He thinks he's the dev from ready player one 🤣😂

This is the gayest thing I've ever seen, and I've had sex with men.

How Americans spend their day 🎥: EEAGLI

Congrats to him :) but with that hairline, he defo needs to be checked properly for roids

here you go buddy give it a day or so and post this with the same title

For those who haven't, please read Born a Crime by trevor noah

The comments under every science post on X

what's stopping you from coding like this?

That techbro knew that highlighting ChatGPT/ Alphafold will make it go viral - Catnip for X bros trying to monetize.. Bet interviewing the researcher involved will reveal how it was done Per Grok - ### How mutations are usually identified from sequencing data (standard pipeline for neoantigens in personalized cancer vaccines) The typical workflow for turning tumor sequencing into potential neoantigens (mutated peptides that could be vaccine targets) is a multi-step bioinformatics pipeline. This is done long before any structure prediction tool like AlphaFold comes into play. Here's the usual process in 2025–2026 clinical/research settings: 1. Sequencing: - Whole-exome sequencing (WES), whole-genome sequencing (WGS), or targeted panels of tumor DNA. - Matched normal (germline) DNA from blood/saliva for comparison. - Often paired with tumor RNA-seq (to check expression of mutated genes). 2. Read alignment & pre-processing: - Align raw reads to the human reference genome (e.g., using BWA, Bowtie2). - Quality filtering, duplicate removal, etc. 3. Somatic mutation calling / variant detection (this is where mutations are actually identified): - Compare tumor vs. normal to find somatic (tumor-specific) variants. - Tools: Mutect2 (GATK), VarScan, Strelka2, MuTect, or ensemble callers for higher confidence. - Focus on nonsynonymous mutations (missense, indels, frameshifts, fusions) that change the protein sequence. - Filter for high variant allele frequency (VAF), expression in RNA-seq, clonal status, etc. 4. Translation to mutated peptides: - Annotate variants with tools like VEP (Variant Effect Predictor) or ANNOVAR. - Generate the altered protein sequence around the mutation (e.g., 21-mer or sliding windows). - Split into possible epitope lengths (8–11 mers for MHC class I, longer for class II). 5. Neoantigen prediction & prioritization: - Peptide-MHC binding affinity prediction (core step): NetMHCpan, MHCflurry, MixMHCpred → rank how well mutated peptides bind patient's HLA alleles (typed via sequencing or PCR). - Additional filters: proteasomal cleavage (NetChop), TAP transport, expression level, clonality, dissimilarity to self (to avoid tolerance), immunogenicity scores. - Pipelines: pVACtools, MuPeXI, nextNEOπ, NeoPredPipe, TruNeo, or commercial ones like those from BioNTech/Moderna/Genentech. Only after this do you have a shortlist of candidate neoantigens (mutated peptides). ### Where AlphaFold actually fits in (limited role) AlphaFold enters much later — and rarely as the main tool for pinpointing neoantigens: - To model the 3D structure of a mutated protein or peptide-MHC complex → assess if the mutation dramatically alters folding, stability, or epitope presentation/conformation. - To help understand why a neoantigen might be immunogenic (e.g., structural changes exposing new surfaces). - In advanced vaccine design: refine peptide structures for better MHC binding or TCR interaction modeling (e.g., with tools like AlphaFold-Multimer or RoseTTAFold). - In some emerging pipelines (post-2024): use AlphaFold structures as input features for improved immunogenicity predictors or to simulate neoantigen presentation. But AlphaFold cannot replace variant calling or read sequencing data — it starts from an already-known mutated amino acid sequence. If someone claims "AlphaFold pinpointed the mutated proteins from genomic sequences," it's either loose wording (they mean used AlphaFold downstream after variant calling) or a misunderstanding of the tools. In short: sequencing variant callers find the mutations → neoantigen pipelines predict peptides → AlphaFold (optionally) models structures for refinement. The heavy lifting for "identifying mutations" is done by dedicated genomic tools, not AlphaFold.

Yup. These cartoon speak volumes about their blind, sycophantic credentialism.

This meme was originally meant as pejorative satire and ended up describing uncomfortable reality.

Atheists are now DEEP FRYING ABORTED EMBRYOS and dipping them in hot sauce. Why is this not FRONT PAGE NEWS?