FAQ

• Can I run ConsensusCruncher in parallel?

  Yes, we provide generate_scripts.sh as a wrapper to create scripts for each individual sample. If you have access to a cluster, you can submit these scripts as separate jobs to be run in parallel.

• Why is ConsensusCruncher taking so long?

  The time it takes ConsensusCruncher to run depends on the sequencing depth, number of UMIs, and the computer you’re using. Time can range from a few minutes to several hours (& up to a day with some of the larger samples). Please be patient!

• Why is ConsensusCruncher taking so much memory?

  The consensus mode of ConsensusCruncher groups reads of a BAM file by UMI and condenses it into consensus sequences. The amount of memory is dependent on the size of your BAM files. If you have large BAMs and have access to a cluster, we recommend running ConsensusCruncher on the high memory queue.

• Why do we need a cytoband bedfile to separate the data?

  Depending on the size of your BAM file, it may be too large to load all the reads into memory. We use coordinates of cytobands to split our data, but you could also use a target bedfile if you’re only interested in specific regions of the genome.

• Can I input BAMs into ConsensusCruncher?

  Yes, as long as your BAM files have UMIs in the queryname of each read, it can be used for the consensus mode of ConsensusCruncher. (Please note: UMIs need to be separated by ‘|’ and paired UMIs split by ‘.’). e.g. “HWI-D00331:196:C900FANXX:5:1101:11551:2948|AC.CT”

• Can I use ConsensusCruncher for single-end sequencing data?

  Yes, you can use the SSCS_maker.py directly to generate consensus sequences. However, please note that single-end sequencing data won’t benefit from Singleton Correction as that requires paired-end data.

Questions

If you have any questions, please post it as an issue on our GitHub.

Alternatively, you can contact: Nina Wang (nina.tt.wang@gmail.com), Trevor Pugh (Trevor.Pugh@uhn.ca), Scott Bratman (Scott.Bratman@rmp.uhn.ca)