SRSLY UMI Post-Processing

For use with SRSLY™ NGS Library preparation kitS with UMI-ADDITION

Though there are many ways to handle UMI sequences with reads, we show only one example on this page. The SRSLY UMI software tool will demultiplex samples with bcl2fastq and place UMI sequences in the FASTQ headerline for each fragment. After mapping FASTQ into BAM format, the UMI sequence can then be moved to an auxiliary read tag (such as RX: or BC:) for further analysis steps.

OPTION 1: UMI-aware demultiplexing with SRSLYumi

1. Install SRSLYumi with:

 pip install srslyumi 

The package is compatible with Python 2.7 and 3, and can be installed in a virtual environment if necessary. You should also have bcl2fastq installed on your system.

Alternatively, manually download the SRSLYumi code from GitHub

2. After sequencing, identify the location of the Illumina run directory that contains both a RunInfo.xml, SampleSheet.csv, and the subdirectory /Data/Intensities/BaseCalls. This is the “run directory” and the first parameter to the script. If these are not located in the same directory, their locations can be specified separately.

3. Run:

srslyumi [rundirectory] [outputdirectory] 

FASTQ will be produced in [outputdirectory].

Download a mock SampleSheet.csv, based on the UMI and UDI length in the SRSLY® kit

For all other questions about UMI deconvolution, write to us at technicalsupport@claretbio.com

For information about sequencer specific set-up for UMI-aware run contact Illumina® technical support.