1
|
Schlusser N, González A, Pandey M, Zavolan M. Current limitations in predicting mRNA translation with deep learning models. Genome Biol 2024; 25:227. [PMID: 39164757 PMCID: PMC11337900 DOI: 10.1186/s13059-024-03369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
BACKGROUND The design of nucleotide sequences with defined properties is a long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of protein synthesis depends on the 5' untranslated region (5'UTR) of the mRNAs, and recently, deep learning models were proposed to predict the translation output of mRNAs from the 5'UTR sequence. At the same time, large data sets of endogenous and reporter mRNA translation have become available. RESULTS In this study, we use complementary data obtained in two different cell types to assess the accuracy and generality of currently available models for predicting translational output. We find that while performing well on the data sets on which they were trained, deep learning models do not generalize well to other data sets, in particular of endogenous mRNAs, which differ in many properties from reporter constructs. CONCLUSIONS These differences limit the ability of deep learning models to uncover mechanisms of translation control and to predict the impact of genetic variation. We suggest directions that combine high-throughput measurements and machine learning to unravel mechanisms of translation control and improve construct design.
Collapse
Affiliation(s)
- Niels Schlusser
- Biozentrum, University of Basel, Spitalstrasse 41, 4056, Basel, Switzerland.
| | - Asier González
- Biozentrum, University of Basel, Spitalstrasse 41, 4056, Basel, Switzerland
- Departament de Bioquímica i Biologia Molecular and Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain
| | - Muskan Pandey
- Biozentrum, University of Basel, Spitalstrasse 41, 4056, Basel, Switzerland
- Current address: Institute of Molecular Biology and Biophysics, Department of Biology, ETH Zurich, 8093, Zurich, Switzerland
| | - Mihaela Zavolan
- Biozentrum, University of Basel, Spitalstrasse 41, 4056, Basel, Switzerland.
| |
Collapse
|
2
|
Zheng D, Wang J, Persyn L, Liu Y, Montoya FU, Cenik C, Agarwal V. Predicting the translation efficiency of messenger RNA in mammalian cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.11.607362. [PMID: 39149337 PMCID: PMC11326250 DOI: 10.1101/2024.08.11.607362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence, RiboNN integrates contributions from the full-length mRNA sequence, learning that the 5' UTR, CDS, and 3' UTR respectively possess ~67%, 31%, and 2% per-nucleotide information density in the specification of mammalian TEs. Interpretation of RiboNN revealed that the spatial positioning of low-level di- and tri-nucleotide features (i.e., including codons) largely explain model performance, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN is predictive of the translational behavior of base-modified therapeutic RNA, and can explain evolutionary selection pressures in human 5' UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability, and localization in mammalian organisms.
Collapse
Affiliation(s)
- Dinghai Zheng
- mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA
| | - Jun Wang
- mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA
| | - Logan Persyn
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Yue Liu
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | | | - Can Cenik
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Vikram Agarwal
- mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA
| |
Collapse
|
3
|
Castillo-Hair S, Fedak S, Wang B, Linder J, Havens K, Certo M, Seelig G. Optimizing 5'UTRs for mRNA-delivered gene editing using deep learning. Nat Commun 2024; 15:5284. [PMID: 38902240 PMCID: PMC11189900 DOI: 10.1038/s41467-024-49508-2] [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: 05/13/2023] [Accepted: 06/07/2024] [Indexed: 06/22/2024] Open
Abstract
mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5'UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5'UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on our datasets and use them to guide the design of high-performing 5'UTRs using gradient descent and generative neural networks. We experimentally test designed 5'UTRs with mRNA encoding megaTALTM gene editing enzymes for two different gene targets and in two different cell lines. We find that the designed 5'UTRs support strong gene editing activity. Editing efficiency is correlated between cell types and gene targets, although the best performing UTR was specific to one cargo and cell type. Our results highlight the potential of model-based sequence design for mRNA therapeutics.
Collapse
Affiliation(s)
- Sebastian Castillo-Hair
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, WA, Seattle, USA
| | | | - Ban Wang
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Johannes Linder
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | | | | | - Georg Seelig
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
| |
Collapse
|
4
|
Chu Y, Yu D, Li Y, Huang K, Shen Y, Cong L, Zhang J, Wang M. A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions. NAT MACH INTELL 2024; 6:449-460. [PMID: 38855263 PMCID: PMC11155392 DOI: 10.1038/s42256-024-00823-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 03/07/2024] [Indexed: 06/11/2024]
Abstract
The 5' UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5' UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5' UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best known benchmark by up to 5% for predicting the Mean Ribosome Loading, and by up to 8% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5' UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5' UTR optimized for therapeutics.
Collapse
Affiliation(s)
- Yanyi Chu
- Center for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dan Yu
- RVAC Medicines, Waltham, MA 02451, USA
| | - Yupeng Li
- RVAC Medicines, Waltham, MA 02451, USA
| | - Kaixuan Huang
- Center for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Yue Shen
- RVAC Medicines, Waltham, MA 02451, USA
| | - Le Cong
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | | | - Mengdi Wang
- Center for Statistics and Machine Learning and Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
| |
Collapse
|
5
|
Zheng W, Fong JHC, Wan YK, Chu AHY, Huang Y, Wong ASL, Ho JWK. Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models. Cell Syst 2023; 14:1103-1112.e6. [PMID: 38016465 DOI: 10.1016/j.cels.2023.10.011] [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] [Received: 03/31/2023] [Revised: 09/18/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023]
Abstract
The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce "GRU-rewiring," a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5' UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs.
Collapse
Affiliation(s)
- Weizhong Zheng
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - John H C Fong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yuk Kei Wan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Athena H Y Chu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Yuanhua Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China; Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Alan S L Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Laboratory of Data Discovery for Health (D24H) Limited, Hong Kong Science Park, Hong Kong SAR, China.
| |
Collapse
|
6
|
Bohn E, Lau TTY, Wagih O, Masud T, Merico D. A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction. Front Mol Biosci 2023; 10:1257550. [PMID: 37745687 PMCID: PMC10517338 DOI: 10.3389/fmolb.2023.1257550] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction: Variants in 5' and 3' untranslated regions (UTR) contribute to rare disease. While predictive algorithms to assist in classifying pathogenicity can potentially be highly valuable, the utility of these tools is often unclear, as it depends on carefully selected training and validation conditions. To address this, we developed a high confidence set of pathogenic (P) and likely pathogenic (LP) variants and assessed deep learning (DL) models for predicting their molecular effects. Methods: 3' and 5' UTR variants documented as P or LP (P/LP) were obtained from ClinVar and refined by reviewing the annotated variant effect and reassessing evidence of pathogenicity following published guidelines. Prediction scores from sequence-based DL models were compared between three groups: P/LP variants acting though the mechanism for which the model was designed (model-matched), those operating through other mechanisms (model-mismatched), and putative benign variants. PhyloP was used to compare conservation scores between P/LP and putative benign variants. Results: 295 3' and 188 5' UTR variants were obtained from ClinVar, of which 26 3' and 68 5' UTR variants were classified as P/LP. Predictions by DL models achieved statistically significant differences when comparing modelmatched P/LP variants to both putative benign variants and modelmismatched P/LP variants, as well as when comparing all P/LP variants to putative benign variants. PhyloP conservation scores were significantly higher among P/LP compared to putative benign variants for both the 3' and 5' UTR. Discussion: In conclusion, we present a high-confidence set of P/LP 3' and 5' UTR variants spanning a range of mechanisms and supported by detailed pathogenicity and molecular mechanism evidence curation. Predictions from DL models further substantiate these classifications. These datasets will support further development and validation of DL algorithms designed to predict the functional impact of variants that may be implicated in rare disease.
Collapse
Affiliation(s)
- Emma Bohn
- Deep Genomics Inc., Toronto, ON, Canada
| | | | | | | | - Daniele Merico
- Deep Genomics Inc., Toronto, ON, Canada
- The Centre for Applied Genomics, Hospital for Sick Children, Toronto, ON, Canada
| |
Collapse
|
7
|
Chehelgerdi M, Chehelgerdi M. The use of RNA-based treatments in the field of cancer immunotherapy. Mol Cancer 2023; 22:106. [PMID: 37420174 PMCID: PMC10401791 DOI: 10.1186/s12943-023-01807-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 06/13/2023] [Indexed: 07/09/2023] Open
Abstract
Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment.
Collapse
Affiliation(s)
- Mohammad Chehelgerdi
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran.
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
| | - Matin Chehelgerdi
- Novin Genome (NG) Lab, Research and Development Center for Biotechnology, Shahrekord, Iran
- Young Researchers and Elite Club, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran
| |
Collapse
|
8
|
Functional characterization of 5' UTR cis-acting sequence elements that modulate translational efficiency in Plasmodium falciparum and humans. Malar J 2022; 21:15. [PMID: 34991611 PMCID: PMC8739713 DOI: 10.1186/s12936-021-04024-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
Background The eukaryotic parasite Plasmodium falciparum causes millions of malarial infections annually while drug resistance to common anti-malarials is further confounding eradication efforts. Translation is an attractive therapeutic target that will benefit from a deeper mechanistic understanding. As the rate limiting step of translation, initiation is a primary driver of translational efficiency. It is a complex process regulated by both cis and trans acting factors, providing numerous potential targets. Relative to model organisms and humans, P. falciparum mRNAs feature unusual 5′ untranslated regions suggesting cis-acting sequence complexity in this parasite may act to tune levels of protein synthesis through their effects on translational efficiency. Methods Here, in vitro translation is deployed to compare the role of cis-acting regulatory sequences in P. falciparum and humans. Using parasite mRNAs with high or low translational efficiency, the presence, position, and termination status of upstream “AUG”s, in addition to the base composition of the 5′ untranslated regions, were characterized. Results The density of upstream “AUG”s differed significantly among the most and least efficiently translated genes in P. falciparum, as did the average “GC” content of the 5′ untranslated regions. Using exemplars from highly translated and poorly translated mRNAs, multiple putative upstream elements were interrogated for impact on translational efficiency. Upstream “AUG”s were found to repress translation to varying degrees, depending on their position and context, while combinations of upstream “AUG”s had non-additive effects. The base composition of the 5′ untranslated regions also impacted translation, but to a lesser degree. Surprisingly, the effects of cis-acting sequences were remarkably conserved between P. falciparum and humans. Conclusions While translational regulation is inherently complex, this work contributes toward a more comprehensive understanding of parasite and human translational regulation by examining the impact of discrete cis-acting features, acting alone or in context. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-021-04024-2.
Collapse
|
9
|
Castillo-Hair SM, Seelig G. Machine Learning for Designing Next-Generation mRNA Therapeutics. Acc Chem Res 2022; 55:24-34. [PMID: 34905691 DOI: 10.1021/acs.accounts.1c00621] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Over just the last 2 years, mRNA therapeutics and vaccines have undergone a rapid transition from an intriguing concept to real-world impact. However, whereas some aspects of mRNA therapeutics, such as the use of chemical modifications to increase stability and reduce immunogenicity, have been extensively optimized for over two decades, other aspects, particularly the selection and design of the noncoding leader and trailer sequences which control translation efficiency and stability, have received comparably less attention. In practice, such 5' and 3' untranslated regions (UTRs) are often borrowed from highly expressed human genes with few or no modifications, as in the case for the Pfizer/BioNTech Covid vaccine. Focusing on the 5'UTR, we here argue that model-driven design is a promising alternative that provides unprecedented control over 5'UTR function. We review recent work that combines synthetic biology with machine learning to build quantitative models that relate ribosome loading, and thus translation efficiency, to the 5'UTR sequence. We first introduce an experimental approach that uses polysome profiling and high-throughput sequencing to quantify ribosome loading for hundreds of thousands of 5'UTRs in parallel. We apply this approach to measure ribosome loading in synthetic RNA libraries with a random sequence inserted into the 5'UTR. We then review Optimus 5-Prime, a convolutional neural network model trained on the experimental data. We highlight that very accurate models of biological regulation can be learned from synthetic data sets with degenerate 5'UTRs. We validate model predictions not only on held-out data sets from our random library but also on a large library of over 30 000 human 5'UTR fragments and using translation reporter data collected independently by other groups. Both the experiment and model are compatible with commonly used chemically modified nucleosides, in particular, pseudouridine (Ψ) and 1-methyl-pseudouridine (m1Ψ). We find that, in general, 5'UTRs have very similar impacts when combined with different protein-coding sequences and even in the context of different chemical modifications. We demonstrate that Optimus 5-Prime can be combined with design algorithms to generate de novo sequences with precisely defined translation efficiencies. We emphasize recent developments in design algorithms that rely on activation maximization and generative modeling to improve both the fitness and diversity of designed sequences. Compared with prior approaches such as genetic algorithms, we show that these approaches are not only faster but also less likely to get stuck in local sequence optima. Finally, we discuss how the approach reviewed here can be generalized to other gene regions and applications.
Collapse
Affiliation(s)
- Sebastian M. Castillo-Hair
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195, United States
- eScience Institute, University of Washington, Seattle, Washington 98195, United States
| | - Georg Seelig
- Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington 98195, United States
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
10
|
Linder J, Seelig G. Fast activation maximization for molecular sequence design. BMC Bioinformatics 2021; 22:510. [PMID: 34670493 PMCID: PMC8527647 DOI: 10.1186/s12859-021-04437-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 10/11/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. RESULTS Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp's capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. CONCLUSIONS Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.
Collapse
Affiliation(s)
- Johannes Linder
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
| | - Georg Seelig
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
| |
Collapse
|
11
|
Schlieben LD, Prokisch H, Yépez VA. How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data. Front Mol Biosci 2021; 8:647277. [PMID: 34141720 PMCID: PMC8204083 DOI: 10.3389/fmolb.2021.647277] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 05/10/2021] [Indexed: 12/11/2022] Open
Abstract
Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10-35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.
Collapse
Affiliation(s)
- Lea D. Schlieben
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Holger Prokisch
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Neurogenomics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Vicente A. Yépez
- School of Medicine, Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Munich, Germany
| |
Collapse
|