# Recipe 7: Trim metagenome and transcriptome reads with variable coverage k-mer trimming¶

This is a recipe for metagenome and transcriptome k-mer trimming. K-mer trimming for genomes (see Recipe 6: Error-trim reads using streaming k-mer abundance trimming) relies on using a hard cutoff to identify low-abundance k-mers that are likely to be erroneous in high coverage genomic shotgun sequencing data sets; however, some or many of these k-mers may not be errors in situations where you have a variety of molecular species at different abundances in your data set – specifically, when you are sequencing metagenomes or transcriptomes. (This will also work for whole-genome amplified data sets.)

Low-abundance k-mer trimming is primarily useful for removing errors from short reads prior to assembly or mapping. This can significantly reduce memory requirements for assembly, in particular. However, note that you should only do this kind of error trimming in cases where your downstream analysis approaches won’t correct the errors for you; see On the optimal trimming of high-throughput mRNA sequence data, MacManes, 2014 for more information.

Suppose you have a metagenome with several different coverage peaks; here, in this simulated data set, there are three: one at 10, one at 100, and one at about 300.

load-into-counting.py -x 1e8 -k 20 reads.kh reads.fa


Suppose you wanted to remove errors with k-mer abundance trimming (as in Recipe 6: Error-trim reads using streaming k-mer abundance trimming) - the problem is that you can’t just use a hard cutoff, because some of the low-abundance k-mers are real, while some are not. For example, the k-mer spectrum of this data set is much broader at 1 than it would be for a high-coverage genome:

abundance-dist.py -s reads.kh reads.fa reads.dist


You can use the -V argument to the script trim-low-abund.py to efficiently trim sequences at low-abundance k-mers that are in high-coverage reads. With -V, low-abundance k-mers in low-coverage reads are kept; these are much more likely to be correct than a low-abundance k-mer in a high-coverage read.

trim-low-abund.py -x 1e8 -k 20 -V reads.fa


(By default, trim-low-abund trims k-mers that are unique in reads that have 20 or higher coverage. You can change the multiplicity of trimming with -C and the trusted coverage with -Z.)

After running trim-low-abund, you’ll note that some but not all of the unique k-mers are now gone:

load-into-counting.py -x 1e8 -k 20 reads-trim.kh reads.fa.abundtrim

load-into-counting.py -x 1e8 -k 20 reads-trim.kh reads.fa.abundtrim