# Biology with Propab

Differential expression, single-cell, structures, variants, trials and the literature behind them, worked through end to end, with every step you can open.

## Real runs

- **Genomics** — [replay](https://propabai.com/replay/share_h1s8bFWvG58GslT9dUy_gERxjXYmfjFk): “Take a public bulk RNA-seq study from GEO comparing treated and control samples in a human cell line (pick one with raw counts available), run a proper differential expression analysis with DESeq2 or an equivalent, and follow it with pathway enrichment. Deliver a volcano plot, a heatmap of the top genes, the ranked gene table and a short report that says which pathways move and how confident we should be.” Made: Volcano plot, Heatmap, Report.
- **Structural biology** — [replay](https://propabai.com/replay/share_LztRLxSuBAPeXcQCNXoR_uxF4SsOB36l): “End-to-end computational study of KRAS G12C. Do real computation, not mock data. Every number you report must come from code you ran. If a step fails or a tool can’t be installed, say so and use the closest working alternative. Fetch human KRAS, HRAS, NRAS and 10–15 RAS orthologs across species, align them, score per-residue conservation and build the phylogenetic tree…” Made: Alignment, Phylogeny, 3D structures.

## Workflows ready today

### Experimental and applied

- **Preclinical literature review** — Searches the literature on a target or drug and extracts in-vitro and in-vivo findings into a table, one source per row.
- **Clinical-trial landscape** — Maps registered trials for a disease by mechanism, phase, status and sponsor, with counts and a timeline.
- **Target–disease evidence** — Pulls Open Targets association scores and the evidence behind them for a target or disease, by evidence type.
- **Disease progression from longitudinal omics** — Models how molecular profiles change over time in patients and groups patients by trajectory.
- **Experimental design and power** — Computes power and sample size, lays out batches and fixes the multiple-testing plan before data are collected.
- **ADMET prediction and docking** — Predicts ADMET liabilities and docks compounds into a target pocket with a re-docking control.
- **Biomarker panel selection** — Selects a minimal biomarker panel with LASSO inside cross-validation and reports held-out performance.
- **Survival analysis** — Kaplan–Meier curves, Cox regression and risk groups for a cohort.
- **Signature reversal (connectivity map)** — Finds compounds whose LINCS L1000 signatures oppose a disease signature.
- **Literature review** — Searches and synthesises the literature on a target, disease or question, with a source for every claim.
- **Trials for a target** — Lists ClinicalTrials.gov records for drugs against a target, with phase, status, sponsor and NCT id.
- **Report and figure deliverables** — Builds the run's report as PDF, Word or PowerPoint with publication-style figures.
- **Tractability and druggability** — Reports Open Targets tractability buckets and detects pockets on the target structure.
- **Drug repurposing** — Proposes new indications for existing drugs from shared targets, network proximity and signature reversal.
- **Adverse-event signals** — Computes disproportionality (PRR/ROR) signals for a drug from FDA adverse-event reports.
- **Virtual screening** — Docks a compound library against a target, triages the hits and summarises structure–activity.
- **Off-target and safety pharmacology** — Predicts secondary-pharmacology, hERG and selectivity liabilities for compounds.
- **Real-world evidence** — Outcomes analysis on an EHR or claims extract the researcher provides.
- **Clinical trial design and simulation** — Simulates adaptive or enrichment designs to compare power and error rates.
- **Meta-analysis** — Pools effect sizes across studies with random-effects models and reports heterogeneity.
- **Microplate layout** — Plate layouts with randomisation, edge-effect handling and covariate balance.

### Bioinformatics

- **Find public omics datasets** — Finds and catalogues public datasets that fit a question across GEO, ENA/SRA, ArrayExpress, PRIDE, MetaboLights, CELLxGENE and GDC, and says which are usable.
- **Pathway enrichment (GSEA / ORA)** — Runs GSEA or over-representation on a gene list against MSigDB, GO and Reactome sets.
- **Methods landscape review** — Compares tools and pipelines for a task from published benchmarks.
- **Phylogenetic tree from sequences** — Aligns homologous sequences, trims the alignment and infers a maximum-likelihood tree with support values.
- **Homology search and domain annotation** — Finds homologues with BLAST and HMMER against Swiss-Prot and Pfam and annotates protein domains.

### Single-cell

- **Target prioritisation from single-cell data** — Finds the cell types that change in a disease in public single-cell data and ranks genes by that signal plus human genetic evidence.
- **scRNA-seq clustering and cell-type annotation** — Takes a count matrix through QC, normalisation, batch integration, clustering and marker-based cell-type labels.
- **Trajectories, pseudotime and RNA velocity** — Orders cells along a differentiation process, finds genes that change along it, and estimates RNA velocity where spliced/unspliced counts exist.
- **Cell–cell communication** — Infers ligand–receptor interactions between annotated cell types.
- **Transcription-factor activity and regulatory networks** — Scores each TF's regulon activity per cell and infers TF–target co-expression networks.
- **Perturb-seq / CROP-seq screen analysis** — Assigns guides to cells, removes unperturbed cells and measures each knockdown's transcriptional effect.
- **Spatial transcriptomics (Visium)** — QC, spatial domains and neighbourhood enrichment between cell types on Visium data.
- **Virtual perturbation prediction** — Predicts single-cell responses to unseen genetic perturbations with a foundation model and scores them on held-out data. (needs a GPU you connect)
- **scATAC, multiome and CITE-seq** — Chromatin-accessibility and multimodal single-cell analysis: peaks, clustering, gene activity and protein–RNA integration.

### Population genetics

- **GWAS to genes (TWAS)** — Tests which genes' predicted expression is associated with a trait, per tissue, from GWAS summary statistics.
- **Fine-mapping GWAS loci** — Gives each variant at a GWAS locus a probability of being causal, with credible sets.
- **Mendelian randomisation** — Estimates whether an exposure causally affects an outcome from two GWAS, with IVW, MR-Egger, weighted median and sensitivity checks.
- **Polygenic risk scores** — Applies a published PGS Catalog score to genotypes and compares score distributions across populations.
- **Population structure and selection scans** — Genotype PCA, population structure and selection statistics from population genotypes.

### Genomics

- **Variant annotation** — Annotates a VCF with consequence, population frequency and clinical significance, and flags likely damaging variants.
- **Bulk RNA-seq differential expression** — Tests genes for differential expression between conditions from a count matrix and returns fold changes, adjusted p-values and plots.
- **Upstream-regulator analysis** — Finds the transcription factors most likely driving an observed differential-expression signature.
- **Genetic constraint gating** — Flags loss-of-function-intolerant genes (pLI, LOEUF) in a gene list.
- **Cancer cohort genomics** — Mutation and copy-number frequencies across TCGA and MSK cohorts, with mutant-versus-wild-type comparisons.
- **Consensus disease signature** — Combines several public expression studies into one up/down disease signature by meta-analysis.
- **Tissue expression and safety** — Shows where a target is expressed across normal tissues and cell types.
- **RNA-seq quantification from FASTQ** — Pseudo-aligns raw RNA-seq reads to a transcriptome and produces gene counts.

### Systems biology

- **Gene co-expression modules** — Finds co-expressed gene modules and their hub genes and relates modules to sample traits.
- **Multi-omics factor analysis (MOFA+)** — Finds shared and layer-specific latent factors across two or more omics layers measured on the same samples.
- **Multi-omics sample clustering** — Clusters samples across transcriptome, proteome and metabolome and checks cluster stability.
- **Knowledge-graph target ranking** — Ranks targets by multi-hop evidence paths through a biomedical knowledge graph.
- **Gene essentiality on a metabolic model** — Knocks out every gene in a genome-scale metabolic model on chosen media and reports which genes are essential and which only on one medium.
- **Kinetic model simulation** — Loads a curated kinetic model from BioModels and runs time courses, steady states and parameter scans.

### Proteomics

- **Proteomics differential abundance** — Tests proteins for differential abundance from a quantified mass-spec table, with FDR control.

### Microbiology

- **Microbiome community analysis** — Compares alpha and beta diversity and differential abundance across groups from a taxon count table.
- **Bacterial genome assembly and gene calling** — Trims reads, assembles an isolate genome, predicts genes and reports assembly statistics.
- **Shotgun read classification against a chosen panel** — Builds a Kraken2 index from the genomes you choose and classifies metagenomic reads against it.

### Molecular biology

- **PCR / qPCR primer design** — Designs primer pairs and TaqMan probes for a target and checks dimers, hairpins and off-target hits.
- **Restriction mapping** — Digests a sequence in silico and predicts fragment sizes and the gel.

### Structural biology

- **Protein structure prediction** — Predicts a protein or complex structure with per-residue confidence. (needs a GPU you connect)
- **De-novo binder and antibody design** — Generates binder backbones against a target site, designs sequences and filters by predicted fold and interface. (needs a GPU you connect)
- **Ligand binding-mode analysis** — Picks the best co-crystal structure and lists the pocket residues that contact the ligand.
- **Targeted protein degradation design** — Models ternary complexes and proposes PROTAC linkers. (needs a GPU you connect)
- **Predicted versus experimental structure** — Superposes an AlphaFold model onto the experimental structure and reports error by region against the model's confidence.
- **Protein molecular dynamics** — Solvates, equilibrates and simulates a protein and analyses its stability and flexibility. (needs a GPU you connect)

### Biochemistry

- **Binding-affinity model** — Trains and validates a model that predicts compound–target affinity from public measurements, with scaffold and cold-target splits.
- **Drug potency and selectivity profile** — Pulls IC50, Ki and Kd for a drug against its target and its off-targets.
- **Enzyme kinetics fitting** — Fits Michaelis–Menten and inhibition models to rate data and compares them.

### Immunology

- **Cell-therapy product QC** — Single-cell QC scorecard for a cell-therapy product: viability, composition, activation and exhaustion states.
- **Immune repertoire (TCR/BCR)** — Clonotypes, clonality and diversity from AIRR-formatted repertoires.
- **Immune-cell deconvolution** — Estimates immune-cell fractions in bulk expression samples.

### Cell biology

- **Flow cytometry analysis** — Gates or clusters cytometry events and reports population frequencies.

### Microscopy

- **Digital pathology** — Tiles whole-slide images and trains slide-level classifiers. (needs a GPU you connect)
- **Nuclei segmentation and measurement** — Segments nuclei or cells in fluorescence images and measures size, shape and intensity per object.

### Neuroscience

- **Intrinsic electrophysiology from patch-clamp** — Extracts rheobase, f–I slope, resting potential, input resistance and membrane time constant from current-clamp sweeps.