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NAalytics / Assemblies Of Putative SARS CoV2 Spike Encoding MRNA Sequences For Vaccines BNT 162b2 And MRNA 1273RNA 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.
tlaplus / DrTLAPlusDr. TLA+ series - learn an algorithm and protocol, study a specification
chaincodelabs / SeminarsStudy materials for Bitcoin & Lightning Protocol
eth-protocol-fellows / Protocol StudiesA study group learning about Ethereum and building protocol wiki
emmanueltouzery / HotwireHotwire allows you to study network traffic of a few popular protocols in a simple way
CrypToolProject / CrypTool 2Official CrypTool 2 (CT2) repository. CT2 is an open-source software for learning about cryptography and cryptanalysis interactively. CT2 provides a comprehensive suite of tools to deeply explore algorithms and protocols, making it perfect for both academic study and personal exploration. It also includes features for analyzing historical ciphers.
214175590 / WeChatProtocolStudy微信协议研究,实现基于Android/MAC/IPAD协议的PC客户端
felipewind / Fix Trading SimulatorA trading simulator between a Broker and a Stock Exchange using the Financial Information eXchange (FIX) Protocol. It's a study project using QuickFIX/J, Quarkus, Angular, Docker, Docker Compose and PostgreSQL.
phoenixctms / CtsmsPhoenix CTMS - the ultimative CTMS/PRS/CDMS.
jvalegre / RobertAutomated machine learning protocols that start from CSV databases of descriptors or SMILES and produce publication-quality results in Chemistry studies with only one command line.
OHDSI / StudyProtocolsRepository of OHDSI Collaborative Research Protocols
givgramacho / CERN Quantum Computing CourseQuantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.
OHDSI / StudyProtocolSandboxThis repository is for developing study packages for OHDSI studies. Once completed, they can be moved to the StudyProtocols repository.
wiepteam / StudygroupA study group initiated by Women in Ethereum Protocol to increase contribution to the Ethereum ecosystem.
ricardoogliari / Flutter MqttOnly my study about communicaton between a Flutter mobile app and a dht11 sensor connected in a NodeMCU. Using MQTT protocol to communication. https://www.youtube.com/watch?v=Y7QrTZtCmBk
psavelis / Enterprise BlockchainTypeScript repository for enterprise blockchain case studies, protocol mappings, and integration patterns.
FebriantiW / Homomorphic Encryption And Federated Learning Based Privacy Preserving CNN Training Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learn- ing techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this prob- lem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomor- phic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.
lichti / ZaplabA Go toolkit for studying and testing the WhatsApp Web protocol, with a built-in REST API for integrations (n8n, webhooks, and more). All events (received messages, receipts, presence, history sync, send errors) are persisted in PocketBase (SQLite) and dispatched to configurable webhooks. Messages can be sent via a REST API.
bsiepe / ADEMP PreRegADEMP preregistration protocol for simulation studies
pdogg / Jt65stegojt65stego project for the purposes of theoretical study and education on the topic of steganography in the JT65 amateur radio protocol
