Best bioinformatics skills, page 9

Calculate sequence statistics (N50, length distribution, GC content, summary reports) using Biopython.

FreedomIntelligence/OpenClaw-Medical-Skills

Integrate multiple scRNA-seq samples/batches using Harmony, scVI, Seurat anchors, and fastMNN.

FreedomIntelligence/OpenClaw-Medical-Skills

Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling.

FreedomIntelligence/OpenClaw-Medical-Skills

Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python).

FreedomIntelligence/OpenClaw-Medical-Skills

Analyze multi-modal single-cell data (CITE-seq, Multiome, spatial).

FreedomIntelligence/OpenClaw-Medical-Skills

Analyze cell-cell communication in spatial transcriptomics data using ligand-receptor analysis with Squidpy.

FreedomIntelligence/OpenClaw-Medical-Skills

Estimate cell type composition in spatial transcriptomics spots using reference-based deconvolution.

FreedomIntelligence/OpenClaw-Medical-Skills

Identify spatial domains and tissue regions in spatial transcriptomics data using Squidpy and Scanpy.

FreedomIntelligence/OpenClaw-Medical-Skills

Build spatial neighbor graphs for spatial transcriptomics data using Squidpy.

FreedomIntelligence/OpenClaw-Medical-Skills

Quality control, filtering, normalization, and feature selection for spatial transcriptomics data.

FreedomIntelligence/OpenClaw-Medical-Skills

Compute spatial statistics for spatial transcriptomics data using Squidpy.

FreedomIntelligence/OpenClaw-Medical-Skills

Visualize spatial transcriptomics data using Squidpy and Scanpy.

FreedomIntelligence/OpenClaw-Medical-Skills

Analyze BCR repertoires for somatic hypermutation, clonal lineages, and B cell phylogenetics using the Immcantation framework.

FreedomIntelligence/OpenClaw-Medical-Skills

Analyze single-cell TCR and BCR data integrated with gene expression using scirpy.

FreedomIntelligence/OpenClaw-Medical-Skills

Guide Claude through omicverse's bulk RNA-seq DEG pipeline, from gene ID mapping and DESeq2 normalization to statistical testing, visualization, and pathway enrichment.

FreedomIntelligence/OpenClaw-Medical-Skills

Query cBioPortal for cancer genomics data including somatic mutations, copy number alterations, gene expression, and survival data across hundreds of cancer studies.

LeonChaoX/qinyan-academic-skills

Query gnomAD (Genome Aggregation Database) for population allele frequencies, variant constraint scores (pLI, LOEUF), and loss-of-function intolerance.

LeonChaoX/qinyan-academic-skills

Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs).

LeonChaoX/qinyan-academic-skills

Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee.

GPTomics/bioSkills

Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal.

GPTomics/bioSkills

Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC.

GPTomics/bioSkills

Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR.

GPTomics/bioSkills

Analyze TF motif accessibility variability across samples or single cells using chromVAR.

GPTomics/bioSkills

Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC.

GPTomics/bioSkills

Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro).

GPTomics/bioSkills

Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA…

GPTomics/bioSkills

Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susierss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS.

GPTomics/bioSkills

Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L.

GPTomics/bioSkills

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with QSNP…

GPTomics/bioSkills

Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes.

GPTomics/bioSkills

Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML.

GPTomics/bioSkills

Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments.

GPTomics/bioSkills

Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS…

GPTomics/bioSkills

Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation…

GPTomics/bioSkills

Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2…

GPTomics/bioSkills

Calls HLA class I and class II alleles at 2/4/6/8-field resolution from WGS/WES/RNA-seq/long-read data using OptiType, HLA-LA, T1K, Polysolver, HLA-HD, arcasHLA, StarPhase, or HIBAG imputation.

GPTomics/bioSkills

Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser…

GPTomics/bioSkills

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory…

GPTomics/bioSkills

Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment)…

GPTomics/bioSkills

Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu.

GPTomics/bioSkills

Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput…

GPTomics/bioSkills

Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream)…

GPTomics/bioSkills

Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical…

GPTomics/bioSkills

Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace…

GPTomics/bioSkills

Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware…

GPTomics/bioSkills

Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count…

GPTomics/bioSkills

Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML…

GPTomics/bioSkills