PhosMap

Update Log

PhosMap 1.2.0 was released in December 2023.

Key features:

- Support the processing of dia and tmt data.

- Introduce a sample quality checker to filter files for obtaining high-quality data.

PhosMap 1.1.0 was released in August 2023.

Key features: Introduce a quality inspector to facilitate iterative preprocessing for obtaining high-quality data.

PhosMap 1.0.0 was released in July 2023.

Notice

The server currently operates on a single-thread and utilizes low-level hardware. To enhance performance and unlock the full potential of PhosMap, we recommend users consider analyzing their data using the server that is specifically designed for small data sets. It is advisable to evaluate the possibility of upgrading to higher-level hardware, considering the potential computational cost associated with the data, to fully maximize the capabilities of PhosMap.

Version	Preprocessing	Analysis [Except MEA]	MEA
Online	✔	✔	✖
Local	✔	✔	✔

Introduction

PhosMap supports multiple function modules for full landscape of phosphoproteomics data analyses including quality control, phosphosite mapping, dimension reduction analysis, time course analysis, kinase activity analysis and survival analysis. Various of publication ready figures and tables could be generated via PhosMap.

- For users without bioinformatics skills, web server can be used for analysis when the data volume is small. For larger data volumes, a local version of PhosMap can be utilized.

- For users with bioinformatics skills, a downloadable R package is provided for more flexible analysis.

Furthermore, our data flow is standardized, ensuring its strong scalability. Therefore, if users have other analysis requirements, they are welcome to submit PR or issue on GitHub.

PhosMap: An Ensemble Bioinformatic Platform to Empower One-stop Interactive Analysis of Quantitative Phosphoproteomics

This website is free and open to all users and there is no login requirement.

Preprocessing

Step1: Parser

no parameter

Step2: p-site Quality Control & Merging

minimum score:

minimum FDR:

Step3: Mapping

species:

id type

fasta type

Step4: Filtering & Normalization & Imputation

minimum detection frequency:

normalization method:

impute by group

impute globally

imputation method:

top

This step also filters modification types, remaining only 'S/T/Y'

Step5: Normalization based on proteomics data

With proteomics data

Without proteomics data

Proteomics data preprocessing parameters

US cutoff:

minimum detection frequency:

normalization method:

imputation method:

control label:

After 'Step1-Step2-Step3-Step4' have been run, you can click 'Go to analysis tools'

Result

Peptide identification files with psites scores:

Peptide data frame through phosphorylation sites quality control:

Data frame mapped ID to Gene Symbol:

PhosMap Matrix:

Proteomics data frame:

Result

Peptide identification files with psites scores:

Peptide data frame through phosphorylation sites quality control:

Data frame mapped ID to Gene Symbol:

PhosMap Matrix:

Step1: p-site Quality Control

minimum score:

minimum localization probability:

minimum detection frequency:

Step2: Normalizaiton & Imputation & Filtering

normalization method:

impute by group

impute globally

imputation method:

top:

Step3: Normalization based on proteomics data

With proteomics data

Without proteomics data

Proteomics data preprocessing parameters

intensity type:

minimum unique peptide:

minimum detection frequency:

normalization method:

imputation method:

control label:

After 'Step1-Step2' have been run, you can click 'Go to analysis tools'

Step3: Normalization based on proteomics data

Result

QC result:

PhosMap Matrix:

Proteomics data frame:

Result

QC result:

PhosMap Matrix:

Step1: p-site Quality Control

minimum score:

minimum localization probability:

minimum detection frequency:

Step2: Normalizaiton & Imputation & Filtering

normalization method:

impute by group

impute globally

imputation method:

top:

Result

QC result:

PhosMap Matrix:

Result

QC result:

PhosMap Matrix:

Step1: Parser & p-site Quality Control

PTM.Site.Confidence threshold:

PTM.Q.Value threshold:

minimum detection frequency:

species:

Step2: Normalizaiton & Imputation & Filtering

normalization method:

impute by group

impute globally

imputation method:

top:

Result

Parser&QC result:

PhosMap Matrix:

Result

Parser&QC result:

PhosMap Matrix:

Step1: Parser & p-site Quality Control

minimum detection frequency:

Step2: Normalizaiton & Imputation & Filtering

normalization method:

impute by group

impute globally

imputation method:

top:

Result

Parser&QC result:

PhosMap Matrix:

Result

Parser&QC result:

PhosMap Matrix:

Dimension Reduction Analysis

This module is used to reduce the dimension of phosphosites and visualize samples.

Parameters Setting

p-sites selection

PCA:

main

legend title

group mean

t-SNE:

main

legend title

random seed

perplexity

UMAP:

main

legend title

neighbors

Time Course Analysis(fuzzy clustering)

Fuzzy clustering is applied to time course analysis for discovering patterns associated with time points in PhosMap.

Parameters Setting

pvalue threshold:

pvalue adjust method:

FC threshold:

minimun membership value:

iteration:

number of clusters:

Kinase-Substrate Enrichment Analysis

This module is used to predict kinase activity.

Kinase-Substrate Regulatory Relation

Clustering Parameters Setting [Step 1]

pvalue threshold:

pvalue adjust method:

FC threshold:

minimun membership value:

iteration:

number of clusters:

KSEA Parameters Setting [Step 2]

species:

scale:

clustering distance rows:

clustering method:

Parameters Setting [Step 1]

Paired data

FC threshold:

KSEA Parameters Setting [Step 2]

species:

scale:

clustering distance rows:

clustering method:

title:

Result

Motif Enrichment Analysis

This module is used to find and visualize enriched motifs.

Motif-Kinase Relation

Parameters Setting

species:

fasta type:

pvalue threshold:

Motif Selection

Heatmap Parameters Setting

Assign quantitative values of peptides to their motif

matched seqs threshold

scale:

distance metric:

clustering method:

title:

Motif enrichment analysis result:

Survival Analysis

This module is used to identify phosphorylation sites or kinases associated with clinical outcomes of patients.

Parameters Setting

pvalue adjust method:

pvalue threshold:

Feature Selection

or input any Psite：

"high" colour

"low" colour

We provide a docker image with PhosMap: https://hub.docker.com/r/liuzandh/phosmap

Pull the docker image of PhosMap: docker pull liuzandh/phosmap:1.0.0

Create a docker container containing PhosMap: docker run -p HostPort:3838 liuzandh/phosmap:1.0.0

Then, you can enter PhosMap by visiting HostIP:HostPort.

For example, HostPort could be set to 8083. This parameter can be changed according to user needs.
docker run -p 8083:3838 liuzandh/phosmap:1.0.0

Next, open 127.0.0.1:8083 in the local browser or remotely access ip:8083 (you should ensure that the machine can be accessed remotely).

In case your browser fails to display this tutorial correctly, please visit https://liuzan-info.github.io/phosmap_r for access.

FAQ

Q1: What is the format of the experimental design file?

Click on the download button below to download the template. Importantly, column 'Experiment_Code' and 'Group' are required.

design file template

Q2: What is the format of the clinical data file?

Click on the download button below to download the template. Importantly, all columns are required.

clinical data file template

Q3: If I want to pre-process a part of samples, can I change only the experimental design file instead the other data?

Sure.

Q4: Where does the example data come from?

WiDr colorectal cancer cells harbouring the BRAF(V600E) mutation after treatment using vemurafenibin a time course of 0, 2, 6, 24, and 48 hour(Ressa,et al.,2018). The raw files were deposited in ProteomeXchange Consortium(PXD007740).

Q5: How is the parameter 'fasta type' in module 'Motif Enrichment Analysis' selected?

This parameter corresponds to the fasta file you use in the search engine. If you analysis MaxQuant example data, select 'uniprot' please.

Q5: When I encounter a bug, how do I contact the author?

Please click the github icon to submit issue, we will reply to you as soon as possible.

Q6: I encountered this error when uploading files: ''Error: zip error: 'Failed to set mtime on 'profiling_gene_txt//Exp_gene.txt' while extracting 'C:\Users\\AppData\Local\Temp\RtmpCa5Mdw\62c04ab3afa3d1ce11d77f19\0.zip'' in file 'zip.c:260'''

PhosMap Calculating...

Update Log

PhosMap 1.2.0 was released in December 2023.

Key features:

- Support the processing of dia and tmt data.

- Introduce a sample quality checker to filter files for obtaining high-quality data.

PhosMap 1.1.0 was released in August 2023.

Key features: Introduce a quality inspector to facilitate iterative preprocessing for obtaining high-quality data.

PhosMap 1.0.0 was released in July 2023.

Notice

Introduction

PhosMap: An Ensemble Bioinformatic Platform to Empower One-stop Interactive Analysis of Quantitative Phosphoproteomics

This website is free and open to all users and there is no login requirement.

Import Data

1. Experimental design file:

2. Mascot xml file:

3. Phosphoproteomics peptide file:

4. Proteomics data[optional]

4.1 Proteomics experimental design file:

4.2 Profiling file:

2. Phospho (STY)Sites.txt:

3. Proteomics data[optional]

3.1 Proteomics experimental design file:

3.2 proteinGroups.txt:

1. Experimental design file:

2. Phospho (STY)Sites.txt:

1. Experimental design file:

2. Report.xls:

2. report.tsv:

1. Experimental design file:

2. Mascot xml file:

3. Phosphoproteomics peptide file:

4. Proteomics data[optional]

4.1 Proteomics experimental design file:

4.2 Profiling file:

2. Phospho (STY)Sites.txt:

3. Proteomics data[optional]

3.1 Proteomics experimental design file:

3.2 proteinGroups.txt:

2. Phospho (STY)Sites.txt:

2. Report.xls:

2. report.tsv:

Preprocessing

Step1: Parser

no parameter

Step2: p-site Quality Control & Merging

Step3: Mapping

Step4: Filtering & Normalization & Imputation

Step5: Normalization based on proteomics data

Proteomics data preprocessing parameters

After 'Step1-Step2-Step3-Step4' have been run, you can click 'Go to analysis tools'

Peptide identification files with psites scores:

Peptide data frame through phosphorylation sites quality control:

Data frame mapped ID to Gene Symbol:

PhosMap Matrix:

PhosMap Matrix:

Proteomics data frame:

Peptide identification files with psites scores:

Peptide data frame through phosphorylation sites quality control:

Data frame mapped ID to Gene Symbol:

PhosMap Matrix:

Step1: p-site Quality Control

Step2: Normalizaiton & Imputation & Filtering

Step3: Normalization based on proteomics data

Proteomics data preprocessing parameters

After 'Step1-Step2' have been run, you can click 'Go to analysis tools'

Step3: Normalization based on proteomics data

QC result:

PhosMap Matrix:

PhosMap Matrix:

Proteomics data frame:

QC result:

PhosMap Matrix:

Step1: p-site Quality Control

Step2: Normalizaiton & Imputation & Filtering

QC result:

PhosMap Matrix:

QC result:

PhosMap Matrix:

Step1: Parser & p-site Quality Control