Advances in Human Genome Resolution: The Role of Pan-Genomic Strategies and Fine-Tuning Pre-trained Genomic Models
Main Article Content
Abstract
The groundbreaking theory of DNA double helix structure has greatly promoted the development of molecular genetics, shaping and refining the genetic central dogma, thus enabling researchers to explore genotype-phenotype regulation at different levels. In particular, with the continued advancement of third-generation sequencing technology, an increasing number of highly accurate human genomes have been assembled, such as T2T-CHM13 and HG002. These high-quality genome sequences not only provide a more comprehensive human reference sequence, but also enable functional genomics studies within a unified coordinate system. To better explore and resolve the complex genetic information encompassed within human genome sequences, scientists have proposed novel research strategies, involving graphical pan-genome and pre-trained genomic models. The graphical pan-genomes provide population- level high-quality references, revealing the genomic diversity within populations and exploring the sequence complexity of specific regions, such as the KIR immune region. Concurrently, related studies of pre-trained models within the human genome offer new perspectives for interpreting sequence functions and delving into the hidden genetic codes, potentially leading to complete DNA decoding. Overall, graphical pan-genome and pre-trained genomic models represent two crucial strategies in genomics research, which will provide more new insights and make greater breakthroughs in the human genome. Together, these approaches have deepened our understanding of the human genome, fostered the development of bioinformatics ecosystems, and will contribute to the establishment and improvement of the entire field. Therefore, this review focuses on DNA sequencing, human genome assembly, high-quality pan-genome and pre-trained genomic large language models (LLMs), highlighting and summarizing the latest achievements and progress in human genome research, discussing existing challenges and providing future perspectives.
Keywords:
Human Genome, Pan-Genomics, Third-Generation Sequencing, Genomic Models, DNA Sequencing, Genome Assembly, Genomic Diversity, Functional Genomics, Graphical Pan-Genome, Pre-Trained Genomic Models
Article Details
How to Cite
DU, Duo et al.
Advances in Human Genome Resolution: The Role of Pan-Genomic Strategies and Fine-Tuning Pre-trained Genomic Models.
Medical Research Archives, [S.l.], v. 12, n. 7, july 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5571>. Date accessed: 15 nov. 2024.
doi: https://doi.org/10.18103/mra.v12i7.5571.
Section
Review Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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3. Timmis JN, Ayliffe MA, Huang CY, Martin W. Endosymbiotic gene transfer: organelle genomes forge eukaryotic chromosomes. Nat Rev Genet 5, 123-135 (2004).
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37. Zhang L, Zhou X, Weng Z, Sidow A. De novo diploid genome assembly for genome-wide structural variant detection. NAR Genom Bioinform 2, lqz018 (2020).
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39. Cohen ASA, et al. Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes. Genet Med 24, 1336-1348 (2022).
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44. Eisenstein M. Every base everywhere all at once: pangenomics comes of age. Nature 616, 618-620 (2023).
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51. Outten J, Warren A. Methods and Developments in Graphical Pangenomics. J Indian Inst Sci 101, 485-498 (2021).
52. Hickey G, et al. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat Biotechnol, (2023).
53. Garrison E, et al. Building pangenome graphs. bioRxiv, (2023).
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55. Liao WW, et al. A draft human pangenome reference. Nature 617, 312-324 (2023).
56. Gao Y, et al. A pangenome reference of 36 Chinese populations. Nature 619, 112-121 (2023).
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58. Wang T, et al. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604, 437-446 (2022).
59. Ji Y, Zhou Z, Liu H, Davuluri RV. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. Bioinformatics 37, 2112-2120 (2021).
60. Yang M, et al. Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution. Nucleic Acids Res 50, e81 (2022).
61. Dalla-Torre H, et al. The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics. bioRxiv, 2023.2001.2011.523679 (2023).
62. Zvyagin M, et al. GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics. bioRxiv, (2022).
63. Fishman V, et al. GENA-LM: A Family of Open-Source Foundational Models for Long DNA Sequences. bioRxiv, 2023.2006.2012.544594 (2023).
64. Liu H, Zhou S, Chen P, Liu J, Huo K-G, Han L. Exploring Genomic Large Language Models: Bridging the Gap between Natural Language and Gene Sequences. bioRxiv, 2024.2002.2026.581496 (2024).
65. Nguyen E, et al. HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. Preprint at https://ui.adsabs.harvard.edu/abs/2023arXiv230615794N (2023).
66. Gu A, Dao T. Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Preprint at https://ui.adsabs.harvard.edu/abs/2023arXiv231200752G (2023).
67. Zhou Z, Ji Y, Li W, Dutta P, Davuluri R, Liu H. DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome. Preprint at https://ui.adsabs.harvard.edu/abs/ 2023arXiv230615006Z (2023).
68. Nguyen E, et al. Sequence modeling and design from molecular to genome scale with Evo. bioRxiv, 2024.2002.2027.582234 (2024).
69. Sun H. Reinforcement Learning in the Era of LLMs: What is Essential? What is needed? An RL Perspective on RLHF, Prompting, and Beyond. Preprint at https://ui.adsabs.harvard.edu/abs/2023arXiv231006147S (2023).
70. Weigmann K. The code, the text and the language of God. When explaining science and its implications to the lay public, metaphors come in handy. But their indiscriminant use could also easily backfire. EMBO Rep 5, 116-118 (2004).
71. Holur P, et al. Embed-Search-Align: DNA Sequence Alignment using Transformer Models. Preprint at https://ui.adsabs.harvard.edu/abs/2023arXiv230911087H (2023).
72. Liu J, Yang M, Yu Y, Xu H, Li K, Zhou X. Large language models in bioinformatics: applications and perspectives. Preprint at https://ui.adsabs.harvard.edu/abs/2024arXiv240104155L (2024).
73. Consens ME, et al. To Transformers and Beyond: Large Language Models for the Genome. Preprint at https://ui.adsabs.harvard.edu/abs/2023arXiv231107621C (2023).