metadata
license: mit
PiperNET data
Data for PiperNET, a multi-omics platform for elucidating the biosynthetic origin of plant specialized metabolites. It holds LC-MS metabolomics and RNA-Seq transcriptomics data from several Piper species and tissues, with biological replicates. Code and pipeline: github.com/titodamiani/PiperNET.
Download
With the hf command line tool:
hf download titodamiani/PiperNET --repo-type dataset --local-dir data
To download one folder only, add --include, for example --include "processed/*".
To run the code on this data, follow the installation steps in the code repository.
Structure
data/
├── README.md # this dataset card
├── raw/
│ ├── lcms/
│ │ ├── rawfiles/ # LC-MS raw files, mzML (140 files)
│ │ └── metadata.tsv # sample metadata, read by MZmine
│ └── rnaseq/piperNN/ # transXpress output, one folder per sample ID (13)
│ ├── transcriptome.fasta # de novo assembly
│ ├── transcriptome.pep # predicted proteins
│ ├── transcriptome_expression_isoform.tsv
│ ├── busco_report.txt
│ └── annotations/ # Pfam, BLASTp, SignalP, TargetP, TMHMM
├── interim/
│ ├── lcms_mzmine/ # MZmine output: feature table, MS/MS spectra (.mgf), annotations, networks (.graphml)
│ └── rnaseq_clstr/piperNN/ # CD-HIT clustered proteomes
├── external/
│ ├── molecular_networks/
│ │ └── gnps2/ # GNPS2 feature-based molecular networking results
│ ├── sirius/ # SIRIUS results: CSI:FingerID structures, CANOPUS classes
│ ├── orthogroups/sonicpd/ # SonicParanoid orthogroups
│ ├── speclibs/ # MS/MS spectral libraries for annotation in MZmine
│ ├── known_enzymes/ # known enzymes table
│ ├── customDB.csv # manually curated list of known LC-MS features, for targeted feature detection in MZmine
│ ├── standardDB.csv # read by MZmine and the LC-MS data preparation
│ └── novelty_scores.csv # preliminary
└── processed/
├── ftable_clean.csv # LC-MS feature table
├── ntable_clean.csv # GNPS2 node table, for Cytoscape
├── proteomes/piperNN/ # proteome.pep, proteome.csv, blastDB/
├── proteomes_all.csv # all proteomes with orthogroups
└── scoring/ # input arrays for network-orthogroup scoring (.npy)
scripts/README.md in the code repository describes how each file is made.