'alignment' 검색 결과

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.

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Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.

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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.

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Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.

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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.

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View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.

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Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.

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선호 답변과 비선호 답변 쌍으로 별도 참조 모델 없이 대규모 언어 모델을 추가 학습합니다. 모델별 설정과 학습 불안정, 메모리 부족에 대한 대응을 안내합니다.

• 답변 선호도 데이터로 모델을 학습합니다
• 지시 응답 모델을 추가 조정합니다
• 수학과 코드용 학습 설정을 조정합니다

Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.

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• Build gene trees from related nucleotide sequences
• Compare evolutionary relationships within protein families
• Review branch support and analysis logs

Turn small-molecule structures into numerical features for similarity searches and models that predict activity or properties. Using Molfeat, the skill covers representation choices, invalid inputs, alignment with experimental labels, and saving feature-generation settings for reuse.

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• Prepare molecular features for activity or property prediction
• Choose molecular representations for similarity searches
• Keep experimental labels aligned after rejecting invalid molecules

Support product strategy with market and competitor analysis, vision setting, team design, and performance measures. Includes a Python script that turns company objectives into linked product and team goals and calculates alignment scores.

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• Turning company goals into product and team goals
• Analyzing markets and competitors
• Defining product vision and KPIs

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

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• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

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• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.

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• Preparing large text collections for language models
• Training a tokenizer on domain-specific text
• Mapping model outputs back to original text

Prepares medical device quality systems for ISO 13485 audits using six focused questions. Reviews design records, corrective actions, process validation, risk files, post-market monitoring, and management reviews, then summarizes readiness gaps and three priority actions.

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• Check readiness before an ISO 13485 internal audit
• Review design history files before a product launch
• Check evidence that corrective actions were effective

Refine interface details such as rounded corners, shadows, typography, icon alignment, and animations. Use concrete implementation rules to improve visual consistency and interaction feedback, with changes documented in before-and-after tables.

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• Refining the appearance of existing UI components
• Improving button and icon interaction feedback
• Reviewing entrance and exit animations

Maintain personal goals and beliefs with timestamped backups, or analyze project documents to connect problems, goals, strategies, and projects. Turn findings about progress, bottlenecks, and alignment into reports, presentation points, or interactive dashboards.

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• Update personal goals while preserving history
• Find project dependencies and bottlenecks
• Prepare key points for a presentation

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

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• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

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• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Prepare large text collections for AI models with fast tokenizers, which split text into model inputs. Train custom vocabularies, trace tokens to original text positions, adjust input lengths, and integrate the results with transformers.

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• Tokenizing large text collections
• Training a custom vocabulary
• Mapping extracted names or answers back to original text

Refine interface surfaces, icons, and motion with specific values for corners, shadows, alignment, and transitions. Preserve the project's existing design conventions while identifying visual inconsistencies, touch interaction problems, and missing alternatives for reduced motion.

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• Refining corners and shadows on nested cards
• Unifying icon appearance across interaction states
• Adjusting button feedback and entrance animations

Review and improve interface grouping, alignment, spacing, and responsive structure. Check whether content and actions remain accessible when screens resize, text grows in translation, zoom increases, or the layout switches to right-to-left reading.

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• Checking clipping and overlap on narrow screens or at higher zoom
• Preparing layouts for longer translated text
• Reviewing right-to-left layouts