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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。
Find restriction enzyme cut sites in DNA sequences using Biopython Bio.Restriction. Searches single enzymes, batches, or commercial enzyme sets and returns cut positions for linear or circular DNA. Use when locating where one or more restriction enzymes cut a…
原文の言語: 英語
Aggregates per-tool QC metrics (FastQC, fastp, alignment, quantification, variant calling, single-cell) into one interactive MultiQC report, and guides module scoping, sample-name resolution, large-cohort behavior, and turning the report into an actual QC…
原文の言語: 英語
Exports publication-ready figures with the correct vector/raster split, embedded editable fonts, color-space-robust palettes, and journal-correct sizing and resolution in matplotlib and ggplot2. Use when preparing figures for journal submission, exporting a…
原文の言語: 英語
Runs parameterized Jupyter notebooks as reproducible batch report generators with papermill, renders them to HTML/PDF with nbconvert, aggregates results across samples, and makes notebook outputs trustworthy. Use when generating per-sample analysis reports,…
原文の言語: 英語
Builds publication-ready tables - descriptive Table 1, regression and differential-expression result tables, and supplementary tables - with gtsummary, gt, flextable, and kableExtra (R) or great_tables, pandas, and tableone (Python), choosing the right…
原文の言語: 英語
Builds reproducible Quarto reports, presentations, and websites across R, Python, and Julia, with correct engine selection, cache-vs-freeze semantics, native cross-references, parameters, and environment pinning. Use when creating a Quarto report of an…
原文の言語: 英語
Creates reproducible R Markdown analysis reports (HTML, PDF, Word) with knitr, covering the render pipeline, the interactive-vs-knit session trap, cache invalidation, bookdown cross-references, parameterization, and environment pinning. Use when generating an…
原文の言語: 英語
Removes sequencing adapters from FASTQ reads with Cutadapt and Trimmomatic, including paired-end read-through, small-RNA 3' adapters, amplicon primers, and anchored/linked adapters. Use when FastQC shows adapter content climbing toward the 3' end, when…
原文の言語: 英語
Detects contamination in sequencing reads - cross-species (FastQ Screen, Kraken2), vector/PhiX/adapter, rRNA, and same-species cross-sample/index-hopping and sample swaps (SNP fingerprints via verifyBamID2/NGSCheckMate/somalier). Use when suspecting…
原文の言語: 英語
Runs all-in-one FASTQ preprocessing with fastp in a single pass - adapter trimming via paired-end overlap analysis, quality/length filtering, 2-color poly-G removal, base correction, optional dedup/UMI/merge, and HTML/JSON reports. Use when preprocessing bulk…
原文の言語: 英語
Filters reads by quality, length, N content, and complexity with Trimmomatic, fastp, and Cutadapt, including sliding-window trimming, per-read unqualified-base filtering, and 2-color poly-G removal. Use when reads have poor-quality tails, when an assembly or…
原文の言語: 英語
Generates and interprets per-file and cross-sample QC reports from FASTQ data with FastQC, falco, and MultiQC, covering Phred quality, per-base composition, GC, duplication, overrepresented sequences, and adapter content. Use when performing initial QC on raw…
原文の言語: 英語
Runs RNA-seq-specific post-alignment QC - strandedness inference, gene-body 5'-3' coverage, read distribution (exonic/intronic/intergenic), rRNA/globin/mitochondrial rate, transcript integrity (TIN), and saturation - with RSeQC, Qualimap, RNA-SeQC, and…
原文の言語: 英語
Extracts UMIs and collapses reads to original molecules with umi_tools (directional dedup) or builds error-corrected single-strand/duplex consensus reads with fgbio. Use when the library has UMIs and accurate molecule counting or below-sequencer-floor error…
原文の言語: 英語
Turns an enrichResult or gseaResult from clusterProfiler/enrichplot into a figure that collapses or shows gene-set redundancy, using dotplot, barplot, cnetplot, emapplot, treeplot, ridgeplot, gseaplot2, and upsetplot. Covers why a default top-20 GO dotplot is…
原文の言語: 英語
Runs Gene Ontology over-representation analysis (ORA) on a gene LIST with clusterProfiler enrichGO, the one-sided hypergeometric/Fisher 2x2 test phyper(k-1, M, N-M, n, lower.tail=FALSE). Covers why the BACKGROUND universe (not the gene list) is the null and…
原文の言語: 英語
Tests a ranked gene vector for coordinated expression shifts in GO, KEGG, Reactome, or MSigDB gene sets with clusterProfiler's gseGO, gseKEGG, gsePathway, and GSEA (fgseaMultilevel engine), and scores per-sample pathway activity with ssGSEA and GSVA. Covers…
原文の言語: 英語
Tests a gene list or ranked gene vector for over-representation or coordinated shifts in Reactome's curated, peer-reviewed, reaction-level pathways using ReactomePA's enrichPathway (ORA) and gsePathway (GSEA), reading the local reactome.db so a run is…
原文の言語: 英語
Aligns DNA short reads to a reference with Bowtie2, choosing end-to-end (whole read must align) vs local (soft-clip read ends) mode and a sensitivity preset; the de-facto aligner for ChIP-seq, ATAC-seq, and CUT&RUN, where fragment-geometry flags (--no-mixed,…
原文の言語: 英語
Aligns DNA short reads (paired- or single-end) to a reference genome with bwa-mem2, the maintained successor to BWA-MEM, for WGS/WES and germline/somatic variant-calling pipelines; covers index build, read-group injection, the collate/fixmate/sort/markdup…
原文の言語: 英語
Aligns RNA-seq reads to a genome with HISAT2, the splice-aware aligner whose hierarchical graph FM-index runs at roughly a quarter of STAR's memory (~7 GB for human), whose SNP/haplotype graph index reduces reference bias in the index itself, and whose MAPQ…
原文の言語: 英語
Aligns RNA-seq reads to a genome with STAR, the fast splice-aware aligner whose splice-junction database (built from a GTF at sjdbOverhang = readlength-1) and two-pass mode set junction sensitivity, whose 255-for-unique MAPQ breaks GATK, and whose GeneCounts…
原文の言語: 英語
Loads mass-spectrometry data into Python/R and strips the search engine's bookkeeping before any number is trusted -- removes decoys (REV__/Reverse), contaminants (CON__/Potential contaminant), Only-identified-by-site groups, and resolves semicolon…
原文の言語: 英語
Analyzes data-independent acquisition (DIA) proteomics by scoring reconstructed fragment-chromatogram peak groups against a decoy null with DIA-NN (library-free directDIA, library-based, or deep-learning predicted-library routes), Spectronaut, OpenSWATH, and…
原文の言語: 英語
Tests for differentially abundant proteins between conditions with limma/DEqMS empirical-Bayes moderation, proDA/msqrob2/MSstats missingness modeling, and Python Welch+BH alternatives. Frames missing values as left-censored MNAR (model, do not impute), makes…
原文の言語: 英語
Peptide-spectrum matching from MS/MS with target-decoy FDR control, framing identification confidence as a property of a ranked list (q-value/PEP) rather than a raw engine score (XCorr, hyperscore, Andromeda, SpecEValue). Covers sequence-database search…
原文の言語: 英語
Groups proteins from peptide identifications and controls protein-level FDR, framing inference as a chosen explanation (parsimony or a probability model) of underdetermined peptide evidence rather than a measurement. Reports protein GROUPS (proteins…
原文の言語: 英語
Quality control for bottom-up proteomics across three levels -- instrument/raw-signal (mass accuracy, RT/iRT fit, FWHM, TIC vs injection time, % MS2 identified), identification/run (missed cleavages, charge states, PTM handling artifacts, contaminants), and…
原文の言語: 英語
Frames PTM/phosphoproteomics analysis as three stacked inference layers on a biased enrichment - chemistry selection, site localization (FLR), and protein-level-adjusted quantification with MSstatsPTM - plus kinase-activity and functional triage. Covers…
原文の言語: 英語
Quantifies protein abundance from mass spectrometry using label-free (LFQ/MaxLFQ, DIA fragment-level), isobaric (TMT/iTRAQ reporter ions, MS2 vs SPS-MS3), and metabolic (SILAC) approaches, including peptide-to-protein summarization (Tukey median polish,…
原文の言語: 英語
Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT…
原文の言語: 英語
Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed)…
原文の言語: 英語
Designs and ranks PCR primer pairs for a target template with primer3-py (design_primers), returning pairs with nearest-neighbor Tm, GC, product size, and complementarity scores. Covers why primer3 is a LOCAL weighted-penalty minimizer over the single…
原文の言語: 英語
Checks whether a PCR primer PAIR amplifies only the intended target genome-wide, using pair-aware in-silico PCR (MFEprimer-3.0, UCSC isPcr, NCBI Primer-BLAST) plus a primer3-py 3'-end-stability prefilter, against the correct database. Covers why plain BLAST…
原文の言語: 英語
Validates chosen PCR/qPCR oligos for intramolecular thermodynamic liabilities with primer3-py - hairpins, self-dimers, cross-dimers (calc_hairpin/homodimer/heterodimer), and 3'-end stability (calc_end_stability) - returning ThermoResult dG/Tm and ASCII…
原文の言語: 英語
Co-designs qPCR/RT-qPCR primers and hydrolysis (TaqMan) or molecular-beacon probes with primer3-py (PRIMER_PICK_INTERNAL_OLIGO, PRIMER_INTERNAL_* tags), for assays whose deliverable is a quantitative measurement device. Covers why amplification efficiency…
原文の言語: 英語
Computes linkage disequilibrium (r2, D', composite Rogers-Huff r2), prunes correlated variants, clumps GWAS summary statistics to lead SNPs, and defines haplotype blocks with PLINK 1.9/2.0 and scikit-allel. r2 and D' answer different questions - r2 (= chi2/N)…
原文の言語: 英語
Manages PLINK genotype filesets - format conversion (VCF, BED/BIM/FAM, PED/MAP, pgen/pvar/psam) and sample/variant QC (missingness, MAF, HWE, sex check, heterozygosity, KING relatedness) with PLINK 1.9 and 2.0. PLINK rewrites allele bookkeeping: PLINK 1.x A1…
原文の言語: 英語
Infers and describes population structure with PCA (plink2 --pca, smartpca/EIGENSOFT, FlashPCA2), model-based clustering (ADMIXTURE, fastSTRUCTURE), FST estimators (Weir-Cockerham vs Hudson), and f-statistics (f3/f4/D via AdmixTools/admixr), plus Python…
原文の言語: 英語
Gene and region-based rare-variant aggregation - burden/collapsing, SKAT, SKAT-O, ACAT-V/ACAT-O, annotation-weighted STAAR - with regenie (--vc-tests), SAIGE-GENE+, and the SKAT R package. Single-variant tests are powerless at low minor allele count, so rare…
原文の言語: 英語