
from bioskills899
Detect syntenic gene blocks and structural rearrangements between genomes using MCScanX, JCVI, GENESPACE, SyRI, AnchorWave and related tools; guidance on pipeli
Provides a practical, publication-oriented workflow for detecting syntenic blocks and structural rearrangements between genomes. The skill explains which tools to use (MCScanX/JCVI for collinearity, GENESPACE for plant multi-genome orthology-anchored synteny, SyRI/plotsr for structural variants, AnchorWave for WGD-aware alignment), concrete command examples, and post-processing checks (repeat-masking, BUSCO, Ks dating). It includes per-tool failure modes and quantitative thresholds for reliable results.
Use this skill when you need to compare genome architectures across species, identify inversions/translocations/duplications, build riparian/pan-gene visualizations for plants, or run synteny-aware ortholog inference in polyploid lineages. It applies to publication analyses, QC of assemblies before comparative work, and generating figures (dotplots, riparian plots, SV maps).
This skill is documentation-focused and tool-agnostic; it maps to Linux/conda environments invoking CLI tools and Python/R pipelines (suitable for agents that can run shell/python/R commands or invoke MCP tools that wrap these bioinformatics tools).
Comprehensive comparative genomics synteny analysis skill from GPTomics/bioSkills. No bundled scripts — purely a knowledge/reference skill covering MCScanX, JCVI, GENESPACE, SyRI, AnchorWave and related tools. Exceptionally detailed content with algorithmic taxonomy, decision trees, failure modes, quantitative thresholds, pipeline examples, and reviewer pushback guidance. Security is clean with no executable code or network calls.
Outstanding knowledge skill for synteny analysis. Covers 12+ tools with practical pipelines, failure modes, and troubleshooting. No security concerns whatsoever — purely educational content. Niche audience (comparative genomics researchers) limits broad usefulness but high value within that niche.
KEGG Pathway Enrichment (clusterProfiler)
Run KEGG pathway and module enrichment using clusterProfiler (enrichKEGG/enrichMKEGG) to find metabolic and signaling pathways over-represented in a gene list.
MS-DIAL metabolomics preprocessing
Headless MS-DIAL preprocessing for LC/GC workflows: peak picking, MS2Dec deconvolution, alignment and honest feature-table import into R or Python.