MMseqs2 VS MethylDackel

A tool I often recommend for sequence clustering is mmseqs2 : https://github.com/soedinglab/MMseqs2, fast and efficient :)

Tools like the the mmseqs2 "taxonomy" module, or DIAMOND v2, can efficiently align contigs to genome databases to assign taxonomy, but it seems like they aren't intended to provide abundance estimates for each taxon (since that would require mapping reads, and mmseqs2 can't even use paired-reads). Can anyone recommend tools or methods for A) connecting per-contig coverage information to contig taxonomy, or B) mapping short reads against genome databases?

Anyway, I'm curious if there are methods used to normalize the data to account for the difference in sequencing depth. I've already employed random subsampling like they used in the EpiQC study to achieve a similar mean coverage across the board, but I'm curious if there are any other known methods which minimize information loss. For context, I won't be doing DML/DMR detection (I know these methods can take coverage into account when merging DMRs and performing statistical analyses), but rather tissue of origin estimation between sets. My data are per-CpG context bedGraph files from MethylDackel, but I have access to the per-Cytosine bedGraphs all the way up to the raw FASTQ files.