1
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Xu L, Liu Y. Identification, Design, and Application of Noncoding Cis-Regulatory Elements. Biomolecules 2024; 14:945. [PMID: 39199333 PMCID: PMC11352686 DOI: 10.3390/biom14080945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 09/01/2024] Open
Abstract
Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.
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Affiliation(s)
- Lingna Xu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China;
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yuwen Liu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China;
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan 528226, China
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2
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Chen V, Yang M, Cui W, Kim JS, Talwalkar A, Ma J. Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments. Nat Methods 2024; 21:1454-1461. [PMID: 39122941 PMCID: PMC11348280 DOI: 10.1038/s41592-024-02359-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 06/24/2024] [Indexed: 08/12/2024]
Abstract
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However, guidelines for using IML in computational biology are generally underdeveloped. We provide an overview of IML methods and evaluation techniques and discuss common pitfalls encountered when applying IML methods to computational biology problems. We also highlight open questions, especially in the era of large language models, and call for collaboration between IML and computational biology researchers.
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Affiliation(s)
- Valerie Chen
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Muyu Yang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Wenbo Cui
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joon Sik Kim
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ameet Talwalkar
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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3
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Sokolova K, Chen KM, Hao Y, Zhou J, Troyanskaya OG. Deep Learning Sequence Models for Transcriptional Regulation. Annu Rev Genomics Hum Genet 2024; 25:105-122. [PMID: 38594933 DOI: 10.1146/annurev-genom-021623-024727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.
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Affiliation(s)
- Ksenia Sokolova
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; , ,
| | - Kathleen M Chen
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; , ,
| | - Yun Hao
- Flatiron Institute, Simons Foundation, New York, NY, USA;
| | - Jian Zhou
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas, USA;
| | - Olga G Troyanskaya
- Princeton Precision Health, Princeton University, Princeton, New Jersey, USA
- Flatiron Institute, Simons Foundation, New York, NY, USA;
- Department of Computer Science and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; , ,
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4
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Gorjifard S, Jores T, Tonnies J, Mueth NA, Bubb K, Wrightsman T, Buckler ES, Fields S, Cuperus JT, Queitsch C. Arabidopsis and maize terminator strength is determined by GC content, polyadenylation motifs and cleavage probability. Nat Commun 2024; 15:5868. [PMID: 38997252 PMCID: PMC11245536 DOI: 10.1038/s41467-024-50174-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.
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Affiliation(s)
- Sayeh Gorjifard
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Jackson Tonnies
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- Graduate Program in Biology, University of Washington, Seattle, WA, 98195, USA
| | - Nicholas A Mueth
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Kerry Bubb
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14853, USA
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY, 14853, USA
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY, 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, 14853, USA
| | - Stanley Fields
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
- Department of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.
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5
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Mendoza-Revilla J, Trop E, Gonzalez L, Roller M, Dalla-Torre H, de Almeida BP, Richard G, Caton J, Lopez Carranza N, Skwark M, Laterre A, Beguir K, Pierrot T, Lopez M. A foundational large language model for edible plant genomes. Commun Biol 2024; 7:835. [PMID: 38982288 PMCID: PMC11233511 DOI: 10.1038/s42003-024-06465-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/17/2024] [Indexed: 07/11/2024] Open
Abstract
Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b for future research purposes.
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Li T, Xu H, Teng S, Suo M, Bahitwa R, Xu M, Qian Y, Ramstein GP, Song B, Buckler ES, Wang H. Modeling 0.6 million genes for the rational design of functional cis-regulatory variants and de novo design of cis-regulatory sequences. Proc Natl Acad Sci U S A 2024; 121:e2319811121. [PMID: 38889146 PMCID: PMC11214048 DOI: 10.1073/pnas.2319811121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 05/14/2024] [Indexed: 06/20/2024] Open
Abstract
Rational design of plant cis-regulatory DNA sequences without expert intervention or prior domain knowledge is still a daunting task. Here, we developed PhytoExpr, a deep learning framework capable of predicting both mRNA abundance and plant species using the proximal regulatory sequence as the sole input. PhytoExpr was trained over 17 species representative of major clades of the plant kingdom to enhance its generalizability. Via input perturbation, quantitative functional annotation of the input sequence was achieved at single-nucleotide resolution, revealing an abundance of predicted high-impact nucleotides in conserved noncoding sequences and transcription factor binding sites. Evaluation of maize HapMap3 single-nucleotide polymorphisms (SNPs) by PhytoExpr demonstrates an enrichment of predicted high-impact SNPs in cis-eQTL. Additionally, we provided two algorithms that harnessed the power of PhytoExpr in designing functional cis-regulatory variants, and de novo creation of species-specific cis-regulatory sequences through in silico evolution of random DNA sequences. Our model represents a general and robust approach for functional variant discovery in population genetics and rational design of regulatory sequences for genome editing and synthetic biology.
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Affiliation(s)
- Tianyi Li
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Hui Xu
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Shouzhen Teng
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Mingrui Suo
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Revocatus Bahitwa
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
- Legumes Research Program, Research and Innovation Division, Tanzania Agricultural Research Institute, Ilonga, Kilosa, Morogoro67410, Tanzania
| | - Mingchi Xu
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Yiheng Qian
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
| | - Guillaume P. Ramstein
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus8000, Denmark
| | - Baoxing Song
- National Key Laboratory of Wheat Improvement, Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agriculture Sciences in Weifang, Weifang, Shandong261325, People’s Republic of China
- Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region of the Ministry of Agriculture, College of Agronomy, Northwest A&F University, Yangling, Shaanxi712100, People’s Republic of China
| | - Edward S. Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY14853
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY14853
| | - Hai Wang
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing100193, People’s Republic of China
- Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, Beijing100193, People’s Republic of China
- Sanya Institute of China Agricultural University, Sanya572025, People’s Republic of China
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7
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Wang Z, Peng Y, Li J, Li J, Yuan H, Yang S, Ding X, Xie A, Zhang J, Wang S, Li K, Shi J, Xing G, Shi W, Yan J, Liu J. DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions. PLANT COMMUNICATIONS 2024:100985. [PMID: 38859587 DOI: 10.1016/j.xplc.2024.100985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 05/25/2024] [Accepted: 06/05/2024] [Indexed: 06/12/2024]
Abstract
Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.
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Affiliation(s)
- Zhenye Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yong Peng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jie Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiying Li
- Microsoft Corporation, Redmond, WA 98052, USA
| | - Hao Yuan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Shangpo Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinru Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Ao Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiangling Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Shouzhe Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China; WIMI Biotechnology Co., Ltd., Changzhou 213000, China
| | - Keqin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiaqi Shi
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Guangjie Xing
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Weihan Shi
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Jianxiao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China; College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
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8
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Lam HYI, Ong XE, Mutwil M. Large language models in plant biology. TRENDS IN PLANT SCIENCE 2024:S1360-1385(24)00118-3. [PMID: 38797656 DOI: 10.1016/j.tplants.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024]
Abstract
Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.
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Affiliation(s)
- Hilbert Yuen In Lam
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Xing Er Ong
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore.
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9
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Kim J, Seok J. ctGAN: combined transformation of gene expression and survival data with generative adversarial network. Brief Bioinform 2024; 25:bbae325. [PMID: 38980369 PMCID: PMC11232285 DOI: 10.1093/bib/bbae325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/29/2024] [Accepted: 06/21/2024] [Indexed: 07/10/2024] Open
Abstract
Recent studies have extensively used deep learning algorithms to analyze gene expression to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival analysis studies on diseases with high mortality rates, such as cancer, are indispensable. However, deep learning models are plagued by overfitting owing to the limited sample size relative to the large number of genes. Consequently, the latest style-transfer deep generative models have been implemented to generate gene expression data. However, these models are limited in their applicability for clinical purposes because they generate only transcriptomic data. Therefore, this study proposes ctGAN, which enables the combined transformation of gene expression and survival data using a generative adversarial network (GAN). ctGAN improves survival analysis by augmenting data through style transformations between breast cancer and 11 other cancer types. We evaluated the concordance index (C-index) enhancements compared with previous models to demonstrate its superiority. Performance improvements were observed in nine of the 11 cancer types. Moreover, ctGAN outperformed previous models in seven out of the 11 cancer types, with colon adenocarcinoma (COAD) exhibiting the most significant improvement (median C-index increase of ~15.70%). Furthermore, integrating the generated COAD enhanced the log-rank p-value (0.041) compared with using only the real COAD (p-value = 0.797). Based on the data distribution, we demonstrated that the model generated highly plausible data. In clustering evaluation, ctGAN exhibited the highest performance in most cases (89.62%). These findings suggest that ctGAN can be meaningfully utilized to predict disease progression and select personalized treatments in the medical field.
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Affiliation(s)
- Jaeyoon Kim
- School of Electrical and Computer Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
| | - Junhee Seok
- School of Electrical and Computer Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
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10
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Wagner A. Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape. Bioinformatics 2024; 40:btae317. [PMID: 38745436 PMCID: PMC11132821 DOI: 10.1093/bioinformatics/btae317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/21/2024] [Accepted: 05/14/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Experimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitness needs to be measured for any one protein. Deep learning may help to predict the fitness of many genotypes from a smaller neural network training sample of genotypes with experimentally measured fitness. Here I use a recently published experimentally mapped fitness landscape of more than 260 000 protein genotypes to ask how such sampling is best performed. RESULTS I show that multilayer perceptrons, recurrent neural networks, convolutional networks, and transformers, can explain more than 90% of fitness variance in the data. In addition, 90% of this performance is reached with a training sample comprising merely ≈103 sequences. Generalization to unseen test data is best when training data is sampled randomly and uniformly, or sampled to minimize the number of synonymous sequences. In contrast, sampling to maximize sequence diversity or codon usage bias reduces performance substantially. These observations hold for more than one network architecture. Simple sampling strategies may perform best when training deep learning neural networks to map fitness landscapes from experimental data. AVAILABILITY AND IMPLEMENTATION The fitness landscape data analyzed here is publicly available as described previously (Papkou et al. 2023). All code used to analyze this landscape is publicly available at https://github.com/andreas-wagner-uzh/fitness_landscape_sampling.
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Affiliation(s)
- Andreas Wagner
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, 8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Genopode,1015 Lausanne, Switzerland
- The Santa Fe Institute, Santa Fe, 87501 NM, United States
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11
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Raoult V, Phillips AA, Nelson J, Niella Y, Skinner C, Tilcock MB, Burke PJ, Szpak P, James WR, Harrod C. Why aquatic scientists should use sulfur stable isotope ratios (ẟ 34S) more often. CHEMOSPHERE 2024; 355:141816. [PMID: 38556184 DOI: 10.1016/j.chemosphere.2024.141816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Over the last few decades, measurements of light stable isotope ratios have been increasingly used to answer questions across physiology, biology, ecology, and archaeology. The vast majority analyse carbon (δ13C) and nitrogen (δ15N) stable isotopes as the 'default' isotopes, omitting sulfur (δ34S) due to time, cost, or perceived lack of benefits and instrumentation capabilities. Using just carbon and nitrogen isotopic ratios can produce results that are inconclusive, uncertain, or in the worst cases, even misleading, especially for scientists that are new to the use and interpretation of stable isotope data. Using sulfur isotope values more regularly has the potential to mitigate these issues, especially given recent advancements that have lowered measurement barriers. Here we provide a review documenting case studies with real-world data, re-analysing different biological topics (i.e. niche, physiology, diet, movement and bioarchaeology) with and without sulfur isotopes to highlight the various strengths of this stable isotope for various applications. We also include a preliminary meta-analysis of the trophic discrimination factor (TDF) for sulfur isotopes, which suggest small (mean -0.4 ± 1.7 ‰ SD) but taxa-dependent mean trophic discrimination. Each case study demonstrates how the exclusion of sulfur comes at the detriment of the results, often leading to very different outputs, or missing valuable discoveries entirely. Given that studies relying on carbon and nitrogen stable isotopes currently underpin most of our understanding of various ecological processes, this has concerning implications. Collectively, these examples strongly suggest that researchers planning to use carbon and nitrogen stable isotopes for their research should incorporate sulfur where possible, and that the new 'default' isotope systems for aquatic science should now be carbon, nitrogen, and sulfur.
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Affiliation(s)
- Vincent Raoult
- Blue Carbon Lab, School of Life and Environmental Science, Deakin University, VIC, Australia; Marine Ecology Group, School of Natural Sciences, Macquarie University, NSW, Australia.
| | - Alexandra A Phillips
- National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, CA, USA
| | - James Nelson
- Department of Marine Science, University of Georgia, Athens, GA, USA
| | - Yuri Niella
- Marine Ecology Group, School of Natural Sciences, Macquarie University, NSW, Australia
| | - Christina Skinner
- Marine Spatial Ecology Lab, School of Biological Sciences, University of Queensland, QLD, Australia
| | | | - Patrick J Burke
- Marine Ecology Group, School of Natural Sciences, Macquarie University, NSW, Australia
| | - Paul Szpak
- Department of Anthropology, Trent University, Peterborough, Ontario, Canada
| | - W Ryan James
- Institute of Environment, Florida International University, Miami, FL, USA
| | - Chris Harrod
- Instituto Ciencias Naturales Alexander von Humboldt, Universidad de Antofagasta, Antofagasta, Chile; Millennium Nucleus INVASAL, Concepción, Chile; Universidad de Antofagasta Stable Isotope Facility, Instituto Antofagasta, Universidad de Antofagasta, Antofagasta, Chile
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12
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Takou M, Bellis ES, Lasky JR. Predicting gene expression responses to environment in Arabidopsis thaliana using natural variation in DNA sequence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.591174. [PMID: 38712066 PMCID: PMC11071634 DOI: 10.1101/2024.04.25.591174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The evolution of gene expression responses are a critical component of adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype to phenotype map is not well resolved for cis regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to environment. We tested if flexible machine learning models could learn some of the underlying cis- regulatory genotype to phenotype map. We tested this approach using cold-responsive transcriptome profiles in 5 diverse Arabidopsis thaliana accessions. We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and down-stream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis- regulatory motifs in DNA sequences to predict expression response to environment. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by biological complexity of regulation and the large potential regulatory code. Overall, DEGs between specific environments can be predicted based on variation in cis- regulatory sequences, although more information needs to be incorporated and better models may be required.
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13
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Ahmad A, Liew AXW, Venturini F, Kalogeras A, Candiani A, Di Benedetto G, Ajibola S, Cartujo P, Romero P, Lykoudi A, De Grandis MM, Xouris C, Lo Bianco R, Doddy I, Elegbede I, D'Urso Labate GF, García del Moral LF, Martos V. AI can empower agriculture for global food security: challenges and prospects in developing nations. Front Artif Intell 2024; 7:1328530. [PMID: 38726306 PMCID: PMC11081032 DOI: 10.3389/frai.2024.1328530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/11/2024] [Indexed: 05/12/2024] Open
Abstract
Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.
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Affiliation(s)
- Ali Ahmad
- Research Institute for Integrated Coastal Zone Management, Polytechnic University of Valencia, Grau de Gandia, Valencia, Spain
| | | | - Francesca Venturini
- Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Winterthur, Switzerland
- TOELT LLC, Dübendorf, Switzerland
| | | | | | | | - Segun Ajibola
- Afridat UG, Bonn, Germany
- NOVA IMS, Universidade Nova de Lisboa, Campus de Campolide, Lisbon, Portugal
| | - Pedro Cartujo
- Department of Electronic and Computer Technology, University of Granada, Granada, Spain
| | - Pablo Romero
- GRANIOT Satellite Technologies S.L, Granada, Spain
| | | | | | - Christos Xouris
- Gaia Robotics Idiotiki Kefalaiouxiki Etaireia, Patras, Greece
| | - Riccardo Lo Bianco
- Department of Agricultural, Food and Forest Sciences, University of Palermo, Viale delle Scienze, Palermo, Italy
| | - Irawan Doddy
- Department of Mechanical Engineering, Universitas Muhammadiyah Pontianak – Universitas, Kalimantan Barat, Indonesia
| | | | | | - Luis F. García del Moral
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
| | - Vanessa Martos
- Department of Plant Physiology, Institute of Biotechnology, University of Granada, Granada, Spain
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14
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Peleke FF, Zumkeller SM, Gültas M, Schmitt A, Szymański J. Deep learning the cis-regulatory code for gene expression in selected model plants. Nat Commun 2024; 15:3488. [PMID: 38664394 PMCID: PMC11045779 DOI: 10.1038/s41467-024-47744-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.
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Affiliation(s)
- Fritz Forbang Peleke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, D-06466 Seeland, OT, Gatersleben, Germany
| | - Simon Maria Zumkeller
- Institute of Bio- and Geosciences, IBG-4: Bioinformatics, Forschungszentrum Jülich, D-52428, Jülich, Germany
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-Universität Düsseldorf, 40225, Düsseldorf, Germany
| | - Mehmet Gültas
- Faculty of Agriculture, South Westphalia University of Applied Sciences, Soest, 59494, Germany
| | - Armin Schmitt
- Breeding Informatics Group, University of Göttingen, Göttingen, 37075, Germany
- Center of Integrated Breeding Research (CiBreed), Göttingen, 37075, Germany
| | - Jędrzej Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, D-06466 Seeland, OT, Gatersleben, Germany.
- Institute of Bio- and Geosciences, IBG-4: Bioinformatics, Forschungszentrum Jülich, D-52428, Jülich, Germany.
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-Universität Düsseldorf, 40225, Düsseldorf, Germany.
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15
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Gorjifard S, Jores T, Tonnies J, Mueth NA, Bubb K, Wrightsman T, Buckler ES, Fields S, Cuperus JT, Queitsch C. Arabidopsis and Maize Terminator Strength is Determined by GC Content, Polyadenylation Motifs and Cleavage Probability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.16.545379. [PMID: 37398426 PMCID: PMC10312805 DOI: 10.1101/2023.06.16.545379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
The 3' end of a gene, often called a terminator, modulates mRNA stability, localization, translation, and polyadenylation. Here, we adapted Plant STARR-seq, a massively parallel reporter assay, to measure the activity of over 50,000 terminators from the plants Arabidopsis thaliana and Zea mays. We characterize thousands of plant terminators, including many that outperform bacterial terminators commonly used in plants. Terminator activity is species-specific, differing in tobacco leaf and maize protoplast assays. While recapitulating known biology, our results reveal the relative contributions of polyadenylation motifs to terminator strength. We built a computational model to predict terminator strength and used it to conduct in silico evolution that generated optimized synthetic terminators. Additionally, we discover alternative polyadenylation sites across tens of thousands of terminators; however, the strongest terminators tend to have a dominant cleavage site. Our results establish features of plant terminator function and identify strong naturally occurring and synthetic terminators.
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Affiliation(s)
- Sayeh Gorjifard
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Jackson Tonnies
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
- Graduate Program in Biology, University of Washington, Seattle, WA 98195
| | - Nicholas A Mueth
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Kerry Bubb
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853
- Agricultural Research Service, United States Department of Agriculture, Ithaca, NY 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
| | - Stanley Fields
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
- Department of Medicine, University of Washington, Seattle, WA 98195
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
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16
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Zeng J, Song K, Wang J, Wen H, Zhou J, Ni T, Lu H, Yu Y. Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model. Microb Cell Fact 2024; 23:7. [PMID: 38172836 PMCID: PMC10763412 DOI: 10.1186/s12934-023-02271-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 12/12/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins. RESULTS We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels. CONCLUSION The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.
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Affiliation(s)
- Junyuan Zeng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Kunfeng Song
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Jingqi Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Haimei Wen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Jungang Zhou
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Hong Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China
| | - Yao Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China.
- Shanghai Engineering Research Center of Industrial Microorganisms, Shanghai, 200438, China.
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17
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Palande S, Kaste JAM, Roberts MD, Segura Abá K, Claucherty C, Dacon J, Doko R, Jayakody TB, Jeffery HR, Kelly N, Manousidaki A, Parks HM, Roggenkamp EM, Schumacher AM, Yang J, Percival S, Pardo J, Husbands AY, Krishnan A, Montgomery BL, Munch E, Thompson AM, Rougon-Cardoso A, Chitwood DH, VanBuren R. Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants. PLoS Biol 2023; 21:e3002397. [PMID: 38051702 PMCID: PMC10723737 DOI: 10.1371/journal.pbio.3002397] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 12/15/2023] [Accepted: 10/20/2023] [Indexed: 12/07/2023] Open
Abstract
Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.
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Affiliation(s)
- Sourabh Palande
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Joshua A. M. Kaste
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Miles D. Roberts
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Kenia Segura Abá
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Carly Claucherty
- Department of Plant, Soil & Microbial Sciences, Michigan State University, East Lansing, Michigan, United States of America
| | - Jamell Dacon
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Rei Doko
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Thilani B. Jayakody
- Department of Plant, Soil & Microbial Sciences, Michigan State University, East Lansing, Michigan, United States of America
| | - Hannah R. Jeffery
- Department of Plant, Soil & Microbial Sciences, Michigan State University, East Lansing, Michigan, United States of America
| | - Nathan Kelly
- Department of Horticulture, Michigan State University, East Lansing, Michigan, United States of America
| | - Andriana Manousidaki
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America
| | - Hannah M. Parks
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Emily M. Roggenkamp
- Department of Plant, Soil & Microbial Sciences, Michigan State University, East Lansing, Michigan, United States of America
| | - Ally M. Schumacher
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Jiaxin Yang
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Sarah Percival
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Jeremy Pardo
- Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Aman Y. Husbands
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Arjun Krishnan
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan, United States of America
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
| | - Beronda L Montgomery
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
- Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, Michigan, United States of America
- MSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, Michigan, United States of America
| | - Elizabeth Munch
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan, United States of America
- Department of Mathematics, Michigan State University, East Lansing, Michigan, United States of America
| | - Addie M. Thompson
- Department of Plant, Soil & Microbial Sciences, Michigan State University, East Lansing, Michigan, United States of America
- Plant Resilience Institute, Michigan State University, East Lansing, Michigan, United States of America
| | - Alejandra Rougon-Cardoso
- Laboratory of Agrigenomic Sciences, Universidad Nacional Autónoma de México, ENES-León, León, Mexico
- Laboratorio Nacional Plantecc, ENES-León, León, Mexico
| | - Daniel H. Chitwood
- Department of Computational Mathematics, Science & Engineering, Michigan State University, East Lansing, Michigan, United States of America
- Department of Horticulture, Michigan State University, East Lansing, Michigan, United States of America
| | - Robert VanBuren
- Department of Horticulture, Michigan State University, East Lansing, Michigan, United States of America
- Plant Resilience Institute, Michigan State University, East Lansing, Michigan, United States of America
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18
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Smet D, Opdebeeck H, Vandepoele K. Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice. FRONTIERS IN PLANT SCIENCE 2023; 14:1212073. [PMID: 37528982 PMCID: PMC10390317 DOI: 10.3389/fpls.2023.1212073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/16/2023] [Indexed: 08/03/2023]
Abstract
Plants have evolved various mechanisms to adapt to adverse environmental stresses, such as the modulation of gene expression. Expression of stress-responsive genes is controlled by specific regulators, including transcription factors (TFs), that bind to sequence-specific binding sites, representing key components of cis-regulatory elements and regulatory networks. Our understanding of the underlying regulatory code remains, however, incomplete. Recent studies have shown that, by training machine learning (ML) algorithms on genomic sequence features, it is possible to predict which genes will transcriptionally respond to a specific stress. By identifying the most important features for gene expression prediction, these trained ML models allow, in theory, to further elucidate the regulatory code underlying the transcriptional response to abiotic stress. Here, we trained random forest ML models to predict gene expression in rice (Oryza sativa) in response to heat or drought stress. Apart from thoroughly assessing model performance and robustness across various input training data, the importance of promoter and gene body sequence features to train ML models was evaluated. The use of enriched promoter oligomers, complementing known TF binding sites, allowed us to gain novel insights in DNA motifs contributing to the stress regulatory code. By comparing genomic feature importance scores for drought and heat stress over time, general and stress-specific genomic features contributing to the performance of the learned models and their temporal variation were identified. This study provides a solid foundation to build and interpret ML models accurately predicting transcriptional responses and enables novel insights in biological sequence features that are important for abiotic stress responses.
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Affiliation(s)
- Dajo Smet
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium
| | - Helder Opdebeeck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium
| | - Klaas Vandepoele
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- Center for Plant Systems Biology, Vlaams Instituut voor Biotechnologie (VIB), Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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19
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Mahdi-Esferizi R, Haji Molla Hoseyni B, Mehrpanah A, Golzade Y, Najafi A, Elahian F, Zadeh Shirazi A, Gomez GA, Tahmasebian S. DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues. BMC Bioinformatics 2023; 24:275. [PMID: 37403016 DOI: 10.1186/s12859-023-05400-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 06/25/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.
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Affiliation(s)
- Roohallah Mahdi-Esferizi
- Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | | | - Amir Mehrpanah
- Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran
| | - Yazdan Golzade
- Department of Mathematics, Faculty of Basic Sciences, Iran University of Science and Technology,(IUST), Tehran, Iran
| | - Ali Najafi
- Molecular Biology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Fatemeh Elahian
- Department of Medical Biotechnology, School of Advanced Technologies, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Amin Zadeh Shirazi
- Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, 5000, Australia
| | - Guillermo A Gomez
- Centre for Cancer Biology, SA Pathology and University of South Australia, Adelaide, SA, 5000, Australia
| | - Shahram Tahmasebian
- Cellular and Molecular Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, Iran.
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20
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Bagnolini G, Luu TB, Hargrove AE. Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA. RNA (NEW YORK, N.Y.) 2023; 29:473-488. [PMID: 36693763 PMCID: PMC10019373 DOI: 10.1261/rna.079497.122] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.
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Affiliation(s)
- Greta Bagnolini
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - TinTin B Luu
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
| | - Amanda E Hargrove
- Department of Chemistry, Duke University, Durham, North Carolina 27708, USA
- Department of Biochemistry, Duke University School of Medicine, Durham, North Carolina 27710, USA
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21
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Patterson A, Elbasir A, Tian B, Auslander N. Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications. Cancers (Basel) 2023; 15:cancers15071958. [PMID: 37046619 PMCID: PMC10093138 DOI: 10.3390/cancers15071958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.
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Affiliation(s)
- Andrew Patterson
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Bin Tian
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Noam Auslander
- The Wistar Institute, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
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22
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Depuydt T, De Rybel B, Vandepoele K. Charting plant gene functions in the multi-omics and single-cell era. TRENDS IN PLANT SCIENCE 2023; 28:283-296. [PMID: 36307271 DOI: 10.1016/j.tplants.2022.09.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/09/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.
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Affiliation(s)
- Thomas Depuydt
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium
| | - Bert De Rybel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium
| | - Klaas Vandepoele
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium; Ghent University, Bioinformatics Institute Ghent, Ghent, Belgium.
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23
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Yasmeen E, Wang J, Riaz M, Zhang L, Zuo K. Designing artificial synthetic promoters for accurate, smart, and versatile gene expression in plants. PLANT COMMUNICATIONS 2023:100558. [PMID: 36760129 PMCID: PMC10363483 DOI: 10.1016/j.xplc.2023.100558] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
With the development of high-throughput biology techniques and artificial intelligence, it has become increasingly feasible to design and construct artificial biological parts, modules, circuits, and even whole systems. To overcome the limitations of native promoters in controlling gene expression, artificial promoter design aims to synthesize short, inducible, and conditionally controlled promoters to coordinate the expression of multiple genes in diverse plant metabolic and signaling pathways. Synthetic promoters are versatile and can drive gene expression accurately with smart responses; they show potential for enhancing desirable traits in crops, thereby improving crop yield, nutritional quality, and food security. This review first illustrates the importance of synthetic promoters, then introduces promoter architecture and thoroughly summarizes advances in synthetic promoter construction. Restrictions to the development of synthetic promoters and future applications of such promoters in synthetic plant biology and crop improvement are also discussed.
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Affiliation(s)
- Erum Yasmeen
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Riaz
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lida Zhang
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Kaijing Zuo
- Single Cell Research Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China.
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24
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Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet 2023; 24:125-137. [PMID: 36192604 DOI: 10.1038/s41576-022-00532-2] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2022] [Indexed: 01/24/2023]
Abstract
Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.
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Affiliation(s)
- Gherman Novakovsky
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.,Bioinformatics Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nick Dexter
- Department of Mathematics, Simon Fraser University, Burnaby, British Columbia, Canada.,School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Maxwell W Libbrecht
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.
| | - Wyeth W Wasserman
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Sara Mostafavi
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA. .,Canadian Institute for Advanced Research, Toronto, Ontario, Canada.
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25
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Deep learning in regulatory genomics: from identification to design. Curr Opin Biotechnol 2023; 79:102887. [PMID: 36640453 DOI: 10.1016/j.copbio.2022.102887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/12/2022] [Accepted: 12/14/2022] [Indexed: 01/14/2023]
Abstract
Genomics and deep learning are a natural match since both are data-driven fields. Regulatory genomics refers to functional noncoding DNA regulating gene expression. In recent years, deep learning applications on regulatory genomics have achieved remarkable advances so-much-so that it has revolutionized the rules of the game of the computational methods in this field. Here, we review two emerging trends: (i) the modeling of very long input sequence (up to 200 kb), which requires self-matched modularization of model architecture; (ii) on the balance of model predictability and model interpretability because the latter is more able to meet biological demands. Finally, we discuss how to employ these two routes to design synthetic regulatory DNA, as a promising strategy for optimizing crop agronomic properties.
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26
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Jubair S, Domaratzki M. Crop genomic selection with deep learning and environmental data: A survey. Front Artif Intell 2023; 5:1040295. [PMID: 36703955 PMCID: PMC9871498 DOI: 10.3389/frai.2022.1040295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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Affiliation(s)
- Sheikh Jubair
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada,*Correspondence: Sheikh Jubair ✉
| | - Mike Domaratzki
- Department of Computer Science, University of Western Ontario, London, ON, Canada
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27
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Liang Z, Meng X, Schnable JC. A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species. Methods Mol Biol 2023; 2698:361-379. [PMID: 37682485 DOI: 10.1007/978-1-0716-3354-0_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predictor for the transcriptional responses of genes to stress. Machine learning methods can quantitatively estimate expression patterns and gene functions using known annotations and collections of features describing each gene. In this chapter, we describe a supervised machine learning framework to predict stress-responsive genes across species using only features derived from nucleotide sequences, using the example of cold stress-responsive genes in different Panicoid grass species.
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Affiliation(s)
- Zhikai Liang
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, USA
| | - Xiaoxi Meng
- Department of Horticultural Science, University of Minnesota, Saint Paul, MN, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE, USA.
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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28
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A survey on gene expression data analysis using deep learning methods for cancer diagnosis. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 177:1-13. [PMID: 35988771 DOI: 10.1016/j.pbiomolbio.2022.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 02/07/2023]
Abstract
Gene Expression Data is the biological data to extract meaningful hidden information from the gene dataset. This gene information is used for disease diagnosis especially in cancer treatment based on the variations in gene expression levels. DNA microarray is an efficient method for gene expression classification and prediction of cancer disease for specific types of cancer. Due to the abundance of computing power, deep learning (DL) has become a widespread technique in the healthcare sector. The gene expression dataset has a limited number of samples but a large number of features. Data augmentation is needed for gene expression datasets to overcome the dimensionality problem in gene data. It is a technique to generating the synthetic samples to increase the diversity of data. Deep learning methods are designed to learn and extract the features that come from the raw input data in the form of multidimensional arrays. This paper reviews the existing research in deep learning techniques like Feed Forward Neural Network (FFN), Convolutional Neural Network (CNN), Autoencoder (AE) and Recurrent Neural Network (RNN) for the classification and prediction of cancer disease and its types through gene expression data analysis.
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29
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Zrimec J, Zelezniak A, Gruden K. Toward learning the principles of plant gene regulation. TRENDS IN PLANT SCIENCE 2022; 27:1206-1208. [PMID: 36100536 DOI: 10.1016/j.tplants.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 08/09/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Advanced machine learning (ML) algorithms produce highly accurate models of gene expression, uncovering novel regulatory features in nucleotide sequences involving multiple cis-regulatory regions across whole genes and structural properties. These broaden our understanding of gene regulation and point to new principles to test and adopt in the field of plant science.
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Affiliation(s)
- Jan Zrimec
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia.
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, 412 96, Gothenburg, Sweden
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 111, 1000 Ljubljana, Slovenia.
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30
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Tan Z, Peng Y, Xiong Y, Xiong F, Zhang Y, Guo N, Tu Z, Zong Z, Wu X, Ye J, Xia C, Zhu T, Liu Y, Lou H, Liu D, Lu S, Yao X, Liu K, Snowdon RJ, Golicz AA, Xie W, Guo L, Zhao H. Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus. Genome Biol 2022; 23:233. [PMID: 36345039 PMCID: PMC9639296 DOI: 10.1186/s13059-022-02801-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/22/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus. RESULTS We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content. CONCLUSIONS We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.
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Affiliation(s)
- Zengdong Tan
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yan Peng
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yao Xiong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Feng Xiong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yuting Zhang
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Ning Guo
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Zhuo Tu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Zhanxiang Zong
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Xiaokun Wu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Jiang Ye
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Chunjiao Xia
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Tao Zhu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Yinmeng Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Hongxiang Lou
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Dongxu Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Shaoping Lu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Xuan Yao
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
| | - Kede Liu
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Rod J. Snowdon
- grid.8664.c0000 0001 2165 8627Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Agnieszka A. Golicz
- grid.8664.c0000 0001 2165 8627Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Weibo Xie
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Liang Guo
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China ,grid.35155.370000 0004 1790 4137Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, China ,grid.488316.00000 0004 4912 1102Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hu Zhao
- grid.35155.370000 0004 1790 4137National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China ,Hubei Hongshan Laboratory, Wuhan, China
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Naqvi RZ, Siddiqui HA, Mahmood MA, Najeebullah S, Ehsan A, Azhar M, Farooq M, Amin I, Asad S, Mukhtar Z, Mansoor S, Asif M. Smart breeding approaches in post-genomics era for developing climate-resilient food crops. FRONTIERS IN PLANT SCIENCE 2022; 13:972164. [PMID: 36186056 PMCID: PMC9523482 DOI: 10.3389/fpls.2022.972164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/15/2022] [Indexed: 06/16/2023]
Abstract
Improving the crop traits is highly required for the development of superior crop varieties to deal with climate change and the associated abiotic and biotic stress challenges. Climate change-driven global warming can trigger higher insect pest pressures and plant diseases thus affecting crop production sternly. The traits controlling genes for stress or disease tolerance are economically imperative in crop plants. In this scenario, the extensive exploration of available wild, resistant or susceptible germplasms and unraveling the genetic diversity remains vital for breeding programs. The dawn of next-generation sequencing technologies and omics approaches has accelerated plant breeding by providing the genome sequences and transcriptomes of several plants. The availability of decoded plant genomes offers an opportunity at a glance to identify candidate genes, quantitative trait loci (QTLs), molecular markers, and genome-wide association studies that can potentially aid in high throughput marker-assisted breeding. In recent years genomics is coupled with marker-assisted breeding to unravel the mechanisms to harness better better crop yield and quality. In this review, we discuss the aspects of marker-assisted breeding and recent perspectives of breeding approaches in the era of genomics, bioinformatics, high-tech phonemics, genome editing, and new plant breeding technologies for crop improvement. In nutshell, the smart breeding toolkit in the post-genomics era can steadily help in developing climate-smart future food crops.
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32
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Wrightsman T, Marand AP, Crisp PA, Springer NM, Buckler ES. Modeling chromatin state from sequence across angiosperms using recurrent convolutional neural networks. THE PLANT GENOME 2022; 15:e20249. [PMID: 35924336 DOI: 10.1002/tpg2.20249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 06/20/2022] [Indexed: 06/06/2024]
Abstract
Accessible chromatin regions are critical components of gene regulation but modeling them directly from sequence remains challenging, especially within plants, whose mechanisms of chromatin remodeling are less understood than in animals. We trained an existing deep-learning architecture, DanQ, on data from 12 angiosperm species to predict the chromatin accessibility in leaf of sequence windows within and across species. We also trained DanQ on DNA methylation data from 10 angiosperms because unmethylated regions have been shown to overlap significantly with ACRs in some plants. The across-species models have comparable or even superior performance to a model trained within species, suggesting strong conservation of chromatin mechanisms across angiosperms. Testing a maize (Zea mays L.) held-out model on a multi-tissue chromatin accessibility panel revealed our models are best at predicting constitutively accessible chromatin regions, with diminishing performance as cell-type specificity increases. Using a combination of interpretation methods, we ranked JASPAR motifs by their importance to each model and saw that the TCP and AP2/ERF transcription factor (TF) families consistently ranked highly. We embedded the top three JASPAR motifs for each model at all possible positions on both strands in our sequence window and observed position- and strand-specific patterns in their importance to the model. With our publicly available across-species 'a2z' model it is now feasible to predict the chromatin accessibility and methylation landscape of any angiosperm genome.
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Affiliation(s)
- Travis Wrightsman
- Section of Plant Breeding and Genetics, Cornell Univ., Ithaca, NY, 14853, USA
| | | | - Peter A Crisp
- School of Agriculture and Food Sciences, Univ. of Queensland, Brisbane, QLD, 4072, Australia
| | - Nathan M Springer
- Dep. of Plant and Microbial Biology, Univ. of Minnesota, Saint Paul, MN, 55108, USA
| | - Edward S Buckler
- Section of Plant Breeding and Genetics, Cornell Univ., Ithaca, NY, 14853, USA
- Institute for Genomic Diversity, Cornell Univ., Ithaca, NY, 14853, USA
- USDA-ARS, Ithaca, NY, 14853, USA
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33
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Guo AJX, Qi H. Using Artificial Neural Networks to Model Errors in Biochemical Manipulation of DNA Molecules. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3060-3067. [PMID: 34115591 DOI: 10.1109/tcbb.2021.3088525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In recent years, the non-biological applications of DNA molecules have made considerable progress; most of these applications were performed in vitro, involving biochemical operations such as synthesis, amplification and sequencing. Because errors may occur with specific sequence patterns or experimental instruments, these biochemical operations are not completely reliable. Modeling errors in these biochemical procedures is an interesting research topic. For example, researchers have proposed several methods to avoid the known vulnerable sequence patterns in the study of storing binary information in DNA molecules. However, there are few end-to-end methods to evaluate these biochemical errors with regard to the DNA sequences. In this article, based on the data generated by a DNA storage research, we use artificial neural networks to predict whether a DNA sequence tends to cause errors in biochemical operations. Through comparative experiments and hyperparameter optimization, we analyze the known and potential problems in the research process. As a result, an end-to-end method to model the biochemical errors of DNA molecules in vitro through a computer system is proposed.
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34
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Kang Y, Jung WJ, Brent MR. Predicting which genes will respond to transcription factor perturbations. G3 (BETHESDA, MD.) 2022; 12:jkac144. [PMID: 35666184 PMCID: PMC9339286 DOI: 10.1093/g3journal/jkac144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022]
Abstract
The ability to predict which genes will respond to the perturbation of a transcription factor serves as a benchmark for our systems-level understanding of transcriptional regulatory networks. In previous work, machine learning models have been trained to predict static gene expression levels in a biological sample by using data from the same or similar samples, including data on their transcription factor binding locations, histone marks, or DNA sequence. We report on a different challenge-training machine learning models to predict which genes will respond to the perturbation of a transcription factor without using any data from the perturbed cells. We find that existing transcription factor location data (ChIP-seq) from human cells have very little detectable utility for predicting which genes will respond to perturbation of a transcription factor. Features of genes, including their preperturbation expression level and expression variation, are very useful for predicting responses to perturbation of any transcription factor. This shows that some genes are poised to respond to transcription factor perturbations and others are resistant, shedding light on why it has been so difficult to predict responses from binding locations. Certain histone marks, including H3K4me1 and H3K4me3, have some predictive power when located downstream of the transcription start site. However, the predictive power of histone marks is much less than that of gene expression level and expression variation. Sequence-based or epigenetic properties of genes strongly influence their tendency to respond to direct transcription factor perturbations, partially explaining the oft-noted difficulty of predicting responsiveness from transcription factor binding location data. These molecular features are largely reflected in and summarized by the gene's expression level and expression variation. Code is available at https://github.com/BrentLab/TFPertRespExplainer.
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Affiliation(s)
- Yiming Kang
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
| | - Wooseok J Jung
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Computer Science and Engineering, Washington University, St. Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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35
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Cho KT, Sen TZ, Andorf CM. Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach. Front Artif Intell 2022; 5:830170. [PMID: 35719692 PMCID: PMC9204276 DOI: 10.3389/frai.2022.830170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.
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Affiliation(s)
- Kyoung Tak Cho
- Department of Computer Science, Iowa State University, Ames, IA, United States
| | - Taner Z. Sen
- USDA-ARS, Crop Improvement and Genetics Research Unit, Albany, CA, United States
| | - Carson M. Andorf
- USDA-ARS, Corn Insects and Crop Genetics Research Unit, Ames, IA, United States
- *Correspondence: Carson M. Andorf
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36
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Akagi T, Masuda K, Kuwada E, Takeshita K, Kawakatsu T, Ariizumi T, Kubo Y, Ushijima K, Uchida S. Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning. THE PLANT CELL 2022; 34:2174-2187. [PMID: 35258588 PMCID: PMC9134063 DOI: 10.1093/plcell/koac079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/20/2022] [Indexed: 06/14/2023]
Abstract
In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expression behaviors from CRE patterns to properly harness them, mainly because the biological processes are complex. In this study, we used cistrome datasets and explainable convolutional neural network (CNN) frameworks to predict genome-wide expression patterns in tomato (Solanum lycopersicum) fruit from the DNA sequences in gene regulatory regions. By fixing the effects of trans-acting factors using single cell-type spatiotemporal transcriptome data for the response variables, we developed a prediction model for crucial expression patterns in the initiation of tomato fruit ripening. Feature visualization of the CNNs identified nucleotide residues critical to the objective expression pattern in each gene, and their effects were validated experimentally in ripening tomato fruit. This cis-decoding framework will not only contribute to the understanding of the regulatory networks derived from CREs and transcription factor interactions, but also provides a flexible means of designing alleles for optimized expression.
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Affiliation(s)
| | | | | | | | - Taiji Kawakatsu
- Institute of Agrobiological Sciences, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8602, Japan
| | - Tohru Ariizumi
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba Plant Innovation Research Center, Tsukuba, Japan
| | - Yasutaka Kubo
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan
| | - Koichiro Ushijima
- Graduate School of Environmental and Life Science, Okayama University, Okayama 700-8530, Japan
| | - Seiichi Uchida
- Department of Advanced Information Technology, Kyushu University, Fukuoka 819-0395, Japan
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37
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Hesami M, Alizadeh M, Jones AMP, Torkamaneh D. Machine learning: its challenges and opportunities in plant system biology. Appl Microbiol Biotechnol 2022; 106:3507-3530. [PMID: 35575915 DOI: 10.1007/s00253-022-11963-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/14/2022] [Accepted: 05/07/2022] [Indexed: 12/25/2022]
Abstract
Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.
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Affiliation(s)
- Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Milad Alizadeh
- Department of Botany, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | | | - Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, G1V 0A6, Canada. .,Institut de Biologie Intégrative Et Des Systèmes (IBIS), Université Laval, Québec City, QC, G1V 0A6, Canada.
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38
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Gage JL, Mali S, McLoughlin F, Khaipho-Burch M, Monier B, Bailey-Serres J, Vierstra RD, Buckler ES. Variation in upstream open reading frames contributes to allelic diversity in maize protein abundance. Proc Natl Acad Sci U S A 2022; 119:e2112516119. [PMID: 35349347 PMCID: PMC9169109 DOI: 10.1073/pnas.2112516119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 02/22/2022] [Indexed: 11/18/2022] Open
Abstract
SignificanceProteins are the machinery which execute essential cellular functions. However, measuring their abundance within an organism can be difficult and resource-intensive. Cells use a variety of mechanisms to control protein synthesis from mRNA, including short open reading frames (uORFs) that lie upstream of the main coding sequence. Ribosomes can preferentially translate uORFs instead of the main coding sequence, leading to reduced translation of the main protein. In this study, we show that uORF sequence variation between individuals can lead to different rates of protein translation and thus variable protein abundances. We also demonstrate that natural variation in uORFs occurs frequently and can be linked to whole-plant phenotypes, indicating that uORF sequence variation likely contributes to plant adaptation.
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Affiliation(s)
- Joseph L. Gage
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695
| | - Sujina Mali
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130
| | - Fionn McLoughlin
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130
| | - Merritt Khaipho-Burch
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
| | - Brandon Monier
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
| | - Julia Bailey-Serres
- Department of Botany and Plant Sciences, Center for Plant Cell Biology, University of California, Riverside, CA 92521
| | - Richard D. Vierstra
- Department of Biology, Washington University in St. Louis, St. Louis, MO 63130
| | - Edward S. Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853
- Agricultural Research Service, US Department of Agriculture, Ithaca, NY 14853
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Liu S, Cheng H, Ashraf J, Zhang Y, Wang Q, Lv L, He M, Song G, Zuo D. Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development. BMC Bioinformatics 2022; 23:91. [PMID: 35291940 PMCID: PMC8922751 DOI: 10.1186/s12859-022-04619-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 02/22/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Upland cotton provides the most natural fiber in the world. During fiber development, the quality and yield of fiber were influenced by gene transcription. Revealing sequence features related to transcription has a profound impact on cotton molecular breeding. We applied convolutional neural networks to predict gene expression status based on the sequences of gene transcription start regions. After that, a gradient-based interpretation and an N-adjusted kernel transformation were implemented to extract sequence features contributing to transcription. RESULTS Our models had approximate 80% accuracies, and the area under the receiver operating characteristic curve reached over 0.85. Gradient-based interpretation revealed 5' untranslated region contributed to gene transcription. Furthermore, 6 DOF binding motifs and 4 transcription activator binding motifs were obtained by N-adjusted kernel-motif transformation from models in three developmental stages. Apart from 10 general motifs, 3 DOF5.1 genes were also detected. In silico analysis about these motifs' binding proteins implied their potential functions in fiber formation. Besides, we also found some novel motifs in plants as important sequence features for transcription. CONCLUSIONS In conclusion, the N-adjusted kernel transformation method could interpret convolutional neural networks and reveal important sequence features related to transcription during fiber development. Potential functions of motifs interpreted from convolutional neural networks could be validated by further wet-lab experiments and applied in cotton molecular breeding.
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Affiliation(s)
- Shang Liu
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Hailiang Cheng
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Javaria Ashraf
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.,Department of Plant Breeding and Genetics, University College of Agriculture and Environmental Sciences, The Islamia University of Bahawalpur, Punjab, 63100, Pakistan
| | - Youping Zhang
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Qiaolian Wang
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Limin Lv
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China.,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China
| | - Man He
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China
| | - Guoli Song
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China. .,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China.
| | - Dongyun Zuo
- Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang, 455000, China. .,Zhengzhou Research Base, State Key Laboratory of Cotton Biology, Zhengzhou University, Zhengzhou, 450001, China.
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40
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Zhou P, Enders TA, Myers ZA, Magnusson E, Crisp PA, Noshay JM, Gomez-Cano F, Liang Z, Grotewold E, Greenham K, Springer NM. Prediction of conserved and variable heat and cold stress response in maize using cis-regulatory information. THE PLANT CELL 2022; 34:514-534. [PMID: 34735005 PMCID: PMC8773969 DOI: 10.1093/plcell/koab267] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/27/2021] [Indexed: 05/04/2023]
Abstract
Changes in gene expression are important for responses to abiotic stress. Transcriptome profiling of heat- or cold-stressed maize genotypes identifies many changes in transcript abundance. We used comparisons of expression responses in multiple genotypes to identify alleles with variable responses to heat or cold stress and to distinguish examples of cis- or trans-regulatory variation for stress-responsive expression changes. We used motifs enriched near the transcription start sites (TSSs) for thermal stress-responsive genes to develop predictive models of gene expression responses. Prediction accuracies can be improved by focusing only on motifs within unmethylated regions near the TSS and vary for genes with different dynamic responses to stress. Models trained on expression responses in a single genotype and promoter sequences provided lower performance when applied to other genotypes but this could be improved by using models trained on data from all three genotypes tested. The analysis of genes with cis-regulatory variation provides evidence for structural variants that result in presence/absence of transcription factor binding sites in creating variable responses. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and defines a framework for developing models to predict expression responses across multiple genotypes.
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Affiliation(s)
- Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Tara A Enders
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Zachary A Myers
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Erika Magnusson
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Peter A Crisp
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
- School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jaclyn M Noshay
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Fabio Gomez-Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
| | - Zhikai Liang
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
| | - Kathleen Greenham
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, Minnesota 55108, USA
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41
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Eslami M, Borujeni AE, Eramian H, Weston M, Zheng G, Urrutia J, Corbet C, Becker D, Maschhoff P, Clowers K, Cristofaro A, Hosseini HD, Gordon DB, Dorfan Y, Singer J, Vaughn M, Gaffney N, Fonner J, Stubbs J, Voigt CA, Yeung E. Prediction of whole-cell transcriptional response with machine learning. Bioinformatics 2022; 38:404-409. [PMID: 34570169 DOI: 10.1093/bioinformatics/btab676] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/29/2021] [Accepted: 09/22/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. RESULTS The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene's dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. AVAILABILITY AND IMPLEMENTATION The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Amin Espah Borujeni
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hamed Eramian
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - Mark Weston
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - George Zheng
- Data Science, Netrias, LLC, Annapolis, MD 21409, USA
| | - Joshua Urrutia
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | | | | | | | | | - Alexander Cristofaro
- TScan Therapeutics, Inc., Waltham, MA 02451, USA.,Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hamid Doost Hosseini
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - D Benjamin Gordon
- Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuval Dorfan
- Foundry for Synthetic Biology, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Matthew Vaughn
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Niall Gaffney
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - John Fonner
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Joe Stubbs
- Life Sciences and Computing, Texas Advanced Computing Center, Austin, TX 78758, USA
| | - Christopher A Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Enoch Yeung
- Bioengineering Center, University of California Santa Barbara, Santa Barbara, CA 93106, USA
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42
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Wang J, Xu J, Yang X, Xu S, Zhang M, Lu F. Boosting the power of transcriptomics by developing an efficient gene expression profiling approach. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:201-210. [PMID: 34510693 PMCID: PMC8710826 DOI: 10.1111/pbi.13706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/09/2021] [Accepted: 08/29/2021] [Indexed: 06/13/2023]
Abstract
Recent advances in plant genomics are scaling up gene expression profiling from the individual level to the population level, making transcriptomics a more powerful tool while deciphering the genome function. This study developed an efficient 3'RNA-seq method, Simplified Poly(A) Anchored Sequencing (SiPAS), to perform large-scale experiments of gene expression quantification. Aside from being cost-effective, by conducting a comprehensive performance assessment of SiPAS in hexaploid wheat, we demonstrated that SiPAS is highly sensitive, accurate, and reproducible while quantifying gene expression. Our method is anticipated to boost studies of population transcriptomics in plants and improve our understanding of genome biology.
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Affiliation(s)
- Jing Wang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijingChina
| | - Jun Xu
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xiaohan Yang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Song Xu
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Ming Zhang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Fei Lu
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
- CAS‐JIC Centre of Excellence for Plant and Microbial Science (CEPAMS)Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina
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43
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Zhu Y, Gu Q, Zhao Y, Wan H, Wang R, Zhang X, Cheng Y. Quantitative Extraction and Evaluation of Tomato Fruit Phenotypes Based on Image Recognition. FRONTIERS IN PLANT SCIENCE 2022; 13:859290. [PMID: 35498696 PMCID: PMC9044966 DOI: 10.3389/fpls.2022.859290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 02/28/2022] [Indexed: 05/08/2023]
Abstract
Tomato fruit phenotypes are important agronomic traits in tomato breeding as a reference index. The traditional measurement methods based on manual observation, however, limit the high-throughput data collection of tomato fruit morphologies. In this study, fruits of 10 different tomato cultivars with considerable differences in fruit color, size, and other morphological characters were selected as samples. Constant illumination condition was applied to take images of the selected tomato fruit samples. Based on image recognition, automated methods for measuring color and size indicators of tomato fruit phenotypes were proposed. A deep learning model based on Mask Region-Convolutional Neural Network (R-CNN) was trained and tested to analyze the internal structure indicators of tomato fruit. The results revealed that the combined use of these methods can extract various important fruit phenotypes of tomato, including fruit color, horizontal and vertical diameters, top and navel angles, locule number, and pericarp thickness, automatically. Considering several corrections of missing and wrong segmentation cases in practice, the average precision of the deep learning model is more than 0.95 in practice. This suggests a promising locule segmentation and counting performance. Vertical/horizontal ratio (fruit shape index) and locule area proportion were also calculated based on the data collected here. The measurement precision was comparable to manual operation, and the measurement efficiency was highly improved. The results of this study will provide a new option for more accurate and efficient tomato fruit phenotyping, which can effectively avoid artificial error and increase the support efficiency of relevant data in the future breeding work of tomato and other fruit crops.
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Affiliation(s)
- Yihang Zhu
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Qing Gu
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yiying Zhao
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Hongjian Wan
- State Key Laboratory for Quality and Safety of Agro-Products, Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Rongqing Wang
- State Key Laboratory for Quality and Safety of Agro-Products, Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xiaobin Zhang
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- *Correspondence: Xiaobin Zhang,
| | - Yuan Cheng
- State Key Laboratory for Quality and Safety of Agro-Products, Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- Yuan Cheng,
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44
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Gardiner LJ, Krishna R. Bluster or Lustre: Can AI Improve Crops and Plant Health? PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10122707. [PMID: 34961177 PMCID: PMC8707749 DOI: 10.3390/plants10122707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/24/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question-Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.
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Abraham-Juárez MJ, Barnes AC, Aragón-Raygoza A, Tyson D, Kur A, Strable J, Rellán-Álvarez R. The arches and spandrels of maize domestication, adaptation, and improvement. CURRENT OPINION IN PLANT BIOLOGY 2021; 64:102124. [PMID: 34715472 DOI: 10.1016/j.pbi.2021.102124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 09/20/2021] [Accepted: 09/21/2021] [Indexed: 06/13/2023]
Abstract
People living in the Balsas River basin in southwest México domesticated maize from the bushy grass teosinte. Nine thousand years later, in 2021, Ms. Deb Haaland - a member of the Pueblo of Laguna tribe of New Mexico - wore a dress adorned with a cornstalk when she was sworn in as the Secretary of Interior of the United States of America. This choice of garment highlights the importance of the coevolution of maize and the farmers who, through careful selection over thousands of years, domesticated maize and adapted the physiology and shoot architecture of maize to fit local environments and growth habits. Some traits such as tillering were directly selected on (arches), and others such as tassel size are the by-products (spandrels) of maize evolution. Here, we review current knowledge of the underlying cellular, developmental, physiological, and metabolic processes that were selected by farmers and breeders, which have positioned maize as a top global staple crop.
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Affiliation(s)
- María Jazmín Abraham-Juárez
- Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Irapuato, 36821, Mexico
| | - Allison C Barnes
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alejandro Aragón-Raygoza
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA; Unidad de Genómica Avanzada, Cinvestav Sede Irapuato, Km. 9.6 Libramiento Norte Carretera Irapuato-León, Guanajuato, Mexico
| | - Destiny Tyson
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC, 27695, USA
| | - Andi Kur
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Josh Strable
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Rubén Rellán-Álvarez
- Department of Molecular and Structural Biochemistry, North Carolina State University, Raleigh, NC, 27695, USA.
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Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 2021; 38:1729-1759. [PMID: 34668509 DOI: 10.1039/d1np00014d] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Amit Rai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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Haplotype associated RNA expression (HARE) improves prediction of complex traits in maize. PLoS Genet 2021; 17:e1009568. [PMID: 34606492 PMCID: PMC8516254 DOI: 10.1371/journal.pgen.1009568] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 10/14/2021] [Accepted: 09/07/2021] [Indexed: 11/19/2022] Open
Abstract
Genomic prediction typically relies on associations between single-site polymorphisms and traits of interest. This representation of genomic variability has been successful for predicting many complex traits. However, it usually cannot capture the combination of alleles in haplotypes and it has generated little insight about the biological function of polymorphisms. Here we present a novel and cost-effective method for imputing cis haplotype associated RNA expression (HARE), studied their transferability across tissues, and evaluated genomic prediction models within and across populations. HARE focuses on tightly linked cis acting causal variants in the immediate vicinity of the gene, while excluding trans effects from diffusion and metabolism. Therefore, HARE estimates were more transferrable across different tissues and populations compared to measured transcript expression. We also showed that HARE estimates captured one-third of the variation in gene expression. HARE estimates were used in genomic prediction models evaluated within and across two diverse maize panels–a diverse association panel (Goodman Association panel) and a large half-sib panel (Nested Association Mapping panel)–for predicting 26 complex traits. HARE resulted in up to 15% higher prediction accuracy than control approaches that preserved haplotype structure, suggesting that HARE carried functional information in addition to information about haplotype structure. The largest increase was observed when the model was trained in the Nested Association Mapping panel and tested in the Goodman Association panel. Additionally, HARE yielded higher within-population prediction accuracy as compared to measured expression values. The accuracy achieved by measured expression was variable across tissues, whereas accuracy by HARE was more stable across tissues. Therefore, imputing RNA expression of genes by haplotype is stable, cost-effective, and transferable across populations. Genomic marker data is widely used in the prediction of many traits. However, prediction has been primarily carried out within populations and without explicit modeling of RNA or protein expression. In this study, we explored the prediction of field traits within and across populations using estimated RNA expression attributable to only the DNA sequence around a gene. We showed that the estimated RNA expression was more transferable across populations and tissues than measured RNA expression. We improved prediction of field traits up to 15% using estimated gene expression as compared to observed expression or gene sequence alone. Overall, these findings indicate that structural and functional information in the gene sequence is highly transferable.
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Zemlyanskaya EV, Dolgikh VA, Levitsky VG, Mironova V. Transcriptional regulation in plants: Using omics data to crack the cis-regulatory code. CURRENT OPINION IN PLANT BIOLOGY 2021; 63:102058. [PMID: 34098218 DOI: 10.1016/j.pbi.2021.102058] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 06/12/2023]
Abstract
Innovative omics technologies, advanced bioinformatics, and machine learning methods are rapidly becoming integral tools for plant functional genomics, with tremendous recent advances made in this field. In transcriptional regulation, an initial lag in the accumulation of plant omics data relative to that of animals stimulated the development of computational methods capable of extracting maximum information from the available data sets. Recent comprehensive studies of transcription factor-binding profiles in Arabidopsis and maize and the accumulation of uniformly processed omics data in public databases have brought plant biologists into the big leagues, with many cutting-edge methods available. Here, we summarize the state-of-the-art bioinformatics approaches used to predict or infer the cis-regulatory code behind transcriptional gene regulation, focusing on their plant research applications.
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Affiliation(s)
- Elena V Zemlyanskaya
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090, Russia; Novosibirsk State University, Novosibirsk, 630090, Russia.
| | - Vladislav A Dolgikh
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090, Russia
| | - Victor G Levitsky
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090, Russia; Novosibirsk State University, Novosibirsk, 630090, Russia
| | - Victoria Mironova
- Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090, Russia; Novosibirsk State University, Novosibirsk, 630090, Russia; Department of Plant Systems Physiology, Institute for Water and Wetland Research, Radboud University, Heyendaalseweg 135, 6525, AJ Nijmegen, the Netherlands.
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Wang Y, Zhang P, Guo W, Liu H, Li X, Zhang Q, Du Z, Hu G, Han X, Pu L, Tian J, Gu X. A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants. THE NEW PHYTOLOGIST 2021; 232:880-897. [PMID: 34287908 DOI: 10.1111/nph.17630] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the scientific utility of epigenomic datasets generated for plants. Here, using deep-learning approaches, we developed a Smart Model for Epigenetics in Plants (SMEP) to predict six types of epigenomic modifications: DNA 5-methylcytosine (5mC) and N6-methyladenosine (6mA) methylation, RNA N6-methyladenosine (m6 A) methylation, and three types of histone modification. Using the datasets from the japonica rice Nipponbare, SMEP achieved 95% prediction accuracy for 6mA, and also achieved around 80% for 5mC, m6 A, and the three types of histone modification based on the 10-fold cross-validation. Additionally, > 95% of the 6mA peaks detected after a heat-shock treatment were predicted. We also successfully applied the SMEP for examining epigenomic modifications in indica rice 93-11 and even the B73 maize line. Taken together, we show that the deep-learning-enabled SMEP can reliably mine epigenomic datasets from diverse plants to yield actionable insights about epigenomic sites. Thus, our work opens new avenues for the application of predictive tools to facilitate functional research, and will almost certainly increase the efficiency of genome engineering efforts.
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Affiliation(s)
- Yifan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Pingxian Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Weijun Guo
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Hanqing Liu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiulan Li
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Qian Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhuoying Du
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Guihua Hu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiao Han
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - Li Pu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jian Tian
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiaofeng Gu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
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Lecca P. Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge. FRONTIERS IN BIOINFORMATICS 2021; 1:746712. [PMID: 36303798 PMCID: PMC9581010 DOI: 10.3389/fbinf.2021.746712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/08/2021] [Indexed: 11/13/2022] Open
Abstract
Most machine learning-based methods predict outcomes rather than understanding causality. Machine learning methods have been proved to be efficient in finding correlations in data, but unskilful to determine causation. This issue severely limits the applicability of machine learning methods to infer the causal relationships between the entities of a biological network, and more in general of any dynamical system, such as medical intervention strategies and clinical outcomes system, that is representable as a network. From the perspective of those who want to use the results of network inference not only to understand the mechanisms underlying the dynamics, but also to understand how the network reacts to external stimuli (e. g. environmental factors, therapeutic treatments), tools that can understand the causal relationships between data are highly demanded. Given the increasing popularity of machine learning techniques in computational biology and the recent literature proposing the use of machine learning techniques for the inference of biological networks, we would like to present the challenges that mathematics and computer science research faces in generalising machine learning to an approach capable of understanding causal relationships, and the prospects that achieving this will open up for the medical application domains of systems biology, the main paradigm of which is precisely network biology at any physical scale.
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Affiliation(s)
- Paola Lecca
- Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani, Bolzano, Italy
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