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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.

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。

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。

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。

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。

JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens) for modeling sgRNA efficacy and gene essentiality. Use when analyzing multiple CRISPR screens simultaneously or when accounting for variable sgRNA efficiency across experiments.

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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.

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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.

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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.

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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。

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。

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。

Detect and track antimicrobial resistance genes using AMRFinderPlus and ResFinder with epidemiological context. Monitor resistance trends and identify emerging resistance patterns. Use when screening genomes for AMR genes or tracking resistance in surveillance programs.

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Infer pathogen transmission networks and identify likely transmission pairs using TransPhylo and outbreak reconstruction algorithms. Estimate who-infected-whom from genomic and epidemiological data. Use when investigating outbreak transmission chains or identifying superspreaders.

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Bead-based normalization for CyTOF and high-parameter flow cytometry. Covers EQ bead normalization, signal drift correction, and batch normalization. Use when correcting instrument drift in CyTOF or harmonizing data across batches.

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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.

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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.

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Detect and remove doublets from flow and mass cytometry data. Covers FSC/SSC gating and computational doublet detection methods. Use when filtering out cell aggregates before clustering or quantitative 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。

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.

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Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.

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。

Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or point mutations.

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 A/B compartments from Hi-C data using cooltools and eigenvector decomposition. Identify active (A) and inactive (B) chromatin compartments from contact matrices. Use when identifying A/B compartments from Hi-C data.

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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.

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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。