cgosorio's repositories
100-Days-Of-ML-Code
100 Days of ML Coding
aima-exercises
Exercises for the book Artificial Intelligence: A Modern Approach
aima-javascript
Javascript visualization of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
aima-pseudocode
Pseudocode descriptions of the algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
aima-python
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
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.
CascadeSVC
Cascade Support Vector Machine Classifier
django-shared-hosting-1and1
Instructions and bash script for installing Django framework in a shared hosting environment (1&1)
docker-alpine-python3
The smallest Docker image with Python 3.6 (~61MB)
EnsemblTSSPrediction
TIS Prediction with Ensembl data.
kaobook_cgo1
A LaTeX class for books, reports or theses based on https://github.com/kenohori/thesis and https://github.com/Tufte-LaTeX/tufte-latex.
LAMDA-SSL
LAMDA-SSL is a comprehensive and easy-to-use toolkit for semi-supervised learning in python.
markdown-element
HTML Element that renders markdown content.
os_quantum_software
Curated list of open-source quantum software projects.
PCVN
TFG web Scrapping
quarto
A Real-Time Quarto Game
quarto-server
Quarto Server
qubiter
Python tools for reading, writing, compiling, simulating quantum computer circuits. “Quantum Space, the final frontier. These are the voyages of the starship Qubiter. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before.”
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
Reinforcement-Learning-Explained
This repository contains the lab files for Microsoft course DAT257x: Reinforcement Learning Explained
strips
A python implementation of the STRIPS planning algorithm
SurveyingPointCode
Automate the process of delineation in CAD, by coding points in a topographic survey
TFG-Amazon-Scraper
Final degree project. The goal is to scrap info from amazon and process it with the aim of using it for a marketing study.
TFG-Neurodegenerative-Disease-Detection
Uso de biomarcadores extraídos de la voz para la detección de la enfermedad del Parkinson
WorkflowManager
Docker and Galaxy based tool for bacterial sequence analysis.
yappy
Yet Another Parser Generator for Python (corrected by me). Now Yappy works in Python3 🥳🎉🎊 .
Yappy3
Repositorio para la subida a PyPi de la biblioteca yappy.