jaechang-hits
@jaechang-hits
Public Skills
gnomad-database
by jaechang-hits
"Query gnomAD v4 population variant frequencies via GraphQL API. Retrieve allele counts and frequencies stratified by ancestry group (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint metrics (pLI, LOEUF, missense z-score), and read depth coverage. Identify variants with low population frequency or under evolutionary constraint. For clinical pathogenicity classifications use clinvar-database; for GWAS associations use gwas-database."
dbsnp-database
by jaechang-hits
"Query NCBI dbSNP for SNP records by rsID, gene, or genomic region via E-utilities (esearch, efetch, epost) and NCBI Variation Services REST API. Retrieve allele data, minor allele frequency, variant class (SNV, indel, MNV), clinical significance links, and cross-database IDs (ClinVar, dbVar, 1000G). Free access; 3 req/sec without API key, 10 req/sec with key. For clinical pathogenicity classifications use clinvar-database; for population frequencies use gnomad-database."
harmony-batch-correction
by jaechang-hits
"Batch correction for single-cell RNA-seq (and other omics) with Harmony. Removes technical batch effects from PCA embeddings while preserving biological variation. Use after PCA, before UMAP/neighbors. Fast and scalable to millions of cells. Python (harmonypy, scanpy integration) and R (Seurat) APIs."
geo-database
by jaechang-hits
"Query NCBI Gene Expression Omnibus (GEO) for gene expression datasets and sample metadata via GEOparse Python library and E-utilities. Search datasets by keyword/organism/platform, download GSE series matrices, parse GPL platform annotations, extract GSM sample metadata, and load expression matrices into pandas. For single-cell data use cellxgene-census; for programmatic multi-DB access use gget-genomic-databases."
gene-database
by jaechang-hits
"Query NCBI Gene via E-utilities for curated gene records across 1M+ taxa. Retrieve official gene symbols, aliases, RefSeq accessions, summary descriptions, genomic coordinates, GO annotations, and interaction data. Use for gene ID resolution, cross-species queries, and gene function summaries. For sequence retrieval use Ensembl; for expression data use geo-database."
omero-integration
by jaechang-hits
"OMERO is an open-source platform for biological image data management. Use the omero-py Python client to connect to an OMERO server, search and retrieve images as numpy arrays, annotate images with tags and key-value pairs, manage ROIs, and integrate OMERO image data into downstream analysis pipelines — all programmatically without the OMERO desktop GUI."
pymc-bayesian-modeling
by jaechang-hits
"Bayesian modeling with PyMC 5. 8-step workflow: define model, set priors, define likelihood, sample (NUTS/ADVI), diagnose (R-hat, ESS, divergences), interpret posteriors, compare models (LOO/WAIC), predict. Hierarchical, logistic, GP model variants. Prior/posterior predictive checks."
bcftools-variant-manipulation
by jaechang-hits
"Command-line toolkit for VCF/BCF variant file manipulation. Filter, merge, annotate, query, normalize, and compute statistics on variant call files. Essential for post-variant-calling pipelines: quality filtering, multi-sample merging, rsID annotation, and genotype extraction. Companion to samtools in the HTSlib ecosystem. Use GATK instead for complex indel realignment during variant calling; use VCFtools instead for population genetics statistics."
flowio-flow-cytometry
by jaechang-hits
"Parse and create FCS (Flow Cytometry Standard) files v2.0-3.1. Read event data as NumPy arrays, extract channel metadata, handle multi-dataset files, export to CSV/FCS. For advanced gating and compensation use FlowKit."
geniml
by jaechang-hits
"Geniml is a Python library for genomic interval machine learning. Train and apply region2vec embeddings to convert BED file regions into numeric vectors, load and index genomic interval datasets for ML pipelines, search embedding spaces with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, cross-sample region comparison, and building ML models on genomic intervals."
anndata-data-structure
by jaechang-hits
"Annotated data matrices for single-cell genomics. AnnData stores expression data (X) with observation metadata (obs), variable metadata (var), layers, embeddings (obsm/varm), graphs (obsp/varp), and unstructured data (uns). Use for .h5ad/.zarr file handling, dataset concatenation, and scverse ecosystem integration. For analysis workflows use scanpy; for probabilistic models use scvi-tools."
gtars
by jaechang-hits
"GTARS is a Rust-backed Python library for fast genomic token arithmetic and BED file processing. Perform high-performance BED file I/O, genomic interval set operations (intersect, merge, complement, subtract), tokenization of genomic regions against a universe, and universe construction. Use for preprocessing large BED file collections, building token vocabularies for ML pipelines, and computing interval statistics at scale."
gseapy-gene-enrichment
by jaechang-hits
"Gene set enrichment analysis (GSEA) and over-representation analysis (ORA) for RNA-seq and proteomics data. Wraps Enrichr API for ORA against MSigDB, KEGG, GO, and 200+ gene set databases; implements preranked GSEA for ranked gene lists from differential expression. Outputs enrichment tables and GSEA running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation of differential expression results."
archs4-database
by jaechang-hits
"Query uniformly processed RNA-seq gene expression profiles, tissue-specific expression patterns, and co-expression networks from the ARCHS4 database REST API. Retrieve z-score normalized expression across 1M+ human and mouse samples, find co-expressed genes, search samples by metadata, and download HDF5 expression matrices. For variant-level population genetics use gnomad-database; for pathway enrichment from gene lists use gget-genomic-databases (Enrichr)."
clinical-decision-support-documents
by jaechang-hits
"Guidelines for generating clinical decision support (CDS) documents: patient cohort analyses (biomarker-stratified outcomes) and treatment recommendation reports (GRADE-graded evidence). Covers document structure, executive summary design, evidence grading (GRADE 1A–2C), statistical reporting (HR, CI, survival), and biomarker integration. Use when creating pharmaceutical research documents, clinical guidelines, or regulatory submissions."
clinvar-database
by jaechang-hits
"Query NCBI ClinVar via E-utilities REST API for clinical significance, pathogenicity classifications, and disease associations of genetic variants. Search by gene, rsID, condition, or review status. Returns structured variant records: ClinSig, submitter data, conditions, HGVS expressions. For GWAS associations use gwas-database; for variant consequence prediction use Ensembl VEP."
bwa-mem2-dna-aligner
by jaechang-hits
"Fast short-read DNA aligner for whole-genome, whole-exome, and ChIP-seq alignment to a reference genome. BWA-MEM2 is the 2× faster successor to BWA-MEM; outputs SAM/BAM with read group headers required by GATK. Produces primary alignments with supplementary records for chimeric reads. Use STAR instead for RNA-seq splice-aware alignment; use Bowtie2 as an alternative with comparable accuracy."
encode-database
by jaechang-hits
"Query the ENCODE Portal REST API for regulatory genomics data: TF ChIP-seq experiments, ATAC-seq/DNase-seq accessibility peaks, histone mark tracks, and RNA-seq datasets across 1000+ cell types and tissues. Search experiments by assay, biosample, or target protein; download BED/bigWig files; retrieve candidate cis-regulatory elements (cCREs) from ENCODE SCREEN by genomic region or gene. Use for finding regulatory tracks to annotate variants, identifying open chromatin in a cell type of interest, and downloading peak files for ChIP-seq or ATAC-seq analysis. For regulatory variant scoring use regulomedb-database; for GWAS associations use gwas-database."
gatk-variant-calling
by jaechang-hits
"GATK Best Practices pipeline for germline SNP and indel variant calling from WGS/WES BAM files. Runs HaplotypeCaller in GVCF mode per sample, consolidates with GenomicsDBImport, joint-genotypes with GenotypeGVCFs, and applies VQSR or hard filters. Requires BWA-MEM2-aligned, markdup, and BQSR-processed BAMs. Use DeepVariant instead for a faster deep-learning alternative; GATK is the ENCODE/NIH standard for research and clinical genomics."
cellpose-cell-segmentation
by jaechang-hits
"Deep learning cell and nucleus segmentation from fluorescence and brightfield microscopy images. Uses pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm that segments cells without requiring retraining on new image types. Outputs label masks for downstream morphology measurement and tracking. Use scikit-image watershed for rule-based segmentation; use Cellpose when deep learning generalization across staining conditions is needed."
deeptools-ngs-analysis
by jaechang-hits
"NGS analysis CLI toolkit for ChIP-seq, RNA-seq, ATAC-seq. BAM→bigWig conversion with normalization (RPGC, CPM, RPKM), sample correlation/PCA, heatmaps and profile plots around genomic features, enrichment fingerprints. For alignment use STAR/BWA; for peak calling use MACS2."
pyimagej-fiji-bridge
by jaechang-hits
"Python bridge to ImageJ2/Fiji enabling macro execution, plugin calls (Bio-Formats, TrackMate, Analyze Particles), bidirectional NumPy↔ImagePlus/ImgLib2 data exchange, and ImageJ Ops from Python. Use for automating Fiji-specific workflows headlessly from Python scripts. Use scikit-image instead for pure Python pipelines that do not require Fiji plugins; use napari for interactive visualization."
etetoolkit
by jaechang-hits
"ETE Toolkit (ETE3) is a Python environment for phylogenetic tree analysis, manipulation, and visualization. Parse Newick/NHX/PhyloXML trees, traverse and annotate nodes, render publication-quality figures with TreeStyle/NodeStyle, integrate NCBI taxonomy for taxon-aware operations, and run PhyloTree workflows for comparative genomics. Use for building species trees, gene family evolution analysis, and annotated tree figures."
gget-genomic-databases
by jaechang-hits
"Unified CLI/Python interface to 20+ genomic databases. Use for quick gene lookups (Ensembl search/info/seq), BLAST/BLAT sequence alignment, AlphaFold structure prediction, enrichment analysis (Enrichr), disease/drug associations (OpenTargets), single-cell data (CELLxGENE), cancer genomics (cBioPortal/COSMIC), and expression correlation (ARCHS4). Covers genomics, proteomics, and disease domains. For batch processing or advanced BLAST use biopython; for multi-database Python SDK workflows use bioservices."