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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.
JayabharathP / The Python Mega Course Build 10 Real World Applications The Python Mega Course is one of the top online Python courses with over 100,000 enrolled students and is targeted toward people with little or no previous programming experience. The course follows a modern-teaching approach where students learn by doing. You will start Python from scratch by first creating simple programs. Once you learn the basics you will then be guided on how to create 10 real-world complex applications in Python 3 through easy video explanations and support by the course instructor. Some of the applications you will build during the course are database web apps, desktop apps, web scraping scripts, webcam object detectors, web maps, and more. These programs are not only great examples to master Python, you can also use any of them as a portfolio once you have built them. By buying the course you will gain lifetime access to all its videos, coding exercises, quizzes, code notebooks, and the Q&A inside the course where you can ask your questions and get an answer the same day. On top of that you are covered by the Udemy 30-day money back guarantee, so you can easily return the course if you don't like it. If you don't know anything about Python, do not worry! In the first two sections, you will learn Python basics such as functions, loops, and conditionals. If you already know the basics, then the first two sections can serve as a refresher. The other 22 sections focus entirely on building real-world applications. The applications you will build cover a wide range of interesting topics: Web applications Desktop applications Database applications Web scraping Web mapping Data analysis Data visualization Computer vision Object-Oriented Programming Specifically, the 10 Python applications you will build are: A program that returns English-word definitions A program that blocks access to distracting websites A web map visualizing volcanoes and population data A portfolio website A desktop-graphical program with a database backend A webcam motion detector A web scraper of real estate data An interactive web graph A database web application A web service that converts addresses to geographic coordinates To consider yourself a professional programmer you need to know how to make professional programs and there's no other course that teaches you that, so join thousands of other students who have successfully applied their Python skills in the real world. Sign up and start learning Python today! What you’ll learn Go from a total beginner to an advanced-Python programmer Create 10 real-world Python programs (no useless programs) Solidify your skills with bonus practice activities throughout the course Create an app that translates English words Create a web-mapping app Create a portfolio website Create a desktop app for storing book information Create a webcam video app that detects objects Create a web scraper Create a data visualization app Create a database app Create a geocoding web app Create a website blocker Send automated emails Analyze and visualize data Use Python to schedule programs based on computer events. Learn OOP (Object-Oriented Programming) Learn GUIs (Graphical-User Interfaces) Are there any course requirements or prerequisites? A computer (Windows, Mac, or Linux). No prior knowledge of Python is required. No previous programming experience needed. Who this course is for: Those with no prior knowledge of Python. Those who know Python basics and want to master Python
isaacovercast / EasySFSEffective selection of population size projection for construction of the site frequency spectrum. Convert VCF to dadi/fastsimcoal style SFS for demographic analysis
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pyrates-neuroscience / PyRatesOpen-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.
ramachandran-lab / PongFast analysis and visualization of latent clusters in population genetic data
kjolley / BIGSdbBacterial Isolate Genome Sequence Database (BIGSdb): A platform for gene-by-gene bacterial population annotation and analysis.
grunwaldlab / Poppr🌶 An R package for genetic analysis of populations with mixed (clonal/sexual) reproduction
statgen / PopscleA suite of population scale analysis tools for single-cell genomics data including implementation of Demuxlet / Freemuxlet methods and auxilary tools
theochem / ChemtoolsA collection of interpretive chemical tools for analyzing outputs of the quantum chemistry calculations.
Aleksobrad / Single Cell Rcc PipelineData files and code for analysis of single-cell ccRCC data for the manuscript "Tumor-Specific Cell Populations in Clear Cell Renal Carcinoma Associated with Clinical Outcome Identified Using Single-Cell Protein Activity Inference." Includes code for VIPER protein activity inference pipeline
AllonKleinLab / PBAPopulation balance analysis
UUPharmacometrics / Xpose4Model building aid for population analysis using NONMEM
metagenopolis / MeteorMeteor is a plateform for quantitative metagenomics profiling of complex ecosystems. Meteor relies on genes catalogue to perform species-level taxonomic profiling (Bacteria, Archaea and Eukaryotes), functional analysis and strain-level population structure inference.
wlz0726 / Population Genomics ScriptsScripts of population genomics analysis
zebrain-lab / Toolbox Romano Et AlComputational toolbox for analysis of calcium imaging data of neuronal populations
GPZ-Bioinfo / Tmaptopological data analysis of population-scale microbiomes
robjhyndman / VitalTidy Analysis Tools for Mortality, Fertility, Migration and Population Data
LynchLab / MAPGDA program for the Maximum-likelihood analysis of population genomic data.
emmanuelparadis / PegasPopulation and Evolutionary Genetics Analysis System
