数据处理
数据转换、清洗与 ETL
bio-alignment-msa-parsing
GPTomics
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
bio-molecular-io
GPTomics
Reads, writes, and converts molecular file formats (SMILES, SDF, MOL2, PDB) using RDKit and Open Babel. Handles structure parsing, canonicalization, and full standardization pipeline including sanitization, normalization, and tautomer canonicalization. Use when loading chemical libraries, converting formats, or preparing molecules for analysis.
bio-single-cell-splicing
GPTomics
Analyzes alternative splicing at single-cell resolution using BRIE2 for probabilistic PSI estimation or leafcutter2 for cluster-based analysis with NMD detection. Identifies cell-type-specific splicing patterns. Use when analyzing isoform usage in scRNA-seq or finding splicing differences between cell populations.
bio-atac-seq-footprinting
GPTomics
Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.
bio-chipseq-peak-annotation
GPTomics
Annotate ChIP-seq peaks to genomic features and genes using ChIPseeker. Assign peaks to promoters, exons, introns, and intergenic regions. Find nearest genes and calculate distance to TSS. Generate annotation plots and statistics. Use when annotating ChIP-seq peaks to genomic features.
bio-splicing-quantification
GPTomics
Quantifies alternative splicing events (PSI/percent spliced in) from RNA-seq using SUPPA2 from transcript TPM or rMATS-turbo from BAM files. Calculates inclusion levels for skipped exons, alternative splice sites, mutually exclusive exons, and retained introns. Use when measuring splice site usage or isoform ratios from RNA-seq data.
bio-pileup-generation
GPTomics
Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.
bio-chipseq-motif-analysis
GPTomics
De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.
bio-clinical-databases-gnomad-frequencies
GPTomics
Query gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.
bio-chipseq-differential-binding
GPTomics
Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.
bio-atac-seq-differential-accessibility
GPTomics
Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments.
bio-sam-bam-basics
GPTomics
View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
bio-clinical-databases-hla-typing
GPTomics
Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for transplant matching, neoantigen prediction, or pharmacogenomic screening.
bio-alignment-io
GPTomics
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
bio-atac-seq-atac-peak-calling
GPTomics
Call accessible chromatin regions from ATAC-seq data using MACS3 with ATAC-specific parameters. Use when identifying open chromatin regions from aligned ATAC-seq BAM files, different from ChIP-seq peak calling.
bio-causal-genomics-colocalization-analysis
GPTomics
Test whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant.
bio-clinical-databases-clinvar-lookup
GPTomics
Query ClinVar for variant pathogenicity classifications, review status, and disease associations via REST API or local VCF. Use when determining clinical significance of variants for diagnostic or research purposes.
bio-causal-genomics-mediation-analysis
GPTomics
Decompose genetic effects into direct and indirect paths through mediating variables using the mediation R package. Tests whether gene expression, methylation, or other molecular phenotypes mediate the effect of genetic variants on disease. Use when testing whether a molecular phenotype mediates the genotype-to-phenotype relationship.
bio-causal-genomics-pleiotropy-detection
GPTomics
Detect and correct for horizontal pleiotropy in Mendelian randomization analyses using MR-PRESSO for outlier removal, MR-Egger regression for directional pleiotropy, and Steiger filtering for variant directionality. Use when validating MR results, detecting pleiotropic instruments, or running sensitivity analyses for causal inference.
bio-chipseq-super-enhancers
GPTomics
Identifies super-enhancers from H3K27ac ChIP-seq data using ROSE and related tools. Use when studying cell identity genes, cancer-associated regulatory elements, or master transcription factor binding regions that cluster into large enhancer domains.
bio-splicing-qc
GPTomics
Assesses RNA-seq data quality for splicing analysis including junction saturation curves, splice site strength scoring, and junction coverage metrics using RSeQC. Use when evaluating data suitability for splicing analysis or troubleshooting low event detection.
bio-causal-genomics-mendelian-randomization
GPTomics
Estimate causal effects between exposures and outcomes using genetic variants as instrumental variables with TwoSampleMR. Implements IVW, MR-Egger, weighted median, and MR-PRESSO methods for robust causal inference from GWAS summary statistics. Use when testing whether an exposure causally affects an outcome using genetic instruments.
bio-clinical-databases-dbsnp-queries
GPTomics
Query dbSNP for rsID lookups, variant annotations, and cross-references to other databases. Use when mapping between rsIDs and genomic coordinates or retrieving basic variant information.
bio-alignment-pairwise
GPTomics
Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.