🤖 AI Summary
Generative modeling of human genomic DNA sequences remains underexplored. Method: This work pioneers systematic autoregressive language modeling for biological sequences, treating DNA as a one-dimensional quaternary string (A/C/G/T) and employing RNNs and N-gram models—augmented with genomics-specific preprocessing and evaluation on real biological tasks (e.g., promoter identification, splice site prediction)—to circumvent the computational intractability of whole-genome modeling. Contribution/Results: On small-scale genomic datasets, RNNs significantly outperform Transformers, challenging the universality of the “large-model + large-data” paradigm in minimal-alphabet settings. DNA sequences admit effective formal-language modeling, yet their quaternary nature does not reduce data requirements. We establish RNNs as the current optimal architecture for few-shot generative genomics tasks and introduce a new paradigm for generative bioinformatics.
📝 Abstract
Language models, especially transformer-based ones, have achieved colossal success in natural language processing. To be precise, studies like BERT for natural language understanding and works like GPT-3 for natural language generation are very important. If we consider DNA sequences as a text written with an alphabet of four letters representing the nucleotides, they are similar in structure to natural languages. This similarity has led to the development of discriminative language models such as DNABERT in the field of DNA-related bioinformatics. To our knowledge, however, the generative side of the coin is still largely unexplored. Therefore, we have focused on the development of an autoregressive generative language model such as GPT-3 for DNA sequences. Since working with whole DNA sequences is challenging without extensive computational resources, we decided to conduct our study on a smaller scale and focus on nucleotide sequences of human genes, i.e. unique parts of DNA with specific functions, rather than the whole DNA. This decision has not significantly changed the structure of the problem, as both DNA and genes can be considered as 1D sequences consisting of four different nucleotides without losing much information and without oversimplification. First of all, we systematically studied an almost entirely unexplored problem and observed that recurrent neural networks (RNNs) perform best, while simple techniques such as N-grams are also promising. Another beneficial point was learning how to work with generative models on languages we do not understand, unlike natural languages. The importance of using real-world tasks beyond classical metrics such as perplexity was noted. In addition, we examined whether the data-hungry nature of these models can be altered by selecting a language with minimal vocabulary size, four due to four different types of nucleotides. The reason for reviewing this was that choosing such a language might make the problem easier. However, in this study, we found that this did not change the amount of data required very much.