Self-hosted alternatives to bulk-rnaseq
5 self-hosted AI Skills apps to use instead, best rated first.
Top picks
- C

claude-to-deerflow
Launchclaude-to-deerflow is an agent skill that lets an assistant send messages and questions to a running DeerFlow instance through its HTTP API. DeerFlow is an AI agent platform built on LangGraph that orchestrates sub-agents for research, code
Self-hosted
- O

open-notebook
Launchopen-notebook is an agent skill for organising research with Open Notebook, a self-hosted alternative to NotebookLM. It arranges sources, notes and AI conversations into research notebooks. Sources can be PDFs, web pages, audio, video and O
Self-hosted
- R

Reactive Resume
LaunchReactive Resume is a free and open-source resume builder for writing resumes and cover letters, checking the work, and tracking job applications. You create a free account, import an existing resume or start from scratch, and see the finish
Open sourceSelf-hosted
- S

self-hosted-funnel-launch
LaunchSelf-hosted funnel launch is an agent skill that takes a funnel from an empty install to published on infrastructure you control, using the Autonnel funnel builder, which is licensed Apache-2.0. The result covers a landing page, checkout, a
Self-hosted
- G

get-convex/convex-backend
LaunchConvex is an open-source reactive database for web app developers. It combines a database, a place to write server functions in pure TypeScript, and client libraries that keep the app's data live, so screens update as the data changes. Beca
Self-hosted
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