diff --git a/lessons/05_alignment_QC.md b/lessons/05_alignment_QC.md
index b5c233b..12851d1 100644
--- a/lessons/05_alignment_QC.md
+++ b/lessons/05_alignment_QC.md
@@ -140,7 +140,7 @@ sbatch picard_metrics_tumor.sbatch
## Collecting Coverage Metrics
-Coverage is the average level of alignment for any random locus in the genome. `Picard` also has a package called [`CollectWgsMetrics`](https://gatk.broadinstitute.org/hc/en-us/articles/360037269351-CollectWgsMetrics-Picard) which is also very nice for collecting data about coverage for alignments. However, **since our data set is whole exome sequencing rather than whole genome sequencing** and thus only compromises about 1-2% of the human genome, average **coverage across the whole genome is not a very useful metric**. However, if one did have whole genome data, then running `CollectWgsMetrics` would be useful and even could be incorporated easily into the downstream MultiQC HTML report. In the dropdown box below we provide the code that you could use to collect this information.
+Coverage is the average level of alignment for any random locus in the genome. `Picard` also has a package called [`CollectWgsMetrics`](https://gatk.broadinstitute.org/hc/en-us/articles/360037269351-CollectWgsMetrics-Picard) which is also very nice for collecting data about coverage for alignments. However, **since our data set is whole exome sequencing rather than whole genome sequencing** and thus only compromises about 1-2% of the human genome, average **coverage across the whole genome is not a very useful metric**. However, if one did have whole genome data, then running `CollectWgsMetrics` would be useful. As such, in the dropdown box below we provide the code that you could use to collect this information.
