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PEIMAN2-vignette

Payman Nickchi, Mohieddin Jafari

2022-11-18

1 Introduction

The annotation enrichment analysis increases the chance of identifying relevant biological pathways in a list of genes or proteins. The post translational enrichment, integration, and matching analysis (PEIMAN v1) software was introduced to provide a systematic framework to identify more probable and enriched post-translational modification (PTM) terms in a list of proteins obtained from high-throughput technologies (Nickchi, Jafari, and Kalantari 2015). PEIMAN maps a large list of proteins to PTM pathways and test for their statistical significance, using a hypergeometric test. PEIMAN uses the most traditional way of enrichment analysis, by getting a list of proteins selected by user, and search for enriched PTM terms one by one. This strategy is called Singular Enrichment Analysis or SEA. Although this is a very promising approach for identifying biological pathways, the quality of selected list by researcher can potentially affect results at the end of the analysis.

To avoid this problem, we extend our enrichment framework to a wider class of enrichment analysis called Gene Set Enrichment Analysis or GSEA (Subramanian et al. 2005). The underlying idea of GSEA is very similar to SEA. Instead of applying a cutoff on input genes obtained from micro array experiments (either p-value or fold-change in gene expression), a ‘no-cutoff’ strategy is considered. The immediate benefits of this approach is to reduce the bias of gene selection and include genes with a low change in their expression level to participate in final analysis. The maximum value of the running score profile for ranked genes in each enrichment category is then calculated and compared with random scores obtained from permutation. More details on (Subramanian et al. 2005). This framework can be expanded to enrichment analysis in proteins. Inspired by GSEA idea, we here introduce a package in R for Protein Set Enrichment Analysis (PSEA).

The database in PEIMAN package updates monthly according to changes in UniProt. The package can be used to perform singular enrichment analysis (SEA) and visualize the results. PEIMAN can also be used to match and integrate results of two SEA analysis (for the same species) by visualizing their common pathways. To correct for biases in SEA, we implement protein set enrichment analysis (PSEA) as a new tool for computational community. Researchers can use this package to run PSEA and visualize the results.

Figure1: Our suggested workflow for a PTM-centric proteomics using PEIMAN software v2.0

2 Example data

We consider two example datasets to demonstrate the features of our package.

  1. exmplData1: We use the first example data for single enrichment analysis. This dataset contains two list of human proteins randomly selected from UniProt. The first list contains 45 proteins and the second list contains of 97 randomly selected proteins. Both lists belongs to Homo Sapiens (Human). Note: Only the first six proteins in each list are shown below.
P31946
P62258
Q04917
P61981
P31947
P27348
P17174
Q9NY61
P00505
Q96GS6
Q5VST6
Q6PCB6
  1. exmplData2: We will use the second dataset to perform protein set enrichment analysis or PSEA. The dataset is described in (Gholizadeh et al. 2021).
beatAML dataset samples
UniProtAC Score
P47819 579.6287
P20428 129.7175
P62982 2139.2700
P0CG51 2139.2700
P62986 2139.2700
Q63429 2139.2700

3 Singular Enrichment analysis (SEA)

In this section, we introduce the functions related to singular enrichment analysis or SEA in PEIMAN2 package. The functions in this section are divided into two parts, functions for enrichment and functions for plotting. We use exmplData1 in this part.

3.1 Enrichment

runEnrichment() function can be used to run singular enrichment analysis for one list of protein. This function takes the following inputs:

As it was mentioned, the taxonomy name of species must be provided, e.g for a list of proteins belongs to human we pass os.name as ‘Homo sapiens (Human)’. The list is available at UniProt website. We also included a helper function named getTaxonomyName to help getting the exact taxonomy name. More on this function later.

The following lines of code illustrate the steps to run SEA on exmplData1. In runEnrichment function, we pass pl1 (a character vector of UniProt accession code) to perform SEA as follows and save the results in enrich1.

# Load PEIMAN2 package
library(PEIMAN2)
#> Loading required package: tidyverse
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✔ ggplot2 3.4.0     ✔ purrr   0.3.4
#> ✔ tibble  3.1.7     ✔ dplyr   1.0.9
#> ✔ tidyr   1.2.0     ✔ stringr 1.4.0
#> ✔ readr   2.1.2     ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

# Extract dataset and assign a variable name to it
pl1 <- exmplData1$pl1

# Run SEA on the list
enrich1 <- runEnrichment(protein = pl1, os.name = 'Homo sapiens (Human)')

The function returns a dataframe with the following columns:

PTM FreqinUniprot FreqinList Sample Population pvalue corrected pvalue AC
N6-(pyridoxal phosphate)lysine 53 5 97 14256 2e-06 6e-05 Q96QU6; Q4AC99; Q8N5Z0; Q8NHS2; P17174
Pyridoxal phosphate 60 5 97 14256 3e-06 6e-05 Q96QU6; Q4AC99; Q8N5Z0; Q8NHS2; P17174
Isoglutamyl cysteine thioester (Cys-Gln) 7 2 97 14256 1e-05 1e-04 P01023; A8K2U0
Thioester bond 11 2 97 14256 5e-05 5e-04 P01023; A8K2U0
S-cysteinyl cysteine 3 1 97 14256 1e-04 1e-03 P01009
Sulfation 57 3 97 14256 6e-04 4e-03 P05408; P08697; P05067

Note: As it was mentioned, the os.name is the exact taxonomy name of species that you are working with. The name should be exactly the same as UniProt definition. To facilitate searching for this name, you can pass your protein list with UniProt accession ID to getTaxonomyName function as follows. The result is the exact taxonomy name of protein list that you need to pass to runEnrichment. In the following example, the exact taxonomy name is printed:

getTaxonomyName(x = exmplData1$pl1)
#> [1] "Please use os.name = `Homo sapiens (Human)`"

Similarly, we can run SEA for the second list of proteins:

# Extract dataset and assign a variable name to it
pl2 <- exmplData1$pl2

# Run SEA on the list
enrich2 <- runEnrichment(protein = pl2, os.name = 'Homo sapiens (Human)')
PTM FreqinUniprot FreqinList Sample Population pvalue corrected pvalue AC
Nucleotide-binding 1800 33 45 14256 0e+00 0.000 O95477; Q9BZC7; Q99758; P78363; Q8WWZ7; Q8N139; Q8IZY2; O94911; Q8IUA7; Q8WWZ4; Q86UK0; Q86UQ4; Q2M3G0; Q9NP58; O75027; Q9NP78; Q9NRK6; O95342; Q09428; O60706; P33897; Q9UBJ2; P28288; O14678; P61221; Q8NE71; Q9UG63; Q9NUQ8; P45844; Q9UNQ0; Q9H172; Q9H222; Q96J66
Glutathionylation 11 1 45 14256 5e-04 0.003 Q9NRK6
Glycoprotein 4691 25 45 14256 5e-04 0.003 O95477; Q9BZC7; Q99758; P78363; Q8WWZ7; Q8N139; Q8IZY2; O94911; Q8IUA7; Q86UK0; Q2M3G0; Q9NP58; O95342; Q09428; O60706; P33897; Q9UBJ2; P28288; Q9UNQ0; Q9H172; Q9H222; Q9H221; Q8N2K0; Q0P651; Q96J66
N6-(pyridoxal phosphate)lysine 53 2 45 14256 6e-04 0.003 P17174; P00505
S-glutathionyl cysteine 8 1 45 14256 3e-04 0.003 Q9NRK6
Pyridoxal phosphate 60 2 45 14256 9e-04 0.004 P17174; P00505

3.2 Plotting SEA results

The plotEnrichment function can be used to visualize singular enrichment analysis for one set of proteins or match, analyse, and integrate results for two sets of proteins. To read more about this match and integration, please read details at (Nickchi, Jafari, and Kalantari 2015). We start by plotting the results for the firs list.

plotEnrichment(x = enrich1, sig.level = 0.05)

The results is a Lollipop plot which presents “Relative frequency” of each “PTM keywords” along with their corrected p-value measured in log scale. Note that only significant PTMs are shown. The default value for significance level is 5 percent. One can also visualize and match the results of two enrichment. For example, we can see the integrated results of enrich1 and enrich2 by the following line of code:

plotEnrichment(x = enrich1, y = enrich2, sig.level = 0.05)

The plot presents the ‘Relative frequency’ of common PTM terms among two enriched list (x and y). The coloring is the corrected p-value measured in log scale. By default a significance level of 5 percent is set to filter results. This can be modified by sig.level parameter.

4 Protein set enrichment analysis (PSEA)

In this section, we introduce the functions for protein set enrichment analysis (PSEA). The functions in this section are divided into two parts, functions for PSEA and functions for plotting the results. We use exmplData2 in this part.

4.1 PSEA

In order to run protein set enrichment analysis (PSEA), you can use runPSEA function. This function takes the following inputs:

psea_res <- runPSEA(protein = exmplData2, os.name = 'Rattus norvegicus (Rat)', nperm = 1000)

The result is a list with 6 elements. The first element of this list is important: A dataframe with protein set enrichment analysis (PSEA) results. Every row corresponds to a post-translational modification (PTM) pathway with the following columns:

knitr::kable(psea_res[[1]], format = 'html')
PTM pval pvaladj FreqinUniProt FreqinList ES NES nMoreExtreme size Enrichment AC leadingEdge
ADP-ribosylglycine 0e+00 0e+00 4 4 0.7707317 1.5787625 297 4 Significant P62986; P62982; P0CG51; Q63429 P62982; P0CG51; P62986; Q63429
Acetylation 0e+00 0e+00 1762 123 0.7521522 1.1819493 4 123 Significant P0C1X8; P11030; P60711; P63259; Q63028; Q62847; Q62848; Q9WUC4; P31399; P29419; P21571; P15999; D3ZAF6; Q9JJW3; O08839; P0DP29; P0DP30; P0DP31; P18418; P26772; P63039; B0K020; P08081; P08082; P45592; Q91ZN1; P11240; Q63768; P10715; P62898; Q9JHL4; Q7M0E3; P62628; Q07266; P84060; P62870; P15429; P07323; P60841; P56571; B0BN94; P55053; P55051; P07483; Q62658; Q32PX7; Q99PF5; Q5XI73; Q63228; P62994; P01946; P02091; P11517; P62959; P82995; P34058; P27321; Q5XI72; P50411; Q6AXU6; Q5BK20; P11980; Q99MZ8; Q792I0; Q66HF9; P15205; Q5M7W5; P02688; B0BN72; P30904; O35763; P62775; Q71UE8; Q9JJ19; P13084; Q01205; P08461; Q920Q0; O88767; P04785; P31044; O55012; P10111; Q6J4I0; Q9R063; Q9EPC6; P02625; Q63475; P51583; Q68A21; P02401; P62982; P62859; Q6RJR6; Q9JK11; Q63945; B0BN85; P07632; Q66HL2; P28042; O35814; P13668; P37377; Q62880; P19332; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; Q6PEC1; P11232; P62076; P62078; Q9WV97; P48500; P04692; P58775; Q63610; P09495; Q7M767; Q9Z1A5; P63045 P62628; P31044; P37377; P45592; P11030; P02625; P29419; P62775; P21571; O88767; P31399; P02688; P08082; P62898; P63045; P62076; P11232; O35814; Q9WUC4; Q62658; Q63228; P07632; Q5XI73; B0K020; P08081; P62959
Cysteine sulfinic acid (-SO2H) 0e+00 0e+00 1 1 0.9423077 65.9672131 49 1 Significant O88767 O88767
N-acetylaspartate 0e+00 0e+00 1 1 -0.9615385 -183.8975297 38 1 Not significant P60711 P31044
N-acetylglutamate 0e+00 0e+00 1 1 -0.9663462 -34.1391187 38 1 Not significant P63259 P31044
N6-acetyllysine 0e+00 0e+00 992 73 0.7226249 1.1498783 40 73 Significant P11030; Q62848; Q9WUC4; P31399; P29419; P21571; P15999; D3ZAF6; Q9JJW3; P0DP29; P0DP30; P0DP31; P18418; P26772; P63039; B0K020; P08081; P08082; P45592; P11240; P62898; Q9JHL4; Q7M0E3; P07323; P56571; Q62658; Q99PF5; Q5XI73; P62994; P01946; P62959; P82995; P34058; P27321; Q6AXU6; Q5BK20; P11980; Q99MZ8; P02688; B0BN72; P30904; O35763; P62775; Q71UE8; P13084; Q01205; P08461; O88767; P04785; P10111; Q9R063; Q63475; P51583; Q68A21; P02401; P62982; Q9JK11; Q63945; P07632; Q66HL2; P28042; O35814; P13668; P19332; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; P11232; P48500; P09495; Q9Z1A5 P45592; P11030; P29419; P62775; P21571; O88767; P31399; P02688; P08082; P62898; P11232; O35814; Q9WUC4; Q62658; P07632; Q5XI73; B0K020; P08081; P62959
Phosphoprotein 0e+00 0e+00 4088 171 0.5995932 0.9309700 754 171 Significant P0C1X8; P11030; Q63028; Q62847; O08838; Q99068; Q05140; Q62848; Q9WUC4; P29419; P21571; P15999; D3ZAF6; Q05175; O08839; O88778; P0DP29; P0DP30; P0DP31; O35783; O35397; P26772; P63039; P08081; P08082; P10354; P45592; Q91ZN1; P11240; P84087; Q5U2U2; Q63768; Q6AY72; P11951; P10715; P62898; Q9JHL4; Q9QXU8; Q7M0E3; Q62950; P47942; Q07266; P84060; Q9WTP0; P62870; P15429; P07323; P60841; Q9Z1Z3; Q5RJL0; B0BN94; P55053; P07483; Q9JIX3; Q62658; Q32PX7; Q99PF5; Q920R4; Q5XI73; P47819; Q63228; P62994; P01946; P02091; P11517; P62959; Q9Z2X5; P82995; P34058; P27321; Q5XI72; Q68FR3; P50411; Q6AXU6; Q5BK20; P07335; P11980; Q99MZ8; Q66HF9; P34926; P15205; Q5M7W5; Q63560; P30009; P02688; B0BN72; Q5FVH7; Q4KM98; Q6XVN8; Q62625; O35763; Q9EPH2; P15146; P62775; P20428; Q05982; P69682; P97603; P07936; Q9JJ19; P13084; Q63083; Q9JI85; Q01205; P08461; Q4V8B0; Q5XIL2; Q9Z0W5; Q920Q0; O88767; P04785; Q5U318; P31044; O55012; Q99MC0; P10111; Q6J4I0; Q9R063; P02625; Q812D1; Q63475; P51583; P86252; Q68A21; P62986; P02401; P62982; P62859; Q64548; Q6RJR6; Q9JK11; O35314; P10362; Q63945; B0BN85; P60881; Q9Z2P6; P07632; Q66HL2; P28042; O35814; P13668; P21818; P09951; Q63537; O70441; Q58DZ9; P37377; Q63754; P21643; Q62880; P19332; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; Q66HC1; P62076; Q9WVA1; P48500; P04692; P58775; Q63610; P09495; P02767; P0CG51; Q63429; P63045; P20156; Q5BJU7 P31044; P37377; P45592; P11030; P02625; P29419; Q05175; P62775; P21571; O88767; P15146; Q63754; P02688; P08082; P62898; P63045; P62076; O35814; Q9WUC4; Q62658; P86252; Q63228; P07632; Q9WVA1; Q5XI73; P08081; P62959; P09951; P60881; P84087; P10362
Phosphothreonine 0e+00 0e+00 1511 92 0.5499037 0.8731984 904 92 Significant P0C1X8; Q63028; O08838; Q05140; Q62848; P15999; Q05175; O08839; O88778; P0DP29; P0DP30; P0DP31; O35783; P26772; P08082; P45592; Q91ZN1; P11240; Q9JHL4; Q9QXU8; Q62950; P47942; Q07266; P84060; Q9WTP0; P62870; P15429; P07323; P60841; Q9Z1Z3; Q5RJL0; B0BN94; P07483; Q32PX7; Q99PF5; Q920R4; P47819; P62994; P01946; P02091; P11517; P82995; P34058; P27321; P50411; Q6AXU6; P07335; P11980; Q99MZ8; P34926; P15205; Q5M7W5; P30009; P02688; B0BN72; Q4KM98; O35763; Q9EPH2; P15146; P62775; P20428; P69682; P97603; P07936; Q9JJ19; P13084; Q63083; Q4V8B0; Q9Z0W5; Q920Q0; P31044; Q99MC0; P10111; Q6J4I0; Q812D1; Q63475; P51583; Q68A21; Q6RJR6; Q9JK11; B0BN85; P60881; Q66HL2; O35814; P09951; Q63537; Q62880; P19332; P48500; P58775; Q63610; P09495 P31044; P45592; Q05175; P62775; P15146; P02688; P08082; O35814
N-acetylalanine 0e+00 0e+00 435 42 0.7139681 1.1185408 178 42 Significant P31399; D3ZAF6; O08839; P0DP29; P0DP30; P0DP31; P26772; P45592; Q63768; Q7M0E3; P62628; Q07266; P15429; B0BN94; P55053; P07483; Q32PX7; Q5XI73; P62959; Q5XI72; P50411; Q792I0; P15205; Q5M7W5; P02688; O88767; P31044; Q9EPC6; P51583; Q68A21; Q6RJR6; B0BN85; P07632; P13668; P19332; Q6PEC1; P62078; Q9WV97; Q63610; P09495; Q7M767; Q9Z1A5 P62628; P31044; P45592; O88767; P31399; P02688; P07632; Q5XI73; P62959; Q9WV97; Q6PEC1; P07483
Phosphoserine 0e+00 0e+00 3634 155 0.5378929 0.8429392 952 155 Significant P0C1X8; Q63028; Q62847; O08838; Q99068; Q05140; Q62848; Q9WUC4; P29419; P21571; P15999; D3ZAF6; Q05175; O08839; O88778; P0DP29; P0DP30; P0DP31; O35783; O35397; P63039; P08081; P08082; P10354; P45592; Q91ZN1; P84087; Q63768; P11951; Q9JHL4; Q9QXU8; Q7M0E3; Q62950; P47942; Q07266; P84060; Q9WTP0; P62870; P15429; P07323; P60841; Q9Z1Z3; Q5RJL0; P55053; P07483; Q62658; Q32PX7; Q99PF5; Q920R4; Q5XI73; P47819; P01946; P02091; P11517; P62959; Q9Z2X5; P82995; P34058; P27321; Q5XI72; Q68FR3; P50411; Q6AXU6; Q5BK20; P07335; P11980; Q99MZ8; Q66HF9; P34926; P15205; Q5M7W5; Q63560; P30009; P02688; B0BN72; Q5FVH7; Q4KM98; Q6XVN8; O35763; Q9EPH2; P15146; P20428; Q05982; P97603; P07936; Q9JJ19; P13084; Q63083; Q9JI85; Q01205; P08461; Q4V8B0; Q5XIL2; Q9Z0W5; Q920Q0; P04785; Q5U318; P31044; O55012; Q99MC0; P10111; Q6J4I0; Q9R063; P02625; Q812D1; Q63475; P51583; P86252; Q68A21; P62986; P02401; P62982; P62859; Q64548; Q6RJR6; Q9JK11; O35314; P10362; Q63945; B0BN85; P60881; Q9Z2P6; P07632; Q66HL2; P28042; O35814; P13668; P21818; P09951; Q63537; O70441; Q58DZ9; P37377; Q63754; P21643; Q62880; P19332; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; Q66HC1; P62076; Q9WVA1; P48500; P04692; P58775; Q63610; P09495; P02767; P0CG51; Q63429; P20156; Q5BJU7 P31044; P37377; P45592; P02625; P29419; Q05175; P21571; P15146; Q63754; P02688; P08082; P62076; O35814; Q9WUC4; Q62658; P86252; P07632; Q9WVA1; Q5XI73; P08081; P62959; P09951; P60881; P84087; P10362; Q9Z0W5; Q63537; P07483; P15999; Q9JHL4; D3ZAF6; P62982; P0CG51; P62986; Q63429; O08838
Phosphotyrosine 0e+00 0e+00 655 48 0.7093412 1.1190650 135 48 Significant P0C1X8; P11030; P0DP29; P0DP30; P0DP31; O35783; P63039; P45592; Q5U2U2; Q63768; Q6AY72; P62898; Q9JHL4; Q62950; P47942; Q9WTP0; P15429; P07323; P55053; P07483; Q9JIX3; P01946; P82995; P34058; P07335; P11980; P34926; P15205; Q63560; P02688; B0BN72; O35763; P15146; P13084; Q9Z0W5; O88767; P51583; Q63945; Q66HL2; O35814; P09951; P37377; P19332; Q6AYZ1; Q68FR8; Q5XIF6; P04692; P58775 P37377; P45592; P11030; O88767; P15146; P02688; P62898; O35814
N6-succinyllysine 0e+00 0e+00 327 31 0.7518702 1.1814730 71 31 Significant P11030; P31399; P21571; P15999; P26772; P63039; P62898; P47942; P07323; P56571; Q62658; Q5XI73; P01946; P02091; P11517; P34058; P11980; Q99MZ8; P30904; O35763; P13084; P08461; O88767; P04785; Q9R063; P02401; P07632; P28042; P11232; P62076; P48500 P11030; P21571; O88767; P31399; P62898; P62076; P11232; Q62658; P07632; Q5XI73
Methylation 0e+00 0e+00 494 39 0.3911697 0.6132559 999 39 Significant P0C1X8; P60711; P63259; Q05140; P15999; O88778; P0DP29; P0DP30; P0DP31; P47942; Q9Z1Z3; Q32PX7; Q99PF5; P47819; P02091; P11517; P34058; Q5XI72; P11980; Q99MZ8; P15205; P02688; Q920Q0; Q63475; Q68A21; P63033; P62986; Q63945; Q66HL2; P13668; P09951; P19332; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; P48500; Q5BJU7 P02688; P09951; P15999; P62986; Q6P9V9; Q6AYZ1; P68370; P48500; Q5XI72
3’-nitrotyrosine 2e-07 1e-06 31 8 0.5834036 0.9581443 667 8 Significant Q62950; P07335; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; P48500 Q6P9V9; Q6AYZ1; P68370; P48500
Nitration 3e-07 1e-06 32 8 0.5834036 0.9621031 657 8 Significant Q62950; P07335; P68370; Q6P9V9; Q6AYZ1; Q68FR8; Q5XIF6; P48500 Q6P9V9; Q6AYZ1; P68370; P48500
N6-methyllysine 6e-06 3e-05 57 9 -0.3200000 -0.5244605 30 9 Not significant P60711; P63259; P0DP29; P0DP30; P0DP31; Q99MZ8; P13668; P19332; P48500 P31044; Q9WUC4; P0DN35; P62859; Q5PPG6; P10715; Q71UE8; O88778; Q6AXU6
Lipoyl 3e-05 1e-04 3 2 -0.7584541 -2.7737591 64 2 Not significant Q01205; P08461 P31044; Q63754
N6-lipoyllysine 3e-05 1e-04 3 2 -0.7584541 -2.6576121 65 2 Not significant Q01205; P08461 P31044; Q63754
N-acetylvaline 4e-05 1e-04 14 4 0.3804878 0.8251630 804 4 Significant P55051; P02091; P11517; P10111 P10111; P55051; P11517; P02091
N6,N6,N6-trimethyllysine 5e-05 2e-04 33 6 0.5493333 0.9787996 683 6 Significant P0DP29; P0DP30; P0DP31; P11980; P62986; Q6P9V9 P62986; Q6P9V9
N6-malonyllysine 8e-05 3e-04 16 4 0.8782475 1.7548040 110 4 Significant P11030; P26772; P63039; P34058 P11030
Isopeptide bond 1e-04 4e-04 708 38 0.6661330 1.0420931 398 38 Significant Q62847; Q05175; P0DP29; P0DP30; P0DP31; P63039; B0K020; P45592; P07323; Q99PF5; Q5XI73; P27321; Q68FR3; P11980; Q66HF9; Q5M7W5; Q05982; Q71UE8; P13084; O88767; O55012; P10111; Q812D1; P62986; P62982; Q63945; B0BN85; Q66HL2; O35814; P19332; P68370; Q6P9V9; Q66HC1; P48500; P0CG51; Q63429; Q5BJP3; P63025 P45592; Q05175; Q5BJP3; O88767; O35814; Q5XI73; B0K020
Omega-N-methylarginine 2e-04 7e-04 256 18 0.4700971 0.7378234 914 18 Significant P0C1X8; Q05140; P15999; O88778; Q9Z1Z3; Q32PX7; Q99PF5; P47819; Q5XI72; P15205; P02688; Q63475; Q68A21; Q66HL2; P09951; P19332; Q6P9V9; Q5BJU7 P02688; P09951; P15999; Q6P9V9; Q5XI72
Phosphatidylethanolamine amidated glycine 3e-04 7e-04 5 2 0.6231884 2.1257031 464 2 Significant Q6XVN8; Q62625 Q62625; Q6XVN8
Phosphatidylserine amidated glycine 3e-04 7e-04 5 2 0.6231884 2.1183246 479 2 Significant Q6XVN8; Q62625 Q62625; Q6XVN8
S-nitrosocysteine 4e-04 1e-03 45 6 0.7352011 1.3056068 363 6 Significant P47942; P82995; P34058; P15205; O35763; P11232 P11232
Methionine (R)-sulfoxide 5e-04 1e-03 6 2 -0.9661836 -3.2497440 2 2 Not significant P60711; P63259 P31044; P37377
N-acetylmethionine 5e-04 1e-03 383 23 0.6967950 1.0902895 328 23 Significant P0C1X8; P60711; P63259; Q63028; P84060; P62870; P62994; Q6AXU6; Q5BK20; Q99MZ8; P13084; Q920Q0; P10111; Q6J4I0; P02401; P62859; Q9JK11; O35814; P37377; Q62880; P62076; P04692; P58775 P37377; P62076; O35814
Oxidation 5e-04 1e-03 23 4 0.9077364 1.8695481 72 4 Significant P60711; P63259; P10354; O88767 O88767
S-nitrosylation 6e-04 1e-03 49 6 0.7352011 1.2510038 365 6 Significant P47942; P82995; P34058; P15205; O35763; P11232 P11232
5-glutamyl polyglutamate 9e-04 2e-03 7 2 0.7053140 2.4210168 373 2 Significant P68370; Q6P9V9 Q6P9V9; P68370
Tele-methylhistidine 1e-03 3e-03 8 2 -0.9661836 -3.4628886 2 2 Not significant P60711; P63259 P31044; P37377
ADP-ribosylation 2e-03 4e-03 43 5 0.7465420 1.3694143 325 5 Significant P13084; P62986; P62982; P0CG51; Q63429 P62982; P0CG51; P62986; Q63429
Deamidated glutamine 3e-03 6e-03 3 1 0.9230769 -433.1865169 68 1 Not significant P02688 P02688
Arginine amide 5e-03 1e-02 4 1 0.5144231 -22.8345354 466 1 Not significant O35314 O35314
Glycine amide 5e-03 1e-02 4 1 -0.7644231 -30.2412945 242 1 Not significant P10354 P31044
N6-(2-hydroxyisobutyryl)lysine 7e-03 1e-02 26 3 0.9429546 2.1759290 31 3 Significant P11030; P18418; P07323 P11030
N,N,N-trimethylalanine 9e-03 2e-02 5 1 -0.7115385 -72.3475728 277 1 Not significant Q63945 P31044
Methionine sulfoxide 1e-02 2e-02 6 1 -0.7644231 -72.2008141 245 1 Not significant P10354 P31044
N6-methylated lysine 1e-02 2e-02 6 1 -0.8365385 -62.6976242 151 1 Not significant P34058 P31044
Asymmetric dimethylarginine 1e-02 2e-02 105 7 0.5311510 0.8965908 726 7 Significant Q05140; O88778; P47942; P02091; P11517; P09951; Q5BJU7 P09951
N-acetylglycine 1e-02 2e-02 17 2 0.8299358 2.7063825 224 2 Significant P10715; P62898 P62898
Pyruvate 2e-02 4e-02 8 1 -0.7692308 -77.8671837 249 1 Not significant P11980 P31044
N-acetylserine 2e-02 4e-02 210 11 0.8333308 1.3299214 73 11 Significant P11030; Q62847; Q91ZN1; P07323; P60841; Q99PF5; Q63228; Q9JJ19; O55012; P02625; P63045 P11030; P02625; P63045; Q63228
4-carboxyglutamate 3e-02 4e-02 9 1 -0.7596154 76.0936455 242 1 Significant P02767 P31044
Citrulline 3e-02 4e-02 39 3 0.8494968 2.0468695 167 3 Significant P47819; P02688; Q812D1 P02688

4.2 Plotting

We now introduce the plotting features for protein set enrichment analysis. Two functions are included to visualize PSEA results returned from runPSEA function. The first plot is generated by plotPSEA function and shows Normalized Enrichment Score (NES) for each PTM pathway. User can restrict the number of pathways to draw based by adjusting sig.level parameter (default value is 0.05). The coloring of the plot indicates if the pathway is enriched or not.

plotPSEA(x = psea_res)

The second plot is generated by plotRunningScore function. A running enrichment score plot for each PTM can be plotted.

5. Translate PEIMAN results for Mass spectrometry searching tools

In addition to the introduced features and extensions from previous version, the results from PEIMAN can also be utilized in Mass spectrometry searching tools. The enriched PTM terms in list of proteins generated by runPSEA function in the previous step can be searched in subset of protein modifications database. psea2mass function takes PSEA results and a significant level (default value is 0.05) and returns protein modification of statistically significant pathways for later searches in mass spectrometry tools. For example, continuing from exmplData2 for PSEA, we call psea2mass function as follows:

MS <- psea2mass(x = psea_res, sig.level = 0.05)
MS
#>      MOD_ID                     name
#> 1 MOD:00064       N6-acetyl-L-lysine
#> 2 MOD:00085       N6-methyl-L-lysine
#> 3 MOD:00322    1'-methyl-L-histidine
#> 4 MOD:00051 N-acetyl-L-aspartic acid
#> 5 MOD:00053 N-acetyl-L-glutamic acid
#>                                                                                                                                                                                                                                                           def
#> 1 "converts an L-lysine residue to N6-acetyl-L-lysine." [ChEBI:17752, DeltaMass:214, OMSSA:24, PubMed:11369851, PubMed:11857757, PubMed:11999733, PubMed:12175151, PubMed:14730666, PubMed:15350136, PubMed:1680872, PubMed:670159, RESID:AA0055, Unimod:1#K]
#> 2                                                                                                              "converts an L-lysine residue to N6-methyl-L-lysine." [ChEBI:17604, DeltaMass:165, PubMed:11875433, PubMed:3926756, RESID:AA0076, Unimod:34#K]
#> 3                                                                                "converts an L-histidine residue to tele-methyl-L-histidine." [PubMed:10601317, PubMed:11474090, PubMed:11875433, PubMed:6692818, PubMed:8076, PubMed:8645219, RESID:AA0317]
#> 4                                                                                                                              "converts an L-aspartic acid residue to N-acetyl-L-aspartic acid." [ChEBI:21547, PubMed:1560020, PubMed:2395459, RESID:AA0042]
#> 5                                                                                                                                              "converts an L-glutamic acid residue to N-acetyl-L-glutamic acid." [ChEBI:17533, PubMed:6725286, RESID:AA0044]
#>   FreqinList
#> 1         73
#> 2          9
#> 3          2
#> 4          1
#> 5          1

Note that list of proteins generated by runEnrichment function can be passed to sea2mass function too.

References

Gholizadeh, Elham, Reza Karbalaei, Ali Khaleghian, Mona Salimi, Kambiz Gilany, Rabah Soliymani, Ziaurrehman Tanoli, et al. 2021. “Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus.” Molecular Pharmacology 99 (5): 308–18. https://doi.org/https://doi.org/10.1124/molpharm.120.000210.
Nickchi, Payman, Mohieddin Jafari, and Shiva Kalantari. 2015. PEIMAN 1.0: Post-translational modification Enrichment, Integration and Matching ANalysis.” Database 2015 (April). https://doi.org/10.1093/database/bav037.
Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.

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