「ax」の検索結果

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.

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Analyzes cfDNA fragment size distributions and fragmentomics features using FinaleToolkit or Griffin. Extracts nucleosome positioning patterns, fragment ratios, and DELFI-style fragmentation profiles for cancer detection. Use when leveraging fragment patterns for tumor detection or tissue-of-origin analysis.

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Workflow from differential expression results to functional enrichment analysis. Covers GO, KEGG, Reactome enrichment with clusterProfiler and visualization. Use when taking DE results to pathway enrichment.

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Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.

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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.

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Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.

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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.

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Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).

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Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices.

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Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.

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Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.

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Read and manipulate Flow Cytometry Standard (FCS) files. Covers loading data, accessing parameters, and basic data exploration. Use when loading and inspecting flow or mass cytometry data before preprocessing.

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End-to-end DNA sequencing workflow from FASTQ files to variant calls. Covers QC, alignment with BWA, BAM processing, and variant calling with bcftools or GATK HaplotypeCaller. Use when calling variants from raw sequencing reads.

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Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.

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Perform flux balance analysis (FBA) and flux variability analysis (FVA) on genome-scale metabolic models using COBRApy. Predict growth rates, metabolic fluxes, and optimal resource utilization. Use when predicting metabolic phenotypes or optimizing flux distributions.

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All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

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Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.

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Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.

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Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.

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Work with FASTQ quality scores using Biopython. Use when analyzing read quality, filtering by quality, trimming low-quality bases, or generating quality reports.

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Predict B-cell and T-cell epitopes using BepiPred, IEDB tools, and structure-based methods for vaccine and antibody design. Identify immunogenic regions in antigens. Use when designing vaccines, mapping antibody binding sites, or predicting immunogenic peptides.

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Predict protein-coding genes in eukaryotic genomes using BRAKER3 for combined RNA-seq and protein evidence, or GALBA for protein-only evidence. Runs Augustus with trained parameters for accurate gene models. Use when annotating a newly assembled eukaryotic genome or improving existing gene predictions.

日本語の概要は準備中です。原文の説明を表示しています。