Human Genome Book: Words, Sentences and Paragraphs (2501.16982v1)

Abstract: Since the completion of the human genome sequencing project in 2001, significant progress has been made in areas such as gene regulation editing and protein structure prediction. However, given the vast amount of genomic data, the segments that can be fully annotated and understood remain relatively limited. If we consider the genome as a book, constructing its equivalents of words, sentences, and paragraphs has been a long-standing and popular research direction. Recently, studies on transfer learning in LLMs have provided a novel approach to this challenge.Multilingual transfer ability, which assesses how well models fine-tuned on a source language can be applied to other languages, has been extensively studied in multilingual pre-trained models. Similarly, the transfer of natural language capabilities to "DNA language" has also been validated. Building upon these findings, we first trained a foundational model capable of transferring linguistic capabilities from English to DNA sequences. Using this model, we constructed a vocabulary of DNA words and mapped DNA words to their English equivalents.Subsequently, we fine-tuned this model using English datasets for paragraphing and sentence segmentation to develop models capable of segmenting DNA sequences into sentences and paragraphs. Leveraging these models, we processed the GRCh38.p14 human genome by segmenting, tokenizing, and organizing it into a "book" comprised of genomic "words," "sentences," and "paragraphs." Additionally, based on the DNA-to-English vocabulary mapping, we created an "English version" of the genomic book. This study offers a novel perspective for understanding the genome and provides exciting possibilities for developing innovative tools for DNA search, generation, and analysis.

Insights into Constructing the Human Genome Book through LLMs

The paper "Human Genome Book: Words, Sentences, and Paragraphs" presents an innovative approach to genomic data analysis by leveraging capabilities of LLMs. By conceptualizing the human genome as a "book", this paper explores the potential of transferring NLP abilities to DNA sequences, enabling a more intricate representation of genomic data.

The authors propose a structured conversion of DNA sequences into genomic "words," "sentences," and "paragraphs". Utilizing a GPT-2-based model fine-tuned for genomic data, the research delineates the process from LLM pre-training to genomic data segmentation. The pre-trained foundation model, gpt2-gene-eng, was developed using a sizable corpus that includes English text, DNA sequences, and protein data. To facilitate the transfer of natural language capabilities to DNA language, the model was fine-tuned using an English semantic similarity task, resulting in the gpt2-gene-eng-ft model. The fine-tuned model demonstrated a translation mechanism by mapping linguistic constructs to genomic elements, thus creating a bilingual genomic book in which DNA sequences have their English equivalents.

A critical component of this paper is the segmentation process of genome data into coherent textual structures. The methodology involved using English datasets to further fine-tune models for sentence splitting, paragraph segmentation, and summarization; these models were then applied to the GRCh38.p14 human genome. A significant aspect of this work is the encapsulation of DNA sequences into hierarchies akin to natural linguistic structures. Chromosome data was divided into chapters, sections, and paragraphs, facilitating novel ways of genomic representation and analysis.

Strong quantitative results are presented in the form of accuracy metrics from sequence similarity judgment tasks. Notably, the application of the gpt2-gene-eng-ft model on DNA datasets achieved over 79% accuracy, indicating successful knowledge transfer from natural language capabilities to DNA comprehension. The visualization through PCA further illustrates the convergence of DNA and English word vector spaces post fine-tuning.

The implications of this research extend across various genomic applications. The paper speculates on efficient DNA search mechanisms where genomic indexing aligns with hierarchical text structures, potentially enhancing the retrieval and accuracy of genomic data searches. The introduction of genomic identifiers through summarization capabilities provides a robust mechanism for comparative genomics and fault-tolerant genome identification. Moreover, the research suggests potential directions in data storage optimization through compressed, yet interpretable, genomic representations.

From a theoretical standpoint, this paper contributes to interdisciplinary methodologies, bridging computational linguistics and molecular biology through artificial intelligence. While the current iteration of the genomic book does not offer conclusive biological interpretations, it exemplifies the tangible application of LLMs in genomic data processing and invites further exploration into the utility of NLP in bioinformatics.

Future developments may focus on refining these techniques to enhance biological insight derivation. As the field progresses, improved interoperability between LLMs and genomic data could facilitate more nuanced understandings, further paving the way for AI-driven innovations in genomics research.