기타 스킬

Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Perform differential expression analysis of miRNAs between conditions using DESeq2 or edgeR with small RNA-specific considerations. Use when identifying miRNAs that change between treatment groups, disease states, or developmental stages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Differentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Estimate divergence times using molecular clock models with BEAST2, MCMCTree, and TreePL. Use when dating speciation events, calibrating phylogenies with fossils, choosing between strict and relaxed clock models, or estimating evolutionary rates across lineages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Align DNA short reads to reference genomes using bwa-mem2, the faster successor to BWA-MEM. Use when aligning DNA short reads to a reference genome.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Align short reads using Bowtie2 with local or end-to-end modes. Supports gapped alignment. Use when aligning ChIP-seq, ATAC-seq, or when flexible alignment modes are needed.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Infer cis-regulatory connections (peak-to-peak co-accessibility) from scATAC-seq using Cicero, ArchR getCoAccessibility, or SCENIC+. Use when linking enhancer accessibility to promoter accessibility, identifying enhancer-gene pairs from chromatin alone (without paired RNA), running gene-regulatory inference combining ATAC + RNA, or comparing predicted regulatory contacts against Hi-C/Micro-C ground truth.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Polish genome assemblies to reduce errors using short reads (Pilon), long reads (Racon), or ONT-specific tools (medaka). Essential for improving long-read assembly accuracy. Use when improving assembly accuracy with polishing tools.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates standardized quality control reports by aggregating metrics from FastQC, alignment, and other tools using MultiQC. Use when summarizing QC metrics across samples, creating shareable quality reports, or building automated QC pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.