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K12 体育培训 (K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。) Master OS — automated mastery of K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on K12 / 青少年体育培训 (从业者视角) — 面向 3-18 岁青少年的校外体育培训生意，政策改革驱动的高增长赛道。覆盖: (a) 业态分层 — 少儿体适能 (3-12 岁基础运动能力/感统/协调) vs 专项培训 (篮球/游泳/跳绳/羽毛球/网球/击剑/武术等单项技能) vs 中考体育备考 (初中冲刺，按各地体育中考考纲提分); (b) 政策脊椎 — 双减 (2021 教育部「双减」: 学科类压减、非学科类规范) 把体育从「兴趣班」推成刚需，体育中考分值改革 (云南/北京/各省把分值提到 50-100 分) 制造确定性需求，《国家学生体质健康标准》+《义务教育体育与健康课程标准 (2022 版)》是教学纲领; (c) 单店模型与连锁扩张 — 坪效/师生比/续费率/转介绍率/课消，直营 vs 加盟，社区店 vs Mall 店 vs 进校 (课后服务/课后三点半); (d) 教研与师资 — 课程体系标准化 (分龄分级)、教练认证 (社会体育指导员/各单项协会教练员/中国少儿体适能等)、教练「能打 ≠ 能教」断层; (e) 运营 SOP — 招生获客 (地推/社群/试听转化)、排课与场地利用率、家长沟通与续费、安全与运动损伤防控/保险; (f) 中考体育教研 — 考纲拆解 (耐力跑/跳绳/引体/坐位体前屈/球类技能)、提分训练计划、体测数据追踪。学派分歧: 体适能通识派 vs 专项技能派、中考应试提分派 vs 长期运动素养派、直营重模型 vs 加盟轻扩张、进校 (toB/课后服务) vs 校外门店 (toC)、引进体系 (如美国 SPARK/体适能认证) vs 本土自研课程。不含: 竞技体校/专业运动员青训选材深水区、成人健身/私教 (见 personal-trainer)、学科类教培、纯体育用品零售。 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「K12 体育培训」「青少年体育培训」「少儿体适能」「中考体育」「体育中考」

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Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen et al 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.

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Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

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7人の専門家による多角的レビューを経て、法的リスクを最小化した取引基本契約書を対話形式で自動生成するスキル。 AI事業・IT事業・コンサルティング等に対応し、フリーランス新法（2024）・民法改正（2020）に準拠。 Anchors: - 契約書の作成実務と書式（牧野和夫）/ 適用: 契約構成・条項設計 / 目的: 実務レベルの契約書構造 - AIと著作権に関する考え方について（文化庁 令和6年）/ 適用: AI著作権条項 / 目的: AI利用免責設計 - フリーランスとして安心して働ける環境を整備するためのガイドライン（公取委・厚労省）/ 適用: 支払条件・解約予告 / 目的: 法令準拠 Trigger: 取引基本契約書, 業務委託契約, 契約書作成, フリーランス契約, AI事業契約, 請負契約, 準委任契約, 著作権譲渡契約

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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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.

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Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

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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.

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Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen & Zwaenepoel 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DTL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.

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External SSL VPN / remote-access appliance attack matrix — Cisco ASA/AnyConnect, Fortinet FortiGate/FortiOS, Citrix NetScaler/ADC, Palo Alto GlobalProtect, Pulse Secure / Ivanti Connect Secure, SonicWall, F5 Big-IP. Covers version fingerprinting, CVE matrix (2018-2026), AAA backend identification, default credentials, configuration-disclosure paths, pre-auth RCE/SSRF/path-traversal exploits where applicable. Built from authorized-engagement Cisco ASA testing plus 2024-2026 enterprise VPN CVE landscape. Use whenever the target's perimeter exposes any SSL VPN appliance or remote-access gateway — these are the most common initial-access points in 2024-2026 actor TTPs.

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Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.

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Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the gene-independent DNA-damage / G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.

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Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 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.

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Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.

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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 2024 bioRxiv; 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.

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Expert SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (26 mandatory, 6 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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United Arab Emirates Governance, Risk & Compliance advisor — a jurisdiction-first compliance router. In the UAE, WHERE an organization sits determines its law: mainland (Federal PDPL, Decree-Law 45/2021 — executive regulations still pending), DIFC (DP Law No. 5 of 2020 as amended 2025, with a private right of action), ADGM (DP Regulations 2021), CBUAE-licensed financial institutions (consumer-data residency, outsourcing approvals), healthcare (ICT Health Law data localization), and government/CNI (UAE IA Regulation, Cyber Security Council; Dubai ISR, ADHICS). Use for any UAE / Dubai / Abu Dhabi / Emirates compliance question: UAE data protection, DIFC or ADGM privacy, free-zone vs mainland obligations, health-data residency, CBUAE cyber and outsourcing rules, market entry ("expanding to the UAE"), breach notification, gap assessments, and mapping UAE requirements to ISO 27001 / NIST CSF / SOC 2. Trigger for any UAE privacy, cybersecurity, or regulatory question even if no framework is named.

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天涯神贴大神智囊团 — 20位传奇大神组成的多Agent决策系统。 基于ClawTeam多Agent协作原理，实现「一个目标 → 20位大神并行分析 → 综合决策」的完整工作流。 核心机制： - 智能领导者Agent（协调者）接收目标，分配给20位专业大神 - 20位大神并行分析，各自输出专业视角 - 综合决策Agent整合所有视角，输出最终决策 使用方式： - 「启动天涯智囊团」+ 你的问题/目标 - 「召集大神」+ 你的问题/目标 - 「20位大神分析」+ 你的问题/目标 涵盖领域：权谋分析、经济预言、历史解读、文化解构、都市情感、技术分析、 黑帮江湖、盗墓探险、玄幻武侠、都市异能、悬疑推理、法医刑侦、历史穿越等。

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Expert SWIFT Customer Security Programme (CSP) advisor covering the Customer Security Controls Framework (CSCF v2026). Use this skill whenever a user asks about SWIFT CSP, CSCF controls, SWIFT security attestation, KYC-SA portal, SWIFT architecture types (A1/A2/A3/A4/B), mandatory vs advisory controls, independent assessment, SWIFT secure zone, secure flow zone, MFA for operators, SWIFT messaging security, payment fraud prevention on SWIFT, gap analysis for CSCF, or compliance with SWIFT's 32 controls (25 mandatory, 7 advisory in v2026) across the three objectives: Secure Your Environment, Know and Limit Access, Detect and Respond. Control 2.4 (Back-Office Data Flow Security) is now mandatory in v2026. v2026 attestation window is July 1– December 31, 2026. Trigger for any SWIFT CSP or CSCF compliance question.

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Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the p53-dependent G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.

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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.

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Analyzes base-editing screens for variant function. Covers library design (Sanson 2020 GRACE, Hanna 2021 BRCA1/2 SNV scanning, Cuella-Martin 2021), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end, wider for ABE8e), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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