调研与学习技能

Profile microbial species and their relative abundances in metagenomic sequencing data with MetaPhlAn 4. Use marker genes, sequences characteristic of particular groups, to analyze samples, combine abundance tables, and reuse saved sequence mappings when adjusting analysis settings.

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• Estimate microbial species composition in a metagenome
• Combine abundance profiles from multiple samples
• Extract abundance tables at species or genus level

Design guide sequences for C-to-T or A-to-G base editing by comparing target positions, nearby bases that may also change, and sequence context. Includes Python examples for ranking candidates and considering paired edits.

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• Select guide candidates for a C-to-T edit
• Compare nearby unintended edits for an A-to-G change
• Explore guide pairs for simultaneous C-to-T and A-to-G edits

Design guide RNA sequences for prime editing, a method for precise DNA changes. Generate components for substitutions, small insertions, and deletions, compare primer binding lengths, and assemble complete sequences for experimental planning.

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• Designing a guide RNA for a specific point mutation
• Comparing primer binding site lengths
• Preparing templates for small insertions or deletions

Design donor DNA templates for precise CRISPR insertions or sequence replacement. Choose between single-stranded DNA, double-stranded DNA, and plasmid donors, then construct matching flanking sequences and primers for amplification or assembly.

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• Designing donors for point mutations or small insertions
• Planning donor sequences for protein tagging
• Choosing donor formats for larger insertions

Find candidate guide RNAs for CRISPR experiments by scanning target DNA sequences. Rank candidates using sequence features and predicted activity, optionally restrict them to coding regions, and identify guides for Cas12a.

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• Finding guide RNA candidates for gene knockout experiments
• Restricting guide candidates to coding exons
• Ranking guides by predicted target activity

Build evolutionary trees with a time axis from genetic sequences and sample dates using TreeTime. Estimate outbreak origins, mutation rates, ancestral sequences, and geographic spread, with guidance for BEAST2 analyses that include uncertainty estimates.

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• Estimating when an outbreak originated
• Building an evolutionary tree with a time axis
• Reconstructing ancestral sequences and geographic spread

Plan and troubleshoot molecular binding experiments using SPR and BLI, two optical measurement methods. Compare platforms, investigate weak signals or unwanted binding, adjust experimental conditions, and assess the quality of binding-rate measurements.

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• Choosing between SPR and BLI for a sample
• Investigating weak or absent binding signals
• Reducing unwanted binding and surface transport effects

Choose tools for designing proteins that bind a target. Compare BoltzGen, BindCraft, RFdiffusion, and Germinal by target type, design diversity, and validation needs, then plan target preparation, design generation, and candidate filtering.

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• Choosing a protein binder design tool
• Planning a design and validation workflow
• Exploring diverse candidate structures

Identify super-enhancers, large clusters of gene regulatory regions, from H3K27ac ChIP-seq data. Use ROSE to join nearby peaks and rank their signals, then associate regions with nearby genes or compare experimental conditions.

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• Identifying super-enhancer candidates from H3K27ac data
• Comparing super-enhancer regions between conditions
• Associating super-enhancers with nearby genes

Look up genetic variants in ClinVar to retrieve pathogenicity classifications, review status, and associated diseases. Search by variant ID, gene, or HGVS notation, a standard variant description, or annotate local variant files in batches.

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• Looking up a variant by ID or HGVS notation
• Finding pathogenic variants reported for a gene
• Checking review status and conflicting classifications

Search BindingDB for measured interactions between compounds and protein targets. Compare binding measurements, examine which targets a compound binds to, and prepare datasets for drug discovery research or machine learning using target identifiers, compound names, or chemical structures.

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• Find compounds that bind to a target protein
• Compare binding measurements across related compounds
• Examine a compound's binding across multiple targets

Search scientific papers through the BGPT MCP server and retrieve structured experimental details extracted from full texts. Gather methods, numerical results, sample sizes, quality assessments, and conclusions to compare studies and build evidence tables.

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• Gather papers for systematic or scoping reviews
• Compare experimental methods across studies
• Collect numerical results and sample sizes

Search arXiv preprints, research papers shared before peer review, using everyday language. Find work across physics, mathematics, computer science, and quantitative biology, with article text and figure links returned through Valyu’s meaning-based search.

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• Finding preprints on a research topic
• Exploring recent AI research
• Searching across physics and mathematics

Predict structures for designed protein sequences and protein complexes with AlphaFold2 or ColabFold. Inspect structure files and confidence scores to assess folding and chain interfaces, with guidance for local runs, Modal execution, and common prediction errors.

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• Check predicted folding of a designed protein sequence
• Predict a binder–target protein complex
• Review structure and interface confidence scores

Plan A/B experiments to measure the effect of a design change. Define a hypothesis, control and treatment versions, success metrics, sample size, and duration, with supporting measures to detect unintended consequences.

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• Planning a comparison between current and proposed designs
• Defining success metrics for a change
• Estimating sample size and test duration

Turn user research into an empathy map covering what one user or segment says, thinks, does, and feels. Summarize goals, frustrations, design implications, and unanswered questions to help teams share an understanding of their users.

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• Organizing interview and observation notes into an empathy map
• Sharing an understanding of a specific user segment
• Identifying user goals, frustrations, and design implications

Design a shared research library that connects individual findings to studies and source data. Define tags, confidence levels, and maintenance routines so teams can find existing evidence, reuse past work, and track findings that become outdated.

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• Find past evidence before planning new research
• Standardize tags across teams' research findings
• Link findings to studies and source data

Design surveys to measure user opinions, needs, and satisfaction across a population. Plan neutral questions, clear response scales, participant sampling, and analysis methods to quantify findings and support decisions.

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• Quantifying findings from user interviews
• Tracking satisfaction over time
• Planning a survey for a specific user segment

Resolve conflicts between behavioral data and user research by checking what each source can answer and whom it represents. Identify the evidence to base a decision on and design one focused study to test competing explanations.

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• Investigating abandonment despite positive user reports
• Examining complaints absent from aggregate metrics
• Checking improved metrics against a worse experience

Create a diary study plan to understand user behavior over days or weeks. Define participant criteria, recording prompts, timing, capture methods, check-ins, and analysis, including ways to keep participants engaged throughout the study.

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• Studying everyday use over several weeks
• Designing daily or event-triggered diary prompts
• Planning participant onboarding and check-ins

Prepare a user interview guide with opening remarks, background questions, deeper exploration, and a closing section. It includes neutral questions, follow-up prompts, and facilitator notes to help uncover real experiences, behaviors, and motivations.

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• Prepare a user interview guide
• Rewrite leading questions neutrally
• Add follow-up questions about past experiences

Turn a single user interview transcript into a structured summary with key themes, supporting quotes, pain points, and follow-up actions. Distinguish explicit statements from inferred insights to help stakeholders assess the findings.

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• Summarizing an interview immediately after the session
• Organizing user pain points with supporting quotes
• Sharing interview findings with stakeholders

Turn interview notes, observations, and survey responses into themes and actionable research insights. Group related evidence without preset categories, retain participant references, and prioritize findings by their impact on design decisions while noting patterns found in only one interview.

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• Synthesize findings across multiple interviews
• Organize observation notes and survey responses
• Prioritize research insights for design decisions

Investigate a defined question using web sources, data experiments, or both. Keep evidence and experiment records together, then produce a structured verdict with caveats, including an inconclusive result when the available evidence does not settle the question.

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• Testing a hypothesis against a dataset
• Comparing approaches with existing benchmarks
• Investigating differences between two releases