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 :



·        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:

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


Running SSP


Run SSP from the command line via the script, 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:






            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.




            The length of RNA-seq short reads.




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




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




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




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




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


An example command for running SSP : – 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:


  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








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


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