README.md
In ginnyintifa/PTMscape:
Table of contents
- Installation
- Functions
- Input and feature files
- Input parameters
- Reference score threshold
- Example script
- Description of output files
1.Installation
PTMscape can be downloaded and installed in R as follows. As a prerequisite, devtools must be installed:
```{r, eval = F}
install.packages("devtools")
Next, install `PTMscape`:
```{r, eval = F}
library("devtools")
devtools::install_github("ginnyintifa/PTMscape")
library(PTMscape)
Finally, PTMscape requires installation of Liblinear library, which can be downloaded from Liblinear website. After downloading and unzipping, a small change of source code in the file linear.cpp (see below) is required so as to produce probability score for SVM prediction.
int check_probability_model(const struct model *model_)
{
return (model_->param.solver_type==L2R_LR ||
model_->param.solver_type==L2R_LR_DUAL ||
model_->param.solver_type==L1R_LR);
}
to
int check_probability_model(const struct model *model_)
{
return 1;
}
After the change is made, compile the files following the instructions below:
On Unix systems, type make to build the train and predict.
programs. Run them without arguments to show the usages.
On other systems, consult Makefile to build them (e.g., see
'Building Windows binaries' in this file) or use the pre-built
binaries (Windows binaries are in the directory `windows').
This software uses some level-1 BLAS subroutines. The needed functions are
included in this package. If a BLAS library is available on your
machine, you may use it by modifying the Makefile: Unmark the following line
#LIBS ?= -lblas
and mark
LIBS ?= blas/blas.a
Note: we provide a modified version of linear.cpp in the repository.
2. Functions
Prediction of PTM events
Whole proteome scale prediction
In this mode of analysis, it is assumed that the user wants to discover all possible PTM sites in proteins of interest. Position information of known PTM sites will be mapped to all proteins. Then the whole data will be divided into k folds as specified by the user. Each time (k-1) folds of the data will be used as training data to fit a linear SVM model, and subsequently the remaining one fold of the data will be predicted by the model trained. This process is repeated for k times so that each fold of the data will be predicted once by the rest of the data. Finally known PTM sites and predicted PTM sites (at a specificity level set by the user) will be the regarded as positive PTM sites.
For this analysis, the function predict_on_whole_proteome() should be called. Input files and parameters are described below.
Targeted prediction
In this mode of analysis, it is assumed that the user provides a set of reliable PTM sites and a fasta file containing associated protein sequences i.e. user is confident about the positive/negative designation of PTM sites in these protein sequences). The aim here is to predict PTM events in previously uncharted proteins sequences with model trained from the reliable set. After prediction, the score threshold of positive/negative site will be selected by either referring to the threshold derived from large dataset (provided by PTMscape tool, see the chart below) or by conducting cross validation within the reliable data and select the cutoff corresponding to user specified specificity level.
For this mode of analysis, the function predict_on_targeted_proteome() should be called. Input files and parameters are described in the following sections.
PTM crosstalk analysis
Positive crosstalk
Positive crosstalk in a protein domain is defined as two types of PTM occurring on two residues close to one another (e.g. 5AA apart). The distance between the two residues can be specified by the user. PTMscape takes the mapped files (these files are the output files of the prediction functions) of the two PTM types as input and performs Fisher exact test to see whether the frequency of co-occurrence of PTMs is higher than expected.
The function calculate_tbt_positive_ptms() should be called for this analysis. Input files and parameters are described in the following sections.
Negative crosstalk
Negative crosstalk in a protein domain is defined as two types of PTM occurring on the same residue. The two PTM types may compete with each other for the chance of modifying the target site. PTMscape takes the mapped files (these files are the output files of the prediction functions) of the two PTM types as input and performs Fisher exact test to see whether the frequency of co-occurrence of PTMs is higher than expected.
The function calculate_tbt_negative_ptms() should be called for this analysis. Input files and parameters are described in the following sections.
3.Input and feature files
User provided input files
-
- Known PTM sites
User has to provide the Uniprot accession ID of proteins and position of the PTM sites in the required format. See sample_known_ps.tsv.
-
- Fasta file for proteins of interest
In the whole proteome scale prediction mode, only one fasta file is required. In targeted prediction mode, two fasta files are needed. One consists proteins containing the reliable PTM sites information, and the other consists protein sequences from which PTM sites are to be predicted. See the sample data: sample_known_fasta.tsv and sample_predict_fasta.tsv.
PTMscape provided feature files
Several files need to be downloaded from the following SOURCE before running PTMscape:
-
- Clustered AAindex parameters in aaindex_cluster_order.tsv.
-
- Extracted SPIDER3 position specific structural features for the whole eligible Swiss-prot human proteome in extract_spider.Rds (this file is applicable when flanking size is set to 12 (window size 25). We also provide extracted_spider_7.Rds and extracted_spider_5.Rds for window size 15 and 11 respectively).
-
- spider_protID.tsv is needed in the feature extraction step. It contains all the Uniprot accession IDs of proteins, for which SPIDER3 features can be extracted.
-
- uniprotID_genename.tsv contains mapping between Uniprot accession numbers and gene names.
-
- domain_df_pure.Rds contains domain specifications compiled from the
Pfam tool.
-
- Subcellular locations of each protein is in the file named subcellusr_location_df_pure.Rds, the information is retrieved from the database called
COMPARTMENTS.
Download these files and put them in the same working directory where you installed PTMscape.
4.Input parameters
PTMscape requires several user specified parameters.
Prediction of PTM events.
Whole proteome prediction
ptm_site The target amino acid of the given PTM type, in upper-case single letter representation.
flanking_size The number of residues surrounding each side of the center residue, The total window size will be 2*flanking_size+1 (default to 12).
SPIDER A boolean variable indicating whether to use SPIDER3 features (default set to TRUE).
positive_info_file A text file containing the positive PTM sites in the required format.
protein_fasta_file A text file containing the protein sequences of interest in fasta format.
liblinear_dir The path for the Liblinear tool.
n_fold The number of folds used for training and prediction in cross validation stage (default set to 2).
feature_file_path The path for the feature files.
lower_bound The lower bound of the scaled data range (default to -1).
upper_bound The upper bound of the scaled data range (default to 1).
cvlog_path_name The path and name of the log files, which hold the details of Liblinear procedures.
specificity_level A number ranges from 0 to 1 indicating the specificity user requires the classifier to achieve (default to 0.99).
output_label The string to tag the output files.
Targeted prediction
ptm_site The target amino acid of the given PTM type, in upper-case single letter representation.
flanking_size The number of residues surrounding each side of the center residue, The total window size will be 2*flanking_size+1 (default to 12).
SPIDER A boolean variable indicating whether to use SPIDER3 features (default set to TRUE).
positive_info_file A text file containing the positive PTM sites in the required format.
protein_fasta_file A text file containing the protein sequences of interest in fasta format.
known_protein_fasta_file A text file containing the proteins sequences of interest and known PTM sites in fasta format.
predict_protein_fasta_file A text file containing the proteins sequences with PTM sites to be predicted in fasta format.
output_label_training The string to tag the output files associated with training proteins.
output_label_predict The string to tag the output files associated with prediction proteins.
liblinear_dir The path for the Liblinear tool.
n_fold The number of folds used for training and prediction in cross validation stage (default set to 2).
feature_file_path The path for the feature files.
lower_bound The lower bound of the scaled data range (default to -1).
upper_bound The upper bound of the scaled data range (default to 1).
cvlog_path_name The path and name of the log files, which hold the details of Liblinear procedures.
specificity_level A number ranges from 0 to 1 indicating the specificity user requires the classifier to achieve (default to 0.99).
flag_for_score_threshold_chosen A string indicating whether use reference score threshold or get from the user supplied training data (default set to "reference").
score_threshold A numerical value between 0 to 1 indicating the reference score threshold (required in "reference" mode).
PTM crosstalk analysis
Positive crosstalk
distance A numerical value indicating distance between two PTM sites (default to 5).
anchor_mod A string indicating the anchor modification.
cross_mod A string indicating the other modification.
anchor_mapped_df_Rds An Rds file containing the window score file of anchor PTM mapped domain.
cross_mapped_df_Rds An Rds file containing the window score file of the other PTM with mapped domain.
output_label The string to tag the output files.
Negative crosstalk
anchor_mod A string indicating the anchor modification.
cross_mod A string indicating the other modification in competition with the anchor modification.
anchor_mapped_df_Rds An Rds file containing the window score file of anchor PTM mapped domain.
cross_mapped_df_Rds An Rds file containing the window score file of the other PTM with mapped domain.
output_label The string to tag the output files.
5.Reference score threshold derived from PhosphoSitePlus PTM data
| PTM_type | Score_at_spec0.9 | Score_at_spec0.95 | Score_at_spec0.99|
| ------------- | ------------- | ------------- | ------------- |
| phosphoS | 0.577 |0.613|0.683|
| phosphoT | 0.572 |0.607|0.675|
| phosphoY | 0.575 |0.604|0.663|
| ubiquitinK | 0.566 |0.586|0.621|
| acetylK | 0.563 |0.59|0.646|
| SUMOyK | 0.554|0.604|0.688|
| methylK| 0.584|0.621|0.688|
| methylR| 0.562|0.608|0.704|
6.Example script
Whole proteome prediction
Let us assume that we predict lysine acetylation on all proteins provided. A two-fold cross validation will be conducted. Score threshold will be determined at specificity level 0.99. Two text output files will be produced: ps_samle_wp_mapped_df.tsv and ps_sample_wp_test.tsv.
```{r, eval=F}
predict_on_whole_proteome(ptm_site = "S",
flanking_size = 12,
SPIDER = TRUE,
positive_info_file = "sample_known_ps.tsv",
protein_fasta_file = "sample_known_fasta.tsv",
n_fold = 2,
lower_bound = -1,
upper_bound = 1,
liblinear_dir = "/data/ginny/liblinear-2.11/",
feature_file_path = "/data/ginny/PTMscape_test/",
cvlog_path_name = "/data/ginny/PTMscape_test/cvlog.txt",
specificity_level = 0.99,
output_label = "ps_sample_wp")
Note: it is important to have "/" appended in the end for the input parameters of `liblinear_dir` and `feature_file_path`.
##### Running time needed for serine phosphorylation on whole proteome scale
The program was run on a Linux server with 32 Intel Xeon(R) E4-2630 v3 (2.40GHz) processors and 64Gb memory.
| Total time | Feature generation | Training/prediction preparation | Liblinear processing| Prediction score annotation | Domain enrichment analysis |
| ------------- | ------------- | ------------- | ------------- |------------- | ------------- |
| 39min | 9min | 17min |4min| 3min | 6min |
### Targeted prediction
Known phosphoS sites and proteins are used to train a linear SVM model. Select a score threshold by cross validating within known PTM sites. The score corresponding to specificity 0.99 will be chosen as the threshold. Two text output files will be produced: **ps_sample_predict_mapped_df.tsv** and **ps_sample_predict_test.tsv**.
```{r, eval=F}
predict_on_targeted_proteome (ptm_site = "S",
flanking_size=12,
SPIDER = TRUE,
positive_info_file = "sample_known_ps.tsv",
known_protein_fasta_file = "sample_known_fasta.tsv",
predict_protein_fasta_file = "sample_predict_fasta.tsv",
output_label_training = "ps_sample_training",
output_label_predict = "ps_sample_predict",
lower_bound = -1,
upper_bound = 1,
liblinear_dir = "/data/ginny/liblinear-2.11/",
feature_file_path = "/data/ginny/PTMscape_test/",
cvlog_path_name = "/data/ginny/PTMscape_test/cvlog.txt",
specificity_level = 0.99,
n_fold = 2,
flag_for_score_threshold_chosen = "cv",
score_threshold = NULL)
Select a score threshold by looking up to the reference table.
```{r, eval=F}
predict_on_targeted_proteome (ptm_site = "S",
flanking_size=12,
SPIDER = T,
positive_info_file = "sample_known_ps.tsv",
known_protein_fasta_file = "sample_known_fasta.tsv",
predict_protein_fasta_file = "sample_predict_fasta.tsv",
output_label_training = "ps_sample_training",
output_label_predict = "ps_sample_predict",
lower_bound = -1,
upper_bound = 1,
liblinear_dir = "/data/ginny/Liblinear_prep/",
feature_file_path = "/data/ginny/PTMscape_test/",
cvlog_path_name = "/data/ginny/PTMscape_test/cvlog.txt",
specificity_level = NULL,
n_fold = 2,
flag_for_score_threshold_chosen = "reference",
score_threshold = 0.683)
### Crosstalk analysis
Let us assume that we analyze positive crosstalk events between methylationK and phosphorylationS. The output file will be **sample_ps_ubi_positive_test.tsv**.
```{r, eval = F}
calculate_tbt_positive_ptms(distance = 5,
anchor_mod = "ps",
cross_mod = "ubi",
anchor_mapped_df_Rds = "sample_ps_mapped_df.Rds",
cross_mapped_df_Rds = "sample_ubi_mapped_df.Rds",
output_label = "sample_ps_ubi_positive")
Meanwhile, this time we analyze negative crosstalk events between ubiquitination and methylationK. The output will be sample_methy_k_ubi_negative_test.tsv.
```{r, eval = F}
calculate_tbt_negative_ptms(anchor_mod = "methy_k",
cross_mod = "ubi",
anchor_mapped_df_Rds = "sample_methy_k_mapped_df.Rds",
cross_mapped_df_Rds = "sample_ubi_mapped_df.Rds",
output_label = "sample_methy_k_ubi_negtive")
```
7.Description of output files.
The user can ignore all the .Rds files produced by the functions as they are used internally by the functions in this package. Redundant files are removed by the program at the last stage. After the function finishes the running process, the following .tsv files should be examined:
output_label_mapped_df.tsv contains the predicted score for each modifiable site in proteins given.
output_label_test.tsv contains the significance score of enrichment for each domain where the PTM takes place.
ginnyintifa/PTMscape documentation built on Nov. 9, 2021, 10:39 p.m.
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Table of contents
- Installation
- Functions
- Input and feature files
- Input parameters
- Reference score threshold
- Example script
- Description of output files
1.Installation
PTMscape can be downloaded and installed in R as follows. As a prerequisite, devtools must be installed:
```{r, eval = F} install.packages("devtools")
Next, install `PTMscape`:
```{r, eval = F}
library("devtools")
devtools::install_github("ginnyintifa/PTMscape")
library(PTMscape)
Finally, PTMscape requires installation of Liblinear library, which can be downloaded from Liblinear website. After downloading and unzipping, a small change of source code in the file linear.cpp (see below) is required so as to produce probability score for SVM prediction.
int check_probability_model(const struct model *model_)
{
return (model_->param.solver_type==L2R_LR ||
model_->param.solver_type==L2R_LR_DUAL ||
model_->param.solver_type==L1R_LR);
}
to
int check_probability_model(const struct model *model_)
{
return 1;
}
After the change is made, compile the files following the instructions below:
On Unix systems, type
maketo build thetrainandpredict. programs. Run them without arguments to show the usages.On other systems, consult
Makefileto build them (e.g., see 'Building Windows binaries' in this file) or use the pre-built binaries (Windows binaries are in the directory `windows').This software uses some level-1 BLAS subroutines. The needed functions are included in this package. If a BLAS library is available on your machine, you may use it by modifying the Makefile: Unmark the following line
#LIBS ?= -lblas
and mark
LIBS ?= blas/blas.a
Note: we provide a modified version of linear.cpp in the repository.
2. Functions
Prediction of PTM events
Whole proteome scale prediction
In this mode of analysis, it is assumed that the user wants to discover all possible PTM sites in proteins of interest. Position information of known PTM sites will be mapped to all proteins. Then the whole data will be divided into k folds as specified by the user. Each time (k-1) folds of the data will be used as training data to fit a linear SVM model, and subsequently the remaining one fold of the data will be predicted by the model trained. This process is repeated for k times so that each fold of the data will be predicted once by the rest of the data. Finally known PTM sites and predicted PTM sites (at a specificity level set by the user) will be the regarded as positive PTM sites.
For this analysis, the function predict_on_whole_proteome() should be called. Input files and parameters are described below.
Targeted prediction
In this mode of analysis, it is assumed that the user provides a set of reliable PTM sites and a fasta file containing associated protein sequences i.e. user is confident about the positive/negative designation of PTM sites in these protein sequences). The aim here is to predict PTM events in previously uncharted proteins sequences with model trained from the reliable set. After prediction, the score threshold of positive/negative site will be selected by either referring to the threshold derived from large dataset (provided by PTMscape tool, see the chart below) or by conducting cross validation within the reliable data and select the cutoff corresponding to user specified specificity level.
For this mode of analysis, the function predict_on_targeted_proteome() should be called. Input files and parameters are described in the following sections.
PTM crosstalk analysis
Positive crosstalk
Positive crosstalk in a protein domain is defined as two types of PTM occurring on two residues close to one another (e.g. 5AA apart). The distance between the two residues can be specified by the user. PTMscape takes the mapped files (these files are the output files of the prediction functions) of the two PTM types as input and performs Fisher exact test to see whether the frequency of co-occurrence of PTMs is higher than expected.
The function calculate_tbt_positive_ptms() should be called for this analysis. Input files and parameters are described in the following sections.
Negative crosstalk
Negative crosstalk in a protein domain is defined as two types of PTM occurring on the same residue. The two PTM types may compete with each other for the chance of modifying the target site. PTMscape takes the mapped files (these files are the output files of the prediction functions) of the two PTM types as input and performs Fisher exact test to see whether the frequency of co-occurrence of PTMs is higher than expected.
The function calculate_tbt_negative_ptms() should be called for this analysis. Input files and parameters are described in the following sections.
3.Input and feature files
User provided input files
-
- Known PTM sites
User has to provide the Uniprot accession ID of proteins and position of the PTM sites in the required format. See sample_known_ps.tsv.
-
- Fasta file for proteins of interest
In the whole proteome scale prediction mode, only one fasta file is required. In targeted prediction mode, two fasta files are needed. One consists proteins containing the reliable PTM sites information, and the other consists protein sequences from which PTM sites are to be predicted. See the sample data: sample_known_fasta.tsv and sample_predict_fasta.tsv.
PTMscape provided feature files
Several files need to be downloaded from the following SOURCE before running PTMscape:
-
- Clustered AAindex parameters in aaindex_cluster_order.tsv.
-
- Extracted SPIDER3 position specific structural features for the whole eligible Swiss-prot human proteome in extract_spider.Rds (this file is applicable when flanking size is set to 12 (window size 25). We also provide extracted_spider_7.Rds and extracted_spider_5.Rds for window size 15 and 11 respectively).
-
- spider_protID.tsv is needed in the feature extraction step. It contains all the Uniprot accession IDs of proteins, for which SPIDER3 features can be extracted.
-
- uniprotID_genename.tsv contains mapping between Uniprot accession numbers and gene names.
-
- domain_df_pure.Rds contains domain specifications compiled from the
Pfamtool.
- domain_df_pure.Rds contains domain specifications compiled from the
-
- Subcellular locations of each protein is in the file named subcellusr_location_df_pure.Rds, the information is retrieved from the database called
COMPARTMENTS.
- Subcellular locations of each protein is in the file named subcellusr_location_df_pure.Rds, the information is retrieved from the database called
Download these files and put them in the same working directory where you installed PTMscape.
4.Input parameters
PTMscape requires several user specified parameters.
Prediction of PTM events.
Whole proteome prediction
ptm_site The target amino acid of the given PTM type, in upper-case single letter representation.
flanking_size The number of residues surrounding each side of the center residue, The total window size will be 2*flanking_size+1 (default to 12).
SPIDER A boolean variable indicating whether to use SPIDER3 features (default set to TRUE).
positive_info_file A text file containing the positive PTM sites in the required format.
protein_fasta_file A text file containing the protein sequences of interest in fasta format.
liblinear_dir The path for the Liblinear tool.
n_fold The number of folds used for training and prediction in cross validation stage (default set to 2).
feature_file_path The path for the feature files.
lower_bound The lower bound of the scaled data range (default to -1).
upper_bound The upper bound of the scaled data range (default to 1).
cvlog_path_name The path and name of the log files, which hold the details of Liblinear procedures.
specificity_level A number ranges from 0 to 1 indicating the specificity user requires the classifier to achieve (default to 0.99).
output_label The string to tag the output files.
Targeted prediction
ptm_site The target amino acid of the given PTM type, in upper-case single letter representation.
flanking_size The number of residues surrounding each side of the center residue, The total window size will be 2*flanking_size+1 (default to 12).
SPIDER A boolean variable indicating whether to use SPIDER3 features (default set to TRUE).
positive_info_file A text file containing the positive PTM sites in the required format.
protein_fasta_file A text file containing the protein sequences of interest in fasta format.
known_protein_fasta_file A text file containing the proteins sequences of interest and known PTM sites in fasta format.
predict_protein_fasta_file A text file containing the proteins sequences with PTM sites to be predicted in fasta format.
output_label_training The string to tag the output files associated with training proteins.
output_label_predict The string to tag the output files associated with prediction proteins.
liblinear_dir The path for the Liblinear tool.
n_fold The number of folds used for training and prediction in cross validation stage (default set to 2).
feature_file_path The path for the feature files.
lower_bound The lower bound of the scaled data range (default to -1).
upper_bound The upper bound of the scaled data range (default to 1).
cvlog_path_name The path and name of the log files, which hold the details of Liblinear procedures.
specificity_level A number ranges from 0 to 1 indicating the specificity user requires the classifier to achieve (default to 0.99).
flag_for_score_threshold_chosen A string indicating whether use reference score threshold or get from the user supplied training data (default set to "reference").
score_threshold A numerical value between 0 to 1 indicating the reference score threshold (required in "reference" mode).
PTM crosstalk analysis
Positive crosstalk
distance A numerical value indicating distance between two PTM sites (default to 5).
anchor_mod A string indicating the anchor modification.
cross_mod A string indicating the other modification.
anchor_mapped_df_Rds An Rds file containing the window score file of anchor PTM mapped domain.
cross_mapped_df_Rds An Rds file containing the window score file of the other PTM with mapped domain.
output_label The string to tag the output files.
Negative crosstalk
anchor_mod A string indicating the anchor modification.
cross_mod A string indicating the other modification in competition with the anchor modification.
anchor_mapped_df_Rds An Rds file containing the window score file of anchor PTM mapped domain.
cross_mapped_df_Rds An Rds file containing the window score file of the other PTM with mapped domain.
output_label The string to tag the output files.
5.Reference score threshold derived from PhosphoSitePlus PTM data
| PTM_type | Score_at_spec0.9 | Score_at_spec0.95 | Score_at_spec0.99| | ------------- | ------------- | ------------- | ------------- | | phosphoS | 0.577 |0.613|0.683| | phosphoT | 0.572 |0.607|0.675| | phosphoY | 0.575 |0.604|0.663| | ubiquitinK | 0.566 |0.586|0.621| | acetylK | 0.563 |0.59|0.646| | SUMOyK | 0.554|0.604|0.688| | methylK| 0.584|0.621|0.688| | methylR| 0.562|0.608|0.704|
6.Example script
Whole proteome prediction
Let us assume that we predict lysine acetylation on all proteins provided. A two-fold cross validation will be conducted. Score threshold will be determined at specificity level 0.99. Two text output files will be produced: ps_samle_wp_mapped_df.tsv and ps_sample_wp_test.tsv.
```{r, eval=F}
predict_on_whole_proteome(ptm_site = "S", flanking_size = 12, SPIDER = TRUE, positive_info_file = "sample_known_ps.tsv", protein_fasta_file = "sample_known_fasta.tsv", n_fold = 2, lower_bound = -1, upper_bound = 1, liblinear_dir = "/data/ginny/liblinear-2.11/", feature_file_path = "/data/ginny/PTMscape_test/", cvlog_path_name = "/data/ginny/PTMscape_test/cvlog.txt", specificity_level = 0.99, output_label = "ps_sample_wp")
Note: it is important to have "/" appended in the end for the input parameters of `liblinear_dir` and `feature_file_path`.
##### Running time needed for serine phosphorylation on whole proteome scale
The program was run on a Linux server with 32 Intel Xeon(R) E4-2630 v3 (2.40GHz) processors and 64Gb memory.
| Total time | Feature generation | Training/prediction preparation | Liblinear processing| Prediction score annotation | Domain enrichment analysis |
| ------------- | ------------- | ------------- | ------------- |------------- | ------------- |
| 39min | 9min | 17min |4min| 3min | 6min |
### Targeted prediction
Known phosphoS sites and proteins are used to train a linear SVM model. Select a score threshold by cross validating within known PTM sites. The score corresponding to specificity 0.99 will be chosen as the threshold. Two text output files will be produced: **ps_sample_predict_mapped_df.tsv** and **ps_sample_predict_test.tsv**.
```{r, eval=F}
predict_on_targeted_proteome (ptm_site = "S",
flanking_size=12,
SPIDER = TRUE,
positive_info_file = "sample_known_ps.tsv",
known_protein_fasta_file = "sample_known_fasta.tsv",
predict_protein_fasta_file = "sample_predict_fasta.tsv",
output_label_training = "ps_sample_training",
output_label_predict = "ps_sample_predict",
lower_bound = -1,
upper_bound = 1,
liblinear_dir = "/data/ginny/liblinear-2.11/",
feature_file_path = "/data/ginny/PTMscape_test/",
cvlog_path_name = "/data/ginny/PTMscape_test/cvlog.txt",
specificity_level = 0.99,
n_fold = 2,
flag_for_score_threshold_chosen = "cv",
score_threshold = NULL)
Select a score threshold by looking up to the reference table.
```{r, eval=F} predict_on_targeted_proteome (ptm_site = "S", flanking_size=12, SPIDER = T, positive_info_file = "sample_known_ps.tsv", known_protein_fasta_file = "sample_known_fasta.tsv", predict_protein_fasta_file = "sample_predict_fasta.tsv", output_label_training = "ps_sample_training", output_label_predict = "ps_sample_predict", lower_bound = -1, upper_bound = 1, liblinear_dir = "/data/ginny/Liblinear_prep/", feature_file_path = "/data/ginny/PTMscape_test/", cvlog_path_name = "/data/ginny/PTMscape_test/cvlog.txt", specificity_level = NULL, n_fold = 2, flag_for_score_threshold_chosen = "reference", score_threshold = 0.683)
### Crosstalk analysis
Let us assume that we analyze positive crosstalk events between methylationK and phosphorylationS. The output file will be **sample_ps_ubi_positive_test.tsv**.
```{r, eval = F}
calculate_tbt_positive_ptms(distance = 5,
anchor_mod = "ps",
cross_mod = "ubi",
anchor_mapped_df_Rds = "sample_ps_mapped_df.Rds",
cross_mapped_df_Rds = "sample_ubi_mapped_df.Rds",
output_label = "sample_ps_ubi_positive")
Meanwhile, this time we analyze negative crosstalk events between ubiquitination and methylationK. The output will be sample_methy_k_ubi_negative_test.tsv.
```{r, eval = F} calculate_tbt_negative_ptms(anchor_mod = "methy_k", cross_mod = "ubi", anchor_mapped_df_Rds = "sample_methy_k_mapped_df.Rds", cross_mapped_df_Rds = "sample_ubi_mapped_df.Rds", output_label = "sample_methy_k_ubi_negtive")
```
7.Description of output files.
The user can ignore all the .Rds files produced by the functions as they are used internally by the functions in this package. Redundant files are removed by the program at the last stage. After the function finishes the running process, the following .tsv files should be examined:
output_label_mapped_df.tsv contains the predicted score for each modifiable site in proteins given.
output_label_test.tsv contains the significance score of enrichment for each domain where the PTM takes place.
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