DNA augmentations for semi-supervised learning in genomic sequence classification.

Semi-supervised learning (SSL) is a natural fit for genomics, where unlabeled sequences are abundant but high-quality labels are scarce due to costly curation. However, most SSL pipelines rely on generic perturbations that are not tailored to biological constraints, and it remains unclear which augmentation designs yield reliable gains for DNA sequence classification. In this application note, we present an empirical study of genome-aware weak-strong augmentation recipes for pseudo-label-based SSL, focusing on FixMatch and FlexMatch with a GROVER DNA language model backbone. Across two classification benchmarks-antimicrobial resistance (AMR) drug-class prediction from AMR gene sequences and multi-class oncovirus family classification from simulated 150 bp tumor-like reads-we systematically evaluate 25 weak/strong augmentation pairs constructed from biologically motivated operators, including reverse-complement, codon-level back-translation, mild masking/substitution (NN), high-rate substitution, and length-preserving insertion-deletion (indel). We find consistent trends: (i) moderate, sequence-preserving perturbations yield the best accuracy-stability trade-off, (ii) excessive masking or aggressive mutation increases variance and degrades performance, and (iii) short-segment masking (NN) for weak augmentation followed by an indel rate (around 5%) for strong augmentation emerge as a robust strategy to learn from unlabeled genomic data. We release implementations and recommended defaults to facilitate reproducible SSL for genomic sequence modeling under limited-label regimes.