umis provides tools for estimating expression in RNA-Seq data which performs sequencing of end tags of trancsript, and incorporate molecular tags to correct for amplification bias.

There are three steps in this process.

1. Formatting reads
2. Pseodomapping to cDNAs
3. Counting molecular identifiers

We want to strip out all non-biological segments of the sequenced reads for the sake of mapping. While also keeping this information for later use. We consider non-biological information such as Cellular Barcode and Molecular Barcode. To later be able to extract the optional CB and the MB these are put in the read header, with the followign format.

@HWI-ST808:130:H0B8YADXX:1:1101:2088:2222:CELL_GGTCCA:UMI_CCCT
AGGAAGATGGAGGAGAGAAGGCGGTGAAAGAGACCTGTAAAAAGCCACCGN
+
@@@DDBD>=AFCF+<CAFHDECII:DGGGHGIGGIIIEHGIIIGIIDHII#

The command umis fastqtransform is for transforming a (pair of) read(s) to this format based on a transform file. The transform file is a json file which has a Python flavored regular expression for each read, made to extract the necessary components of the reads.

This is done by pseduoaligners, either Kallisto or RapMap. The SAM file output from these tools need to be saved.

The final step is to infer which cDNA was the origin of the tag a UMI was attached to. We use the pseudoalignments to the cDNAs, and consider a tag assigned to a cDNA as a partial evidence for a (cDNA, UMI) pairing. For actual counting, we only count unique UMIs for (gene, UMI) pairings with sufficient evidence.