RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.

该系列资源是Python疫情大数据分析，涉及网络爬虫、可视化分析、GIS地图、情感分析、舆情分析、主题挖掘、威胁情报溯源、知识图谱、预测预警及AI和NLP应用等。推荐大家结合作者CSDN博客阅读，武汉必胜、湖北必胜、中国必胜！

2019新型冠状病毒疫情可视化。COVID-2019(原NCOV)，武汉(WuHan)疫情、全国疫情地图及时间轴变化，各省市地图及疫情曲线。疫情数据分析系统，疫情小区可视化，COVID-2019 Data Visualization Analysis System (前端+后端+数据清洗)

Data analysis and visualizations of daily COVID cases report

A collection of data analysis and visualization projects designed to uncover insights from diverse datasets. These projects include analyses on COVID-19 trends, stock trading patterns, housing market prices, IoT data, and more, showcasing the power of data-driven storytelling.

Public Available Code and Data to Reproduce Analyses in "Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis."

R script for the covid balf data analysis

A list of high quality open datasets for COVID-19 data analysis

Analysis with Covid-19 data

COVID-19-sentiment-analysis-dataset-Weibo

Analysis and cuts of data from the team behind the Oxford COVID policy tracker

No description available

Data analysis of the COVID-19 outbreak in Italy updated daily from official sources

[Cell Patterns] COVID-19 EHR data analysis pipeline

Analysis and visualization of Taiwan’s COVID-19 data

Data and analysis for the early COVID-19 outbreak

Content Material for the lecture Covid-19 data analysis

Source code and data for an analysis of Covid-19 excess deaths in Mexico City

Analysis code and data for COVID-19 age-specific clinical fraction

“Analyzed and predicted COVID-19 mortality trends across U.S. states, age groups, and time periods using Python-based data analysis and machine learning techniques.”

If you want to analyze Covid 19 data on your own from reliable sources using the same software used by professional data scientists this is for you

Contains functions for pulling publicly-available global COVID-19 case and testing data for analysis and populating a Power BI dashboard. ARCHIVED: See ITF-Dashboard Repository for further development

No description available

ViReport analysis of COVID-19 using GISAID data

Analysis of high resolution clinical data for COVID-19 patients

This project aims to use the Hadoop framework to analyze unstructured data that we obtain from Twitter and perform sentiment and trend analysis using Hive on MapReduce and Spark on keyword “COVID19”. We then compare the Hive and Spark approaches to determine the best performance.

No description available

Covid-19 India's statewide analysis with census data 2011 and Kaggle data

Data ETL & Analysis on the global and Mexican datasets of the COVID-19 pandemic.

Data, analysis and graphs of the advancement of COVID-19 in Brazil