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Please work through the tutorial and add your comments on the bottom of this page. Or send comments per email to Thank you.



  1. A CyVerse account. (Register for an CyVerse account here -
  2. Mandatory arguments 
    1. Transcript file name (in fasta format)
    2. FASTQ files ( Paired or Single end reads in Fastq or fastq.gz format) 
    3. Read File type (Enter whether the library is compressed or uncompressed )is Paired or Single end reads )
    4. Library- the type of sequenicng library, leave default to A if not sure else read the doc for value to enter:
  3. Optional arguments
    1. Number of bootstraps ( This option takes a positive integer that dictates the number of bootstrap samples to compute. The more samples computed, the better the estimates of varaiance, but the more computation (and time) required)
    2. Number of GibbsSamples (this option produces samples that allow us to estimate the variance in abundance estimates. However, in this case the samples are generated using posterior Gibbs sampling over the fragment equivalence classes rather than bootstrapping)

Test/sample data 

The following test data are provided for testing Sailfish_align_quant-0.9.2 in here - :


  1. logs
  2. Index
  3. reads_1
    1. quant.sfWhen the quantification step is finished, the directory <quant_dir> will contain a file named “quant.sf” (and, if bias correction is enabled, an additional file names “quant_bias_corrected.sf”). This file contains the result of the Sailfish quantification step. This file contains a number of columns (which are listed in the last of the header lines beginning with ‘#’). Specifically, the columns are (1) Transcript ID, (2) Transcript Length, (3) Transcripts per Million (TPM) and (6) Estimated number of reads (an estimate of the number of reads drawn from this transcript given the transcript’s relative abundance and length).

More information on the tool can be found here -