Read Processing and Quality Control Toolkit for Dual-Indexed Metabarcoding
Read Processing and Quality Control Toolkit for Dual-Indexed Metabarcoding. That is what the metaplex mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
For a full list of MetaPlex primers and library preparation information, see here
The server publishes 4 tools. What each one is for:
Input — The remultiplexing process can start from either a raw unmapped bam file such as what is given by an Ion Torrent sequencer, or a fastq/fastq.gz fileOutput — Remultiplexing produces a single gzipped fastq containing all sequences where both a forward and reverse barcode were foundInputs — This workflow requires starting from data which has been demultiplexed utilizing QIIME2. Specifically, we start with a data typeOutputs — Return : Integer value of maximum number of index jumps (false reads) expected in a single samplemetaplex on PyPI is all you need. Most clients run it directly, so configuration is a few lines and a restart.
This sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Metaplex's toolset — Input, Output, Inputs and 1 more — is a fair guide to whether it matches your workflow. It is maintained by Nick Gabry; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Input | The remultiplexing process can start from either a raw unmapped bam file such as what is given by an Ion Torrent sequencer, or a fastq/fastq.gz file. Aside from the sequences, all that is needed is a .csv containing all |
| Output | Remultiplexing produces a single gzipped fastq containing all sequences where both a forward and reverse barcode were found. |
| Inputs | This workflow requires starting from data which has been demultiplexed utilizing QIIME2. Specifically, we start with a data type SampleData[SequencesWithQuality], though it isn't necessary for the data to have quality va |
| Outputs | Return : Integer value of maximum number of index jumps (false reads) expected in a single sample |
{
"mcpServers": {
"metaplex": {
"command": "uvx",
"args": ["metaplex"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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