「omics」の検索結果

Find and work with ENCODE single-cell genomics data including scRNA-seq and scATAC-seq. Use when the user asks about single-cell experiments, cell type resolution, clustering from ENCODE data, deconvolution of bulk signals using single-cell references, or comparing single-cell vs bulk profiles. Covers platform differences (10X Chromium, Smart-seq2, Drop-seq), quality limitations of single-cell data, multimodal integration (RNA+ATAC), and cross-study reproducibility concerns. Also use for cell type annotation, gene detection limits, dropout artifacts, and single-cell data structure in ENCODE.

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Search and explore ENCODE Project genomics data. Use when the user wants to find experiments, files, or explore what data is available for specific assays, organs, cell lines, or targets.

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Build financial models — revenue forecasts, unit economics, scenario analysis, and investor-grade spreadsheets.

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Create comics and graphic narratives with panel flow, lettering, pacing, and visual storytelling.

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End-to-end bioinformatics analysis — sequence QC, alignment, and omics pipelines for research data.

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Analyze behavioral segmentation systems for RFM scoring, cohort tracking, churn propensity, engagement scoring, persona clustering, and journey mapping using behavioral economics frameworks..

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Calculate and model paid-media CPA, CPL, CPC, CPM, ROAS, MER, break-even targets, contribution margin, LTV:CAC, impression-share opportunity, budgets, forecasts, and experiment economics. Use for PPC math, ad calculator, break-even analysis, ROAS calculator, CPA calculator, budget forecast, LTV CAC, or MER.

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Create a professional paid-advertising strategy covering objectives, economics, platform selection, campaign architecture, audiences, budget, creative, measurement, experiments, governance, rollout, and reporting. Use for ad plan, media plan, PPC strategy, paid-social strategy, campaign architecture, advertising roadmap, or channel planning.

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Product Discovery through an 18-task methodology in 6 blocks — from market analysis to an investor-ready financial plan. Use when someone wants to run Product Discovery, validate a startup, assess a market hypothesis, prepare for an investment round, or raises an adjacent topic — idea validation, niche sizing, competitor teardown, product-market fit, pitch preparation, startup unit economics, geographic expansion into a new country, go-to-market for SEA / Asia / MENA. People rarely name PD explicitly; they say 'is there a market for this', 'assess my idea', 'I need a deck for an angel', 'we are entering Thailand/Singapore/UAE, how do we size it'. Use the skill in those cases too. Three modes: Light (~45 min, idea stage), Full (~2-3 h, MVP with customers), Geographic Expansion (~2 h, product entering a new geography). Produces 3-4 artifacts in /mnt/user-data/outputs/: one-pager.pptx, financial-plan.xlsx, presentation.pptx, optionally interview-guide.docx.

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Drafts filing-ready Delaware Amended and Restated Certificates of Incorporation for venture-financing rounds. Converts term-sheet economics and governance terms into a defensible charter document under DGCL 242/245. Trigger when user needs to reauthorize capital structure, designate preferred stock series, set investor protections, or prepare a charter filing for closing.

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Drafts a Delaware Amended and Restated Certificate of Incorporation implementing VC term sheet economics and governance into DGCL-compliant charter provisions. Use when drafting or revising a Delaware A&R certificate, preferred stock charter terms, Series Seed/A/B financing charter, blank check preferred authority, or implementing cap structure from a term sheet or SPA.

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Use this skill for ANY of the following — do not skip it even if the query seems simple: strategic decisions, business analysis, thesis building, complex problem solving, geopolitical analysis (non-market), philosophical reasoning, academic research, system design, decision under uncertainty, long-form thinking, or any question that benefits from First Principle deconstruction and multi-agent reasoning. ALWAYS use this skill when the user asks about: why something works the way it does, whether a decision is correct, how to think about a complex problem, what could go wrong, analyzing a situation from multiple angles, or any "should I..." question involving real-world trade-offs. This is NOT a market/trading skill — for financial markets, BTC, gold, stocks, forex, macro economics, use money-atlas-intelligence-os instead.

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Systematic literature review methodology for social science research. Use when conducting systematic reviews, research synthesis, meta-analyses, scoping reviews, or comprehensive literature searches across economics, political science, sociology, education, public health, and development. Produces structured notebook cells with search strategy, evidence table, thematic synthesis, and gap analysis.

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Applies game theory to multi-agent coordination protocols with advisory claims, repeated interactions, daemon-mediated correlation, and deviation penalties. Use for Nash equilibrium checks, folk-theorem cooperation analysis, correlated-equilibrium routing, and price-of-anarchy audits in strategic agent systems. NOT for consumer pricing, card-game strategy, or general microeconomics outside agent coordination.

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Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.

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Analyze bonded agent systems, escrow workflows, slashing rules, oracle trust, Sybil cost, and front-running risk in protocols where agents post bonds or stake reputation. Use for collateral sizing, griefing analysis, dispute design, and bonded-agent security reviews. NOT for smart contract auditing, DeFi tokenomics, blockchain consensus design, or trading strategy.

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Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.

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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.

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