'r2' 검색 결과

Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.

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Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.

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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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Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.

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Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.

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Manage MATLAB packages with the in-product MATLAB Package Manager (mpm) on MATLAB R2026b or later: install, uninstall, update, search, or inspect packages; declare package dependencies in a matlab.toml project; install a shareable .mltbx package; or manage repositories. Covers dependency-resolution errors and anything involving mpminstall / mpmuninstall / mpmupdate / mpmlist / mpmsearch / mpmAddRepository / matlab.mpm.*. MATLAB *packages* are distinct from MathWorks products, toolboxes, and support packages: a bare "what packages are installed?" means MPM packages (mpmlist / matlab.mpm.Package) — this skill — unless the user explicitly says products, toolboxes, or support packages. NOT for installing MATLAB, Simulink, or toolboxes from a shell via the standalone mpm executable (the OS-level product installer), nor for listing installed products / toolboxes (ver, installedAddons) — use matlab-install-products for those.

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Generate Bluetooth Low Energy (BLE) PHY waveforms. Read BEFORE writing any BLE waveform code to avoid hallucinating API patterns. Covers bleWaveformGenerator, bleIdealReceiver, bleCTEIQSample, bleAngleEstimate, bluetoothTestWaveform for LE1M/LE2M/LE500K/LE125K. Bluetooth Toolbox R2022a+.

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Use whenever a MATLAB robot model (rigidBodyTree) is needed — whether for simulation, visualization, IK, motion planning, pick-and-place, or trajectory generation. Triggers on: any mention of a manipulator by name (UR3, UR5, UR5e, UR10, UR10e, UR16e, UR20, KUKA iiwa, Fanuc, ABB, Panda, Kinova, Sawyer, Baxter), robot modeling verbs (load, create, build, import, simulate, model), tasks implying a robot model (pick, place, lift, reach, move, grasp, plan motion for, animate), kinematics keywords (IK, FK, inverse kinematics, forward kinematics, joint configuration, end-effector pose, gripper), or working with rigidBodyTree, loadrobot, importrobot, URDF, DH parameters, addVisual, addCollision.

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Design and implement adaptive filters using DSP System Toolbox System objects. Use when working with adaptive filtering, system identification, noise cancellation, echo cancellation, active noise control (ANC), channel equalization, inverse system identification, or adaptive prediction. Covers dsp.LMSFilter, dsp.RLSFilter, dsp.FilteredXLMSFilter, dsp.FrequencyDomainAdaptiveFilter, dsp.AffineProjectionFilter, dsp.BlockLMSFilter, dsp.AdaptiveLatticeFilter, dsp.FastTransversalFilter, maxstep(), and algorithm selection for adaptive filtering problems. Replaces deprecated adaptfilt.* objects (removed R2020a).

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Build industrial-grade SCADA/HMI dashboards in MATLAB following industrial-HMI conventions (ISA-101-aligned): gray-field philosophy, alarms at source, write safeguards, fixed-range trends, drill-down layout. Produces a real App Designer app (.mlapp, or plain-text .m+.xml on R2026b+) by handing serialization to the matlab-build-app skill when available, and falls back to a programmatic .m app otherwise. Use when wrapping OPC UA / Modbus / MQTT / OSI PI / PI AF monitoring scripts into a live operator app, building plant overviews, designing operator dashboards, or any time a user asks for a "SCADA dashboard", "HMI", "plant dashboard", "operator screen", or "industrial monitoring app" in MATLAB. Trigger on: SCADA, HMI, industrial dashboard, plant overview, operator screen, uigauge, uilamp, alarm banner, gray-field, ISA-101, OPC UA dashboard, setpoint, write safeguards, alarm visualization, OSIsoft PI, AVEVA PI, PI Server, PI Data Archive, PI AF, PI Asset Framework, piclient, afclient.

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Generate 3GPP-compliant 5G NR downlink and uplink baseband waveforms. Use to create NR signals, test model (TM) waveforms, fixed reference channels (FRC), test and measurement (T&M) signals, or test vectors for conformance testing. Covers configuring data, control, and broadcast channels and signals: PDSCH, PUSCH, PDCCH, PUCCH, SRS, SSBurst, CSI-RS, DM-RS, PT-RS, CORESET, and BWP parameters including bandwidth, subcarrier spacing (SCS), modulation (QPSK, QAM), numerology, FR1, FR2, TDD, FDD, and multi-bandwidth-part setups. Use for signal generation, RF instrument playback, or IQ baseband synthesis. Requires 5G Toolbox.

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Reference for MATLAB audiostreamer (Audio Toolbox R2025a+). Without this skill, agents consistently default to legacy audioDeviceWriter/audioDeviceReader or base MATLAB sound(), producing less capable code. Use when writing code for audio playback, recording, full-duplex device I/O, real-time audio measurements, or audio I/O processing with callbacks. Also use when debugging audiostreamer errors, dropouts, or latency issues, or migrating from audioDeviceReader, audioDeviceWriter, audioPlayerRecorder, or audioplayer/audiorecorder.

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Modernize removed or discouraged MATLAB functions and patterns. Use when the check_matlab_code MCP tool reports that a function "is not recommended", "will be removed in a future release", or "has been removed" — at any severity (info, warning, or error) — when migrating legacy code, or when replacing removed or discouraged APIs with current equivalents. Also applies to discouraged patterns Code Analyzer does not flag (containers.Map, bsxfun, str2num, eval, clear all) and to removed or discouraged functions such as trainNetwork, csvread, xlsread, datenum, guide, svmtrain, ltln2val, webmap.

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Create, edit, and run plain-text MATLAB live scripts (.m files) with rich text formatting, LaTeX equations, section breaks, and inline figures. Use when generating tutorials, analysis notebooks, reports, documentation, or educational content, when modifying existing live scripts, or when converting existing binary .mlx files to .m for version control. Requires R2025a+.

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Style MATLAB charts and figures. colororder palettes for chart series colors, colormap selection, brand color organization, and the R2025a Theme API for uifigure apps (dark mode with fliplightness, ThemeChangedFcn, uistyle, component colors). Use when customizing chart colors, applying a color palette, organizing brand colors, implementing dark mode, or importing an external color scheme. Triggers: theme, dark mode, brand colors, colororder, colormap, palette, fliplightness, color scheme, styling, chart colors.

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Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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Display images and annotations for image processing, computer vision, and visual inspection. Use when displaying images with imageshow, creating image viewers with viewer2d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming video frames, or building apps with image display.

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Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

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Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.

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Use when generating video with Vidu models on Scenario via MCP: text-to-video, image-to-video from a single still, start and end frame interpolation, reference-to-video keeping characters and props consistent from up to 7 reference images or reference videos, anime-style motion, movement amplitude control, or deciding between Q3, Q2, Q1, and 2.0 tiers. Keywords: Vidu Q3 Pro, Q2 Turbo, Shengshu Technology, T2V, I2V, R2V, Reference2V, start end frame, background music toggle.

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Use when generating or extending video with Google Veo models on Scenario via MCP: text-to-video, image-to-video from a first frame, first and last frame transitions, reference-to-video (R2V) with asset or style reference images, native audio with dialogue, sound effects, and ambience, negative prompts, seeded reruns, extending a 16:9 clip, or choosing a quality, fast, or lite tier. Keywords: Veo 3.1, Veo 3.1 Fast, Veo 3.1 Lite, Extend Video, Google, T2V, I2V, V2V, 720p, 1080p.

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Use when generating or editing video with Grok Imagine models on Scenario via MCP: text-to-video, image-to-video from a first frame, reference-to-video with @image tags for consistent people, products, or clothing, prompt-based editing, extending a clip from its last frame, native audio with lip-synced dialogue and sound effects, or choosing between first-frame and reference conditioning. Keywords: Grok Imagine Video 1.5, R2V, Grok Edit Video, Grok Extend Video, xAI, T2V, I2V, V2V.

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Scaffold and deploy Cloudflare Workers with Hono routing, Vite plugin, and Static Assets. Describe project, scaffold structure, configure bindings, deploy. Use whenever the user wants to create a Worker project, set up Hono on Cloudflare, configure D1 / R2 / KV / Queues bindings, or troubleshoot Worker export syntax, API route conflicts, HMR issues, or deployment failures.

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Scaffold a full-stack Cloudflare app from the vite-flare-starter template — React 19 + Hono + D1+Drizzle + better-auth + Tailwind v4+shadcn/ui + TanStack Query + R2 + Workers AI. Run setup.sh to clone, configure, and deploy. Use whenever the user wants a batteries-included Cloudflare full-stack app, vite-flare-starter scaffold, or a React + Cloudflare app with auth + database + Workers AI ready to go.

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