Skills for scientific discovery

Unsupervised multi-omics integration with GAUDI — two-stage UMAP, HDBSCAN clustering, and SHAP metagenes, in R or Python. Use when two or more omics layers share samples and you need sample clusters plus the features driving them.

Retrieve predicted protein structures from the AlphaFold database by UniProt accession, and read their confidence metrics correctly — per-residue pLDDT, the PAE matrix for multi-domain proteins, and AlphaMissense variant annotations.

Molecular biology toolkit with Biopython — sequence manipulation, FASTA/GenBank/PDB parsing, phylogenetics, BLAST automation, and programmatic NCBI/PubMed access via Bio.Entrez.

Predict biomolecular structure and binding affinity with Boltz2 NIM — protein, protein-ligand, DNA and RNA complexes from SMILES or CCD ligands, returning mmCIF and pIC50 scores, via the hosted NVIDIA API or local Docker.

Run EvolutionaryScale ESM protein language models — ESM3 sequence and structure generation, ESMC embeddings, inverse folding, and ESMFold2 structure prediction, locally or through the hosted Biohub API.

Biological data analysis with scikit-bio — sequence handling, alignments, phylogenetic trees, alpha and beta diversity including UniFrac, ordination (PCoA), and PERMANOVA. Built for microbiome work.

Molecular docking with AutoDock Vina — receptor and ligand preparation, grid box definition, pose prediction and scoring. Use to predict how a small molecule binds a protein, to redock a known complex for validation, or to screen a compound set.

Score pathway-pathway association by PCA to k components then first canonical correlation

Work with AnnData annotated matrices and .h5ad files — layers, obs/var metadata, sparse backing, on-disk access, and format conversion. The data-format layer under the single-cell ecosystem.

Query the CZ CELLxGENE Census for versioned public single-cell and spatial transcriptomics data — cell metadata, expression slices, summary counts, source H5AD downloads, and embeddings across organisms, tissues, diseases, and cell types.

Cheminformatics with datamol, a Pythonic layer over RDKit with sensible defaults — SMILES parsing and standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.

Run DiffDock molecular docking via NVIDIA NIM to predict small-molecule binding poses against protein targets — blind ligand docking of SMILES or SDF inputs, returning ranked poses with confidence scores, hosted NVIDIA API or local Docker.

Design a study before data is collected — pick a design, randomize, block and stratify, and lay out treatment combinations. Covers factorial and fractional-factorial DOE, crossover, split-plot, Latin squares, and plate layouts.

Turn a paper or results summary into an accurate, accessible graphical abstract as editable SVG — choose the figure type, compose it, then audit palette contrast, greyscale separation and colour-vision safety before submission.

Fill a journal or preprint submission portal from a manuscript directory — choose a venue, generate its submission file, extract the field values from the paper, validate them, then hand a signed-in browser to the author for the final submit.

Pathway and gene-set enrichment on a gene list or ranked table — over-representation (Enrichr, g:Profiler), preranked GSEA, and ssGSEA/GSVA against GO, KEGG, Reactome, and MSigDB, with background choice and FDR handled correctly.

Genomic interval operations and bioinformatics file I/O on Polars DataFrames — overlap, nearest, merge, coverage, complement, and subtract over BED/VCF/BAM/GFF/BigWig, plus streaming FastQC, with cloud-native paths.

Differential gene expression for bulk RNA-seq with PyDESeq2 — formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.

Evaluate scientific claims and evidence quality — assess experimental design validity, identify bias and confounding, and apply grading frameworks such as GRADE and Cochrane Risk of Bias.

Deep generative models for single-cell omics with scvi-tools — probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, and multi-modal integration (totalVI, MultiVI).

Sample-size and power calculations for planning a study — a priori power, minimum detectable effect, and power curves. Closed-form for t-tests, ANOVA, proportions, correlation, and regression; Monte Carlo simulation for the rest.