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agpmicrobiomehostpredictions

A broad search for significant microbiome-modifying host variables from the American Gut Project public microbiome data

aichpoem

手把手教你搭建人工智能AI写诗平台，从模型搭建到前后端开发到SEO优化推广

android-instagram

Starter Project for Android One Week Bootcamp

android-instagram-clone

Develop your own Instagram Clone App

animegan2-pytorch

PyTorch implementation of AnimeGANv2

animeganv2

[Open Source]. The improved version of AnimeGAN. Landscape photos/videos to anime

arxivtimes

repository to research & share the machine learning articles

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.

bbs

BBS by Django

beamspy

BEAMSpy - Birmingham mEtabolite Annotation for Mass SpectroMetry (Python Package)

bimodal

Supporting code for the paper "Bidirectional Molecule Generation with Recurrent Neural Networks" (J. Chem. Inf. 2020, 60, 3).

bio-ml

Command lines for the BIO-ML resources

bullshitgenerator

Needs to generate some texts to test if my GUI rendering codes good or not. so I made this.

cc-cruiser

chatbot_modified

A chatbot with real time scraping facilities based on NLTK, bag of words, and Neural Networks

chinese-poetry

The most comprehensive database of Chinese poetry 🧶最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。 阿里招 Python P6/P7 上海张江, [email protected]

chinese_chatbot_corpus

中文公开聊天语料库

clone-instagram

This is a clone instragram build it with Django and SQLite

core_metabolome

Contains database of core_metabolome molecules and documents generation of said database.

couplet-dataset

Dataset for couplets. 70万条对联数据库。

covid-19-clinical

cpm

Easy-to-use CPM for Chinese text generation（基于CPM的中文文本生成）

decoid

Metabolomics software for database-assisted deconvolution of MS/MS spectra

dimepy

Python package for the high-throughput nontargeted metabolite fingerprinting of nominal mass direct injection mass spectrometry directly from mzML files.

dimspy

Python package for processing direct-infusion mass spectrometry-based metabolomics and lipidomics data

django_instagramclone

Eleven Fifty Academy Python Blue Badge Project: Django Instagram Clone

download

Lantern官方版本下载 蓝灯 翻墙 代理 科学上网 外网 加速器 梯子 路由 proxy vpn circumvention gfw

drug-efficacy-prediction

Predicting anti-HIV activity of chemical molecules - Python, RDKit, Scikit-learn, Keras, Tensorflow

faceanalysis

fake-zhihu

用django模仿的精简版知乎网站