Please refer to the documentation for more in depth details.

Berg JA, et. al. (2019). XPRESSyourself: Enhancing and Automating the Ribosome
Profiling and RNA-Seq Analysis Toolkit. bioRxiv 704320; doi: https://doi.org/10.1101/704320

The following is a short tutorial showing you how to install XPRESSpipe:

• Make sure you let Anaconda set up the PATH info for you.
• If the help menu is not displayed when testing, try adding the path where you installed XPRESSpipe to the system PATH

$ echo 'export PATH=$PATH:/path/to/xpresspipe' >> ~/.bash_profile

• If you do not have a file names ~/.bash_profile, try looking for one called ~/.profile
• The commands used in the video above are summarized here:

$ curl -L -O https://github.com/XPRESSyourself/XPRESSpipe/archive/v0.2.3b0.zip
$ unzip v0.2.3b0.zip
$ cd XPRESSpipe-0.2.3b0/
$ conda env create -f requirements.yml
$ conda activate xpresspipe
$ python setup.py install
$ xpresspipe -h
$ xpresspipe test

• Be sure to specify the correct release version in the first URL

• Reference building
  

• Running XPRESSpipe on sequence data
  

• You can also use the XPRESSpipe command builder and executor for reference curation or running the pipeline by executing the following:

$ xpresspipe build

• input directory with raw sequence data
  • Sequence data files should be FASTQ format and end in .fastq or .fq and can be .zip or .gz compressed
• An empty output directory
• A reference directory (see documentation for curateReference for more details)

In order for ordered output after alignment (except for generation of a raw counts table), recommended file naming conventions should be followed.

1. Download your raw sequence data and place in a folder -- this folder should contain all the sequence data and nothing else.
2. Make sure files follow a pattern naming scheme. For example, if you had 3 genetic backgrounds of ribosome profiling data, the naming scheme would go as follows:

ExperimentName_BackgroundA_FP.fastq(.qz)
ExperimentName_BackgroundA_RNA.fastq(.qz)
ExperimentName_BackgroundB_FP.fastq(.qz)
ExperimentName_BackgroundB_RNA.fastq(.qz)
ExperimentName_BackgroundC_FP.fastq(.qz)
ExperimentName_BackgroundC_RNA.fastq(.qz)

3. If the sample names are replicates, their sample number needs to be indicated.
4. If you want the final count table to be in a particular order and the samples ordered that way are not alphabetically, append a letter in front of the sample name to force this ordering.

ExperimentName_a_WT.fastq(.qz)
ExperimentName_a_WT.fastq(.qz)
ExperimentName_b_exType.fastq(.qz)
ExperimentName_b_exType.fastq(.qz)

5. If you have replicates:

ExperimentName_a_WT_1.fastq(.qz)
ExperimentName_a_WT_1.fastq(.qz)
ExperimentName_a_WT_2.fastq(.qz)
ExperimentName_a_WT_2.fastq(.qz)
ExperimentName_b_exType_1.fastq(.qz)
ExperimentName_b_exType_1.fastq(.qz)
ExperimentName_b_exType_2.fastq(.qz)
ExperimentName_b_exType_2.fastq(.qz)

• We can run a test dataset as in the associated manuscript by downloading the FASTQ files from GSE65778 using the SRAtoolkit.
• We can curate the reference like so:

$ xpresspipe curateReference -o /path/to/reference -f /path/to/reference/genome_fastas -g /path/to/reference/transcripts.gtf -p -t --sjdbOverhang 49

• And we can process the dataset like so:

xpresspipe riboseq -i /path/to/input -o /path/to/output -r /path/to/reference/ --gtf /path/to/reference//transcripts_CT.gtf -e isrib_test_study -a CTGTAGGCACCATCAAT --sjdbOverhang 49

• The above steps will be very computationally intensive, so we recommend running this on a supercomputing cluster

• Scripts used to analyze this data can be found here and here and here

• Alternatively, smaller test datasets can be found within the XPRESSpipe tests folder and an outline of commands to run can be found here