본문으로 건너뛰기
cccskills
무료GitHub에 공개

bio-isoform-switching

Identify genes whose main transcript form changes between conditions using IsoformSwitchAnalyzeR. Assess predicted effects on protein domains, protein production, and transcript degradation, then visualize individual switches and summarize patterns across the analyzed genes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Comparing transcript usage between control and treatment
  • Investigating protein domain changes caused by transcript switches
  • Assessing changes in coding potential and degradation sensitivity
  • Plotting switches for selected genes

포함된 파일(3)

  • SKILL.md5.7 KB
  • examples/isoform_switch_analysis.R4.9 KB
  • usage-guide.md2.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Version Compatibility

Reference examples tested with: Salmon 1.10+

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Isoform Switching Analysis

Identify isoform switches and predict their functional consequences on protein structure and function.

IsoformSwitchAnalyzeR Workflow

Goal: Identify genes where the dominant isoform switches between conditions.

Approach: Import Salmon quantification, filter low-expression isoforms, and test for isoform usage changes with DEXSeq-based statistics.

"Analyze isoform switching" -> Import transcript quantification, test for dominant isoform changes, and assess functional consequences.

  • R: IsoformSwitchAnalyzeR (importRdata + isoformSwitchTestDEXSeq)
library(IsoformSwitchAnalyzeR)

# Import transcript quantification from Salmon
salmonQuant <- importIsoformExpression(
    parentDir = 'salmon_quant/',
    addIsofomIdAsColumn = TRUE
)

# Create switch analysis object
switchAnalyzeRlist <- importRdata(
    isoformCountMatrix = salmonQuant$counts,
    isoformRepExpression = salmonQuant$abundance,
    designMatrix = data.frame(
        sampleID = colnames(salmonQuant$counts),
        condition = c('control', 'control', 'control', 'treatment', 'treatment', 'treatment')
    ),
    isoformExonAnnoation = 'annotation.gtf',
    isoformNtFasta = 'transcripts.fa'
)

# Filter lowly expressed isoforms
switchAnalyzeRlist <- preFilter(
    switchAnalyzeRlist,
    geneExpressionCutoff = 1,  # Minimum TPM
    isoformExpressionCutoff = 0,
    removeSingleIsoformGenes = TRUE
)

# Test for isoform switches
switchAnalyzeRlist <- isoformSwitchTestDEXSeq(
    switchAnalyzeRlist,
    reduceToSwitchingGenes = TRUE
)

Functional Annotation

Goal: Predict how isoform switches alter protein domains, coding potential, and localization.

Approach: Extract isoform sequences, run external annotation tools (CPC2, Pfam, SignalP, IUPred2), and import results back into the switch analysis object.

# Extract sequences for external analysis
switchAnalyzeRlist <- extractSequence(
    switchAnalyzeRlist,
    pathToOutput = 'sequences/',
    writeToFile = TRUE
)

# Run external tools and import results:
# - CPC2 for coding potential
# - Pfam for protein domains
# - SignalP for signal peptides
# - IUPred2 for intrinsic disorder

# After running external tools, import results
switchAnalyzeRlist <- analyzeCPC2(
    switchAnalyzeRlist,
    pathToCPC2resultFile = 'cpc2_results.txt',
    removeNoncodinORFs = TRUE
)

switchAnalyzeRlist <- analyzePFAM(
    switchAnalyzeRlist,
    pathToPFAMresultFile = 'pfam_results.txt'
)

switchAnalyzeRlist <- analyzeSignalP(
    switchAnalyzeRlist,
    pathToSignalPresultFile = 'signalp_results.txt'
)

switchAnalyzeRlist <- analyzeIUPred2A(
    switchAnalyzeRlist,
    pathToIUPred2AresultFile = 'iupred2_results.txt'
)

Consequence Analysis

Goal: Determine which isoform switches cause functional changes (NMD, domain loss, coding potential shifts).

Approach: Run analyzeSwitchConsequences across multiple consequence types and extract switches with confirmed functional impact.

# Analyze functional consequences of switches
switchAnalyzeRlist <- analyzeSwitchConsequences(
    switchAnalyzeRlist,
    consequencesToAnalyze = c(
        'intron_retention',
        'coding_potential',
        'ORF_seq_similarity',
        'NMD_status',
        'domains_identified',
        'signal_peptide_identified'
    ),
    dIFcutoff = 0.1,  # Minimum isoform fraction change
    showProgress = TRUE
)

# Extract significant switches
significantSwitches <- extractSwitchSummary(
    switchAnalyzeRlist,
    filterForConsequences = TRUE
)

print(significantSwitches)

Visualization

Goal: Visualize isoform switch events and summarize functional consequence patterns.

Approach: Generate per-gene switch plots showing isoform usage changes, and create global summaries of consequence enrichment.

# Plot individual gene switches
switchPlot(
    switchAnalyzeRlist,
    gene = 'GENE_OF_INTEREST',
    condition1 = 'control',
    condition2 = 'treatment'
)

# Summary of consequence types
extractConsequenceSummary(
    switchAnalyzeRlist,
    consequencesToAnalyze = 'all',
    plotGenes = FALSE
)

# Enrichment of consequences
extractConsequenceEnrichment(
    switchAnalyzeRlist,
    consequencesToAnalyze = 'all'
)

Significance Thresholds

ParameterDefaultDescription
Switch q-value< 0.05Significance of isoform switch
dIF (delta isoform fraction)> 0.1Minimum usage change
Consequence q-value< 0.05Significance of consequence

Consequence Types

ConsequenceImpact
NMD sensitiveTranscript targeted for degradation
Domain loss/gainAltered protein function
ORF disruptionTruncated/altered protein
Signal peptide lossChanged localization
Coding potential lossSwitch to non-coding

Related Skills

  • differential-splicing - Identify differential events first
  • splicing-quantification - PSI-level analysis
  • pathway-analysis/go-enrichment - Pathway enrichment of switching genes

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

adaptyv

무료

Support protein design with cloud laboratory testing for binding, expression, thermal stability, and enzyme activity. Covers sequence optimization, submitting experiments through the Adaptyv API, checking progress, and downloading experimental results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Validate designed proteins with laboratory assays
  • Screen and redesign sequences for better solubility
  • Submit protein testing experiments through an API
FreedomIntelligence/OpenClaw-Medical-Skills3.1천2026년 7월 21일 업데이트

Build flexible daily plans that account for ADHD-related difficulties with time estimation and getting started. Narrow priorities to three tasks, add transition buffers, and choose small next actions that match the energy available that day.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Choosing three priorities for today
  • Planning a schedule with transition buffers
  • Finding a small first step when starting feels difficult
FreedomIntelligence/OpenClaw-Medical-Skills3.1천2026년 7월 21일 업데이트

aeon

무료

Build Python workflows for data recorded over time, including classification, forecasting, anomaly detection, and pattern search. Using aeon, the skill guides preprocessing, algorithm selection, feature extraction, and evaluation through interfaces compatible with scikit-learn.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Classify time series by their patterns
  • Forecast future values from historical data
  • Detect anomalies and change points
FreedomIntelligence/OpenClaw-Medical-Skills3.1천2026년 7월 21일 업데이트

Use a browser to research topics, extract page information, fill forms, and test websites. Guides agents through identifying page controls, interacting with web apps, saving screenshots or PDFs, and reusing saved login sessions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Researching topics and reading web articles
  • Filling and submitting online forms
  • Extracting page text and links
FreedomIntelligence/OpenClaw-Medical-Skills3.1천2026년 7월 21일 업데이트

Combine health measurements, exercise, sleep, nutrition, and mental health records to examine trends, unusual changes, and estimated risks. The skill produces personalized suggestions and text or interactive HTML reports using local data without external API calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Review health records together in a comprehensive report
  • Explore links between sleep, mood, exercise, and weight
  • Summarize estimated disease and nutrition risks
FreedomIntelligence/OpenClaw-Medical-Skills3.1천2026년 7월 21일 업데이트

alphafold

무료

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Check predicted folding of a designed protein sequence
  • Predict a binder–target protein complex
  • Review structure and interface confidence scores
FreedomIntelligence/OpenClaw-Medical-Skills3.1천2026년 7월 21일 업데이트

FreedomIntelligence의 스킬 모두 보기

이 스킬의 문제 신고