Data & ReproProtein Structure & DesignFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-isoform-switching

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-isoform-switching🧬 bioinformatics (gptomics bio-* suite)bioinformatics — differential expression & transcriptomicsanalyzes

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-isoform-switching

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Identify isoform switches and predict their functional consequences on protein structure and function.
salmonQuant <- importIsoformExpression( parentDir = 'salmon_quant/', addIsofomIdAsColumn = TRUE ).

Source Doc

Excerpt From SKILL.md

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)

## 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
)