Agent skill

bio-read-qc-umi-processing

Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-read-qc-umi-processing --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 9 KB
Bundled scripts: yes
Path: skills/bio-read-qc-umi-processing/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: pandas 2.2+, samtools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # UMI Processing **"Deduplicate reads using UMIs"** → Extract UMI barcodes, group reads by UMI+position, and collapse PCR duplicates to count unique molecules. - CLI: `umi_tools extract` + `umi_tools dedup` (UMI-tools) - CLI: `fgbio GroupReadsByUmi` + `fgbio CallMolecularConsensusReads` UMIs (Unique Molecular Identifiers) are short random sequences added during library preparation to tag individual molecules before PCR amplification. This enables accurate PCR duplicate removal and molecule counting. ## UMI Workflow Overview ``` Raw FASTQ with UMIs | v [umi_tools extract] --> Move UMI to read header | v [Alignment] --> bwa/STAR/bowtie2 | v [umi_tools dedup] --> Remove PCR duplicates based on UMI + position | v Deduplicated

What's inside
Steps it walks through
  1. Version Compatibility
  2. UMI Workflow Overview
  3. Extract UMIs from Reads
  4. UMI in Read Sequence
  5. UMI Pattern Syntax
  6. Complex Patterns
  7. UMI in Separate Index Read
  8. Quality Filtering During Extraction
  9. Deduplication
  10. Basic Deduplication
  11. Deduplication Methods
  12. Method Selection Guide
  13. Paired-End Deduplication
  14. Gene-Level Deduplication
Ships with 2 files
  • examples/umi_workflow.sh
  • usage-guide.md
Commands it runs
UMI at start of R1 (8bp UMI)
umi_tools extract \
UMI at start of R2
UMI in both reads
Skip bases between barcode and UMI
Fixed anchor sequence
UMI in I1 index read
Filter by UMI quality
Filter UMIs with N bases
Must be sorted and indexed first
More from OpenClaw-Medical-Skills
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About this skill
What does the bio-read-qc-umi-processing skill do?

Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-read-qc-umi-processing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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