clarle

ai-class

Code collection for the Stanford Online Introduction to Artificial Intelligence class

decks

Repository for any given presentations or talks

django-slides

A Django application that makes it easy to add, edit, and deploy slideshows using the Slides.js jQuery plugin

falcor-react-native-demo

Example app using Falcor inside of a React Native application

node-sunlightapi

Node.js client library for the Sunlight Labs Congress API

node-tensorflow-serving-demo

Example app combining Express and TensorFlow Serving (WIP)

phylo-mcmc

MCMC algorithms for phylogenetic trees in Cython

yui3

A library for building richly interactive web applications.

clarle/node-tensorflow-serving-demo

Example app combining Express and TensorFlow Serving (WIP)

clarle/falcor-react-native-demo

Example app using Falcor inside of a React Native application

clarle/vid2vid

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

clarle/yui3

A library for building richly interactive web applications.

clarle/aoc

Advent of Code solutions

clarle/arkhamdb

clarle/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.

clarle/blog

Personal blog generator for @clarle (WIP)

clarle/brain-fitness-mobile

Mobile client for Teacher Brain Fitness app.

clarle/Cockatrice

A cross-platform virtual tabletop for multiplayer card games

clarle/council-catalog

clarle/dasy-mobile

Perform a public service. Message public agencies.

clarle/denoising-gravitational-waves

Deep Learning for NCSA Gravity Group

clarle/digimon-tcg-simulator

clarle/dr4ft

Multiplayer Online MTG Draft and Sealed Simulator

clarle/faceswap-GAN

A denoising autoencoder + adversarial loss for face swapping.

clarle/falcor

A JavaScript library for efficient data fetching

clarle/falcor-lambda

JavaScript library for creating Falcor endpoints on AWS Lambda

clarle/generative_inpainting

DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

clarle/grunt-yui-contrib

YUI Build Tasks

clarle/keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano or TensorFlow.

clarle/mage

Magic Another Game Engine

clarle/mishima-matting

Python implementation of Mishima matting method

clarle/noya

The open design tools SDK. Try our new experimental wireframing tool! 👇

clarle/OpenData

clarle/react-native

A framework for building native apps with React.

clarle/react-native-touch-id

React Native authentication with the native Touch ID popup.

clarle/react-router

Declarative routing for React

clarle/tensorflow

Computation using data flow graphs for scalable machine learning

clarle/vue-cli

🛠️ Standard Tooling for Vue.js Development