Pinned Repositories
Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273
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.
Copula4retirement
llm_guardrail
LLM-based Guardrail Model for Secure GenAI System Resistant to Prompt Injection Attacks and Jailbreaking
allennlp
An open-source NLP research library, built on PyTorch.
alphafold
Open source code for AlphaFold.
biobert-pretrained
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
COYD-IoT
darknet-events
Go-based Darknet parser to extract Darknet events such as scanning and backscatter
darts
A python library for user-friendly forecasting and anomaly detection on time series.
docker-aws-graph-notebook
Dockerized AWS Graph Notebook
bcqguo's Repositories
bcqguo/Gymnasium
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
bcqguo/llm_guardrail
LLM-based Guardrail Model for Secure GenAI System Resistant to Prompt Injection Attacks and Jailbreaking
bcqguo/rdca
Code for "Optimal Decumulation: The Case for Quarterly Withdrawals of Retirement Savings"
bcqguo/Gemini-Chatbot-Interface-with-Streamlit
This project is a Streamlit-based chat application that interacts with the Gemini AI model, allowing users to engage in conversations with an artificial intelligence assistant. The application stores chat history, allowing users to revisit and continue previous conversations.
bcqguo/TimeSeriesAnalysisWithPython
bcqguo/Llama-2-Onnx
bcqguo/NVFlare
NVIDIA Federated Learning Application Runtime Environment
bcqguo/darts
A python library for user-friendly forecasting and anomaly detection on time series.
bcqguo/Copula4retirement
bcqguo/FinGPT
Data-Centric FinGPT. Open-source for open finance! Revolutionize 🔥 We release the trained model on HuggingFace.
bcqguo/Yolo_v8
NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
bcqguo/OFA
Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
bcqguo/qlora
QLoRA: Efficient Finetuning of Quantized LLMs
bcqguo/twitter-algorithm
Source code for Twitter's Recommendation Algorithm
bcqguo/samtools
Tools (written in C using htslib) for manipulating next-generation sequencing data
bcqguo/twitter_graph
bcqguo/gym-anytrading
The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)
bcqguo/llama-dl
High-speed download of LLaMA, Facebook's 65B parameter GPT model
bcqguo/gym
A toolkit for developing and comparing reinforcement learning algorithms.
bcqguo/machine-learning-articles
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
bcqguo/alphafold
Open source code for AlphaFold.
bcqguo/MA-NLP
bcqguo/darknet-events
Go-based Darknet parser to extract Darknet events such as scanning and backscatter
bcqguo/Mirai-Source-Code
Leaked Mirai Source Code for Research/IoC Development Purposes
bcqguo/kubernetes
Production-Grade Container Scheduling and Management
bcqguo/mlflow
Open source platform for the machine learning lifecycle
bcqguo/transformers
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
bcqguo/HuggingFace_notebook
Notebooks using the Hugging Face libraries 🤗
bcqguo/mesh
Mesh TensorFlow: Model Parallelism Made Easier
bcqguo/HanLP
Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification