SSP is a de novo transcriptome assembler that assembles RNA-seq reads into transcripts. SSP aims to reconstructs all the alternatively spliced isoforms and estimates the expression level of them.

 

 

 

·        Installing a pre-compiled binary release

 

            In order to make it easy to install SSP, we provide a few binary packages. To use the binary packages, simply download the appropriate one for your machine, untar it, and use it.

 Linux x86_64 binary

 Mac OS X x86_64 binary

 

Building SSP from source

 

SSP runs on Linux or Mac OS X based computers with GCC 4.0 or greater. 

In order to execute SSP, you must follow below steps to have all the needed requirements installed on your system.

 

·        Installing Integer Linear Programming solver

 

1.      Download LP_solve

2.      Unpack the lp_solve tarball

 

·        Installing Velvet

1.      Download Velvet

2.      Unpack the Velvet tarball and cd to Velvet source directory

3.      Build the Velvet by typing make at the command line.

 

·        Installing Clustering_SSP

1.      Download Clustering_SSP

2.      Unpack the Clustering_SSP source tarball:

tar Clustering_SSP.tar.gz

3.      Change to the Clustering_SSP directory:

cd Clustering_SSP_0.1

4.      Make the Clustering_SSP :

make

 

·        Building SSP

1.      Download SSP

2.      Unpack the SSP source tarball:

                        tar zxvf SSP_Source.tar.gz

3.      Change to the SSP directory:

cd SSP_0.1.01

4.      Configure SSP. You need to specify where to find lp_solve, Velvet and Clustering_SSP binary files:

./configure.sh -lp_solve=/path/to/lp_solve -Clustering_SSP=/path/to/ Clustering_SSP

 

Running SSP

 

Run SSP from the command line via the script SSP.sh, which is available in the base installation directory.

1-      Preprocess the RNA-seq reads using Velvet.

a.      It’s necessary to run velvetg by setting the value of -read_trkg parameter to yes.  Do not set -cov_cutoff and -exp_cov parameters.

b.      Velvet has many parameters that are explained in the Velvet Manual. A sample useful way to run Velvet is as following:

 

 velveth working_directory 21 -shortPaired source-dir/Paired-RNA-seq_reads.fa

 velvetg working_directory -read_trkg yes

 

 

2-      Now run the SSP on the Velvet working_directory.

 

The following is the options used to control SSP:

 

Arguments

 

            -dir_name

 

            Sets the name of the directory in which SSP will write all of its output.  It should be                             the same as the working_directory determined for Velvet in the previous step.

 

            -read_length

           

            The length of RNA-seq short reads.

 

            -Kmer_length

 

            Sets the length of kmer that is equal to the value set to –hash_length parameter                                    in velveth.

 

            -equation_threshold

           

            Sets the length of interval needed to solve Interval Integer Linear Programming.

 

-clustering_threshold

 

Tell SSP the maximum distance between two contigs to cluster them together.

 

-LP_upper_bound

 

Tells SSP the maximum number of contigs in each locus to use linear programming for reconstructing isofoms in that locus.

 

-min_trans_length

 

Tells SSP the minimum length of transcripts to report as reconstructed ones.

 

An example command for running SSP :

 

SSP.sh – dir_name=working_directory – read_length =45 – Kmer_length =21 – equation_threshold =0.5 -clustering_threshold=1 -LP_upper_bound =20 -min_trans_lengt=100

 

Testing the installation

 

  1. Download the Sample Data
  2. In the SSP directory type:

      ./SSP.sh

  1. The Program asks you to enter the required parameters to run the program, so enter the following values one by one

 

-dir_Name=path to Sample folder

-read_length=45

-Kmer_length=21

-equation_threshold=0.5

-clustering_threshold=1

-LP_upper_bound=17

-min_trans_length=100

 


You should see the following output:

ShortReadCount Started at Thu Sep 19 12:31:03 2013
ShortReadCount Finished at Thu Sep 19 12:31:03 2013
No_Short_In_Bin_Contig calc started at Thu Sep 19 12:31:03 2013
No_Short_In_Bin_Contig calc finished at Thu Sep 19 12:31:03 2013
Distance Calc Started at Thu Sep 19 12:31:03 2013
Distance Calc Finished at Thu Sep 19 12:31:03 2013
Reading Distance File ...
Clustering ...
Pruning Edges ... Done
Finding Connected Components ...
Component #1 Done
Component #2 Done
Component #3 Done
Component #4 Done
Saving Clusters ...
Locus 0: 8
Locus 1: 8
Locus 2: 1
Locus 3: 1
 
The Transfrags file in Sample folder contains the reconstructed transcripts by SSP.

 

SSP Development Group

  • Zhaleh Safikhani, University of Tehran
  • Mehdi Sadeghi, National Institute of Genetic Engineering and Biotechnology
  • Hamid Pezeshk, University of Tehran
  • Changiz Ezlahchi, Shahid Beheshti University

 

Contact Us

In the case that you have questions, suggestions or comments,

send email to safikhani@ut.ac.ir

 

 

 

footer
 

webmaster | ipmic@ipm.ir   Copyright © 2014, All rights reserved.