Open source alternatives to bulk-rnaseq
24 open source AI Skills apps to use instead, best rated first.
Top picks
- G
github-ops
Launchgithub-ops is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It manages a GitHub repository with a focus on community health, CI reliability and the experience of contributors. It covers triaging issues by classify
Open source
- O
opensource-pipeline
Launchopensource-pipeline is an agent skill from the ECC collection by affaan-m, listed on skills.sh. It prepares a private project for safe public release through a three-stage pipeline. The fork stage strips secrets from a copy. The sanitise st
Open source
- A
andrej-karpathy-skills
Launchandrej-karpathy-skills is a single CLAUDE.md file from the multica-ai project that is meant to improve how Claude Code behaves on coding tasks. It is derived from Andrej Karpathy's public observations about LLM coding pitfalls. The file dis
Open source
- M
microsoft/markitdown
Launchmarkitdown-mcp is the MCP server package for MarkItDown, Microsoft's tool for converting many file formats to Markdown for language models. It is a lightweight server that runs over stdio, Streamable HTTP or SSE, and exposes one tool, conve
Open source
- O
od-contribute
Launchod-contribute is an agent skill in the OpenDesign project that provides a guided, one-click contribution flow to the OpenDesign repository, written so that people without a coding background can send a real pull request. It is locked to the
Open source
- G
github-deep-research
Launchgithub-deep-research is an agent skill from the DeerFlow project that researches a single GitHub repository in several rounds and writes a report in Markdown. It is for requests that need more than a README summary, such as a comprehensive
Open source
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Learn Claude Code
by shareAI-lab - A really interesting analysis of how coding agents like Claude
About bulk-rnaseq
bulk-rnaseq
Prepares bulk RNA-seq FASTQ, Salmon
bulk-rnaseq is an agent skill that prepares bulk RNA-seq reads and quantification output for a reproducible gene-level comparison. It takes FASTQ files, along with Salmon, STAR or featureCounts output, and gets them ready for differential expression. It owns sample validation, the recipes that turn reads into counts, and the handoff of counts and metadata to later steps. Statistical fitting and enrichment analysis are left to specialist skills. The skill defines defensible work as three things: reproducible, with pinned tool versions, containers where possible, recorded parameters and fixed random seeds; quality-gated, with QC inspected and acted on before, during and after quantification; and statistically sound, with adequate replication, a design that matches the biology, correct handling of counts, and FDR-controlled testing. The pipeline it describes runs FastQC and trimming, then alignment and quantification with STAR or Salmon, then counts, then differential expression with pydeseq2, then enrichment, then figures. It covers nf-core/rnaseq as well as standalone quantification. Use it when you are setting up a bulk RNA-seq comparison.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/bulk-rnaseq