1,605 skills found · Page 1 of 54
firecrawl / Firecrawl🔥 The Web Data API for AI - Turn entire websites into LLM-ready markdown or structured data
D4Vinci / Scrapling🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
ScrapeGraphAI / Scrapegraph AIPython scraper based on AI
getmaxun / Maxun🔥 The open-source no-code platform for web scraping, crawling, search and AI data extraction • Turn websites into structured APIs in minutes 🔥
pymupdf / PyMuPDFPyMuPDF is a high performance Python library for data extraction, analysis, conversion & manipulation of PDF (and other) documents.
Zipstack / UnstractLLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows
vi3k6i5 / FlashtextExtract Keywords from sentence or Replace keywords in sentences.
katanaml / SparrowStructured data extraction and instruction calling with ML, LLM and Vision LLM
charles2gan / GDA Android Reversing Toolthe fastest and most powerful android decompiler(native tool working without Java VM) for the APK, DEX, ODEX, OAT, JAR, AAR, and CLASS file. which supports malicious behavior detection, privacy leaking detection, vulnerability detection, path solving, packer identification, variable tracking, deobfuscation, python&java scripts, device memory extraction, data decryption, and encryption, etc.
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.
jackwener / OpencliMake any website your CLI. A powerful, AI-native runtime for seamless browser automation and dynamic web data extraction.
brightdata / Brightdata MCPA powerful Model Context Protocol (MCP) server that provides an all-in-one solution for public web access.
hhursev / Recipe ScrapersPython package for scraping recipes data
NanoNets / DocextAn on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking toolkit. (https://idp-leaderboard.org/)
MyIntervals / PHP CSS ParserA Parser for CSS Files written in PHP. Allows extraction of CSS files into a data structure, manipulation of said structure and output as (optimized) CSS
shcherbak-ai / ContextgemContextGem: Effortless LLM extraction from documents
alvarobartt / InvestpyFinancial Data Extraction from Investing.com with Python
saifyxpro / HeadlessXThe undetected self-hosted browser automation platform. Powered by Camoufox (Firefox) for 0% detection rates. Built for speed, privacy, and scalability.
yobix-ai / ExtractousFast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
bespokelabsai / CuratorSynthetic data curation for post-training and structured data extraction
