Uninformative transcripts dominate sequencing reads.
One problem faced by single cell RNA-seq methods is that greater than 90% of single cell RNA-seq dataset is noise and uninformative1. While computational algorithms have evolved to parse out the true signal, commonly available microfluidic technologies enable only sparse sampling of RNAs from each cell, with many genes represented by a few sequencing reads. This limit is partially due to an abundance of biologically uninformative RNAs which dominate sequencing reads and limit detection of the moderately expressed transcripts that often drive biological differences between cell types.
What if there was a turnkey molecular solution that removes uninformative reads in-vitro, thereby redistributing sequencing reads to unique, biologically relevant transcripts?
With CRISPRclean, you can gain a deeper view of expression profiles of individual cells.