User guide

Registration to the system

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Click on the signup button and fill out the form:

User datasets

The “User datasets” screen contains the list of the user’s analyses. You can see the run status (RUNNING/SUCCESSFOL/FAILS). You need to refresh the page to see if the status has changed (you will also get an email at the end of the run).

Import Input data

In order to run the transcriptome analysis pipeline, fastq sequence files need to be located on the server.

Click on the “Upload data” button on the navigation bar, and select the folder of the fastq files.

• Instructions for uploading data to the server
  • Uploading folder of fastq files
  • Uploading input for the demultiplexing pipeline

Run analysis

After importing you data (or if you have old data on the server that was imported in the past), you can run the pipeline by selecting the “Run pipeline” option

• Run Pipelines
  • Run Demultiplexing pipeline
  • Run Ribo-Seq Pipeline
  • Run Transcriptome pipelines
  • Run ChIP-Seq pipeline
  • Run ATAC-Seq pipeline

Customization

We chose the various pipeline parameters based on our rich experience in transcriptome analysis. This works very well for users who are not deeply familiar with bioinformatics software, and who prefer to quickly benefit from these choices without having to delve into the pipeline’s architecture. On the other hand, many research groups have their own particular preferences, and can achieve flexibility by making some minor adjustments to the code as follows:

1. System-wide changes:

   The Snakefiles for the pipelines are built using the Snakemake workflow management system (https://snakemake.readthedocs.io) and are located within a Miniconda3 environment in the image/container at:

   /home/labs/bioservices/services/UTAP-data-staging/utap-meta-data/installation/UTAP-staging-singularity/update_utap/UTAP2_RHEL_9.1/utap.sandbox/opt/miniconda3/envs/utap/lib/python3.10/site-packages/ngs-snakemake/snakefiles/

   Users can modify the above scripts by adding new rules, commands and changing parameters.

   In addition, one can customize the following R script’s DESeq2 analyses and report generation:

   /home/labs/bioservices/services/UTAP-data-staging/utap-meta-data/installation/UTAP-staging-singularity/update_utap/UTAP2_RHEL_9.1/utap.sandbox/opt/miniconda3/envs/utap/lib/python3.10/site-packages/ngs-snakemake/DESeq_extend.Rmd

   After the code is changed, subsequent analyses using the web application will reflect these changes.

2. Ad-hoc changes:

   Another option is to change parameters only for a particular run.

   After running the analysis in the usual way, one can navigate to the output folder, which contains a copy of the snakefile and Rmd script. One can then change and re-run the analysis from the linux terminal by executing:

   source $HOST_MOUNT/singularity_variables && singularity exec  $HOST_MOUNT/utap_latest.sif /bin/bash <output_folder>/snakemake_cmd_RUN_ID (where an example of a RUN_ID is a timestamp like “20171205_145424”).

(where $HOST_MOUNT is the folder that contains all UTAP2 installation files - see https://utap2.readthedocs.io/en/latest/rst/installation.html).