Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
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Shotgun metagenomics workflow with host-depletion-aware QC, taxonomic profiling, functional profiling, AMR follow-up, and reproducible community output tables.
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Reference examples assume:
fastp 0.23+kraken2 2.1+bracken 2.8+metaphlan 4+humann 3.9+Verify the environment first:
kraken2 --version, bracken -v, metaphlan --version, humann --versionUse this skill for shotgun metagenomics when the user needs:
kraken2 + bracken is a common pragmatic routehumannresults/taxonomy/bracken_species.tsvresults/taxonomy/bracken_genus.tsvresults/function/pathabundance.tsvresults/amr/amr_summary.tsvqc/read_processing_summary.tsvfastp \
-i sample_R1.fastq.gz \
-I sample_R2.fastq.gz \
-o qc/sample.clean.R1.fastq.gz \
-O qc/sample.clean.R2.fastq.gz \
--html qc/sample.fastp.html \
--json qc/sample.fastp.json
kraken2 \
--db $KRAKEN_DB \
--paired qc/sample.clean.R1.fastq.gz qc/sample.clean.R2.fastq.gz \
--report results/taxonomy/sample.kraken.report \
--output results/taxonomy/sample.kraken.out \
--confidence 0.1
At minimum, inspect read quality, adapter content, and retained reads. For host-associated samples, remove host reads before community interpretation.
Use a k-mer or marker-based profiler. Document the database and version because abundance results depend strongly on the reference.
Convert raw classification to species or genus abundance tables suitable for cohort comparison.
Run pathway or AMR profiling only after confirming taxonomic QC and read retention are reasonable.
Save per-sample tables and merged matrices with clear metadata joins.
results/
├── taxonomy/
│ ├── sample.kraken.report
│ ├── bracken_species.tsv
│ └── bracken_genus.tsv
├── function/
│ └── pathabundance.tsv
└── amr/
└── amr_summary.tsv
qc/
├── read_processing_summary.tsv
└── sample.fastp.html
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Workflow for read alignment, sorting, indexing, mapping statistics, and downstream-ready alignment artifacts.
中文简介正在准备中。当前显示原文说明。
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm. For QC thresholds, use protein-qc.
中文简介正在准备中。当前显示原文说明。
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
中文简介正在准备中。当前显示原文说明。
Workflow for event-level and isoform-level splicing analysis with sashimi-ready outputs and splice QC.
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ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
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End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
中文简介正在准备中。当前显示原文说明。