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Raimondi D, Passemiers A, Verplaetse N, Corso M, Ferrero-Serrano Á, Nazzicari N, Biscarini F, Fariselli P, Moreau Y. Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana. Sci Rep 2024; 14:13188. [PMID: 38851759 PMCID: PMC11162433 DOI: 10.1038/s41598-024-63855-6] [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: 01/17/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024] Open
Abstract
Genome interpretation (GI) encompasses the computational attempts to model the relationship between genotype and phenotype with the goal of understanding how the first leads to the second. While traditional approaches have focused on sub-problems such as predicting the effect of single nucleotide variants or finding genetic associations, recent advances in neural networks (NNs) have made it possible to develop end-to-end GI models that take genomic data as input and predict phenotypes as output. However, technical and modeling issues still need to be fixed for these models to be effective, including the widespread underdetermination of genomic datasets, making them unsuitable for training large, overfitting-prone, NNs. Here we propose novel GI models to address this issue, exploring the use of two types of transfer learning approaches and proposing a novel Biologically Meaningful Sparse NN layer specifically designed for end-to-end GI. Our models predict the leaf and seed ionome in A.thaliana, obtaining comparable results to our previous over-parameterized model while reducing the number of parameters by 8.8 folds. We also investigate how the effect of population stratification influences the evaluation of the performances, highlighting how it leads to (1) an instance of the Simpson's Paradox, and (2) model generalization limitations.
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Affiliation(s)
| | | | | | - Massimiliano Corso
- Université Paris-Saclay, INRAE, AgroParisTech, Institute Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000, Versailles, France
| | - Ángel Ferrero-Serrano
- Department of Biology, Pennsylvania State University, University Park, PA, 16802, USA
| | | | | | - Piero Fariselli
- Department of Medical Sciences, University of Torino, 10123, Turin, Italy
| | - Yves Moreau
- ESAT-STADIUS, KU Leuven, 3001, Leuven, Belgium
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Barreda L, Brosse C, Boutet S, Perreau F, Rajjou L, Lepiniec L, Corso M. Specialized metabolite modifications in Brassicaceae seeds and plants: diversity, functions and related enzymes. Nat Prod Rep 2024; 41:834-859. [PMID: 38323463 DOI: 10.1039/d3np00043e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Covering: up to 2023Specialized metabolite (SM) modifications and/or decorations, corresponding to the addition or removal of functional groups (e.g. hydroxyl, methyl, glycosyl or acyl group) to SM structures, contribute to the huge diversity of structures, activities and functions of seed and plant SMs. This review summarizes available knowledge (up to 2023) on SM modifications in Brassicaceae and their contribution to SM plasticity. We give a comprehensive overview on enzymes involved in the addition or removal of these functional groups. Brassicaceae, including model (Arabidopsis thaliana) and crop (Brassica napus, Camelina sativa) plant species, present a large diversity of plant and seed SMs, which makes them valuable models to study SM modifications. In this review, particular attention is given to the environmental plasticity of SM and relative modification and/or decoration enzymes. Furthermore, a spotlight is given to SMs and related modification enzymes in seeds of Brassicaceae species. Seeds constitute a large reservoir of beneficial SMs and are one of the most important dietary sources, providing more than half of the world's intake of dietary proteins, oil and starch. The seed tissue- and stage-specific expressions of A. thaliana genes involved in SM modification are presented and discussed in the context of available literature. Given the major role in plant phytochemistry, biology and ecology, SM modifications constitute a subject of study contributing to the research and development in agroecology, pharmaceutical, cosmetics and food industrial sectors.
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Affiliation(s)
- Léa Barreda
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
| | - Céline Brosse
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
| | - Stéphanie Boutet
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
| | - François Perreau
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
| | - Loïc Rajjou
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
| | - Loïc Lepiniec
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
| | - Massimiliano Corso
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France.
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Raimondi D, Chizari H, Verplaetse N, Löscher BS, Franke A, Moreau Y. Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients. Sci Rep 2023; 13:19449. [PMID: 37945674 PMCID: PMC10636050 DOI: 10.1038/s41598-023-46887-2] [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: 06/12/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.
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Affiliation(s)
| | | | | | - Britt-Sabina Löscher
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
- University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
- University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Yves Moreau
- ESAT-STADIUS, KU Leuven, 3001, Leuven, Belgium
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Verplaetse N, Passemiers A, Arany A, Moreau Y, Raimondi D. Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease. Genome Biol 2023; 24:224. [PMID: 37798735 PMCID: PMC10552306 DOI: 10.1186/s13059-023-03064-y] [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: 03/21/2023] [Accepted: 09/20/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Despite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects. RESULTS We demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis. CONCLUSIONS In this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.
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Affiliation(s)
- Nora Verplaetse
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
| | - Antoine Passemiers
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Adam Arany
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Yves Moreau
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Daniele Raimondi
- Department of of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium.
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Raimondi D, Orlando G, Verplaetse N, Fariselli P, Moreau Y. Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship. FRONTIERS IN BIOINFORMATICS 2022; 2:1098941. [PMID: 36530385 PMCID: PMC9749061 DOI: 10.3389/fbinf.2022.1098941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 11/12/2023] Open
Affiliation(s)
| | | | | | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, Italy
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Computational interpretation of human genetic variation. Hum Genet 2022; 141:1545-1548. [PMID: 36149496 DOI: 10.1007/s00439-022-02483-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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