AI & LLM Engineering · By GPTomics

Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer.

GPTomics/bioSkills

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect…

GPTomics/bioSkills

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions)…

GPTomics/bioSkills

Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairopdf TrueType embedding…

GPTomics/bioSkills

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data.

GPTomics/bioSkills

Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular…

GPTomics/bioSkills

Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization.

GPTomics/bioSkills

Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and…

GPTomics/bioSkills

Predicts RNA secondary structure with ViennaRNA, treating the Boltzmann ensemble (partition function, base-pair probabilities, centroid, MEA, stochastic samples) as the object rather than a single…

GPTomics/bioSkills