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Perform multi-locus sequence typing (MLST), core genome MLST, and SNP-based strain typing for bacterial isolate characterization using mlst and chewBBACA. Use when identifying strain types, tracking outbreak clones, or characterizing bacterial isolates.

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。

Detects differential alternative splicing between conditions using rMATS-turbo (BAM-based) or SUPPA2 diffSplice (TPM-based). Reports events with FDR-corrected significance and delta PSI effect sizes. Use when comparing splicing patterns between treatment groups, tissues, or disease states.

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。

Analyze time-series RNA-seq data using limma voom with splines, maSigPro, and ImpulseDE2. Identify genes with dynamic expression patterns. Use when analyzing time-series or longitudinal expression data.

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。

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

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。

Remove batch effects from RNA-seq data using ComBat, ComBat-Seq, limma removeBatchEffect, and SVA for unknown batch variables. Use when correcting batch effects in expression data.

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。

Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results.

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。

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.

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。

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.

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。

Load, convert, and manipulate Hi-C contact matrices using cooler format. Read .cool/.mcool files, convert from .hic format, access matrix data, and export to different formats. Use when loading or converting Hi-C contact matrices.

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。

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.

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。

MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.

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。

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

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。

Process Hi-C read pairs using pairtools. Parse alignments, filter duplicates, classify pairs, and generate contact statistics from Hi-C sequencing data. Use when processing raw Hi-C read pairs.

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。

CRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.

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。

Extract, filter, annotate, and export differential expression results from DESeq2 or edgeR. Use for identifying significant genes, applying multiple testing corrections, adding gene annotations, and preparing results for downstream analysis. Use when filtering and exporting DE analysis results.

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。

Spillover compensation and data transformation for flow cytometry. Covers compensation matrix calculation, application, and biexponential/arcsinh transforms. Use when correcting spectral overlap between fluorophores or transforming data for analysis.

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。

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

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。

Perform differential expression analysis using DESeq2 in R/Bioconductor. Use for analyzing RNA-seq count data, creating DESeqDataSet objects, running the DESeq workflow, and extracting results with log fold change shrinkage. Use when performing DE analysis with DESeq2.

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。

Manual and automated gating for defining cell populations in flow cytometry. Covers rectangular, polygon, and data-driven gates. Use when identifying cell populations through hierarchical gating strategies.

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。

Variant calling with GATK HaplotypeCaller following best practices. Covers germline SNP/indel calling, GVCF workflow for cohorts, joint genotyping, and variant quality score recalibration (VQSR). Use when calling variants with GATK HaplotypeCaller.

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。

CRISPR library design for genetic screens. Covers sgRNA selection, library composition, control design, and oligo ordering. Use when designing custom sgRNA libraries for knockout, activation, or interference screens.

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。

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.

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。

Detects somatic mutations in circulating tumor DNA using variant callers optimized for low allele fractions with UMI-based error suppression. Reliably detects mutations at VAF above 0.5 percent using consensus-based approaches. Use when identifying tumor mutations from plasma DNA or tracking specific variants.

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。

Quality control for pooled CRISPR screens. Covers library representation, read distribution, replicate correlation, and essential gene recovery. Use when assessing screen quality before hit calling or diagnosing poor screen performance.

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。