diff --git a/README.md b/README.md
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@@ -28,7 +28,7 @@ See [README.md in the config directory](https://github.com/rnabioco/aa-tRNA-seq-
 
 ## Workflow overview
 
-Given a directory of pod5 files, this pipeline merges all files from the sample into a single pod5, rebasecalls them to generate an unmapped bam with move table information (for downstream use by Remora), converts the bam into a fastq, and aligns that fastq to a reference containing tRNA + adapter sequences with BWA MEM. The resulting data (pod5s and aligned reads) are then fed to a model trained using Remora to classify charged vs. uncharged reads in the rule `cca_classify`, generating numeric values indicating the likelihood of a read being aminoacylated in the `ML` tag of the BAM file. For classifying charged vs. uncharged reads, we treat ML values of 200-255 as aminoacylated in downstream steps.
+Given a directory of pod5 files, this pipeline merges all files from the sample into a single pod5, rebasecalls them to generate an unmapped bam with move table information (for downstream use by Remora), converts the bam into a fastq, and aligns that fastq to a reference containing tRNA + adapter sequences with BWA MEM. The resulting data (pod5s and aligned reads) are then fed to a model trained using Remora to classify charged vs. uncharged reads in the rule `cca_classify`, generating numeric values indicating the likelihood of a read being aminoacylated in the `ML` tag of the BAM file. For classifying charged vs. uncharged reads, we treat ML values of 200-255 as aminoacylated in downstream steps, and values <200 as uncharged.
 
 The final steps of the pipeline calculate a number of outputs that may be useful for analysis and visualization, including normalized counts for charged and uncharged tRNA (`get_cca_trna_cpm`), basecalling error values (`bcerror`), alignment statistics (`align_stats`) and information on raw nanopore signal from Remora (`remora_signal_stats`).