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Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such

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🇺🇸 Product Launch & Product Hunt #1 Playbook — Multi-channel launch timing and sequencing. T-14 warm-up → T-0 launch day → T+7 momentum. Hunter selection, maker comment templates, comment cadence, anti-shadowban rules. Distilled from 30+ Product Hunt #1 daily launches across 150+ AI startups. **Supports 3 product paths: developer/maker (PH+GitHub), B2C consumer (Reddit/communities/TikTok, skip PH), B2B (LinkedIn+media).** 🇨🇳 产品发布与 Product Hunt 夺冠手册 — 多渠道发布的时序与节奏。T-14 预热 → T-0 发布日 → T+7 后续。Hunter 选择、maker comment 模板、评论节奏、防 shadowban。提炼自 30+ 次 Product Hunt 日冠、150+ AI 创业公司实战。**支持三种产品路径：开发者/maker（PH+GitHub）、2C 消费者（社区种子/TikTok，跳过 PH）、B2B（LinkedIn+媒体）。** 🇯🇵 プロダクトローンチ＆Product Hunt 1位プレイブック — マルチチャネルのローンチ時系列。T-14準備→T-0当日→T+7モメンタム。ハンター選定、メーカーコメント、コメントリズム、シャドウバン回避。**3つの製品パス対応：開発者/maker（PH+GitHub）、2C消費者（コミュニティ種・TikTok、PHスキップ）、B2B（LinkedIn+メディア）。** 🇰🇷 제품 출시 & Product Hunt 1위 플레이북 — 멀티채널 출시 타이밍과 시퀀싱. T-14 워밍업 → T-0 출시일 → T+7 모멘텀. 헌터 선정, 메이커 코멘트, 댓글 리듬, 섀도밴 방지. **3가지 제품 경로 지원: 개발자/maker(PH+GitHub), 2C 소비자(커뮤니티 씨드/TikTok, PH 건너뜀), B2B(LinkedIn+미디어).** Triggers: "product launch" | "launch strategy" | "Product Hunt" | "PH launch" | "how to launch" | "go to market" | "GTM" | "launch day" | "find a hunter" | "maker comment" | "launch sequencing" | "发布策略" | "产品发布" | "发 PH" | "Product Hunt 准备" | "找 hunter" | "ローンチ" | "출시 전략"

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Run nf-core workflows on local sequencing files or public GEO/SRA datasets to analyze gene expression, genetic variants, or chromatin accessibility. The skill guides environment checks, sample table preparation, workflow configuration, execution, and output verification.

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• Analyze gene expression from RNA-seq data
• Identify genetic variants in WGS/WES data
• Analyze chromatin accessibility from ATAC-seq data

Check whether a next-generation sequencing workflow has the tools, packages, reference data, and databases it needs. Generate installation and resource setup plans, record available resources, and compare them before rerunning an analysis.

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• Check installed tools before starting a sequencing workflow
• Prepare an installation plan for missing packages
• Audit reference data and database readiness

Find the right workflow for next-generation sequencing data by inspecting files and clarifying only missing setup details. Receive a recommended analysis skill, checks of available tools, and plans for software installation and reference data.

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• Choosing an analysis workflow from sequencing files
• Clarifying requirements for RNA-seq or variant analysis
• Checking tools and preparing an installation plan

Plan or run inherited DNA variant detection from whole-genome, exome, or targeted-panel sequencing. Check reference compatibility, sample quality, and combined analysis across samples, with documented commands, resource versions, and paths to variant and annotation outputs.

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• Planning inherited DNA variant detection from sequencing data
• Preparing joint variant analysis for cohorts or families
• Checking reference resources and sequencing quality

帮助使用GSAP编排多个动画的顺序与重叠，指定起始位置、嵌套动画组，并实现播放、暂停、倒放及跳转到指定位置。

• 按设定顺序播放多个动画
• 调整起始时间以重叠动作
• 将动作分组并统一控制播放

Build coordinated animation sequences with GSAP timelines. Set when each movement starts, overlap effects, name key moments, combine smaller sequences, and control playback, including pause, reverse, restart, and jumps to a specific point.

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• Animating several elements in a planned order
• Overlapping effects at precise times
• Organizing animation sections with labels

Process next-generation sequencing data with deepTools: convert alignment files into coverage tracks, normalize signals, compare samples, and check quality. Create heatmaps and profiles around genes or peak regions for ChIP-seq, RNA-seq, and ATAC-seq experiments.

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• Converting BAM files to normalized bigWig tracks
• Checking sequencing quality and replicate agreement
• Plotting heatmaps around transcription start sites

Plan and track the month-end accounting close with daily checklists, task dependencies, owners, deadlines, and blockers. Use the included schedules and review questions to coordinate work, assess delays, and identify process improvements.

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• Planning a daily month-end close calendar
• Tracking task owners, deadlines, and progress
• Identifying blockers and their impact on the schedule

Get oriented with a biological research plugin by checking connected research tools and reviewing available analysis skills. Receive a connection summary, optional setup guidance, and suggested starting points for literature searches, sequencing analysis, drug discovery, or research planning.

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• Getting started with the biological research plugin
• Checking connections to literature and drug discovery tools
• Reviewing available sequencing analysis skills

Build coordinated animation sequences with GSAP timelines. Set when each movement starts, overlap effects, name key moments, combine smaller sequences, and control playback, including pause, reverse, restart, and jumps to a specific point.

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• Animating several elements in a planned order
• Overlapping effects at precise times
• Organizing animation sections with labels

Validate FASTQ sequencing files, run local quality checks, and interpret reports to decide whether trimming is needed. Optionally remove adapters or low-quality read ends while preserving original reads and their quality reports.

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• Check sequencing quality before downstream analysis
• Summarize quality reports across multiple samples
• Assess whether adapter or quality trimming is needed

Analyze single-cell RNA sequencing data with scVI and scANVI. Combine experimental batches while correcting technical differences, produce compact cell features for clustering, assign cell types using reference labels, and assess differences in gene expression between groups.

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• Integrating single-cell RNA sequencing batches
• Preparing cell features for clustering
• Assigning cell types from reference annotations

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.

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Calls DNA methylation from Oxford Nanopore sequencing data using signal-level analysis. Use when detecting 5mC or 6mA modifications directly from nanopore reads without bisulfite conversion.

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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.

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Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-aware duplicate removal using fgbio. Applies cfDNA-specific quality thresholds and fragment length filtering. Use when processing plasma cfDNA sequencing data before downstream analysis.

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Apply the Law of Continuity — the eye follows alignment and unbroken paths. Use when sequencing steps, aligning content, or designing carousels and timelines. For grouping rather than sequencing, use `law-of-proximity`.

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Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.

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Permutation and sequencing integer-programming formulations with ordered local-window variables, prefix/suffix continuity, and overlap penalties. Use when assigning exams, jobs, tasks, visits, blocks, or resources to ordered positions and costs depend on adjacent pairs, sliding triples, n-grams, short-horizon pressure, or overlapping local patterns.

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Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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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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