makdl's Stars
SinaKhalili/Folders.py
Implementation of the Folders📂 esoteric programming language, a language with no code and just folders.
linkedin/school-of-sre
At LinkedIn, we are using this curriculum for onboarding our entry-level talents into the SRE role.
Iceman12k/fteqw-mguns
Official git mirror of FTEQW.
opentofu/opentofu
OpenTofu lets you declaratively manage your cloud infrastructure.
opentofu/manifesto
The OpenTF Manifesto expresses concern over HashiCorp's switch of the Terraform license from open-source to the Business Source License (BSL) and calls for the tool's return to a truly open-source license.
albertzsigovits/malware-study
Resources for learning malware analysis and reverse engineering
vaporup/ssh-tools
Making SSH more convenient
alexellis/k3sup
bootstrap K3s over SSH in < 60s 🚀
dora-team/fourkeys
Platform for monitoring the four key software delivery metrics of software delivery
vaniacer/sshto
Small bash script to manage your ssh connections. It builds menu (via dialog) from your ~/.ssh/config. It can not only connect but also to run commands, copy files, tunnel ports.
alex/what-happens-when
An attempt to answer the age old interview question "What happens when you type google.com into your browser and press enter?"
briandrawert/SPH-BVF
livialima/linuxupskillchallenge
Learn the skills required to sysadmin a remote Linux server from the commandline.
mikepound/enigma
A java implementation of Enigma, and a modern attack to decrypt it.
NAalytics/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.
caddyserver/caddy
Fast and extensible multi-platform HTTP/1-2-3 web server with automatic HTTPS
StochSS/stochss-compute
A compute delegation server for the StochSS family of stochastic simulators.
nasa/fprime
F´ - A flight software and embedded systems framework
jdreeve/remote-solver
A "remote solver" (client/server implementation) for GillesPy2.
nassarhuda/juliacon2020files
JuliaCon 2020 notebooks for the tutorial: Make your Julia code faster and compatible with non-Julia code
TheAlgorithms/Python
All Algorithms implemented in Python
mikepound/feistel
A toy Feistel network implementation
StochSS/SpatialPy
Simulation of spatial deterministic/stochastic reaction-diffusion-advection problems embedded in Lagrangian reference frame particle based fluid dynamics domain
ammaraskar/sphinx-action
Github action that builds docs using sphinx and places errors inline
briandrawert/SpatialPy-sdpd
SpatialPy with SPH/SDPD
briandrawert/hybrid_SSA_SDPD
A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection-diffusion-reaction problems
briandrawert/pyurdme
Python API for URDME solvers
scidash/sciunit
A Python framework for test-driven validation of scientific models.
sn4k3/Ender3
Supercharge your Ender3 with Guides, Mods and Addons
github/gitignore
A collection of useful .gitignore templates