Command Line Pipeline Execution And Runtime Benchmarking

command-line-pipeline-execution-and-runtime-benchmarking

Summary

Execute a fully automated command-line pipeline on FT-ICR MS data and measure wall-clock runtime across sample-size conditions to validate that observed performance matches published benchmarks. This skill verifies reproducibility of computational efficiency claims by comparing empirical runtimes against reference thresholds.

When to use

When you have peak-abundance .csv files and assigned molecular formula data from FT-ICR MS preprocessing, and you need to verify that a published pipeline's runtime claims (e.g., '<1 min for 40 samples', '~2 min for 120 samples') hold true on your own datasets or reference datasets. Use this skill when computational efficiency is a selection criterion or a reproducibility target.

When NOT to use

• If raw FT-ICR MS spectra preprocessing is required; MetaboDirect does not provide raw spectra data preprocessing.
• If your datasets lack pre-assigned molecular formulas; the pipeline accepts only peak abundance and assigned molecular formula data.
• If you need to benchmark individual filter steps or sub-tasks in isolation; this skill measures end-to-end main pipeline runtime only.

Inputs

• Peak-abundance .csv files (average ~1025 peaks per sample)
• Assigned molecular formula data (.csv format with elemental composition)
• MetaboDirect v0.3.4 or later (cloned from GitHub)
• Python 3.8+ and R 4.0+ environments

Outputs

• Wall-clock runtime in seconds for pipeline execution
• Output .csv files (data matrices, transformed features, analysis results)
• Visualization files (Van Krevelen diagrams, elemental/molecular class composition plots)
• Runtime comparison report against reference benchmarks

How to apply

Clone the pipeline repository and install Python dependencies (NumPy, pandas, seaborn, py4cytoscape, matplotlib) and R libraries (vegan, SYNCSA, tidyverse, RColorBrewer, etc.). Obtain or download peak-abundance .csv files with assigned molecular formulas from public repositories (e.g., OSF). Execute the main pipeline via command-line interface with default parameters, excluding optional steps (e.g., KEGG database queries or transformation network calculation) unless benchmarking the full pipeline. Record wall-clock runtime in seconds using system timing utilities (e.g., Unix time command). Verify that all expected output .csv files and visualization files are generated without errors. Compare observed runtime against reference benchmarks; for the bacterium-phage dataset (36 samples, ~495 assigned molecular formulas per sample), expect ~36 seconds for main pipeline steps.

Related tools

• MetaboDirect (Command-line pipeline for automated analysis and visualization of FT-ICR MS data; executed to measure runtime performance) — https://github.com/Coayala/MetaboDirect
• Python 3.8 (Runtime environment and dependency management for MetaboDirect execution)
• R 4.0.2 (Statistical and visualization environment required by MetaboDirect R modules (vegan, SYNCSA, tidyverse))
• NumPy, pandas (Python dependencies for data manipulation and matrix operations in MetaboDirect pipeline)
• vegan, SYNCSA (R packages for chemodiversity analysis and multivariate statistics within MetaboDirect)

Examples

time metabodirect -i peak_abundance.csv -f molecular_formulas.csv -o output_dir

Evaluation signals

• Wall-clock runtime for bacterium-phage dataset (36 samples, ~495 assigned formulas per sample) is approximately 36 seconds (or <1 min as published).
• All expected output .csv files are generated without errors during pipeline execution.
• All visualization files (Van Krevelen diagrams, elemental composition plots) are successfully created.
• Observed runtime falls within or below published reference benchmarks for the same sample size and peak/formula count.
• No runtime warnings, missing file errors, or incomplete data matrix outputs are reported in standard output or logs.

Limitations

• MetaboDirect does not provide raw spectra data preprocessing; input data must already be peak-detected and molecular formulas must be pre-assigned (typically via separate MS processing software).
• Benchmark times may vary depending on hardware specifications (CPU, RAM), which should be documented alongside runtime measurements.
• KEGG database queries and transformation network calculation are optional steps that significantly increase runtime; published benchmarks exclude these, so runtime cannot be fairly compared if they are included.
• Pipeline runtime is sensitive to the number of detected peaks and formula assignments per sample; datasets with very high peak counts may not match published benchmarks for smaller datasets.

Evidence

• [other] Execute the main MetaboDirect pipeline using the command-line interface with default parameters for data pre-processing, data diagnostics, data exploration, and chemodiversity analysis, excluding KEGG database queries and transformation network calculation.: "Execute the main MetaboDirect pipeline using the command-line interface with default parameters for data pre-processing, data diagnostics, data exploration, and chemodiversity analysis, excluding"
• [other] Record wall-clock runtime (seconds) for the phage dataset execution and the S. fallax leachate dataset execution using system timing utilities.: "Record wall-clock runtime (seconds) for the phage dataset execution and the S. fallax leachate dataset execution using system timing utilities."
• [other] For the bacterium-phage dataset (36 samples with average 495 assigned molecular formulas per sample), the main MetaboDirect pipeline steps without KEGG mapping or transformation network calculation completed in less than 1 min (~36 s).: "For the bacterium-phage dataset (36 samples with average 495 assigned molecular formulas per sample), the main MetaboDirect pipeline steps without KEGG mapping or transformation network calculation"
• [results] 40 samples were processed in less than 1 min whereas 120 samples took as little as 2 min to generate all the figures, plots, and outputs.: "40 samples were processed in less than 1 min whereas 120 samples took as little as 2 min to generate all the figures, plots, and outputs"
• [methods] MetaboDirect does not provide raw spectra data preprocessing.: "MetaboDirect does not provide raw spectra data preprocessing"
• [readme] MetaboDirect requires Python (3.5 and above), R (4 and above) and Cytoscape (3.8 and above) with the following libraries/modules.: "MetaboDirect requires Python (3.5 and above), R (4 and above) and Cytoscape (3.8 and above) with the following libraries/modules"