10.1016/j.dib.2022.108386. eCollection 2022 Aug.
Long-read sequencing (LRS) approaches shed new light on the complexity of viral (Kakuk et al., 2021 [1]; Boldogkői et al., 2019 [2]; Depledge et a., 2019 [3]), bacterial (Yan et al., 2018 [4]) and eukaryotic (Tilgner et al., 2014 [5]) transcriptomes. Emerging RNA viruses are zoonotic (Woolhouse et al., 2016 [6]) and create public health problems, e.g. influenza pandemic caused by H1N1 virus in (Fraser et al., 2009 [7]), as well as the current SARS-CoV-2 pandemic (Kim et al., 2020 [8]). In this study, we carried out nanopore sequencing for generating transcriptomic data valuable for structural and kinetic profiling of six important human pathogen RNA viruses, the H1N1 subtype of Influenza A virus (IVA), the Zika virus (ZIKV), the West Nile virus (WNV), the Crimean-Congo hemorrhagic fever virus (CCHFV), the Coxsackievirus [group B serotype 5 (CVB5)] and the Vesicular stomatitis Indiana virus (VSIV), and the response of host cells upon viral infection. The raw sequencing data were filtered during basecalling and only high quality reads (Qscore ≥ 7) were mapped to the appropriate viral and host genomes. Length distribution of sequencing reads were assessed and statistics of data were plotted by the ReadStat.4 python script. The datasets can be used to profile the transcriptomic landscape of RNA viruses, provide information for novel gene annotations, can serve as resource for studying the virus-host interactions, and for the analysis of RNA base modifications. These datasets can be used to compare the different sequencing techniques, library preparation approaches, bioinformatics pipelines, and to analyze the RNA profiles of viruses with small RNA genomes.
长读长测序（LRS）方法为病毒（Kakuk等人，2021 [1]；Boldogkői等人，2019 [2]；Depledge等人，2019 [3]）、细菌（Yan等人，2018 [4]）和真核生物（Tilgner等人，2014 [5]）转录组的复杂性带来了新的认识。新兴的RNA病毒是人畜共患病原体（Woolhouse等人，2016 [6]），会引发公共卫生问题，例如H1N1病毒导致的流感大流行（Fraser等人，2009 [7]），以及当前的SARS-CoV-2大流行（Kim等人，2020 [8]）。在本研究中，我们进行了纳米孔测序，以生成对六种重要的人类病原体RNA病毒、甲型流感病毒（IVA）的H1N1亚型、寨卡病毒（ZIKV）、西尼罗河病毒（WNV）、克里米亚-刚果出血热病毒（CCHFV）、柯萨奇病毒[B组5型血清型（CVB5）]和印第安纳州水疱性口炎病毒（VSIV）的结构和动力学分析有价值的转录组数据，以及病毒感染后宿主细胞的反应。原始测序数据在碱基识别过程中进行了过滤，只有高质量读数（Qscore≥7）被映射到相应的病毒和宿主基因组。通过ReadStat.4 Python脚本评估测序读数的长度分布并绘制数据统计图表。这些数据集可用于分析RNA病毒的转录组图谱，为新基因注释提供信息，可作为研究病毒-宿主相互作用的资源，以及用于RNA碱基修饰的分析。这些数据集可用于比较不同的测序技术、文库制备方法、生物信息学流程，并分析具有小RNA基因组病毒的RNA图谱。
