An alignment and analysis pipeline for RNAseq data

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Please refer to the documentation for more in depth details.

Development Notes:

• XPRESSpipe is still in beta production
• The current release XPRESSpipe-v0.1.4b2 is running relatively stable on MacOS and Linux (including HPCs)
  • The meta-analysis plotting seems to work on a local MacOS, but not when running on a Linux HPC, will hopefully have kinks worked out in the next couple of weeks
  • Yet to incorporate UMI handling, representative gene housekeeping, along with some other features in the near future

Citation:

Berg, JA, et. al. (2019). XPRESSyourself: Automating and Democratizing High-Throughput Sequencing. https://github.com/XPRESSyourself.

Installation:

Installing from source

1. Installation requires Python (distributed with most operating systems automatically) and setuptools. If you have a more current version of Python, you can install setuptools as follows:

$ pip install setuptools

If this does not work, please refer to this site for more information 2. Get XPRESSpipe by downloading and unpacking the most recent archive found here 3. Unzip the folder and navigate to the appropriate directory in the command line

$ cd /path/to/XPRESSpipe

4. Install XPRESSpipe

$ python setup.py install

5. Test the installation

$ xpresspipe -h

If the help menu is not displayed, 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

Using a Docker container

1. Install Docker
2. Download the XPRESSpipe Docker container

$ docker pull docker push jordanberg/xpresspipe:latest

3. Run the Docker container

docker run jordanberg/xpresspipe --help

QuickStart:

$ xpresspipe riboprof -i /path/to/raw/data/ -o /path/to/output/ -r /path/to/reference/ ...

Important Notes:

Basic Starting Input

• 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)

Naming Conventions

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)