1,844 件 ・ 関連度順
概要と使いどころ
Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.
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PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.
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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
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Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.
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Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
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Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.
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Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.
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Imports an FBX vehicle into a CARLA source build as a spawnable vehicle.<make>.<model> — checks the FBX carries CARLA's 4-wheel bone rig, imports the mesh, then assembles the vehicle through CarlaTools' VehicleAuthoringLibrary (physics asset with kinematic wheel bodies, retargeted anim blueprint, four configured wheel blueprints, the vehicle blueprint), registers it in VehicleFactory through CARLA's own add_vehicle_to_vehicle_factory.py, and verifies it spawns, drives and steers. Use when the user asks to "add a car/truck/van to CARLA", "import a vehicle FBX", or needs a custom drivable vehicle spawnable from the Python API.
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Writes a new ScenarioRunner scenario — a Python class deriving from BasicScenario with its behaviour tree assembled from srunner atomic behaviours and criteria, plus the XML config that gives it a town, ego spawn and parameters — and registers it so `--scenario MyScenario_1` works. Also covers adding a scenario to a route via --additionalScenario. Generates a working skeleton and validates it without a simulator. Use when the user asks to "create/write a new scenario", "add a custom scenario", "make my own traffic situation", or wants to extend the scenario library.
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Library of realistic adversarial attack vectors and anti-patterns to avoid. Contains examples of valid attacks and subtle gaming patterns to reject.
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Taxonomia de 86 layouts de apresentação/marketing (hero, pricing, auth, nav, CTA, testimonials, footer, FAQ, onboarding, blog, contact, stats, bento, states) adaptada do VibeUI. Consultar ANTES de construir landing pages, onboarding flows, auth screens, ou qualquer UI de camada de apresentação. Complementa o design-library/solutions (que cobre módulos verticais de produto).
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SSL/БСП subsystems guidance and search via 1c-mcp_ssl_server MCP. Use when working with standard library subsystems - users, files, print forms, background jobs, email, common utilities.
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ライブラリの情報を確認するためのスキル。Next.js、shadcn、その他のライブラリについて、適切なMCPサーバーを使用して最新のドキュメントと使用方法を取得します。
Check whether a Playwright, Puppeteer, Selenium or CDP-driven browser presents a coherent fingerprint, using liarjs as a library against a Page you already have - navigator.webdriver, HeadlessChrome tokens, worker versus main-thread identity, patched-API integrity, WebGL versus WebGPU GPU identity. Use when asked whether an automated browser looks like a normal one, when a headless setup or a stealth plugin's effect needs measuring rather than assuming, or when an assertion on fingerprint quality belongs in a test suite.
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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
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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 c_curve / lc_extrap, 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 — see Tips), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for downstream cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for downstream visualisation.
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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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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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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.
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Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.
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Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.
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。
Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.
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。
Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
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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。