「eq」の検索結果

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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。

Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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。

Build sequence logos from aligned DNA, RNA, or protein motifs using ggseqlogo (R), Logomaker (Python), or WebLogo with explicit bits vs probability encoding, background-frequency correction, custom alphabets, and multi-logo stacking. Use when visualizing motif PWMs (TF binding, splice sites, CRISPR spacers), aligned-position composition, or comparing two motif sets.

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 distant homologs using profile and structure-aware methods that go beyond standard BLAST. Use when sequence identity falls into the twilight zone (<35% pairwise), when BLAST fails to find homologs that should exist, when working at metagenomic scale (DIAMOND, MMseqs2), or when structure beats sequence (Foldseek). Covers PSI-BLAST (iterative PSSM), jackhmmer (iterative HMM), HHblits/HHsearch (profile-profile), DIAMOND, MMseqs2, and Foldseek (3Di structural alphabet, van Kempen 2024).

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。

Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP 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。

Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.

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。

Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.

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。

Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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。

Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2025 Nat Genet; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new 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。

Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.

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。

Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, and applies the Patel et al 2024 *Nat Biotechnol* review's failure-mode framework to validate that normalization is correctly applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.

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 differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.

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。

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.

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。

Find and identify the CCSPlayer_WeaponServices_EquipWeapon function and the CCSPlayer_WeaponServices_CanUse virtual function in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the weapon-equip helper and the "is this weapon usable right now" vfunc by RTTI-walking the CCSPlayer_WeaponServices vtable and confirming each candidate's decompiled body against the known CCSPlayer_WeaponServices_PickupItem anchor's callee shapes. Trigger: CCSPlayer_WeaponServices_EquipWeapon, CCSPlayer_WeaponServices_CanUse

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。

Guide the user through starting a new web application project — elicit requirements (MoSCoW scoping), create architecture and requirements documents, break work into tasks, and register them as a GitHub Project kanban board. Use when the user says "新しいWebアプリを作りたい", "Webアプリの立ち上げを手伝って", "start a new web app project", "プロジェクトの要件を整理したい", or "GitHub Projectにタスクを登録したい".

Documents minimum SDK/OS version requirements for all OneKey platforms. Use when checking platform compatibility, understanding deployment targets, verifying version requirements, or when user asks if their device can run the project. Triggers on minimum version, SDK version, API level, deployment target, platform requirements, iOS version, Android version, Chrome version, Electron version, can I run, environment check, device compatibility, check environment.

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。

Create GitHub Pull Request for feature request from specification file using pull_request_template.md template.

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。

[BETA] AEM Cloud Service expert skill — add explicit connect/read/socket timeouts to outbound HTTP clients constructed without one (Apache HttpClient 4.x/5.x, OkHttp, JDK java.net.http.HttpClient), including the JDK per-request read timeout on HttpRequest. Use for "add HTTP timeouts", "this external/outbound call has no timeout", or a scan that flags a timeout-less client. Highest-frequency CSO outage cause: a client on default (effectively infinite) timeouts holds request threads on a slow upstream until the Jetty pool saturates and the site goes down. The analyzer locates timeout-less construction sites; mechanical per-library remediation applies CSO-backed default timeouts (recipe.md). This skill is in beta. Verify all outputs before applying them to production projects.

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。

AIエージェントの作業ログから、GitHubのPRやissueに添える記録を作ります。関連部分を絞り、機密情報を除いてプレビューと掲載を扱います。

• PRに添えるエージェント作業記録の作成
• ローカルログから関連する依頼と結果を抽出
• 機密情報を除いた記録のプレビュー

GitHubのローカル保存データからIssueやPR、重複候補を調べ、同期状況や情報の不足を確認しながら、判断前に最新の状態を照合するスキル。

• 不具合に関連するIssueやPRを探したいとき
• 重複候補のIssueを確認したいとき
• 対応判断前に保存情報の鮮度を確かめたいとき

OpenClawの課題やプルリクエストを、現在のソースとテスト結果に基づいて調査・レビューし、許可された範囲で修正からマージ完了の確認まで進めるスキル。

• OpenClawのPRをレビューしたいとき
• 報告された不具合のローカル修正
• 課題が既に解決済みか確認したいとき

GitHubのIssueを条件で絞り込み、修正・テスト・PR作成をバックグラウンドのエージェントに任せます。重複作業の回避やレビュー指摘への対応も扱います。

• ラベルや担当者で課題を選びたいとき
• 複数の課題の修正PRを作りたいとき
• 作成したPRのレビュー対応

Gemini CLIで質問への回答や文章生成、ファイル内容の要約を単発で実行します。モデルやJSON出力の指定、拡張機能などの管理コマンドも扱います。

• メモファイルを要約したいとき
• 単発の依頼で文章を生成したいとき
• 回答をJSON形式で受け取りたいとき

ClawHubで目的に合うプラグインやスキルを探し、スキルの検証・導入・更新・削除から公開・同期まで、OpenClawと専用CLIで扱うスキル。

• 必要な機能を持つプラグインを探したいとき
• 指定したスキルを検証して導入したいとき
• 導入済みスキルを更新・削除したいとき