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Call for papers - Application of large language models in genome analysis

Guest Editors:
Ramana Davuluri, PhD, State University of New York at Stony Brook, USA
Casey S. Greene, PhD, University of Colorado School of Medicine, USA
Qin Ma, PhD, College of Medicine and The James Comprehensive Cancer Center, The Ohio State University, USA
Jiangning Song, PhD, Monash Biomedicine Discovery Institute, Monash University, Australia

Submission Status: Open   |   Submission Deadline: 28 November 2025


Genome Biology is calling for submissions to our Collection on the employment of LLMs in biology in areas such as genomics, transcriptomics, and proteomics, to decode complex biological data and predict genetic variations, and advance our understanding of disease mechanisms.

Meet the Guest Editors

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Ramana Davuluri, PhD, State University of New York at Stony Brook, USA

Ramana is currently “State University of New York (SUNY) Distinguished Professor”, in the Department of Biomedical Informatics, School of Medicine, Stony Brook University (SBU) on Long Island. He is also Graduate Program Director of Biomedical Informatics and Director of Bioinformatics, SBU Cancer Center. Prior to this, he held faculty positions at Northwestern University School of Medicine, Chicago, The Wistar Institute, Philadelphia, and The Ohio State University, Columbus. His research program, continuously funded by NLM/NIH, focuses on development of machine learning and artificial intelligence methods for understanding the DNA language, and how genetic and epigenetic changes in the non-coding genome alter DNA linguistics in cancer. Ramana is well-known for the development of DNABERT, the first language model for human DNA, which has been incorporated in NVIDIA BioNeMo drug discovery AI framework.

Casey S. Greene, PhD, University of Colorado School of Medicine, USA

Dr Greene is the Chair of and a Professor in the Department of Biomedical Informatics at the University of Colorado School of Medicine. His lab develops and evaluates approaches to integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such data. The overarching theme of his work has been the development and evaluation of methods that acknowledge the emergent complexity of biological systems.

Qin Ma, PhD, College of Medicine and The James Comprehensive Cancer Center, The Ohio State University, USA

Qin Ma is a Professor and Division Chief of the Bioinformatics and Computational Biology Section in the Department of Biomedical Informatics, the Ohio State University (OSU), and Leader of the Immuno-Oncology Informatics Group, Pelotonia Institute for Immuno-Oncology at The OSU Comprehensive Cancer Center. He received his PhD in Operational Research from Shandong University and then did his postdoc in Bioinformatics at the University of Georgia, specializing in high-throughput sequencing data mining and modeling. His lab focuses on developing cutting-edge deep-learning methods for single-cell and spatial omics data, aiming to discover underlying mechanisms (e.g., gene regulation, cell-cell interactions, and cellular senescence) in diverse biological systems and complex diseases.

Jiangning Song, PhD, Monash Biomedicine Discovery Institute, Monash University, Australia

Jiangning Song is a Professor of Bioinformatics and Group Leader at the Biomedicine Discovery Institute (BDI) within the Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. He directs the AI-driven Bioinformatics and Biomedicine Laboratory and is an Associate Investigator at the ARC Centre of Excellence in Advanced Molecular Imaging and The Centre to Impact AMR (Antimicrobial Resistance). Trained as a bioinformatician and data-savvy biomedical scientist, his research interests span AI, bioinformatics, comparative genomics, cancer genomics, bacterial genomics, computational biomedicine, data mining, infection and immunity, machine learning, proteomics, and biomedical big data analytics. Jiangning is an Associate Editor for several leading journals and serves on the editorial boards of many others. Jiangning and his team have developed over 60 bioinformatics algorithms and tools widely used in the research community.

About the Collection

Genome Biology is calling for submissions to our Collection on the applications of large language models (LLMs) in genome analysis.

The integration of machine learning, particularly LLMs built using transformers, into biological research has opened up new avenues for understanding complex biological data. These advanced computational models have shown promise in processing vast datasets, from genomic sequences to protein structures, enabling researchers to extract meaningful insights and identify patterns that were previously unattainable.

The employment of LLMs in biology has enhanced our capabilities in areas such as genomics, transcriptomics, and proteomics. For instance, LLMs have been successfully applied to predict the functional consequences of genetic variants in both coding and non-coding genomes, facilitating targeted precision medicine and personalized therapies. Furthermore, these models enable researchers to automate the annotation of DNA and protein sequences, accelerating the pace of discovery and innovation in biotechnology and pharmaceutical research. Additionally, LLMs are increasingly being used to explore the regulation of gene expression, including the identification and characterization of regulatory elements such as enhancers, promoters, and transcription factor binding sites.

Future advancements may lead to the creation of more sophisticated models capable of integrating multi-omic data, facilitating the understanding of complex biological systems. Such innovations could potentially enable real-time analysis and prediction of biological responses, transforming our approach to disease modeling, drug discovery, and synthetic biology. The incorporation of regulatory elements into machine learning models will be crucial for uncovering the mechanisms that govern cellular behavior and tissue-specific gene regulation.

Topics accepted for submission include, but are not limited to, the following:

  • Applications of LLMs in genomic data analysis
  • Transformer models in protein sequence prediction
  • Comparative studies of LLMs and traditional methods
  • LLMs for functional prediction and annotation of proteins
  • Machine learning approaches to the study of regulatory elements in gene expression


All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Image credit: [M] NicoElNino / Getty Images / iStock

  1. Promoters, as essential cis-regulatory elements in prokaryotes, govern gene expression by mediating RNA polymerase binding through core motifs and long-range regulatory interactions, playing a pivotal role in ...

    Authors: Wei Su, Yuhe Yang, Yafei Zhao, Shishi Yuan, Xueqin Xie, Yuduo Hao, Hongqi Zhang, Dongxin Ye, Hao Lyu and Hao Lin
    Citation: Genome Biology 2025 26:353
    Content type: Research Published on:

  2. Single-cell foundation models (scFMs) have emerged as powerful tools for integrating heterogeneous datasets and exploring biological systems. Despite high expectations, their ability to extract unique biologic...

    Authors: Jialu Wu, Qing Ye, Yilin Wang, Renling Hu, Yiheng Zhu, Mingze Yin, Tianyue Wang, Jike Wang, Chang-Yu Hsieh and Tingjun Hou
    Citation: Genome Biology 2025 26:334
    Content type: Research Published on:

  3. RNA metabolism is tightly regulated by cis-elements and trans-acting factors. Most information guiding such regulation is encoded in RNA sequences. Deciphering the regulatory rules is critical for RNA biology and...

    Authors: Hanwen Zhou, Yue Hu, Yulong Zheng, Jiefu Li, Jielong Peng, Jiang Hu, Yun Yang, Wei Chen, Guoqing Zhang and Zefeng Wang
    Citation: Genome Biology 2025 26:301
    Content type: Research Published on:

  4. Analysis of single-cell RNA sequencing (scRNA-seq) data has revolutionized our understanding of cellular heterogeneity, yet current approaches face challenges in efficiency, interpretability, and connecting mo...

    Authors: Haohuai He, Zhenchao Tang, Guanxing Chen, Fan Xu, Yao Hu, Yinglan Feng, Jibin Wu, Yu-An Huang, Zhi-An Huang and Kay Chen Tan
    Citation: Genome Biology 2025 26:300
    Content type: Research Published on:

  6. Single-cell RNA sequencing has revolutionized cellular heterogeneity research, but analyzing the abundance of unannotated public datasets remains challenging. We present scExtract, a framework leveraging large...

    Authors: Yuxuan Wu and Fuchou Tang
    Citation: Genome Biology 2025 26:174
    Content type: Software Published on:

Submission Guidelines

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This Collection welcomes submission of original Research, Method, Short Report, Review, and Database article types. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Application of large language models in genome analysis" from the dropdown menu.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.