Generative Language Models on Nucleotide Sequences of Human Genes
Musa Nuri Ihtiyar, Arzucan Ozgur
TL;DR
The study treats nucleotide sequences of human genes as a textual language and benchmarks N-gram, LSTM, and Transformer generative language models on gene-level data sourced from NCBI. It finds that, despite advances in NLP, simple N-gram models often outperform deep learning approaches in perplexity and in real-world discriminative tasks, with deep models incurring higher computational costs. The work demonstrates the viability and limitations of applying language-model paradigms to small-vocabulary genomic data and provides reproducible datasets and code. It highlights the need for real-world task-oriented evaluation beyond perplexity and suggests directions for expanding datasets and hyperparameter optimization in genomic contexts.
Abstract
Language models, especially transformer-based ones, have achieved colossal success in NLP. To be precise, studies like BERT for NLU and works like GPT-3 for NLG 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 rather than the whole DNA. This decision has not 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. Firstly, we systematically studied an almost entirely unexplored problem and observed that 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.