  Decoding the linguistic intricacies of the genome is a crucial problem in
biology, and pre-trained foundational models such as DNABERT and Nucleotide
Transformer have made significant strides in this area. Existing works have
largely hinged on k-mer, fixed-length permutations of A, T, C, and G, as the
token of the genome language due to its simplicity. However, we argue that the
computation and sample inefficiencies introduced by k-mer tokenization are
primary obstacles in developing large genome foundational models. We provide
conceptual and empirical insights into genome tokenization, building on which
we propose to replace k-mer tokenization with Byte Pair Encoding (BPE), a
statistics-based data compression algorithm that constructs tokens by
iteratively merging the most frequent co-occurring genome segment in the
corpus. We demonstrate that BPE not only overcomes the limitations of k-mer
tokenization but also benefits from the computational efficiency of
non-overlapping tokenization. Based on these insights, we introduce DNABERT-2,
a refined genome foundation model that adapts an efficient tokenizer and
employs multiple strategies to overcome input length constraints, reduce time
and memory expenditure, and enhance model capability. Furthermore, we identify
the absence of a comprehensive and standardized benchmark for genome
understanding as another significant impediment to fair comparative analysis.
In response, we propose the Genome Understanding Evaluation (GUE), a
comprehensive multi-species genome classification dataset that amalgamates $36$
distinct datasets across $9$ tasks, with input lengths ranging from $70$ to
$10000$. Through comprehensive experiments on the GUE benchmark, we demonstrate
that DNABERT-2 achieves comparable performance to the state-of-the-art model
with $21 \times$ fewer parameters and approximately $92 \times$ less GPU time
in pre-training.
