| Data File(s) | Type | Description | Action |
|---|---|---|---|
986127_v1_proteomics.csv (81.55 KB) | Comma Separated Values (.csv) | Primary data file for dataset ID 986127, version 1 | |
| Supplemental File(s) | Type | Description | Action |
EZ55_proteome.faa (276.73 KB) | FASTA | Input: NCBI Entrez protein records. Process: Compilation of retrieved amino acid sequences into a single FASTA file. Output: Complete protein sequence dataset. Purpose: Reference proteome for all downstream functional and localization analyses. | |
Master_Proteomics.csv (285.38 KB) | Comma Separated Values (.csv) | Input: WIFF files. Process: Spectral processing and peptide identification as described in the manuscript methods. Output: Consolidated table of predicted peptides with sample metadata. Purpose: Central record of all detected peptides and associated confidence metrics. Columns: Organism: The Prochlorococcus/Alteromonas co-culture from which the Alteromonas strain assayed was isolated. LTPE26 was ... | |
Master_Proteomics_simplified.csv (136.74 KB) | Comma Separated Values (.csv) | Input: Master_Proteomics.csv. Process: Simplification to accession number, peptide name, and a combined column identifying Organism and Method. Output: Reduced dataset optimized for computational reshaping. Purpose: Streamlined input for presence/absence analysis. | |
Protein_localization.tsv (19.10 KB) | Tab Separated Values (.tsv) | Input: EZ55_proteome.faa. Process: Prediction of subcellular localization using PSORTb with Bacteria / Gram‑negative settings. Output: Localization predictions with confidence scores. Purpose: Determines likely cellular compartment of each protein. Columns: SeqID: identical to accession number in the fasta file Localization: predicted subcellular localization of the protein Score:confidence of th... | |
Proteome.py (9.14 KB) | Python Script | Input: Accession numbers from Proteomics_output.csv. Process: Automated querying of the NCBI Entrez database to retrieve corresponding protein sequences. Output: FASTA file of identified proteins. Purpose: Links detected peptides to full protein sequences for annotation. | |
Proteome_1.R (389.00 B) | R Script | Input: Master_Proteomics_simplified.csv. Process: Conversion from long-format peptide listings to a wide-format table indicating presence or absence across samples. Output: Proteomics_output.csv. Purpose: Enables comparative proteomic analysis across experimental conditions. | |
Proteome_2.R (780.00 B) | R Script | Input: KO numbers and KEGG pathway–KO links. Process: Retrieval of pathway names and higher-level categories using KEGGREST. Output: pathways.csv. Purpose: Groups proteins into metabolic and functional pathways. | |
Proteome_3.R (7.75 KB) | R Script | Input: Proteomics_combined_output.csv. Process: Statistical analysis and visualization of protein distributions across samples and pathways. Output: Figure 2 and Supplemental Figures 12–15. Purpose: Generates final analytical results and figures for the manuscript. | |
Proteomics_combined_output.csv (82.13 KB) | Comma Separated Values (.csv) | Input: Proteomics_output.csv, Protein_localization.tsv, user_ko_definition.tsv, mw.tsv, pathways.csv. Process: Manual merging of all annotations into a single master table. Output: Fully annotated proteomics dataset (Table S1). Purpose: Primary data table used for analysis and publication. Columns: Accession: Accession number of the protein KO: mapped KO number Name: Gene group definition of KO n... | |
Proteomics_output.csv (21.17 KB) | Comma Separated Values (.csv) | Input: Simplified master file. Process: Logical transformation into a binary presence/absence matrix. Output: Table with accession numbers and six sample columns. Purpose: Defines which proteins are detected in each proteome sample. | |
README.proteome.txt (5.00 KB) | Plain Text | README file describing files and order in which files are produced in this proteomics dataset. WIFF Files: Raw mass spectrometry data generated by the MS facility from Alteromonas samples. Master_Proteomics.csv: Produced by the MS facility by processing the WIFF files to identify peptides and associated metadata. Master_Proteomics_simplified.csv: Created from Master_Proteomics.csv by reducing ... | |
WIFF Files.zip (2.40 GB) | ZIP Archive (ZIP) | Input: Alteromonas proteome samples analyzed by LC–MS/MS. Process: Raw spectral data acquisition by the mass spectrometry facility. Output: WIFF files containing unprocessed MS data. Purpose: Primary experimental data source for peptide identification. | |
mw.tsv (72.19 KB) | Tab Separated Values (.tsv) | Input: EZ55_proteome.faa. Process: Computation of molecular weight for each protein sequence. Output: Accession-to-molecular-weight mappings. Purpose: Provides protein size information for downstream comparisons. | |
pathways.csv (14.38 KB) | Comma Separated Values (.csv) | Input: KEGG REST API results. Process: Assembly of KO–pathway relationships with names and categories. Output: Structured pathway annotation table. Purpose: Enables pathway-level enrichment and classification analyses. Columns: KO: KO number Pathway: KO of mapped pathway Name: specific pathway map Category: higher-level classification of pathway | |
user_ko.tsv (11.04 KB) | Tab Separated Values (.tsv) | Input: EZ55_proteome.faa. Process: Sequence comparison against KEGG databases to assign KO numbers. Output: KO mappings for identified proteins. Purpose: Enables functional annotation via KEGG orthology. Columns are: Column 1: the accession number from the fasta file Column 2: the mapped KO number Column 3: the gene group definition | |
user_ko_definition.tsv (33.23 KB) | Tab Separated Values (.tsv) | Input: user_ko.tsv and KEGG KO list. Process: Manual addition of gene group definitions corresponding to KO numbers. Output: KO mappings with functional descriptions. Purpose: Improves interpretability of KO annotations. Columns are: Column 1: the accession number from the fasta file Column 2: the mapped KO number Column 3: the gene group definition |
Files
Type: Comma Separated Values (.csv)
Primary data file for dataset ID 986127, version 1
Supplemental Files
Type: FASTA
Input: NCBI Entrez protein records. Process: Compilation of retrieved amino acid sequences into a single FASTA file. Output: Complete protein sequence dataset. Purpose: Reference proteome for all downstream functional and localization analyses.
Type: Comma Separated Values (.csv)
Input: WIFF files. Process: Spectral processing and peptide identification as described in the manuscript methods. Output: Consolidated table of predicted peptides with sample metadata. Purpose: Central record of all detected peptides and associated ...
Type: Comma Separated Values (.csv)
Input: Master_Proteomics.csv. Process: Simplification to accession number, peptide name, and a combined column identifying Organism and Method. Output: Reduced dataset optimized for computational reshaping. Purpose: Streamlined input for presence/absence analysis.
Type: Tab Separated Values (.tsv)
Input: EZ55_proteome.faa. Process: Prediction of subcellular localization using PSORTb with Bacteria / Gram‑negative settings. Output: Localization predictions with confidence scores. Purpose: Determines likely cellular compartment of each protein. ...
Type: Python Script
Input: Accession numbers from Proteomics_output.csv. Process: Automated querying of the NCBI Entrez database to retrieve corresponding protein sequences. Output: FASTA file of identified proteins. Purpose: Links detected peptides to full protein sequences for annotation.
Type: R Script
Input: Master_Proteomics_simplified.csv. Process: Conversion from long-format peptide listings to a wide-format table indicating presence or absence across samples. Output: Proteomics_output.csv. Purpose: Enables comparative proteomic analysis across experimental conditions.
Type: R Script
Input: KO numbers and KEGG pathway–KO links. Process: Retrieval of pathway names and higher-level categories using KEGGREST. Output: pathways.csv. Purpose: Groups proteins into metabolic and functional pathways.
Type: R Script
Input: Proteomics_combined_output.csv. Process: Statistical analysis and visualization of protein distributions across samples and pathways. Output: Figure 2 and Supplemental Figures 12–15. Purpose: Generates final analytical results and figures for the manuscript.
Type: Comma Separated Values (.csv)
Input: Proteomics_output.csv, Protein_localization.tsv, user_ko_definition.tsv, mw.tsv, pathways.csv. Process: Manual merging of all annotations into a single master table. Output: Fully annotated proteomics dataset (Table S1). Purpose: Primary data ...
Type: Comma Separated Values (.csv)
Input: Simplified master file. Process: Logical transformation into a binary presence/absence matrix. Output: Table with accession numbers and six sample columns. Purpose: Defines which proteins are detected in each proteome sample.
Type: Plain Text
README file describing files and order in which files are produced in this proteomics dataset. WIFF Files: Raw mass spectrometry data generated by the MS facility from Alteromonas samples. Master_Proteomics.csv: Produced by the MS facility by proc...
Type: ZIP Archive (ZIP)
Input: Alteromonas proteome samples analyzed by LC–MS/MS. Process: Raw spectral data acquisition by the mass spectrometry facility. Output: WIFF files containing unprocessed MS data. Purpose: Primary experimental data source for peptide identification.
Type: Tab Separated Values (.tsv)
Input: EZ55_proteome.faa. Process: Computation of molecular weight for each protein sequence. Output: Accession-to-molecular-weight mappings. Purpose: Provides protein size information for downstream comparisons.
Type: Comma Separated Values (.csv)
Input: KEGG REST API results. Process: Assembly of KO–pathway relationships with names and categories. Output: Structured pathway annotation table. Purpose: Enables pathway-level enrichment and classification analyses. Columns: KO: KO number Pathway...
Type: Tab Separated Values (.tsv)
Input: EZ55_proteome.faa. Process: Sequence comparison against KEGG databases to assign KO numbers. Output: KO mappings for identified proteins. Purpose: Enables functional annotation via KEGG orthology. Columns are: Column 1: the accession number f...
Type: Tab Separated Values (.tsv)
Input: user_ko.tsv and KEGG KO list. Process: Manual addition of gene group definitions corresponding to KO numbers. Output: KO mappings with functional descriptions. Purpose: Improves interpretability of KO annotations. Columns are: Column 1: the a...