メニュー
このリポジトリの skills
SkillsMP は GPTomics/bioSkills から 562 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
GPTomics/bioSkills収集済み skill 562 件中 40 件を表示しています。
Infers person-to-person transmission from pathogen genomes using outbreaker2, TransPhylo, phybreak, BadTrIP, SCOTTI, BEASTLIER, and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific…
原文の言語: 英語
Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot),…
原文の言語: 英語
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM,…
原文の言語: 英語
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and…
原文の言語: 英語
Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet…
原文の言語: 英語
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools…
原文の言語: 英語
Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input…
原文の言語: 英語
Retrieves and interprets AlphaFold Protein Structure Database (AFDB) models by UniProt accession, reading pLDDT and PAE confidence correctly. Use when treating pLDDT as PER-RESIDUE confidence (not global accuracy) and recognizing a long low-pLDDT stretch as…
原文の言語: 英語
Detects putative ligand-binding pockets and druggable cavities de novo on an apo protein structure with fpocket, P2Rank, CASTp, and DoGSiteScorer, ranking them by druggability/ligandability score. Use when detecting cavities on an apo structure with no bound…
原文の言語: 英語
Measures geometric properties of protein structures with Biopython Bio.PDB - interatomic distances, distance matrices, bond and dihedral angles (phi/psi/chi, Ramachandran), superposition and RMSD, center of mass, radius of gyration, and solvent accessible…
原文の言語: 英語
Maps protein-protein and protein-ligand interfaces with Bio.PDB, computing contact residues and buried surface area (BSA). Use when choosing a contact cutoff and stating its rationale (heavy-atom 4-5A vs CA-CA 8A vs a SASA-based definition); deciding a…
原文の言語: 英語
Predicts protein and complex structures with deep-learning models (ESMFold, AlphaFold2/ColabFold, AlphaFold3, Chai-1, Boltz-1/2) and reconciles them with confidence metrics. Use when choosing a predictor by input and question rather than novelty (ESMFold…
原文の言語: 英語
Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB. Use when choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDB's ~62-chain / 99,999-atom limits; when residue numbers do not match…
原文の言語: 英語
Modifies protein structures in place with Biopython Bio.PDB - transforms coordinates, strips waters/heteroatoms, overloads the B-factor column, renumbers, and builds entities. Use when applying a rotation matrix and needing to know whether it is…
原文の言語: 英語
Navigate the Bio.PDB SMCRA hierarchy (Structure-Model-Chain-Residue-Atom) safely, surfacing the heterogeneity it hides by default. Use when deciding how to handle altloc/DisorderedAtom conformers before a distance or RMSD, indexing residues…
原文の言語: 英語
Prepares a deposited or predicted structure for docking, molecular dynamics, or electrostatics by adding hydrogens, assigning protonation and tautomer states, and filling missing atoms and short loops with PDBFixer, reduce, PROPKA, and PDB2PQR. Use when…
原文の言語: 英語
Judges whether a macromolecular model (or a region of it) is reliable enough to build on, using resolution, R-free, B-factors, MolProbity geometry, and predicted-model confidence with Bio.PDB. Use when deciding if a structure or a specific region is…
原文の言語: 英語
Reconstructs single cells from sub-cellular spatial capture units (Visium HD 2um bins, Stereo-seq DNB spots, Slide-seqV2 beads) by aggregating bins UP into cells rather than deconvolving a mixture DOWN. Use when choosing a bin size and recognizing the…
原文の言語: 英語
Segments cells/nuclei and extracts image features from imaging spatial transcriptomics (Xenium, MERFISH/MERSCOPE, CosMx) and H&E/IF tissue images using Cellpose, StarDist, Baysor, and Squidpy. Use when choosing a segmentation strategy (DAPI nucleus +…
原文の言語: 英語
Maps cell-cell communication and ligand-receptor co-expression in spatial transcriptomics (Visium, Xenium, MERFISH, CosMx, Slide-seq) with Squidpy ligrec, COMMOT, stLearn, CellChat-spatial, and NicheNet. Use when choosing a method by whether spatial distance…
原文の言語: 英語
Estimates per-spot cell type composition of spatial transcriptomics mixtures (Visium, Slide-seq, Stereo-seq) from an scRNA-seq reference with cell2location, RCTD, SPOTlight, stereoscope, SpatialDWLS, or reference-free STdeconvolve. Use when deciding whether a…
原文の言語: 英語
Integrates spatial RNA with a second modality (protein, ATAC, or histone marks) on spatial CITE-seq, DBiT-seq, spatial-ATAC, or Visium CytAssist data. Use when deciding vertical (same-pixel co-profiling -> WNN/MOFA joint factors) versus diagonal (serial…
原文の言語: 英語
Quality control, filtering, and normalization for spatial transcriptomics (Visium, Visium HD, Xenium, MERFISH/MERSCOPE, CosMx, Slide-seq) with Squidpy and Scanpy. Use when setting QC floors that do NOT delete real low-count imaging cells (an scRNA…
原文の言語: 英語
Analyzes multiplexed antibody-imaging data (CODEX/PhenoCycler, MIBI-TOF, IMC, CyCIF, Opal/Vectra mIF) as continuous protein intensity rather than transcript counts, using scimap and squidpy. Use when choosing an intensity transform/normalization (arcsinh…
原文の言語: 英語
Detects spatially variable genes, spatial autocorrelation, and cell-type colocalization for spatial transcriptomics using Squidpy with PySAL/esda for local statistics. Use when choosing an SVG method by its null and scaling (SpatialDE/SPARK GP…
原文の言語: 英語
Plots spatial transcriptomics expression, clusters, and annotations on tissue using Squidpy and Scanpy. Use when choosing the plotter and spot size by platform fork (sc.pl.spatial / sq.pl.spatial_scatter with real scalefactors and capture diameter for…
原文の言語: 英語
Tests miRNAs for differential expression with DESeq2 or edgeR using small-RNA-aware normalization and filtering. Use when deciding which normalization survives a library dominated by a few hyper-abundant miRNAs (compositional fragility); choosing DESeq2 vs…
原文の言語: 英語
Discovers novel miRNAs and quantifies known miRNAs with miRDeep2 by scoring genome-mapped read stacks against the Dicer/Drosha biogenesis signature. Use when deciding whether a study needs de novo discovery at all versus known-miRNA quantification; choosing…
原文の言語: 英語
Quantifies known miRNAs, isomiRs, tRFs, and A-to-I editing fast with miRge3.0 by aligning collapsed reads to curated miRBase or MirGeneDB libraries. Use when choosing miRBase versus MirGeneDB as the reference; deciding whether to collapse isomiRs to the…
原文の言語: 英語
Trims kit-specific 3' adapters, strips UMIs or 4N degenerate ends, size-selects, and collapses small RNA-seq reads (miRNA, piRNA, tRF) with cutadapt or fastp. Use when choosing the kit's 3' adapter; setting the size window (18-26 nt miRNA vs 24-32 nt piRNA);…
原文の言語: 英語
Predicts and prioritizes miRNA target genes with seed-based tools (miRanda, TargetScan, miRDB) and experimentally validated databases (miRTarBase, multiMiR). Use when deciding that a predicted target is a hypothesis not a finding; ranking by the right score…
原文の言語: 英語
Profiles non-miRNA small RNAs - tRNA-derived fragments (tRFs/tsRNAs), piRNAs, and rRNA/snoRNA-derived species - with MINTmap, unitas, SPORTS, and proTRAC. Use when annotating all small-RNA classes in a library; quantifying tRFs at locus resolution where tRNA…
原文の言語: 英語
Integrate multiple scRNA-seq samples or batches with Harmony, scVI/scANVI, Seurat (CCA/RPCA), fastMNN, Scanorama, or BBKNN. Resolves which method to use for the dataset size and design, how strongly to correct, when integration is the wrong move (confounded…
原文の言語: 英語
Automated reference-based cell type annotation for single-cell RNA-seq using CellTypist, SingleR, Azimuth, scANVI, and scmap to transfer labels from a reference. Use when annotating cell types from a reference atlas or pretrained model, transferring labels…
原文の言語: 英語
Infers ligand-receptor cell-cell communication from scRNA-seq with a consensus-first workflow (LIANA), plus CellPhoneDB specificity tests, CellChat pathway probabilities, and NicheNet downstream ligand-activity. Use when ranking ligand-receptor interactions…
原文の言語: 英語
Dimensionality reduction and graph-based clustering for single-cell RNA-seq with Scanpy (Python) and Seurat (R). Resolves which algorithm to use (Leiden vs Louvain), how many PCs and neighbors to set, how to sweep and validate resolution, when a split is…
原文の言語: 英語
Infer large-scale copy-number alterations from tumor single-cell or single-nucleus RNA-seq to separate malignant from normal cells and call subclones, using inferCNV, copyKAT, Numbat, and SCEVAN. Use when separating malignant from normal cells in a tumor…
原文の言語: 英語
Read, write, create, and convert single-cell objects across AnnData (Python), Seurat (R), and SingleCellExperiment (R). Use when loading 10X Cell Ranger output (raw vs filtered), importing or exporting h5ad/RDS/h5mu/zarr, building AnnData or Seurat objects…
原文の言語: 英語
Test whether cell-type proportions or composition changed between conditions in single-cell data using Milo (miloR), scCODA, sccomp, and propeller. Use when comparing cell-type proportions / composition between conditions, asking which populations expanded or…
原文の言語: 英語
Detect and remove doublets (two or more cells in one droplet) from single-cell RNA-seq using scDblFinder (R), Scrublet (Python), and DoubletFinder (R). Use when flagging artificial intermediate populations before clustering, setting the expected doublet rate…
原文の言語: 英語