'sequences' 검색 결과

Analyze CTF cryptography challenges by identifying the cipher and matching known parameters to possible attacks. It provides formulas, attack parameters, and Python snippets for RSA, AES, elliptic curves, random number generators, classical ciphers, and lattice problems.

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• Choose RSA approaches from the given parameters
• Investigate weaknesses in AES modes
• Analyze random number sequences for predictability

Build PyTorch machine learning workflows for molecules, proteins, and biomedical relationships. Get guidance on datasets, model selection, training, and evaluation for property prediction, protein modeling, molecule generation, and planning synthesis routes from target molecules.

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• Predicting molecular solubility and toxicity
• Predicting protein function from sequences or structures
• Exploring new drug uses through biomedical relationships

Build tests with fast-check for TypeScript and JavaScript or Hypothesis for Python. Generate varied inputs to check rules code should always satisfy, discover edge cases, and simplify failing inputs into small examples for debugging.

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• Checking that encoding and decoding restore the original data
• Testing rules that sorting must preserve
• Generating inputs with complex constraints

Write Python tests with Hypothesis that generate varied inputs and check properties, rules that should always hold. Discover edge cases, reduce failures to simpler examples, and save failing inputs for replay alongside conventional tests.

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• Finding edge cases in parsers and input handling
• Checking mathematical properties across many inputs
• Testing data encoding and decoding round trips

Analyze genomic regions with gtars using Python or command-line tools. Find overlaps, generate coverage tracks showing read density, prepare regions as tokens for machine learning, and split single-cell fragment data for further analysis.

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• Finding overlaps between peaks and promoter regions
• Generating BigWig coverage tracks from sequencing fragments
• Converting genomic regions into tokens for machine learning

Turn relationships, sequences, hierarchies, or quantities into a self-contained HTML diagram. The skill selects a suitable visual form and adds filtering, path tracing, or zoom only when those interactions help readers understand the subject.

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• Explain components and their connections
• Visualize process steps and handoffs
• Show state transitions and their conditions

Generate protein structures and sequences with specified conditions using NVIDIA's hosted API or a local NIM service. Supports binder and antibody design, reward-guided search, and inspection of generated candidates, outputs, and validation results.

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• Generate protein candidates for a specified binding target
• Design antibodies or structures around a retained motif
• Explore candidates with reward-guided search

Prepare or run variant scoring with public CodonFM Encodon models. Validate coding sequences and codon positions in CSV inputs, assemble the scoring command, and check output arrays that compare reference and alternate codon probabilities in context.

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• Validating a variant CSV before scoring
• Preparing an Encodon scoring command
• Running scoring and checking output arrays

Guides fine-tuning public Encodon models on labeled protein-coding sequences or variants. Supports numerical prediction and classification, with data checks, LoRA adapters that train selected parameters, head-only training, or full training, followed by checkpoint and metric checks.

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• Fine-tuning Encodon for sequence-level numerical prediction
• Training a classifier on labeled coding variants
• Preparing training data and a launch command

Guides hierarchical context parallelism in Megatron-Bridge, which splits input sequences across nested GPU groups. Covers configuration, implementation locations, constraints, and log checks to help enable the feature and investigate memory errors or performance regressions.

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• Configure hierarchical context parallelism
• Scale context parallelism beyond KV head counts
• Investigate memory errors after configuration changes

Choose how to split large-model training across GPUs in Megatron Bridge. This guide helps combine parallelism methods, estimate GPU requirements, match settings to hardware connections, and check memory use and performance after configuration changes.

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• Choosing parallelism settings for a model and GPU count
• Estimating GPU requirements for MoE models
• Adjusting parallelism for long input sequences

Integrate a machine learning weather model into Earth2Studio for forecasts that advance through time. The skill guides wrapper implementation, coordinate handling, model loading, tests, dependency declarations, and documentation updates using a reference inference script.

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• Add an external weather model to Earth2Studio
• Align model inputs, outputs, and coordinates
• Test forecast steps and sequences

Agent guide for operating Sequenzy. Use when Codex needs to authenticate, inspect identity, manage subscribers, create or edit campaigns/sequences/templates, generate draft email content, send a transactional email, read delivery stats, or decide whether a requested Sequenzy workflow is currently supported. Prefer the CLI when it is implemented, and fall back to the dashboard or direct API use when the current CLI surface is only partial.

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Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.

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Searches for homologous protein sequences using MMseqs2 (fast, default) or BLAST (comprehensive, fallback). Trigger this whenever the user provides a protein sequence or FASTA file and asks to find homologues, sequence matches, or wants to infer protein function based on sequence similarity, but not when the user wants to infer protein function based on structural similarity.

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Access protein metadata, function, taxonomy, and sequences across UniProtKB, UniParc, and UniRef. Use when searching for proteins, mapping identifiers, or retrieving functional annotations and publications. Don't use for sequence alignment, protein folding, or sequence similarity search (use specialized skills for those tasks).

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Dataset annotation management — COCO labels, sequences, export, and Kaggle upload

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Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.

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Remove sequencing adapters from FASTQ files using Cutadapt and Trimmomatic. Supports single-end and paired-end reads, Illumina TruSeq, Nextera, and custom adapter sequences. Use when FastQC shows adapter contamination or before alignment of short reads.

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Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.

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Navigate protein structure hierarchy using Biopython Bio.PDB SMCRA model. Use when accessing models, chains, residues, and atoms, iterating over structure levels, or extracting sequences from PDB files.

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Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) using Biopython Bio.SeqIO. Use when parsing sequence files, iterating multi-sequence files, random access to large files, or high-performance parsing.

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Analyzes isoform switching events and functional consequences using IsoformSwitchAnalyzeR. Predicts protein domain changes, NMD sensitivity, ORF alterations, and coding potential shifts between conditions. Use when investigating how splicing changes affect protein function.

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Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.

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