Python Package Installation And Dependency Management

Python Package Installation and Dependency Management

Summary

Install and configure Python bioinformatics packages with pinned dependencies to ensure reproducible execution of mass spectrometry analysis workflows. This skill addresses version-specific compatibility constraints that arise when combining multiple scientific packages.

When to use

When setting up a new computational environment for tandem MS/MS spectrum clustering or other proteomics analysis, and you need to install a tool (like falcon) that depends on specific versions of auxiliary packages (like spectrum-utils==0.3.5). Use this skill when the tool's documentation specifies exact version pins and platform constraints (e.g., Linux/OSX only, Python 3.8+ required).

When NOT to use

• Environment already has incompatible package versions installed that cannot be reconciled (e.g., spectrum-utils 0.4.0 required by another tool)
• Platform is Windows (falcon is documented for Linux and OSX only)
• Python version is 3.7 or earlier (falcon requires Python 3.8+)

Inputs

• Python 3.8+ runtime environment
• pip package manager
• Internet access to PyPI package repository
• Tool installation specification with pinned dependencies (e.g., from tool README or publication)

Outputs

• Installed falcon-ms command-line tool
• Installed spectrum-utils==0.3.5 library
• Python environment with validated package versions

How to apply

First, verify your Python version meets the minimum requirement (Python 3.8+) and that your operating system is supported (Linux or OSX). Then install the primary tool and its pinned dependencies together using pip, specifying exact versions as documented: pip install falcon-ms spectrum-utils==0.3.5. After installation, verify successful setup by invoking the tool's help or running it on a small test dataset to confirm no import errors or version conflicts occur. Version pinning is critical because spectrum-utils and falcon use feature hashing and nearest neighbor indexing that may break across minor version boundaries.

Related tools

• falcon-ms (Primary spectrum clustering tool; installed via pip with pinned dependencies) — https://github.com/bittremieux/falcon
• spectrum-utils (Auxiliary library for spectrum handling and preprocessing; pinned to version 0.3.5 for compatibility with falcon)
• pip (Package manager used to install falcon-ms and spectrum-utils with exact version specifications)

Examples

pip install falcon-ms spectrum-utils==0.3.5

Evaluation signals

• pip install command completes without errors and reports successful installation of both falcon-ms and spectrum-utils==0.3.5
• Running falcon --help or falcon -h produces help text without ImportError or version mismatch warnings
• Invoking falcon on a small test mzML/mzXML/MGF file runs to completion without exceptions
• Installed package versions match documentation requirements: python -c 'import falcon; import spectrum_utils; print(spectrum_utils.__version__)' returns '0.3.5'
• Cluster output files (*.csv) are generated with non-empty cluster assignments

Limitations

• falcon is restricted to Linux and OSX platforms; Windows users cannot use this tool
• Older Python versions (< 3.8) are incompatible; users on legacy environments must upgrade
• spectrum-utils version 0.3.5 is a strict requirement; newer or older versions may introduce incompatibilities in feature hashing or vector construction steps
• pip requires internet connectivity to download packages from PyPI; offline environments need pre-cached wheels

Evidence

• [readme] falcon requires Python 3.8+ and is available on the Linux and OSX platforms: "falcon requires Python 3.8+ and is available on the Linux and OSX platforms."

• [readme] pip installation with pinned spectrum-utils version: "You can easily install falcon with pip:

  pip install falcon-ms spectrum-utils==0.3.5"

• [readme] Feature hashing and nearest neighbor indexing depend on spectrum-utils implementation details: "First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity"