Spectral Library Parallel Ingestion

---
name: spectral-library-parallel-ingestion
description: Use when you have multiple MSP or spectral library files (e.g., one per
  batch of analytical standards, or organized in a directory structure) that need
  to be read and merged into a single library object for downstream enrichment (SMILES
  assignment, RI annotation, write operations).
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3434
  edam_topics:
  - http://edamontology.org/topic_0625
  tools:
  - mspcompiler
  - R
  - future
  - future.apply
  - parallel
  techniques:
  - mass-spectrometry
  license_tier: open
  provenance_tier: literature
derived_from:
- doi: 10.1021/acs.analchem.2c05389
  title: mspcompiler
evidence_spans:
- library(mspcompiler)
- library(future)
- library(future.apply)
- library(parallel)
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_mspcompiler
    doi: 10.1021/acs.analchem.2c05389
    title: mspcompiler
  dedup_kept_from: coll_mspcompiler
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1021/acs.analchem.2c05389
  all_source_dois:
  - 10.1021/acs.analchem.2c05389
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# spectral-library-parallel-ingestion

## Summary

Parallelize the reading and merging of multiple MSP spectral library files using the R future and future.apply packages with multisession workers. This skill accelerates in-house or batch library compilation by distributing file I/O across multiple CPU cores while guaranteeing structure and content identity with serial execution.

## When to use

You have multiple MSP or spectral library files (e.g., one per batch of analytical standards, or organized in a directory structure) that need to be read and merged into a single library object for downstream enrichment (SMILES assignment, RI annotation, write operations). File count or total data volume makes serial read_lib() calls noticeably slow.

## When NOT to use

- Single MSP file: use read_lib() directly; parallelization overhead exceeds benefit.
- Real-time or low-latency requirements: worker initialization and inter-process communication introduce startup latency (~seconds).
- Memory-constrained systems: multisession workers each load a copy of shared R libraries; total RAM may exceed available capacity for very large spectral collections.

## Inputs

- directory path containing multiple MSP spectral library files (e.g., 'D:/MS_libraries/in_house')
- multisession worker configuration (worker count derived from detectCores())

## Outputs

- merged library object (S3 class from mspcompiler, combining all spectra from input MSP files)
- identical structure and content as serial read_lib() execution

## How to apply

Load the mspcompiler, future, future.apply, and parallel R packages. Call plan(multisession(workers = detectCores() - 1)) to configure the parallel backend before reading; this spawns worker sessions on all but one core to avoid system saturation. Invoke read_multilibs(folder_path) on the directory containing all MSP files; the function leverages future.apply internally to distribute file parsing across workers. After merging completes, restore sequential execution by calling plan(sequential) to release worker resources. Verify the merged library object structure (class, column names, row count) and spot-check spectrum entries against the serially-read result to confirm identical content despite parallelization.

## Related tools

- **mspcompiler** (Provides read_multilibs() function to distribute MSP file reading across parallel workers; also supplies read_lib() for serial baseline and library object structure.) — https://github.com/QizhiSu/mspcompiler
- **future** (Unified parallel backend abstraction; plan(multisession()) configures worker spawning and synchronization.)
- **future.apply** (Parallel apply family (flapply, etc.) used internally by read_multilibs() to map file reads to workers.)
- **parallel** (detectCores() utility for querying system CPU count to set appropriate worker count.)

## Examples

```
library(mspcompiler); library(future); plan(multisession(workers = detectCores() - 1)); in_house <- read_multilibs("D:/MS_libraries/in_house"); plan(sequential)
```

## Evaluation signals

- Merged library object row count equals sum of row counts from all input MSP files.
- Merged library S3 class structure and column names match serial read_lib() output.
- Random spot-checks of spectrum names, m/z peaks, and intensities in merged object are identical to serial execution.
- Execution time is measurably shorter than serial read_lib() loop (wall-clock comparison).
- No data loss or corruption in merge: unique spectrum identifiers are preserved and duplicates (if any) are handled consistently with serial behavior.

## Limitations

- Worker startup and synchronization introduce latency; benefit is marginal for <5 MSP files or file sizes <100 MB total.
- Each worker maintains independent R environment; large shared data structures (e.g., structure annotation tables) are replicated in memory.
- Platform-dependent behavior: Linux/macOS use fork() (fast, shared memory); Windows uses socket-based workers (slower, isolated memory). Relative speedup on Windows may be modest.
- No changelog provided in repository, limiting visibility into stability or recent bug fixes.
- Read_multilibs() function not documented in README; users must infer from code or vignette that it is the designated parallel entry point.

## Evidence

- [readme] read_multilibs function for reading all libraries at once: "When you have multiple libraries to be read in, for instance if you are building your in-house library and you have one msp file for each batch of standards, then you will have many msp files to"
- [readme] future multisession parallel backend configuration before library reading: "Set up parallel computing. Just remember to set it back once you have the library compiled by "plan(sequential)". We will include it later. plan(multisession(workers = detectCores() - 1))"
- [readme] Time-consuming compilation benefits from parallel computing: "The whole process is time-consuming (several hours, depending on the capability of your PC), so we suggests to use parallel computing."
- [other] Research question on multisession spawning and merged output identity: "Does configuring the future::plan(multisession) parallel backend before calling read_multilibs on a directory of MSP files successfully spawn multiple worker sessions while producing a merged library"
- [readme] In-house library use case for read_multilibs: "in_house <- read_multilibs("D:/MS_libraries/in_house")"