Tagged bioinformatics

Normalize expression matrices with log2, z-score or min-max scaling before analysis

Map gene structure and exon layouts for analysis and publication figures

Plan phenotype scoring studies that intersect gene signatures with DEGs and outcomes

Train embeddings on genomic interval BED files for chromatin accessibility studies

Search NCBI GEO for expression datasets by keyword, organism or author

Query 20+ bioinformatics databases by gene or protein ID from a CLI or Python

Run GO and KEGG enrichment on a gene list and plot a combined dot chart

Run GSEA on a ranked gene list and produce enrichment tables and plots

Run fast genomic interval overlaps, coverage tracks and tokenization in Rust

Beautify gene expression heatmaps with clustering trees and annotation tracks

Look up official gene symbols, names and identifiers in HGNC

Run immune pathway GSVA or ssGSEA with Reactome gene sets and export a heatmap

Retrieve KEGG genes, pathways, compounds and drugs through the KEGG API

Query KEGG pathways, genes and compounds over direct REST calls and map IDs

Impute missing values in expression matrices with group-aware KNN after gene filtering

Interpret alpha and beta diversity results from 16S rRNA sequencing studies

Generate publication-quality sequence logos for DNA and protein motifs

Plan studies that combine Mendelian randomization with single-cell transcriptomics

Plan bioinformatics and machine learning papers on non-cancer diseases

Check whether bioinformatics software licenses allow commercial use, such as GPL vs MIT

Plan cancer transcriptome studies built around programmed cell death genes

Search and download RCSB Protein Data Bank structures by text, sequence or 3D shape

Build protein-protein interaction networks from gene lists using an offline STRING cache

Plan diagnostic biomarker studies built on process-related genes and nomograms

Visualize single-cell pseudotime trajectories and branch structure in review-ready plots

Run DESeq2-style differential expression on bulk RNA-seq counts in Python

Query Reactome pathways, reactions and gene list enrichment results

Check Sanger sequencing chromatograms for quality and flag unreliable base calls

Run single-cell RNA-seq QC, clustering, UMAP and differential expression with Scanpy

Label single-cell clusters with cell types using marker gene evidence

Correct batch effects and integrate single-cell omics with scVI deep generative models

Align nucleotide and protein sequences against databases using the NCBI BLAST API

Design complete single-cell research plans for disease, biomarker and validation studies

Plan network pharmacology studies for one drug adverse effect using hub gene analysis

Plan oncology studies on one candidate gene covering expression and prognosis

Search Broad Single Cell Portal datasets by organism, tissue, disease or cell type

Estimate immune infiltration with ssGSEA or GSVA and compare case and control groups

Map protein IDs and pull STRING interaction networks with enrichment analysis

Identify regulating transcription factors for a gene list and plot a TF-target network

Build graph neural networks for molecules and proteins in PyTorch

Search UniProtKB, fetch protein entries and map identifiers across databases

Annotate gene variants with ClinVar and dbSNP records

Label volcano plots of differential expression results with key genes

Build WGCNA co-expression modules from bulk expression and correlate them with traits

Interpret mechanisms behind imaging and genomic links with competing explanations

Run and audit bulk, single-cell and spatial RNA workflows with traceable quality checks

Formulate ordered sequencing with local window variables and overlap penalties in MIP

Find small-molecule binders for protein targets through virtual screening and filtering

Discover bioinformatics tools, APIs and R packages before writing analysis code