jaechang-hits
@jaechang-hits
Public Skills
clinpgx-database
by jaechang-hits
"Query PharmGKB (Clinical Pharmacogenomics) database via REST API for drug-gene interactions, clinical annotations, dosing guidelines (CPIC, DPWG), variant-drug associations, and pharmacogenomic pathways. Search by gene, drug, rsID, or pathway. No authentication required. For somatic cancer pharmacogenomics use cosmic-database or opentargets-database; for drug structures use chembl-database-bioactivity."
bioservices-multi-database
by jaechang-hits
Unified Python interface to 40+ bioinformatics web services via bioservices library. Query UniProt proteins, KEGG pathways, ChEMBL/ChEBI/PubChem compounds, run BLAST searches, map identifiers across databases, retrieve GO annotations, and find protein-protein interactions. For single-database deep queries use dedicated tools (gget for Ensembl, pubchempy for PubChem); bioservices excels at cross-database integration workflows.
nnunet-segmentation
by jaechang-hits
"Train and deploy automated medical image segmentation models using nnU-Net's self-configuring framework that auto-selects optimal architecture, preprocessing, and training for any modality. Supports CT, MRI, microscopy, and ultrasound with 2D, 3D full-res, 3D low-res, and cascade configurations. Pipeline: convert dataset → plan and preprocess → train (5-fold cross-validation) → find best configuration → predict → ensemble. Use when classical segmentation fails and annotated training data is available."
deseq2-differential-expression
by jaechang-hits
"Differential expression analysis for bulk RNA-seq using R/Bioconductor DESeq2. Negative binomial GLM with empirical Bayes shrinkage, Wald and LRT tests, multi-factor designs, interaction terms, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap visualization. The R gold standard for DE analysis with native Bioconductor integration. Use pydeseq2-differential-expression for Python-based pipelines; use edgeR for TMM normalization."
cosmic-database
by jaechang-hits
"Query COSMIC (Catalogue Of Somatic Mutations In Cancer) for cancer somatic mutations, gene census data, mutational signatures, drug resistance variants, and cancer gene annotations. REST API v3.1 supports gene/sample/variant queries. Free registration required. For germline clinical variants use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity."
napari-image-viewer
by jaechang-hits
"Interactive multi-dimensional image viewer for scientific microscopy data. Napari displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, and Tracks layers; supports real-time annotation, plugin-based analysis, and headless screenshot export. Core visualization tool for bioimage analysis workflows. Use ImageJ/FIJI for macro-based processing; use napari for Python-native interactive visualization and plugin-based deep learning segmentation review."
ena-database
by jaechang-hits
"European Nucleotide Archive (ENA) REST API access for genomic sequences, raw reads, assemblies, and annotations. Portal API search with query syntax, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM download URLs, taxonomy queries, cross-references. For multi-database Python queries prefer bioservices; for NCBI-specific queries use pubmed-database or Biopython Entrez."
celltypist-cell-annotation
by jaechang-hits
"Automated cell type annotation for scRNA-seq data using pre-trained logistic regression models. CellTypist ships 45+ models covering immune cells, gut, lung, brain, fetal tissues, and cancer microenvironments. Inputs a normalized AnnData; outputs per-cell predicted labels, majority-vote cluster labels, and confidence scores. Use when you want fast, reproducible, reference-model-backed annotation without manual marker inspection."
featurecounts-rna-counting
by jaechang-hits
"Counts aligned RNA-seq reads overlapping gene features in a GTF annotation. Takes sorted BAM files from STAR alignment and a GTF file; outputs a tab-delimited count matrix per gene across all samples. Handles strandedness (0=unstranded, 1=stranded, 2=reverse-stranded), paired-end, and multi-sample batch counting in a single command. Use Salmon instead for alignment-free quantification; use featureCounts when STAR BAMs already exist and a gene-level count matrix is needed."
cellxgene-census
by jaechang-hits
"Query CELLxGENE Census (61M+ cells) programmatically. Search by cell type, tissue, disease, organism. Get expression matrices as AnnData, stream large queries out-of-core, train PyTorch models on single-cell data. For analyzing your own data use scanpy; for annotated data manipulation use anndata."
ensembl-database
by jaechang-hits
"Query Ensembl REST API for gene/transcript/variant annotations across 300+ species. Retrieve gene info by symbol/ID, sequence, cross-references (HGNC, RefSeq, UniProt), variants, regulatory features, comparative genomics. For bulk local access use pyensembl; for pathway lookups use kegg-database or reactome-database."
pydicom-medical-imaging
by jaechang-hits
"Pure Python DICOM library for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM files, extract pixel data as NumPy arrays, access/modify metadata tags, apply windowing (VOI LUT), anonymize PHI, build DICOM from scratch, process series into 3D volumes. For whole-slide pathology images use histolab; for NIfTI neuroimaging use nibabel."
bedtools-genomic-intervals
by jaechang-hits
"Toolkit for genomic interval operations on BED, BAM, GFF, VCF files. Find overlapping regions, merge adjacent intervals, calculate coverage depth, extract FASTA sequences, find nearest features, and manipulate interval coordinates. Essential for ChIP-seq peak annotation, target region filtering, and genome arithmetic. Use tabix instead for indexed single-region queries; use deeptools for normalized bigWig coverage."
simpleitk-image-registration
by jaechang-hits
"Register, segment, filter, and resample 3D medical images (MRI, CT, microscopy) using SimpleITK's Python API with support for DICOM, NIfTI, and multi-modal image analysis. Provides rigid/affine/deformable registration, threshold and region-growing segmentation, Gaussian and morphological filtering, label statistics, and format conversion. Use when aligning volumetric images across timepoints or modalities, automating segmentation of fluorescence microscopy, or converting DICOM series to NIfTI for analysis pipelines."
trackpy-particle-tracking
by jaechang-hits
"Python library for tracking particles (fluorescent spots, colloids, vesicles, cells) in video microscopy using the Crocker-Grier algorithm. Core modules: locate particles in single frames, batch-process image sequences, link positions into trajectories, filter short-lived tracks, and compute mean squared displacement (MSD) for diffusion analysis. Supports 2D and 3D tracking with subpixel accuracy. Integrates with pims for reading TIF stacks, AVI, and image series. Use when you need quantitative single-particle tracking (SPT) from fluorescence or brightfield video and downstream diffusion coefficient extraction."
imaging-data-commons
by jaechang-hits
"Query NCI Imaging Data Commons (IDC) for cancer radiology and pathology imaging datasets hosted on Google Cloud. Search DICOM collections by modality, anatomical site, cancer type, or collection name. Download images via Google Cloud Storage or IDAT tool. 50TB+ of publicly accessible DICOM images. Requires Google Cloud account for large downloads; small queries work without billing. For local DICOM processing use pydicom-medical-imaging; for whole-slide pathology use histolab."
cnvkit-copy-number
by jaechang-hits
"Detect somatic copy number variants (CNVs) from WES, WGS, or targeted sequencing BAM files with CNVkit v0.9.x. Pipeline: calculate bin-level coverage in target/antitarget regions, normalize against a reference, segment copy ratios with CBS or HMM, call amplifications and deletions, generate scatter/diagram plots, estimate tumor purity and ploidy, and export to VCF/SEG. Both CLI and Python API (cnvlib) shown. Use GATK CNV instead for deep WGS with population-scale controls; use CNVkit for targeted or exome sequencing where antitarget bins are critical."
gwas-database
by jaechang-hits
"NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published genome-wide association studies. Query studies, associations, variants, traits, genes, and summary statistics. Build polygenic risk score candidates, analyze variant pleiotropy, download summary statistics for Manhattan plots. No authentication required."
histolab-wsi-processing
by jaechang-hits
"Whole slide image processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC preprocessing. Use for dataset preparation, tile-based deep learning, and slide quality assessment. For advanced spatial proteomics or multiplexed imaging use pathml."
pyhealth
by jaechang-hits
"PyHealth is a Python library for healthcare machine learning. Build clinical prediction models from EHR (Electronic Health Record) data: process MIMIC-III/IV, eICU, and OMOP-CDM datasets, encode medical codes (ICD, ATC, NDC), construct patient-level datasets, and train models (Transformer, RETAIN, GRASP, MedBERT) for tasks including mortality prediction, drug recommendation, readmission, and diagnosis prediction. Alternatives: FIDDLE (EHR preprocessing only), clinical-longformer (NLP on clinical notes only), ehr-ml (EHR embedding only)."
biopython-sequence-analysis
by jaechang-hits
"Biopython toolkit for sequence analysis workflows: parse FASTA/FASTQ/GenBank/GFF with SeqIO, query NCBI databases via Entrez (esearch/efetch/elink), run remote and local BLAST with result parsing, perform pairwise and multiple sequence alignment (PairwiseAligner, MUSCLE/ClustalW), and build/visualize phylogenetic trees (Phylo module). Use for gene family studies, phylogenomics, comparative genomics, and programmatic NCBI pipelines. For PCR design, restriction digestion, and cloning workflows use biopython-molecular-biology; for SAM/BAM alignments use pysam."
scikit-survival-analysis
by jaechang-hits
"Survival analysis and time-to-event modeling with scikit-survival. Cox proportional hazards (standard/elastic net), Random Survival Forests, Gradient Boosting, SVMs for censored data. C-index (Harrell/Uno), Brier score, time-dependent AUC evaluation. Kaplan-Meier, Nelson-Aalen, competing risks. scikit-learn Pipeline/GridSearchCV compatible. For frequentist regression use statsmodels; for Bayesian survival use pymc; for simpler parametric models use lifelines."
cbioportal-database
by jaechang-hits
"Access TCGA and other cancer genomics datasets via cBioPortal REST API. Retrieve somatic mutations, copy number alterations, gene expression profiles, and clinical data (survival, stage, treatment) for thousands of cancer studies. Use for tumor mutation burden analysis, oncoprint queries, and survival analysis. For population variant frequencies use gnomad-database; for drug-gene interactions use dgidb-database."