literature

name	literature
description	Parse scholarly articles (PDF, DOI, URL) to extract metadata, GEO accessions, and acquisition links using OpenAlex + GROBID pipelines.
version	0.2.0
author	OmicsClaw
license	MIT
tags	["literature","metadata","openalex","grobid","text-mining"]
metadata	{"omicsclaw":{"domain":"literature","emoji":"📚","trigger_keywords":["literature search","openalex","pdf parsing","grobid","accession mining"]}}
source_reliability	[{"source":"https://github.com/ourresearch/OpenAlex","description":"Primary open-source stack powering the OpenAlex API plus CLI/tutorial repos for scholarly metadata retrieval.","score":0.88,"rationale":"Maintained by OurResearch with multiple active repositories (CLI, API, docs) and open governance for the scholarly graph.\n"},{"source":"https://github.com/kermitt2/grobid","description":"GROBID ML pipeline for PDF parsing and citation extraction; used for high-fidelity metadata recovery.","score":0.86,"rationale":"Long-lived, peer-reviewed project with automated PDF extraction benchmarks; aligns with OmicsClaw’s PDF parsing approach.\n"}]

Literature Parsing Skill

Purpose

Parse scientific literature (PDFs, URLs, DOIs) to extract GEO accessions and metadata, then download datasets for downstream omics analysis.

Methodology

1. Input Processing

Accepts multiple input types:

• URL: PubMed, bioRxiv, journal article links
• DOI: Digital Object Identifier (e.g., 10.1038/s41586-021-03569-1)
• PubMed ID: PMID (e.g., 33234567)
• PDF: Uploaded scientific paper
• Text: Raw text containing GEO references

2. Metadata Extraction

Extracts structured information:

• GEO Accessions: GSE (study-level), GSM (sample-level)
• Organism: Species (e.g., Homo sapiens, Mus musculus)
• Tissue: Tissue type or organ
• Cell Type: Cell type if specified
• Technology: Sequencing platform (10x, Visium, etc.)

3. Data Download

Downloads datasets from GEO:

• Resolves GSE to find all associated GSM samples
• Downloads expression matrices (.h5ad, .mtx, .csv)
• Organizes files by accession: data/GSE123456/
• Generates metadata.json with extracted information

4. Error Handling

• Retry with fallbacks: PDF parsing → text extraction → manual patterns
• Partial results: Returns successfully extracted data even if some downloads fail
• Logging: Detailed logs for debugging

Output

• *data/GSE/**: Downloaded datasets organized by accession
• output/literature-parse_*/report.md: Extraction report
• output/literature-parse_*/metadata.json: Structured metadata

Usage

# Parse from URL
python skills/literature/literature_parse.py \
  --input "https://pubmed.ncbi.nlm.nih.gov/12345" \
  --output output/literature_results

# Parse from DOI
python skills/literature/literature_parse.py \
  --input "10.1038/s41586-021-03569-1" \
  --input-type doi \
  --output output/literature_results

# Parse PDF
python skills/literature/literature_parse.py \
  --input paper.pdf \
  --input-type file \
  --output output/literature_results