Article Figures & Data
Figures
- Fig. 1.
An overview of the pipeline for peptide/protein identification from metaproteomic MS/MS data. The pipeline integrate two approaches: the Graph2Pro approach that uses assembly uncertainties and the new variant-aware approach Var2Pep.
- Fig. 2.
Comparison of peptide identification by the different approaches on the ocean data sets. The barplot shows the total number of unique peptides identified from six ocean metaproteomic MS/MS data sets, by different approaches.
- Fig. 3.
Comparison of peptide identification results by the different approaches on the wastewater data sets. The barplot shows the total number of unique peptides identified from three wastewater samples (SD3, SD6, and SD7), using either matching metagenomic data alone (MG), or both metagenomic and metaproteomic data (MGMT) as the reference.
- Fig. 4.
Selected examples of identified proteins and variants supported by metaproteomic MS/MS data (ocean water sample CS51 and wastewater sample SD3). The plots depict the regions in the proteins that are supported by identified peptides. The black lines on the top represent the proteins, with boxes in different colors showing predicted PFAM domains in these proteins (orange box represents the SBP_bac_5 domain, gray box represents DUF2815, and the blue boxes represent the SLH domains. The green lines below the protein lines represent MS/MS supported peptides from each protein (no mismatches), and the red lines represent peptide variants that share similarity with the protein, with the number of mismatches indicated by the bar on the left.
- Fig. 5.
Var2Pep detected variations that are preferred substitutions among homologous proteins. The y axis shows the BLOSUM score of pairs of amino acids. The “all” box is for all pairs of possible amino acids (excluding pairs of identical residues), whereas the other box is for variations found by Var2Pep in Protein366.
Tables
BSt CS Number of peptides in metapeptides database (obtained from 30) 15,911,893 19,194,693 Number of mismatched/unmatched reads 148,502,311 221,702,454 Number of peptides in Var2Pep database 2,702,655 4,891,690 Note: Bst, Bering strait; CS, Chukchi sea.
BSt45 (90,072 spectra) CS51 (100,588 spectra) PSMs (%) Unique peptide PSMs (%) Unique peptide Reads based 7795 (8.6%) 2958 15,167 (15.1%) 5652 Contigs based (S*) 1892 (2.10%) 817 6526 (6.49%) 2442 Graph2Pro (S*) 12,728 (14.1%) 4542 26,576 (26.4%) 9932 Contigs based (M*) 8631 (9.6%) 3367 17,427 (17.3%) 6388 Graph2Pro (M*) 15,172 (16.8%) 5857 29,072 (28.9%) 11,463 Graph2Pro (M*) + Var2Pep 15,913 (17.7%) 6240 31,145 (31.0%) 12,380 Note: S* represents SOAPdenovo2, M* represents MegaHit, PSM stands for peptide spectrum match. Bst stands for Bering strait, and CS stands for Chukchi sea. Graph2Pro (S*) and Graph2Pro (M*) represent using assembly graph from SOAPdenovo2 (S*) and MegaHit (M*) as the reference in Graph2Pro, respectively. In all cases, FDR (false discovery rate) was estimated using a target-decoy search approach, and a cutoff of 1% at spectrum level was applied. This table only shows the results for two datasets. See Fig. 2 and supplementary Data File S1 for results of all data sets.
- Table III Comparison of additional spectra and unique peptides identified by searching against the Var2Pep database using the separate search approach (our approach) versus cascaded search
Separate (our approach) Cascaded PSMs Peptides PSMs Peptides CS51 2073 917 1598 629 CS52 2282 816 1692 533 CS53 1969 625 1466 392 Bst45 741 383 495 221 Bst46 651 318 390 173 Bst47 640 322 418 192 Note: PSM stands for peptide spectrum match. Bst, Bering strait; CS, Chukchi sea.
- Table IV Comparison of the performance using metagenome assembly from MegaHit and MetaSpades as the reference on SD3-MG dataset
MegaHit MetaSpades Number of contigs 111,739 446,773 Total bases (MB) 78 166 Contig only (PSM/peptide) 43650 (11173) 40161 (10337) Graph2Pro (PSM/peptide) 63178 (17452) 57760 (17651) Graph2Pro + Var2pep (PSM/peptide) 74317 (21662) 71366 (22043)
Supplemental Data
- Supplementary Data 3 - Proteins identified from the CS51 dataset and their annotations
- Supplementary Data 1 - Peptide identification results for the ocean datasets
- Supplementary Figure 4 - Comparison of peptide identification results using FDR at spectrum and peptide level for the SD3-MG dataset.
- Supplementary Figure 3 - Comparison of the different approaches on all wastewater datasets, using FDR <= 0.01 at peptide level.
- Supplementary Figure 2 - Comparison of peptide identification results using FDR at spectrum and peptide level for the CS51 dataset.
- Supplementary Figure 1 - Comparison of the different approaches on all ocean water datasets, using FDR <= 0.01 at peptide level.
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