Browse Skills — Page 42

binance-address-info

TermiX-official/cryptoclaw

Binance Web3 official skill — query any wallet address for token holdings, balances, and portfolio data across BSC, Base, and Solana. Sourced from github.com/binance/binance-skills-hub.

binance-market-rank

TermiX-official/cryptoclaw

Binance Web3 official skill — crypto market rankings including trending tokens, smart money inflow, social hype, meme ranks, and top trader PnL leaderboards. Sourced from github.com/binance/binance-skills-hub.

binance-meme-rush

TermiX-official/cryptoclaw

Binance Web3 official skill — real-time meme token launchpad tracking and AI-powered trending topic discovery on Solana and BSC. Sourced from github.com/binance/binance-skills-hub.

binance-spot

TermiX-official/cryptoclaw

Binance official spot trading skill — place orders, manage accounts, and access real-time market data via Binance Spot API. Sourced from github.com/binance/binance-skills-hub.

binance-token-audit

TermiX-official/cryptoclaw

Binance Web3 official skill — security audit for token contracts, detecting honeypots, rug pulls, and malicious functions across BSC, Base, Solana, and Ethereum. Sourced from github.com/binance/binance-skills-hub.

binance-token-info

TermiX-official/cryptoclaw

Binance Web3 official skill — search tokens, retrieve metadata, real-time market data, and candlestick charts across BSC, Base, and Solana. Sourced from github.com/binance/binance-skills-hub.

binance-trading-signal

TermiX-official/cryptoclaw

Binance Web3 official skill — Smart Money on-chain trading signals tracking professional investor buy/sell activity on BSC and Solana. Sourced from github.com/binance/binance-skills-hub.

binary-analysis-patterns

wshobson/agents

Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing static analysis on binaries.

binary-analysis-patterns

benjaminasterA/antigravity-awesome-skills

Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing...

binary-analysis-patterns-v2

diegosouzapw/awesome-omni-skills

Binary Analysis Patterns workflow skill. Use this skill when the user needs Comprehensive patterns and techniques for analyzing compiled binaries, understanding assembly code, and reconstructing program logic and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

binary-exploitation-methodology

wgpsec/AboutSecurity

二进制漏洞利用基础方法论。涵盖漏洞识别（栈溢出/堆溢出/格式化字符串/UAF）、保护机制分析（ASLR/NX/Stack Canary/PIE/RELRO）、利用策略选择（ret2win/ret2shellcode/ROP/ret2libc）、调试与利用开发流程。当 Agent 需要分析二进制程序漏洞、选择利用策略、或理解保护机制绕过方法时触发。区别于 CTF pwn 技巧，侧重实战方法论。

binary-exploitation-tools

wgpsec/AboutSecurity

二进制漏洞利用工具集参考。涵盖调试工具（GDB/GEF/pwndbg/r2）、利用开发框架（pwntools/ROPgadget/one_gadget）、反编译工具（Ghidra/IDA/Binary Ninja）、动态分析工具（ltrace/strace/valgrind）。当 Agent 需要选择合适的二进制分析工具、了解工具使用方法、或搭建利用开发环境时触发。

binary-triage

cyberkaida/reverse-engineering-assistant

Performs initial binary triage by surveying memory layout, strings, imports/exports, and functions to quickly understand what a binary does and identify suspicious behavior. Use when first examining a binary, when user asks to triage/survey/analyze a program, or wants an overview before deeper reverse engineering.

bindcraft

adaptyvbio/protein-design-skills

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binder-design

adaptyvbio/protein-design-skills

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binding_site_characterization

InternScience/scp

Binding Site Characterization - Characterize binding sites: predict pockets with fpocket and P2Rank, get binding site info from ChEMBL, and visualize. Use this skill for structural biology tasks involving run fpocket pred pocket prank get binding site by id visualize protein. Combines 4 tools from 3 SCP server(s).

binding-characterization

adaptyvbio/protein-design-skills

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bindingdb-database

swaruplab/operon

Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.

bindingdb-skill

openai/plugins

Submit compact BindingDB REST API requests for ligand-target binding lookups by PDB, UniProt, or similarity search. Use when a user wants concise BindingDB summaries; save raw payloads only on request.

binlog-failure-analysis

microsoft/testfx

Analyze MSBuild binary logs to diagnose build failures. USE FOR: build errors that are unclear from console output, diagnosing cascading failures across multi-project builds, tracing MSBuild target execution order, and generally any MSBuild build issues. Requires an existing .binlog file. DO NOT USE FOR: generating binlogs (use binlog-generation), non-MSBuild build systems.

binlog-generation

microsoft/testfx

Generate MSBuild binary logs (binlogs) for build diagnostics and analysis. USE FOR: adding /bl:{} to any dotnet build, test, pack, publish, or restore command to capture a full build execution trace, prerequisite for binlog-failure-analysis and build-perf-diagnostics skills, enabling post-build investigation of errors or performance. Requires MSBuild 17.8+ / .NET 8 SDK+ for {} placeholder; PowerShell needs -bl:{{}}. DO NOT USE FOR: non-MSBuild build systems (npm, Maven, CMake), analyzing an existing binlog (use binlog-failure-analysis instead).

bio-admet-prediction

GPTomics/bioSkills

Predicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety.

bio-alignment-amplicon-clipping

GPTomics/bioSkills

Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.

bio-alignment-filtering

GPTomics/bioSkills

Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.

bio-alignment-indexing

GPTomics/bioSkills

Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.

bio-alignment-io

GPTomics/bioSkills

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-alignment-msa-parsing

GPTomics/bioSkills

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-alignment-msa-statistics

GPTomics/bioSkills

Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.

bio-alignment-multiple

GPTomics/bioSkills

Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.

bio-alignment-pairwise

GPTomics/bioSkills

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.

bio-alignment-sorting

GPTomics/bioSkills

Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.

bio-alignment-structural

GPTomics/bioSkills

Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.

bio-alignment-trimming

GPTomics/bioSkills

Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.

bio-alignment-validation

GPTomics/bioSkills

Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.

bio-atac-seq-allele-specific-accessibility

GPTomics/bioSkills

Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.

bio-atac-seq-atac-peak-calling

GPTomics/bioSkills

Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.

bio-atac-seq-atac-qc

GPTomics/bioSkills

ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.

bio-atac-seq-co-accessibility

GPTomics/bioSkills

Infer cis-regulatory connections (peak-to-peak co-accessibility) from scATAC-seq using Cicero, ArchR getCoAccessibility, or SCENIC+. Use when linking enhancer accessibility to promoter accessibility, identifying enhancer-gene pairs from chromatin alone (without paired RNA), running gene-regulatory inference combining ATAC + RNA, or comparing predicted regulatory contacts against Hi-C/Micro-C ground truth.

bio-atac-seq-consensus-peakset

GPTomics/bioSkills

Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.

bio-atac-seq-deep-learning-atac

GPTomics/bioSkills

Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.

bio-atac-seq-differential-accessibility

GPTomics/bioSkills

Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.

bio-atac-seq-enhancer-gene-linking

GPTomics/bioSkills

Predict enhancer-gene regulatory connections from ATAC-seq using ABC, ENCODE-rE2G, HiChIP, or Cicero. Use when linking distal enhancers to target genes, choosing between contact-aware (ABC, ENCODE-rE2G), accessibility-only (Cicero), and orthogonal (HiChIP H3K27ac, EpiMap) approaches, validating predictions against CRISPRi-FlowFISH gold-standard, or building cell-type-specific regulatory maps for fine-mapping or therapeutic target discovery.

bio-atac-seq-footprinting

GPTomics/bioSkills

Detect transcription factor binding footprints in ATAC-seq using TOBIAS, HINT-ATAC, Wellington, or scprinter. Use when identifying bound TF sites within accessible regions, correcting Tn5 insertion bias before footprinting, choosing between cleavage-based and aggregate-based footprinters, or comparing differential TF activity between conditions.

bio-atac-seq-motif-deviation

GPTomics/bioSkills

Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalents, or distinguishing motif-accessibility signal from per-site footprinting.

bio-atac-seq-nucleosome-positioning

GPTomics/bioSkills

Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.

bio-atac-seq-single-cell-atac

GPTomics/bioSkills

Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peaksets, integrating with paired scRNA-seq, doublet detection (AMULET vs ArchR vs scDblFinder), or running pseudobulk differential accessibility per cluster.

bio-bam-statistics

GPTomics/bioSkills

Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.

bio-basecalling

GPTomics/bioSkills

Convert raw Nanopore signal data (FAST5/POD5) to nucleotide sequences using Dorado basecaller. Covers model selection, GPU acceleration, modified base detection, and quality filtering. Use when processing raw Nanopore data before alignment. Note: Guppy is deprecated; use Dorado for all new analyses.

bio-batch-downloads

GPTomics/bioSkills

Download large datasets from NCBI efficiently using history server, batching, and rate limiting. Use when performing bulk sequence downloads, handling large query results, or production-scale data retrieval.

bio-batch-processing

GPTomics/bioSkills

Process multiple sequence files in batch using Biopython. Use when working with many files, merging/splitting sequences, or automating file operations across directories.

bio-bedgraph-handling

GPTomics/bioSkills

Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.

bio-blast-searches

GPTomics/bioSkills

Run remote BLAST searches against NCBI databases using Biopython Bio.Blast. Use when identifying unknown sequences, finding homologs, or searching for sequence similarity against NCBI's nr/nt databases.

bio-causal-genomics-colocalization-analysis

GPTomics/bioSkills

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-causal-genomics-fine-mapping

GPTomics/bioSkills

Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.

bio-causal-genomics-mediation-analysis

GPTomics/bioSkills

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-mendelian-randomization

GPTomics/bioSkills

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-causal-genomics-pleiotropy-detection

GPTomics/bioSkills

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-cfdna-preprocessing

GPTomics/bioSkills

Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.

bio-chipseq-differential-binding

GPTomics/bioSkills

Identifies differentially bound ChIP-seq regions between conditions using DiffBind (from BAMs), DESeq2, or PyDESeq2 (from count matrices). Handles normalization, statistical testing, and fold-change estimation with ChIP-seq-specific considerations. Use when comparing ChIP-seq binding between experimental conditions.

bio-chipseq-motif-analysis

GPTomics/bioSkills

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-chipseq-peak-annotation

GPTomics/bioSkills

Annotate ChIP-seq peaks to genomic features and nearest genes. Classify peaks as promoter, exon, intron, or intergenic using ChIPseeker (R), HOMER annotatePeaks.pl (CLI), or Python (pandas/pyranges). Supports pre-built annotation databases and custom GTF files. Handles promoter definition, feature priority, category collapsing, and signed distance-to-TSS. Use when assigning genomic context to ChIP-seq peaks or linking peaks to target genes.

bio-chipseq-peak-calling

GPTomics/bioSkills

ChIP-seq peak calling using MACS3 and HOMER findPeaks. Call narrow peaks for transcription factors or broad peaks for histone modifications. Supports single-caller and multi-caller consensus approaches, input control, fragment size modeling, and various output formats. Use when calling peaks from ChIP-seq alignments.

bio-chipseq-qc

GPTomics/bioSkills

ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.

bio-chipseq-super-enhancers

GPTomics/bioSkills

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-chipseq-visualization

GPTomics/bioSkills

Visualize ChIP-seq data using deepTools, Gviz, and ChIPseeker. Create heatmaps, profile plots, and genome browser tracks. Visualize signal around peaks, TSS, or custom regions. Use when visualizing ChIP-seq signal and peaks.

bio-clinical-biostatistics-categorical-tests

GPTomics/bioSkills

Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, and Cochran-Mantel-Haenszel tests. Computes effect sizes and post-hoc pairwise comparisons. Use when analyzing categorical outcomes or testing treatment-outcome independence in clinical trials.

bio-clinical-biostatistics-cdisc-data

GPTomics/bioSkills

Reads and prepares CDISC SDTM clinical trial data for analysis. Handles domain tables (DM, AE, EX, VS, LB), USUBJID-based joins, event-to-subject aggregation, and SUPPQUAL pivoting. Use when working with clinical trial datasets in CDISC/SDTM format or .xpt files.

bio-clinical-biostatistics-effect-measures

GPTomics/bioSkills

Computes and interprets treatment effect measures including odds ratios, risk ratios, number needed to treat, and confidence intervals from clinical trial data. Covers crude and adjusted measures, non-collapsibility of odds ratios, and forest plot visualization. Use when reporting treatment effects or comparing effect sizes across clinical studies.

bio-clinical-biostatistics-logistic-regression

GPTomics/bioSkills

Performs logistic regression for clinical trial outcomes including binary, ordinal, and multinomial models. Extracts odds ratios with confidence intervals, handles covariate adjustment, and provides Firth penalized regression for rare events or separation. Use when modeling binary or ordinal endpoints from clinical data.

bio-clinical-biostatistics-subgroup-analysis

GPTomics/bioSkills

Performs stratified and subgroup analyses for clinical trial data. Covers Mantel-Haenszel pooling, Breslow-Day homogeneity testing, interaction terms in regression, multiple comparisons correction, and forest plot visualization. Use when analyzing treatment effects across patient subgroups or controlling for stratification variables.

bio-clinical-biostatistics-trial-reporting

GPTomics/bioSkills

Prepares statistical tables and reports for clinical trials following regulatory standards. Generates Table 1 baseline characteristics, defines analysis populations (ITT, per-protocol, safety), performs multiple imputation for missing data, and follows CONSORT and ICH E9 guidelines. Use when creating analysis reports, handling missing data, or preparing regulatory submissions from clinical trials.

bio-clinical-databases-clinvar-lookup

GPTomics/bioSkills

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-clinical-databases-dbsnp-queries

GPTomics/bioSkills

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-clinical-databases-gnomad-frequencies

GPTomics/bioSkills

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-clinical-databases-hla-typing

GPTomics/bioSkills

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-clinical-databases-myvariant-queries

GPTomics/bioSkills

Query myvariant.info API for aggregated variant annotations from multiple databases (ClinVar, gnomAD, dbSNP, COSMIC, etc.) in a single request. Use when annotating variants with clinical and population data from multiple sources simultaneously.

bio-clinical-databases-pharmacogenomics

GPTomics/bioSkills

Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response from genetic variants or implementing clinical pharmacogenomics.

bio-clinical-databases-polygenic-risk

GPTomics/bioSkills

Calculate polygenic risk scores using PRSice-2, LDpred2, or PRS-CS from GWAS summary statistics. Use when predicting disease risk from genome-wide genetic variants.

bio-clinical-databases-somatic-signatures

GPTomics/bioSkills

Extract and analyze mutational signatures from somatic variants using SigProfiler or MutationalPatterns to characterize mutagenic processes. Use when identifying DNA damage mechanisms or etiology in cancer genomes.

bio-clinical-databases-tumor-mutational-burden

GPTomics/bioSkills

Calculate tumor mutational burden from panel or WES data with proper normalization and clinical thresholds. Use when assessing immunotherapy eligibility or characterizing tumor immunogenicity.

bio-clinical-databases-variant-prioritization

GPTomics/bioSkills

Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.

bio-clip-seq-binding-site-annotation

GPTomics/bioSkills

Annotate CLIP-seq binding sites to genomic features including 3'UTR, 5'UTR, CDS, introns, and ncRNAs. Use when characterizing where an RBP binds in transcripts.

bio-clip-seq-clip-alignment

GPTomics/bioSkills

Align CLIP-seq reads to the genome with crosslink site awareness. Use when mapping preprocessed CLIP reads for peak calling.

bio-clip-seq-clip-motif-analysis

GPTomics/bioSkills

Identify enriched sequence motifs at CLIP-seq binding sites for RBP binding specificity. Use when characterizing the sequence preferences of an RNA-binding protein.

bio-clip-seq-clip-peak-calling

GPTomics/bioSkills

Call protein-RNA binding site peaks from CLIP-seq data using CLIPper, PureCLIP, or Piranha. Use when identifying RBP binding sites from aligned CLIP reads.

bio-clip-seq-clip-preprocessing

GPTomics/bioSkills

Preprocess CLIP-seq data including adapter trimming, UMI extraction, and PCR duplicate removal. Use when preparing raw CLIP, iCLIP, or eCLIP reads for peak calling.

bio-codon-usage

GPTomics/bioSkills

Analyze codon usage, calculate CAI (Codon Adaptation Index), and examine synonymous codon bias using Biopython. Use when analyzing coding sequences for expression optimization or evolutionary analysis.

bio-comparative-genomics-ancestral-reconstruction

GPTomics/bioSkills

Reconstruct ancestral sequences at phylogenetic nodes using PAML and IQ-TREE marginal likelihood methods. Infer ancient protein sequences and trace evolutionary trajectories through sequence history. Use when inferring ancestral states for protein resurrection or tracing evolutionary history.

bio-comparative-genomics-hgt-detection

GPTomics/bioSkills

Detect horizontal gene transfer events using HGTector, compositional analysis, and phylogenetic incongruence methods. Identify foreign genes in bacterial and archaeal genomes from anomalous composition or unexpected phylogenetic placement. Use when searching for horizontally transferred genes or analyzing genome evolution in prokaryotes.

bio-comparative-genomics-ortholog-inference

GPTomics/bioSkills

Infer orthologous gene groups across species using OrthoFinder and ProteinOrtho. Identify orthologs, paralogs, and co-orthologs for comparative genomics and functional annotation transfer. Use when identifying gene orthologs across species or building orthogroups for evolutionary analysis.

bio-comparative-genomics-positive-selection

GPTomics/bioSkills

Detect positive selection using dN/dS (omega) tests with PAML codeml and HyPhy. Identify sites and branches under adaptive evolution through codon models and branch-site tests. Use when testing for adaptive evolution in gene families or identifying positively selected sites.

bio-comparative-genomics-synteny-analysis

GPTomics/bioSkills

Analyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying conserved genomic regions.

bio-compressed-files

GPTomics/bioSkills

Read and write compressed sequence files (gzip, bzip2, BGZF) using Biopython. Use when working with .gz or .bz2 sequence files. Use BGZF for indexable compressed files.

bio-consensus-sequences

GPTomics/bioSkills

Generate consensus FASTA sequences by applying VCF variants to a reference using bcftools consensus. Use when creating sample-specific reference sequences or reconstructing haplotypes.

bio-copy-number-cnv-annotation

GPTomics/bioSkills

Annotate CNVs with genes, pathways, and clinical significance. Use when interpreting CNV calls or identifying affected genes from copy number analysis.

bio-copy-number-cnv-visualization

GPTomics/bioSkills

Visualize copy number profiles, segments, and compare across samples. Create publication-quality plots of CNV data from CNVkit, GATK, or other callers. Use when creating genome-wide CNV plots, sample heatmaps, or chromosome-level visualizations.

bio-copy-number-cnvkit-analysis

GPTomics/bioSkills

Detect copy number variants from targeted/exome sequencing using CNVkit. Supports tumor-normal pairs, tumor-only, and germline CNV calling. Use when detecting CNVs from WES or targeted panel sequencing data.

bio-copy-number-gatk-cnv

GPTomics/bioSkills

Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

bio-crispr-screens-base-editing-analysis

GPTomics/bioSkills

Analyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.

bio-crispr-screens-batch-correction

GPTomics/bioSkills

Batch effect correction for CRISPR screens. Covers normalization across batches, technical replicate handling, and batch-aware analysis. Use when combining screens from multiple batches or correcting systematic technical variation.