Fine-Tuning Transformers For Genomic Tasks

Abstract

Transformers are a type of neural network architecture that has been successfully used to achieve state-of-the-art performance in numerous natural language processing tasks. However, what about DNA, the language life written in the four-letter alphabet? In this paper, we review the current state of Transformers usage in genomics and molecular biology in general, introduce a collection of benchmark datasets for the classification of genomic sequences, and compare the performance of several model architectures on those benchmarks, including a BERT-like model for DNA sequences DNABERT as implemented in HuggingFace (armheb/DNA_bert_6 model). In particular, we explore the effect of pre-training on a large DNA corpus vs training from scratch (with randomized weights). The results presented here can be used for identification of functional elements in human and other genomes.