Bioconductor Psmatch logo

Bioconductor Psmatch

Organization
bioMate-AI
bioconductor-psmatch

The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and m

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-psmatch
Stars
804
Forks
66
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Psmatch AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/proteomics/psmatch
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/proteomics/psmatch .claude/skills/bioconductor-psmatch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Psmatch in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Psmatch on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Psmatch is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PSMatch

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 1.16.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Depends: S4Vectors, PTMods
  • Imports: igraph, Spectra, Matrix, BiocParallel, BiocGenerics, ProtGenerics, QFeatures, MsCoreUtils
  • Install: BiocManager::install("PSMatch")

When to Use

  • Fragment Ion Calculation: Calculating theoretical MS2 fragment ions (b and y ions) for a peptide sequence using calculateFragments.
  • Fragment Visualisation: Visualising matched b- and y-ion fragment sequences directly on an MS spectrum using plotSpectraPTM.
  • Peptide-Protein Modelling: Modelling the relation between peptides and proteins as an adjacency matrix using makeAdjacencyMatrix.
  • Protein Group Resolution: Partitioning complex peptide-protein graphs into independent subgraphs using ConnectedComponents to explore shared and unique peptides.

When NOT to Use

  • For handling and processing raw mass spectrometry spectra, use Spectra because PSMatch relies on it for raw data representation and extraction.
  • For managing multi-level quantitative proteomics data, use QFeatures because PSMatch focuses on PSM-level filtering and structural relationships.
  • For applying and managing peptide modifications, use PTMods because PSMatch delegates canonical modification handling to it.

Data Requirements

  • Identification Data: Imported as a PSM object containing columns for peptide sequences (e.g., "sequence") and protein accessions (e.g., "DatabaseAccess").
  • Raw MS Data: Loaded as a Spectra object for fragment ion visualisation.
  • Quantitative Data: Can be imported as a SummarizedExperiment object with protein groups named by peptide sequences.

Key Parameters

  • addCarbamidomethyl: Logical in calculateFragments and plotSpectraPTM to apply carbamidomethylation of cysteines by default.
  • variableModifications: Named numeric vector in calculateFragments or plotSpectraPTM to apply variable mass modifications.
  • binary: Logical in makeAdjacencyMatrix to create a binary matrix instead of counting occurrences.
  • protColors: Numeric or character in plotAdjacencyMatrix to control protein node colours based on string distances.
  • pepColors: Character vector in plotAdjacencyMatrix to colour peptide nodes (e.g., by search engine score).
  • split: Character in makeAdjacencyMatrix (e.g., ";") to split protein groups into individual proteins.

Best Practices

  • Filter out decoy hits and low-rank matches using filterPsmDecoy and filterPsmRank before constructing adjacency matrices.
  • Merge raw Spectra data with PSM identification data using joinSpectraData to enable fragment visualisation.
  • Use prioritiseConnectedComponents to rank and identify the most interesting connected components (e.g., those with multiple shared peptides).
  • Extract specific subgraphs using connectedComponents to manually inspect complex protein groups with plotAdjacencyMatrix.

Common Pitfalls

  • Overlapping fragment ions in visualisations: Restricted plot windows can cause text to overlap. Fix: Run plotSpectraPTM locally or adjust the xlim parameter in plotSpectra.
  • Duplicate keys when merging: Merging spectra and PSMs can result in duplicates. Fix: Be aware that joinSpectraData will only keep the last instance if duplicates are found in the key.
  • Missing modifications: Fragments might not match the spectrum if fixed modifications are ignored. Fix: Ensure addCarbamidomethyl = TRUE or explicitly pass modifications via fixedModifications.

Alternatives

  • Spectra: For core MS data infrastructure and raw spectra manipulation.
  • QFeatures: For downstream quantitative protein-level aggregation and statistical analysis.
  • PTMods: For defining and retrieving canonical peptide modifications.

Citations

  • Gatto L, Rainer J, Gibb S, Wieczorek S, Burger T, Deflandre G (2026). PSMatch: Handling and Managing Peptide Spectrum Matches. R package.

References


Run this on BioMate

This skill is the knowledge layer — when, why, and how to use psmatch. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open psmatch on BioMate →

Frequently asked questions

What does the Bioconductor Psmatch AI skill do?

The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and m

Why use Bioconductor Psmatch on TypingMind?

Because you install it once and use it with any model. Bioconductor Psmatch is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Psmatch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/proteomics/psmatch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Psmatch?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Psmatch?

As many as you like. As long as a model supports skills, you can use Bioconductor Psmatch with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Psmatch AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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