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ProteinLP 2013-12-08
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Readme.txt 2012-03-28 2.7 kB
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ProteinLP:
In this paper, we present a linear programming model (ProteinLP) for protein inference, which is built on two simple probability equations. ProteinLP is implemented in Java and can run on any Java Virtual Machine (JVM) regardless of computer architecture. Since ProteinLP introduces linear programming to solve protein inference problem, the software uses a standard LP software package, Glpk for Java (v4.47).



How to try the program:

1. Install and run Eclipse into your computer.
Important prerequisite: you must first install the Java Development Kit (JDK). Eclipse requires a JRE or JDK to start because Eclipse is itself a - sophisticated - Java application with millions of lines of Java code. JDK and Eclipse are both free which are available at: http://www.eclipse.org/downloads/. During the Eclipse startup, it will ask you to specify the location of the workspace - where you will store the source codes. 

2. Download the source codes from this website into your computer.
Click on the link http://sourceforge.net/projects/prolp/ to download ProteinLP.rar into the folder you chose as the workspace and decompress the project.


3. Execute ProteinLasso with Eclipse.

You can use the Import Wizard to import the ProteinLasso project into workspace. 
	From the main menu bar, select " File > Import and select General". 	The Import wizard opens. 
	Select "General > Existing Project into Workspace" and click "Next". 
	Choose "Select root directory" and click the associated "Browse" to 	locate the directory where you put the ProteinLP project. 
	Under "Projects" select the ProteinLP project to import. 
	Click "Finish" to start the import. 

Select PILP.java and click the white arrow in a green circle to start the Java program.

The input data is put in the folder "real_data". You can change the file name in the main function to run your own data.



Input and output files.

1. Input files
peptideFile			input file with a list of sequences and confidence score for candidate peptide identifications.

2. Output files
resultFile			output file with the full inference result including the probabilities for the proteins


3. Formats of input and output files
3.1. Accepted formats for peptide identification file pospepfile

Format (tab delimited):
pospep1	protein1	peptide_probability1
pospep2	protein2	peptide_probability2
...
Note: peptide_probability refers to the peptide spectra matching score. It can be obtained from software such as Peptideprophet. "protein" represent the candidate proteins in the database which contain at least one identified peptide.


3.2. Format of output resultFile(tab delimited):
All_protein     protein probabilit
Source: Readme.txt, updated 2012-03-28