「dna」の検索結果

Use this skill to configure a Lightning Service Console app in Salesforce — Console Navigation (split view), workspace tabs and subtabs, the utility bar with Omni-Channel, Macros and History, Quick Text, keyboard shortcuts, and navigation rules per object. Trigger keywords: Service Console, console app, workspace tabs, subtabs, utility bar macros, Omni-Channel utility, split view, Quick Text, console navigation rules, keyboard shortcuts service console, navType Console, workspaceConfig mappings, WorkspaceMapping fieldName, UtilityBar FlexiPage, tabLimitConfig, listPlacement, MacroInstruction, QuickText Channel multipicklist, isNavTabPersistenceDisabled. NOT for opening or refreshing console tabs from code — use lwc/lwc-console-workspace-api. NOT for Omni-Channel routing setup — use admin/omni-channel-routing-setup.

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Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before phenotyping, choosing a doublet method for flow vs CyTOF, or diagnosing a suspicious double-positive cluster.

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。

Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis.

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。

Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.

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。

Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

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。

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.

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。

Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.

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。

Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).

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。

Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.

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。

Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.

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。

Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.

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。

Infers genetic super-population ancestry from a 23andMe/AncestryDNA file and computes ancestry-stratified odds ratios with an exploratory Ancestry Elevation Score (AES) showing where ancestry-specific GWAS effect sizes diverge from European reference estimates.

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。

Run a safe, structured wellness intake that escalates red flags to professionals.

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。

Expert-level genetics covering Mendelian inheritance, molecular genetics, mutations, linkage analysis, population genetics, epigenetics, and GWAS. Covers classical and modern approaches from Mendel to CRISPR.

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。

可变体的 warm editorial 演示设计系统，用于 presentation、case sharing、项目汇报、战略叙事和产品发布。支持横向全屏 HTML deck 与程序化 PPTX，通过 style profile、语义 slide manifest、构图新颖度规划和反重复验证，在保持编辑设计 DNA 的同时避免固定配色、固定卡片和固定 11 页模板。用户要求 Pfizer/Anthropic/warm editorial/编辑设计风格、类似血液科那套，或需要高规格且非模板化的 HTML/PPT 演示时使用。

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。

Coach German writing and conversation with minimal corrections, persistent root-cause mistake tracking (FehlerDNA), mistake-based spaced repetition with a new sentence every time, roleplay whose words return in spaced reviews, continuing preparation for real-life goals across sessions, and an honest progress profile. Use for German correction, personalized review, vocabulary, progress, roleplay, or preparation for an interview, presentation, appointment, or other concrete event; do not use for translation-only requests without learning or feedback.

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。

Researches a company from its URL and produces a Brand DNA file covering positioning, audience, competitors, voice, and messaging. Use when starting work with a new brand or customer.

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。

Researches Reddit using a brand's Brand DNA to find promotable pain-point discussions, target subreddits, and real user search language. Produces a prioritized Reddit opportunity list for content seeding, helpful replies, and prompt research inspiration.

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。

Reads existing brand DNA, keywords.csv, and prompts.csv, then produces a plan.csv — a strictly-schema'd content architecture telling the next pipeline step which pages to create, for which keyword/prompt clusters, and in what build order.

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。

Verifies truthfulness, accuracy, and link integrity of content before publishing. Catches fabricated statistics, dead URLs, misattributed sources, and company claims that contradict the brand DNA.

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。

Use when screenshots, websites, moodboards, competitors, or visual examples are provided and the task needs design DNA extraction, comparison, remixing, or translation into an original design direction.

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。

Set up a paid-media client, brand, account, data-source, privacy, and mutation-guardrail profile for Claude Ads. Use for onboarding, initial configuration, brand DNA, API tokens or credential profiles, environment-variable or keychain setup, connecting exports or read adapters, safe native or verified-local installation, curl-pipe-bash install requests, declaring KPIs, or preparing a new advertising project.

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 genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

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。