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Huminiecki Ł. Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science. ENTROPY (BASEL, SWITZERLAND) 2021; 24:17. [PMID: 35052043 PMCID: PMC8774939 DOI: 10.3390/e24010017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/02/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.
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Affiliation(s)
- Łukasz Huminiecki
- Evolutionary, Computational, and Statistical Genetics, Department of Molecula Biology, Institute of Genetics and Animal Biotechnology, Polish Academy of Sciences, Postępu 36A, Jastrzębiec, 05-552 Warsaw, Poland
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202
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Lin JL, Hsieh TT, Tung YA, Chen XJ, Hsiao YC, Yang CL, Liu TL, Chen CY. ezGeno: an automatic model selection package for genomic data analysis. Bioinformatics 2021; 38:30-37. [PMID: 34398217 DOI: 10.1093/bioinformatics/btab588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/15/2021] [Accepted: 08/13/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION To facilitate the process of tailor-making a deep neural network for exploring the dynamics of genomic DNA, we have developed a hands-on package called ezGeno. ezGeno automates the search process of various parameters and network structures and can be applied to any kind of 1D genomic data. Combinations of multiple abovementioned 1D features are also applicable. RESULTS For the task of predicting TF binding using genomic sequences as the input, ezGeno can consistently return the best performing set of parameters and network structure, as well as highlight the important segments within the original sequences. For the task of predicting tissue-specific enhancer activity using both sequence and DNase feature data as the input, ezGeno also regularly outperforms the hand-designed models. Furthermore, we demonstrate that ezGeno is superior in efficiency and accuracy compared to the one-layer DeepBind model and AutoKeras, an open-source AutoML package. AVAILABILITY AND IMPLEMENTATION The ezGeno package can be freely accessed at https://github.com/ailabstw/ezGeno. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jun-Liang Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | | | | | - Xuan-Jun Chen
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
| | | | - Chia-Lin Yang
- Taiwan AI Labs, Taipei 10355, Taiwan.,Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Tyng-Luh Liu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan.,Taiwan AI Labs, Taipei 10355, Taiwan
| | - Chien-Yu Chen
- Taiwan AI Labs, Taipei 10355, Taiwan.,Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan
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203
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Musolf AM, Holzinger ER, Malley JD, Bailey-Wilson JE. What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics. Hum Genet 2021; 141:1515-1528. [PMID: 34862561 PMCID: PMC9360120 DOI: 10.1007/s00439-021-02402-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 11/08/2021] [Indexed: 01/26/2023]
Abstract
Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are commonly used in genetics to predict or classify subjects, but some methods evaluate which features (variables) are responsible for creating a good prediction; this is called feature importance. This is critical in genetics, as researchers are often interested in which features (e.g., SNP genotype or environmental exposure) are responsible for a good prediction. This allows for the deeper analysis beyond simple prediction, including the determination of risk factors associated with a given phenotype. Feature importance further permits the researcher to peer inside the black box of many ML algorithms to see how they work and which features are critical in informing a good prediction. This review focuses on ML methods that provide feature importance metrics for the analysis of genetic data. Five major categories of ML algorithms: k nearest neighbors, artificial neural networks, deep learning, support vector machines, and random forests are described. The review ends with a discussion of how to choose the best machine for a data set. This review will be particularly useful for genetic researchers looking to use ML methods to answer questions beyond basic prediction and classification.
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Affiliation(s)
- Anthony M Musolf
- Statistical Genetics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive Suite 1200, Baltimore, MD, 21224, USA
| | - Emily R Holzinger
- Target Sciences, Informatics and Predictive Sciences, Bristol Myers Squibb, Cambridge, MA, USA
| | - James D Malley
- Statistical Genetics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive Suite 1200, Baltimore, MD, 21224, USA
| | - Joan E Bailey-Wilson
- Statistical Genetics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive Suite 1200, Baltimore, MD, 21224, USA.
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204
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Thibodeau A, Khetan S, Eroglu A, Tewhey R, Stitzel ML, Ucar D. CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data. PLoS Comput Biol 2021; 17:e1009670. [PMID: 34898596 PMCID: PMC8699717 DOI: 10.1371/journal.pcbi.1009670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 12/23/2021] [Accepted: 11/19/2021] [Indexed: 02/06/2023] Open
Abstract
Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n = 40 samples) that were not used in model training (mean average precision = 0.80, mean F1 score = 0.70). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis-RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis-RE functions of rare cells without the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases.
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Affiliation(s)
- Asa Thibodeau
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Shubham Khetan
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Alper Eroglu
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Michael L. Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
- Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut, United States of America
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, Connecticut, United States of America
| | - Duygu Ucar
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
- Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut, United States of America
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, Connecticut, United States of America
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205
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Yoshimura Y, Hamada A, Augey Y, Akiyama M, Sakakibara Y. Genomic style: yet another deep-learning approach to characterize bacterial genome sequences. BIOINFORMATICS ADVANCES 2021; 1:vbab039. [PMID: 36700086 PMCID: PMC9710696 DOI: 10.1093/bioadv/vbab039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/15/2021] [Accepted: 11/26/2021] [Indexed: 01/28/2023]
Abstract
Motivation Biological sequence classification is the most fundamental task in bioinformatics analysis. For example, in metagenome analysis, binning is a typical type of DNA sequence classification. In order to classify sequences, it is necessary to define sequence features. The k-mer frequency, base composition and alignment-based metrics are commonly used. On the other hand, in the field of image recognition using machine learning, image classification is broadly divided into those based on shape and those based on style. A style matrix was introduced as a method of expressing the style of an image (e.g. color usage and texture). Results We propose a novel sequence feature, called genomic style, inspired by image classification approaches, for classifying and clustering DNA sequences. As with the style of images, the DNA sequence is considered to have a genomic style unique to the bacterial species, and the style matrix concept is applied to the DNA sequence. Our main aim is to introduce the genomics style as yet another basic sequence feature for metagenome binning problem in replace of the most commonly used sequence feature k-mer frequency. Performance evaluations showed that our method using a style matrix has the potential for accurate binning when compared with state-of-the-art binning tools based on k-mer frequency. Availability and implementation The source code for the implementation of this genomic style method, along with the dataset for the performance evaluation, is available from https://github.com/friendflower94/binning-style. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Yuka Yoshimura
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
| | - Akifumi Hamada
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
| | - Yohann Augey
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
| | - Manato Akiyama
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan
| | - Yasubumi Sakakibara
- Department of Biosciences and Informatics, Keio University, Yokohama 223-8522, Japan,To whom correspondence should be addressed.
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206
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Li R, Li L, Xu Y, Yang J. Machine learning meets omics: applications and perspectives. Brief Bioinform 2021; 23:6425809. [PMID: 34791021 DOI: 10.1093/bib/bbab460] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/29/2021] [Accepted: 10/07/2021] [Indexed: 02/07/2023] Open
Abstract
The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.
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Affiliation(s)
- Rufeng Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Lixin Li
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China
| | - Yungang Xu
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Juan Yang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an 710061, P. R. China.,Key Laboratory of Environment and Genes Related to Diseases (Xi'an Jiaotong University), Ministry of Education of China, Xi'an 710061, P. R. China
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207
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Zhang Y, Liu Y, Xu J, Wang X, Peng X, Song J, Yu DJ. Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites. Brief Bioinform 2021; 22:bbab351. [PMID: 34459479 PMCID: PMC8575024 DOI: 10.1093/bib/bbab351] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/02/2021] [Accepted: 08/09/2021] [Indexed: 11/12/2022] Open
Abstract
DNA N6-methyladenine is an important type of DNA modification that plays important roles in multiple biological processes. Despite the recent progress in developing DNA 6mA site prediction methods, several challenges remain to be addressed. For example, although the hand-crafted features are interpretable, they contain redundant information that may bias the model training and have a negative impact on the trained model. Furthermore, although deep learning (DL)-based models can perform feature extraction and classification automatically, they lack the interpretability of the crucial features learned by those models. As such, considerable research efforts have been focused on achieving the trade-off between the interpretability and straightforwardness of DL neural networks. In this study, we develop two new DL-based models for improving the prediction of N6-methyladenine sites, termed LA6mA and AL6mA, which use bidirectional long short-term memory to respectively capture the long-range information and self-attention mechanism to extract the key position information from DNA sequences. The performance of the two proposed methods is benchmarked and evaluated on the two model organisms Arabidopsis thaliana and Drosophila melanogaster. On the two benchmark datasets, LA6mA achieves an area under the receiver operating characteristic curve (AUROC) value of 0.962 and 0.966, whereas AL6mA achieves an AUROC value of 0.945 and 0.941, respectively. Moreover, an in-depth analysis of the attention matrix is conducted to interpret the important information, which is hidden in the sequence and relevant for 6mA site prediction. The two novel pipelines developed for DNA 6mA site prediction in this work will facilitate a better understanding of the underlying principle of DL-based DNA methylation site prediction and its future applications.
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Affiliation(s)
- Ying Zhang
- School of Computer Science and Engineering at Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Yan Liu
- School of Computer Science and Engineering at Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Jian Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Xiaoyu Wang
- Monash Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Xinxin Peng
- Monash Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
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208
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Benner P, Vingron M. Quantifying the tissue-specific regulatory information within enhancer DNA sequences. NAR Genom Bioinform 2021; 3:lqab095. [PMID: 34729474 PMCID: PMC8557370 DOI: 10.1093/nargab/lqab095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 09/23/2021] [Accepted: 09/28/2021] [Indexed: 12/04/2022] Open
Abstract
Recent efforts to measure epigenetic marks across a wide variety of different cell types and tissues provide insights into the cell type-specific regulatory landscape. We use these data to study whether there exists a correlate of epigenetic signals in the DNA sequence of enhancers and explore with computational methods to what degree such sequence patterns can be used to predict cell type-specific regulatory activity. By constructing classifiers that predict in which tissues enhancers are active, we are able to identify sequence features that might be recognized by the cell in order to regulate gene expression. While classification performances vary greatly between tissues, we show examples where our classifiers correctly predict tissue-specific regulation from sequence alone. We also show that many of the informative patterns indeed harbor transcription factor footprints.
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Affiliation(s)
- Philipp Benner
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 73, 14195 Berlin, Germany
| | - Martin Vingron
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestraße 73, 14195 Berlin, Germany
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209
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Downes DJ, Cross AR, Hua P, Roberts N, Schwessinger R, Cutler AJ, Munis AM, Brown J, Mielczarek O, de Andrea CE, Melero I, Gill DR, Hyde SC, Knight JC, Todd JA, Sansom SN, Issa F, Davies JOJ, Hughes JR. Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus. Nat Genet 2021; 53:1606-1615. [PMID: 34737427 PMCID: PMC7611960 DOI: 10.1038/s41588-021-00955-3] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 09/22/2021] [Indexed: 12/21/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) disease (COVID-19) pandemic has caused millions of deaths worldwide. Genome-wide association studies identified the 3p21.31 region as conferring a twofold increased risk of respiratory failure. Here, using a combined multiomics and machine learning approach, we identify the gain-of-function risk A allele of an SNP, rs17713054G>A, as a probable causative variant. We show with chromosome conformation capture and gene-expression analysis that the rs17713054-affected enhancer upregulates the interacting gene, leucine zipper transcription factor like 1 (LZTFL1). Selective spatial transcriptomic analysis of lung biopsies from patients with COVID-19 shows the presence of signals associated with epithelial-mesenchymal transition (EMT), a viral response pathway that is regulated by LZTFL1. We conclude that pulmonary epithelial cells undergoing EMT, rather than immune cells, are likely responsible for the 3p21.31-associated risk. Since the 3p21.31 effect is conferred by a gain-of-function, LZTFL1 may represent a therapeutic target.
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Affiliation(s)
- Damien J Downes
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Amy R Cross
- Nuffield Department of Surgical Sciences, Transplantation Research and Immunology Group,University of Oxford, Oxford, UK
| | - Peng Hua
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Nigel Roberts
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Ron Schwessinger
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Department of Medicine, Medical Research Council Weatherall Institute of Molecular Medicine Centre for Computational Biology, University of Oxford, Oxford, UK
| | - Antony J Cutler
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Immunology Research Unit, GlaxoSmithKline, Stevenage, UK
| | - Altar M Munis
- Department of Medicine, Gene Medicine Group, Nuffield Division of Clinical Laboratory Sciences, Radcliffe University of Oxford, Oxford, UK
| | - Jill Brown
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Olga Mielczarek
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Carlos E de Andrea
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Ignacio Melero
- Division of Immunology and Immunotherapy, Centre for Applied Medical Research, University of Navarra, Pamplona, Spain
| | - Deborah R Gill
- Department of Medicine, Gene Medicine Group, Nuffield Division of Clinical Laboratory Sciences, Radcliffe University of Oxford, Oxford, UK
| | - Stephen C Hyde
- Department of Medicine, Gene Medicine Group, Nuffield Division of Clinical Laboratory Sciences, Radcliffe University of Oxford, Oxford, UK
| | - Julian C Knight
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford, UK
| | - John A Todd
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Stephen N Sansom
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Fadi Issa
- Nuffield Department of Surgical Sciences, Transplantation Research and Immunology Group,University of Oxford, Oxford, UK
- Oxford University Hospitals National Health Service Foundation Trust, Oxford, UK
| | - James O J Davies
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Oxford University Hospitals National Health Service Foundation Trust, Oxford, UK.
| | - Jim R Hughes
- Department of Medicine, Medical Research Council Molecular Haematology Unit, Medical Research Council Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
- Department of Medicine, Medical Research Council Weatherall Institute of Molecular Medicine Centre for Computational Biology, University of Oxford, Oxford, UK.
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210
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The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation. Nat Genet 2021; 53:1564-1576. [PMID: 34650237 PMCID: PMC8763320 DOI: 10.1038/s41588-021-00947-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 09/01/2021] [Indexed: 01/24/2023]
Abstract
Transcription factors bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multiomic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep-learning framework to model the cis-regulatory logic of chromatin accessibility. This analysis framework identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter assay analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach shows the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code of dynamic gene regulation.
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211
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Srinivasan C, Phan BN, Lawler AJ, Ramamurthy E, Kleyman M, Brown AR, Kaplow IM, Wirthlin ME, Pfenning AR. Addiction-Associated Genetic Variants Implicate Brain Cell Type- and Region-Specific Cis-Regulatory Elements in Addiction Neurobiology. J Neurosci 2021; 41:9008-9030. [PMID: 34462306 PMCID: PMC8549541 DOI: 10.1523/jneurosci.2534-20.2021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 06/18/2021] [Accepted: 07/10/2021] [Indexed: 12/14/2022] Open
Abstract
Recent large genome-wide association studies have identified multiple confident risk loci linked to addiction-associated behavioral traits. Most genetic variants linked to addiction-associated traits lie in noncoding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied stratified linkage disequilibrium score regression to compare genome-wide association studies to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of female and male mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Last, we developed machine learning models to predict mouse cell type-specific open chromatin, enabling us to further categorize human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction.SIGNIFICANCE STATEMENT We combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. Our computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type- and tissue-specific manner for numerous complex traits.
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Affiliation(s)
- Chaitanya Srinivasan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - BaDoi N Phan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Medical Scientist Training Program, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Alyssa J Lawler
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Easwaran Ramamurthy
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Michael Kleyman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Ashley R Brown
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Irene M Kaplow
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Morgan E Wirthlin
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Andreas R Pfenning
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
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212
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Asada K, Takasawa K, Machino H, Takahashi S, Shinkai N, Bolatkan A, Kobayashi K, Komatsu M, Kaneko S, Okamoto K, Hamamoto R. Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines 2021; 9:biomedicines9111513. [PMID: 34829742 PMCID: PMC8614827 DOI: 10.3390/biomedicines9111513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/06/2021] [Accepted: 10/19/2021] [Indexed: 01/14/2023] Open
Abstract
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Koji Okamoto
- Division of Cancer Differentiation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryuji Hamamoto
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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213
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Tran T, Rekabdar B, Ekenna C. Deep Learning Methods in Predicting Gene Expression Levels for the Malaria Parasite. Front Genet 2021; 12:721068. [PMID: 34630516 PMCID: PMC8493083 DOI: 10.3389/fgene.2021.721068] [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: 06/28/2021] [Accepted: 08/25/2021] [Indexed: 11/13/2022] Open
Abstract
Malaria is a mosquito-borne disease caused by single-celled blood parasites of the genus Plasmodium. The most severe cases of this disease are caused by the Plasmodium species, Falciparum. Once infected, a human host experiences symptoms of recurrent and intermittent fevers occurring over a time-frame of 48 hours, attributed to the synchronized developmental cycle of the parasite during the blood stage. To understand the regulated periodicity of Plasmodium falciparum transcription, this paper forecast and predict the P. falciparum gene transcription during its blood stage life cycle implementing a well-tuned recurrent neural network with gated recurrent units. Additionally, we also employ a spiking neural network to predict the expression levels of the P. falciparum gene. We provide results of this prediction on multiple genes including potential genes that express possible drug target enzymes. Our results show a high level of accuracy in being able to predict and forecast the expression levels of the different genes.
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Affiliation(s)
- Tuan Tran
- Department of Computer Science, University at Albany, Albany, NY, United States
| | - Banafsheh Rekabdar
- Department of Computer Science, Southern Illinois University, Carbondale, IL, United States
| | - Chinwe Ekenna
- Department of Computer Science, University at Albany, Albany, NY, United States
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214
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Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021; 19:5762-5790. [PMID: 34765093 PMCID: PMC8566780 DOI: 10.1016/j.csbj.2021.10.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/05/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
We review the current applications of artificial intelligence (AI) in functional genomics. The recent explosion of AI follows the remarkable achievements made possible by "deep learning", along with a burst of "big data" that can meet its hunger. Biology is about to overthrow astronomy as the paradigmatic representative of big data producer. This has been made possible by huge advancements in the field of high throughput technologies, applied to determine how the individual components of a biological system work together to accomplish different processes. The disciplines contributing to this bulk of data are collectively known as functional genomics. They consist in studies of: i) the information contained in the DNA (genomics); ii) the modifications that DNA can reversibly undergo (epigenomics); iii) the RNA transcripts originated by a genome (transcriptomics); iv) the ensemble of chemical modifications decorating different types of RNA transcripts (epitranscriptomics); v) the products of protein-coding transcripts (proteomics); and vi) the small molecules produced from cell metabolism (metabolomics) present in an organism or system at a given time, in physiological or pathological conditions. After reviewing main applications of AI in functional genomics, we discuss important accompanying issues, including ethical, legal and economic issues and the importance of explainability.
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Affiliation(s)
- Claudia Caudai
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Antonella Galizia
- CNR, Institute of Applied Mathematics and Information Technologies (IMATI), Genoa, Italy
| | - Filippo Geraci
- CNR, Institute for Informatics and Telematics (IIT), Pisa, Italy
| | - Loredana Le Pera
- CNR, Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies (IBIOM), Bari, Italy
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Veronica Morea
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Emanuele Salerno
- CNR, Institute of Information Science and Technologies “A. Faedo” (ISTI), Pisa, Italy
| | - Allegra Via
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
| | - Teresa Colombo
- CNR, Institute of Molecular Biology and Pathology (IBPM), Rome, Italy
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215
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Dibaeinia P, Sinha S. Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks. Nucleic Acids Res 2021; 49:10309-10327. [PMID: 34508359 PMCID: PMC8501998 DOI: 10.1093/nar/gkab765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/18/2021] [Accepted: 08/25/2021] [Indexed: 11/18/2022] Open
Abstract
Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer 'grammar' in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.
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Affiliation(s)
- Payam Dibaeinia
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Saurabh Sinha
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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216
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Huang K, Xiao C, Glass LM, Critchlow CW, Gibson G, Sun J. Machine learning applications for therapeutic tasks with genomics data. PATTERNS (NEW YORK, N.Y.) 2021; 2:100328. [PMID: 34693370 PMCID: PMC8515011 DOI: 10.1016/j.patter.2021.100328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Thanks to the increasing availability of genomics and other biomedical data, many machine learning algorithms have been proposed for a wide range of therapeutic discovery and development tasks. In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development. We investigate the interplay among genomics, compounds, proteins, electronic health records, cellular images, and clinical texts. We identify 22 machine learning in genomics applications that span the whole therapeutics pipeline, from discovering novel targets, personalizing medicine, developing gene-editing tools, all the way to facilitating clinical trials and post-market studies. We also pinpoint seven key challenges in this field with potentials for expansion and impact. This survey examines recent research at the intersection of machine learning, genomics, and therapeutic development.
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Affiliation(s)
- Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Cao Xiao
- Amplitude, San Francisco, CA 94105, USA
| | - Lucas M. Glass
- Analytics Center of Excellence, IQVIA, Cambridge, MA 02139, USA
| | | | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jimeng Sun
- Computer Science Department and Carle's Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61820, USA
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217
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Jing R, Wen T, Liao C, Xue L, Liu F, Yu L, Luo J. DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework. NAR Genom Bioinform 2021; 3:lqab086. [PMID: 34617013 PMCID: PMC8489581 DOI: 10.1093/nargab/lqab086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 08/12/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022] Open
Abstract
Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge. Here, we present DeepT3 2.0 (http://advintbioinforlab.com/deept3/), a novel web server that integrates different deep learning models for genome-wide predicting T3SEs from a bacterium of interest. DeepT3 2.0 combines various deep learning architectures including convolutional, recurrent, convolutional-recurrent and multilayer neural networks to learn N-terminal representations of proteins specifically for T3SE prediction. Outcomes from the different models are processed and integrated for discriminating T3SEs and non-T3SEs. Because it leverages diverse models and an integrative deep learning framework, DeepT3 2.0 outperforms existing methods in validation datasets. In addition, the features learned from networks are analyzed and visualized to explain how models make their predictions. We propose DeepT3 2.0 as an integrated and accurate tool for the discovery of T3SEs.
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Affiliation(s)
- Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Tingke Wen
- School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Chengxiang Liao
- School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou 646000, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang 550018, China
| | - Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang 550018, China
| | - Jiesi Luo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China
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218
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Patel ZM, Hughes TR. Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms. Genome Biol 2021; 22:285. [PMID: 34620190 PMCID: PMC8496038 DOI: 10.1186/s13059-021-02503-y] [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: 10/31/2020] [Accepted: 09/16/2021] [Indexed: 01/07/2023] Open
Abstract
Background Mammalian genomes contain millions of putative regulatory sequences, which are delineated by binding of multiple transcription factors. The degree to which spacing and orientation constraints among transcription factor binding sites contribute to the recognition and identity of regulatory sequence is an unresolved but important question that impacts our understanding of genome function and evolution. Global mechanisms that underlie phenomena including the size of regulatory sequences, their uniqueness, and their evolutionary turnover remain poorly described. Results Here, we ask whether models incorporating different degrees of spacing and orientation constraints among transcription factor binding sites are broadly consistent with several global properties of regulatory sequence. These properties include length, sequence diversity, turnover rate, and dominance of specific TFs in regulatory site identity and cell type specification. Models with and without spacing and orientation constraints are generally consistent with all observed properties of regulatory sequence, and with regulatory sequences being fundamentally small (~ 1 nucleosome). Uniqueness of regulatory regions and their rapid evolutionary turnover are expected under all models examined. An intriguing issue we identify is that the complexity of eukaryotic regulatory sites must scale with the number of active transcription factors, in order to accomplish observed specificity. Conclusions Models of transcription factor binding with or without spacing and orientation constraints predict that regulatory sequences should be fundamentally short, unique, and turn over rapidly. We posit that the existence of master regulators may be, in part, a consequence of evolutionary pressure to limit the complexity and increase evolvability of regulatory sites. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02503-y.
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Affiliation(s)
- Zain M Patel
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Timothy R Hughes
- Donnelly Centre for Cellular and Biomolecular Research and Department of Molecular Genetics, University of Toronto, Toronto, ON, M5S 3E1, Canada.
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219
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Wang S, He Y, Chen Z, Zhang Q. FCNGRU: Locating Transcription Factor Binding Sites by combing Fully Convolutional Neural Network with Gated Recurrent Unit. IEEE J Biomed Health Inform 2021; 26:1883-1890. [PMID: 34613923 DOI: 10.1109/jbhi.2021.3117616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Deciphering the relationship between transcription factors (TFs) and DNA sequences is very helpful for computational inference of gene regulation and a comprehensive understanding of gene regulation mechanisms. Transcription factor binding sites (TFBSs) are specific DNA short sequences that play a pivotal role in controlling gene expression through interaction with TF proteins. Although recently many computational and deep learning methods have been proposed to predict TFBSs aiming to predict sequence specificity of TF-DNA binding, there is still a lack of effective methods to directly locate TFBSs. In order to address this problem, we propose FCNGRU combing a fully convolutional neural network (FCN) with the gated recurrent unit (GRU) to directly locate TFBSs in this paper. Furthermore, we present a two-task framework (FCNGRU-double): one is a classification task at nucleotide level which predicts the probability of each nucleotide and locates TFBSs, and the other is a regression task at sequence level which predicts the intensity of each sequence. A series of experiments are conducted on 45 in-vitro datasets collected from the UniPROBE database derived from universal protein binding microarrays (uPBMs). Compared with competing methods, FCNGRU-double achieves much better results on these datasets. Moreover, FCNGRU-double has an advantage over a single-task framework, FCNGRU-single, which only contains the branch of locating TFBSs. In additionwe combine with in vivo datasets to make a further analysis and discussion. The source codes are avaiable at https://github.com/wangguoguoa/FCNGRU.
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220
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Abstract
A sequence-to-expression machine learning model achieves higher accuracy by incorporating information about potential long-range interactions.
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221
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Shen Y, Chen LL, Gao J. CharPlant: A De Novo Open Chromatin Region Prediction Tool for Plant Genomes. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:860-871. [PMID: 33662624 PMCID: PMC9170768 DOI: 10.1016/j.gpb.2020.06.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/17/2020] [Accepted: 10/28/2020] [Indexed: 11/01/2022]
Abstract
Chromatin accessibility is a highly informative structural feature for understanding gene transcription regulation, because it indicates the degree to which nuclear macromolecules such as proteins and RNAs can access chromosomal DNA. Studies have shown that chromatin accessibility is highly dynamic during stress response, stimulus response, and developmental transition. Moreover, physical access to chromosomal DNA in eukaryotes is highly cell-specific. Therefore, current technologies such as DNase-seq, ATAC-seq, and FAIRE-seq reveal only a portion of the open chromatin regions (OCRs) present in a given species. Thus, the genome-wide distribution of OCRs remains unknown. In this study, we developed a bioinformatics tool called CharPlant for the de novo prediction of OCRs in plant genomes. To develop this tool, we constructed a three-layer convolutional neural network (CNN) and subsequently trained the CNN using DNase-seq and ATAC-seq datasets of four plant species. The model simultaneously learns the sequence motifs and regulatory logics, which are jointly used to determine DNA accessibility. All of these steps are integrated into CharPlant, which can be run using a simple command line. The results of data analysis using CharPlant in this study demonstrate its prediction power and computational efficiency. To our knowledge, CharPlant is the first de novo prediction tool that can identify potential OCRs in the whole genome. The source code of CharPlant and supporting files are freely available from https://github.com/Yin-Shen/CharPlant.
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Affiliation(s)
- Yin Shen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Ling-Ling Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Junxiang Gao
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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222
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He X, Zhang S, Zhang Y, Lei Z, Jiang T, Zeng J. Characterizing RNA Pseudouridylation by Convolutional Neural Networks. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:815-833. [PMID: 33631424 PMCID: PMC9170758 DOI: 10.1016/j.gpb.2019.11.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 09/15/2019] [Accepted: 11/13/2019] [Indexed: 12/12/2022]
Abstract
Pseudouridine (Ψ) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Ψs (especially in mRNAs) still remain largely unclear. The landscape of Ψs across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Ψ sites and characterize the contextual sequence features of pseudouridylation. PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Ψ sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.
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Affiliation(s)
- Xuan He
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Sai Zhang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Yanqing Zhang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Zhixin Lei
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA; MOE Key Lab of Bioinformatics and Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Institute of Integrative Genome Biology, University of California, Riverside, CA 92521, USA
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China.
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223
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Avsec Ž, Agarwal V, Visentin D, Ledsam JR, Grabska-Barwinska A, Taylor KR, Assael Y, Jumper J, Kohli P, Kelley DR. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods 2021; 18:1196-1203. [PMID: 34608324 PMCID: PMC8490152 DOI: 10.1038/s41592-021-01252-x] [Citation(s) in RCA: 322] [Impact Index Per Article: 107.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 07/27/2021] [Indexed: 02/08/2023]
Abstract
How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequences through the use of a deep learning architecture, called Enformer, that is able to integrate information from long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Furthermore, Enformer learned to predict enhancer-promoter interactions directly from the DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of human disease associations and provide a framework to interpret cis-regulatory evolution.
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224
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Novakovsky G, Saraswat M, Fornes O, Mostafavi S, Wasserman WW. Biologically relevant transfer learning improves transcription factor binding prediction. Genome Biol 2021; 22:280. [PMID: 34579793 PMCID: PMC8474956 DOI: 10.1186/s13059-021-02499-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 09/15/2021] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall model performance, compared to training a separate model for each new task. RESULTS We assess a transfer learning strategy for TF binding prediction consisting of a pre-training step, wherein we train a multi-task model with multiple TFs, and a fine-tuning step, wherein we initialize single-task models for individual TFs with the weights learned by the multi-task model, after which the single-task models are trained at a lower learning rate. We corroborate that transfer learning improves model performance, especially if in the pre-training step the multi-task model is trained with biologically relevant TFs. We show the effectiveness of transfer learning for TFs with ~ 500 ChIP-seq peak regions. Using model interpretation techniques, we demonstrate that the features learned in the pre-training step are refined in the fine-tuning step to resemble the binding motif of the target TF (i.e., the recipient of transfer learning in the fine-tuning step). Moreover, pre-training with biologically relevant TFs allows single-task models in the fine-tuning step to learn useful features other than the motif of the target TF. CONCLUSIONS Our results confirm that transfer learning is a powerful technique for TF binding prediction.
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Affiliation(s)
- Gherman Novakovsky
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6H 3 N1, Canada
| | - Manu Saraswat
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6H 3 N1, Canada
| | - Oriol Fornes
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada.
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6H 3 N1, Canada.
| | - Sara Mostafavi
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6H 3 N1, Canada
- Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Canadian Institute for Advanced Research, CIFAR AI Chair, and Child and Brain Development, Toronto, ON, M5G 1 M1, Canada
| | - Wyeth W Wasserman
- Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Vancouver, BC, V5Z 4H4, Canada.
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, V6H 3 N1, Canada.
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225
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Umarov R, Li Y, Arakawa T, Takizawa S, Gao X, Arner E. ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation. PLoS Comput Biol 2021; 17:e1009376. [PMID: 34491989 PMCID: PMC8448322 DOI: 10.1371/journal.pcbi.1009376] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/17/2021] [Accepted: 08/23/2021] [Indexed: 11/19/2022] Open
Abstract
Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understanding gene expression patterns. While there are many attempts to develop computational promoter and enhancer identification methods, reliable tools to analyze long genomic sequences are still lacking. Prediction methods often perform poorly on the genome-wide scale because the number of negatives is much higher than that in the training sets. To address this issue, we propose a dynamic negative set updating scheme with a two-model approach, using one model for scanning the genome and the other one for testing candidate positions. The developed method achieves good genome-level performance and maintains robust performance when applied to other vertebrate species, without re-training. Moreover, the unannotated predicted regulatory regions made on the human genome are enriched for disease-associated variants, suggesting them to be potentially true regulatory elements rather than false positives. We validated high scoring "false positive" predictions using reporter assay and all tested candidates were successfully validated, demonstrating the ability of our method to discover novel human regulatory regions.
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Affiliation(s)
- Ramzan Umarov
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
- * E-mail: (RU); (XG); (EA)
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), Hong Kong, People’s Republic of China
| | - Takahiro Arakawa
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Satoshi Takizawa
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
| | - Xin Gao
- King Abdullah University of Science and Technology, Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, Thuwal, Saudi Arabia
- * E-mail: (RU); (XG); (EA)
| | - Erik Arner
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
- Laboratory for Applied Regulatory Genomics Network Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
- * E-mail: (RU); (XG); (EA)
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226
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Friedman RZ, Granas DM, Myers CA, Corbo JC, Cohen BA, White MA. Information content differentiates enhancers from silencers in mouse photoreceptors. eLife 2021; 10:67403. [PMID: 34486522 PMCID: PMC8492058 DOI: 10.7554/elife.67403] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 09/03/2021] [Indexed: 12/12/2022] Open
Abstract
Enhancers and silencers often depend on the same transcription factors (TFs) and are conflated in genomic assays of TF binding or chromatin state. To identify sequence features that distinguish enhancers and silencers, we assayed massively parallel reporter libraries of genomic sequences targeted by the photoreceptor TF cone-rod homeobox (CRX) in mouse retinas. Both enhancers and silencers contain more TF motifs than inactive sequences, but relative to silencers, enhancers contain motifs from a more diverse collection of TFs. We developed a measure of information content that describes the number and diversity of motifs in a sequence and found that, while both enhancers and silencers depend on CRX motifs, enhancers have higher information content. The ability of information content to distinguish enhancers and silencers targeted by the same TF illustrates how motif context determines the activity of cis-regulatory sequences. Different cell types are established by activating and repressing the activity of specific sets of genes, a process controlled by proteins called transcription factors. Transcription factors work by recognizing and binding short stretches of DNA in parts of the genome called cis-regulatory sequences. A cis-regulatory sequence that increases the activity of a gene when bound by transcription factors is called an enhancer, while a sequence that causes a decrease in gene activity is called a silencer. To establish a cell type, a particular transcription factor will act on both enhancers and silencers that control the activity of different genes. For example, the transcription factor cone-rod homeobox (CRX) is critical for specifying different types of cells in the retina, and it acts on both enhancers and silencers. In rod photoreceptors, CRX activates rod genes by binding their enhancers, while repressing cone photoreceptor genes by binding their silencers. However, CRX always recognizes and binds to the same DNA sequence, known as its binding site, making it unclear why some cis-regulatory sequences bound to CRX act as silencers, while others act as enhancers. Friedman et al. sought to understand how enhancers and silencers, both bound by CRX, can have different effects on the genes they control. Since both enhancers and silencers contain CRX binding sites, the difference between the two must lie in the sequence of the DNA surrounding these binding sites. Using retinas that have been explanted from mice and kept alive in the laboratory, Friedman et al. tested the activity of thousands of CRX-binding sequences from the mouse genome. This showed that both enhancers and silencers have more copies of CRX-binding sites than sequences of the genome that are inactive. Additionally, the results revealed that enhancers have a diverse collection of binding sites for other transcription factors, while silencers do not. Friedman et al. developed a new metric they called information content, which captures the diverse combinations of different transcription binding sites that cis-regulatory sequences can have. Using this metric, Friedman et al. showed that it is possible to distinguish enhancers from silencers based on their information content. It is critical to understand how the DNA sequences of cis-regulatory regions determine their activity, because mutations in these regions of the genome can cause disease. However, since every person has thousands of benign mutations in cis-regulatory sequences, it is a challenge to identify specific disease-causing mutations, which are relatively rare. One long-term goal of models of enhancers and silencers, such as Friedman et al.’s information content model, is to understand how mutations can affect cis-regulatory sequences, and, in some cases, lead to disease.
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Affiliation(s)
- Ryan Z Friedman
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, United States.,Department of Genetics, Washington University School of Medicine, St. Louis, United States
| | - David M Granas
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, United States.,Department of Genetics, Washington University School of Medicine, St. Louis, United States
| | - Connie A Myers
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, United States
| | - Joseph C Corbo
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, United States
| | - Barak A Cohen
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, United States.,Department of Genetics, Washington University School of Medicine, St. Louis, United States
| | - Michael A White
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, United States.,Department of Genetics, Washington University School of Medicine, St. Louis, United States
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227
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Zhao H, Li J, Yang L, Qin G, Xia C, Xu X, Su Y, Liu Y, Ming L, Chen LL, Xiong L, Xie W. An inferred functional impact map of genetic variants in rice. MOLECULAR PLANT 2021; 14:1584-1599. [PMID: 34214659 DOI: 10.1016/j.molp.2021.06.025] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/20/2021] [Accepted: 06/28/2021] [Indexed: 06/13/2023]
Abstract
Interpreting the functional impacts of genetic variants (GVs) is an important challenge for functional genomic studies in crops and next-generation breeding. Previous studies in rice (Oryza sativa) have focused mainly on the identification of GVs, whereas systematic functional annotation of GVs has not yet been performed. Here, we present a functional impact map of GVs in rice. We curated haplotype information for 17 397 026 GVs from sequencing data of 4726 rice accessions. We quantitatively evaluated the effects of missense mutations in coding regions in each haplotype based on the conservation of amino acid residues and obtained the effects of 918 848 non-redundant missense GVs. Furthermore, we generated high-quality chromatin accessibility (CA) data from six representative rice tissues and used these data to train deep convolutional neural network models to predict the impacts of 5 067 405 GVs for CA in regulatory regions. We characterized the functional properties and tissue specificity of the GV effects and found that large-effect GVs in coding and regulatory regions may be subject to selection in different directions. Finally, we demonstrated how the functional impact map could be used to prioritize causal variants in mapping populations. This impact map will be a useful resource for accelerating gene cloning and functional studies in rice, and can be freely queried in RiceVarMap V2.0 (http://ricevarmap.ncpgr.cn).
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Affiliation(s)
- Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jiacheng Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ling Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Gang Qin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Chunjiao Xia
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xingbing Xu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yangmeng Su
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yinmeng Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Luchang Ming
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Ling-Ling Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning, China
| | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
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228
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Trevino AE, Müller F, Andersen J, Sundaram L, Kathiria A, Shcherbina A, Farh K, Chang HY, Pașca AM, Kundaje A, Pașca SP, Greenleaf WJ. Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 2021; 184:5053-5069.e23. [PMID: 34390642 DOI: 10.1016/j.cell.2021.07.039] [Citation(s) in RCA: 185] [Impact Index Per Article: 61.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 05/18/2021] [Accepted: 07/28/2021] [Indexed: 12/20/2022]
Abstract
Genetic perturbations of cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Base-pair-resolution neural network models identified strong cell-type-specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD individuals and identified frequently disrupted TF binding sites. This approach illustrates how cell-type-specific mapping can provide insights into the programs governing human development and disease.
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Affiliation(s)
| | - Fabian Müller
- Department of Genetics, Stanford University, Stanford, CA, USA; Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Jimena Andersen
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Stanford Brain Organogenesis Program, Wu Tsai Neuroscience Institute Stanford University, Stanford, CA, USA
| | | | - Arwa Kathiria
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Anna Shcherbina
- Biomedical Data Science Program, Stanford University, Stanford CA, USA
| | - Kyle Farh
- Illumina Artificial Intelligence Laboratory, Illumina Inc, San Diego, CA, USA
| | - Howard Y Chang
- Department of Genetics, Stanford University, Stanford, CA, USA; Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Anca M Pașca
- Department of Pediatrics, Division of Neonatology, Stanford University, Stanford, CA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Sergiu P Pașca
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Stanford Brain Organogenesis Program, Wu Tsai Neuroscience Institute Stanford University, Stanford, CA, USA.
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Applied Physics, Stanford University, Stanford, CA, USA; Chan-Zuckerberg Biohub, San Francisco, CA, USA.
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229
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Yao Q, Ferragina P, Reshef Y, Lettre G, Bauer DE, Pinello L. Motif-Raptor: a cell type-specific and transcription factor centric approach for post-GWAS prioritization of causal regulators. Bioinformatics 2021; 37:2103-2111. [PMID: 33532840 PMCID: PMC11025460 DOI: 10.1093/bioinformatics/btab072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWASs) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype. RESULTS We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait-associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. AVAILABILITY AND IMPLEMENTATION Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiuming Yao
- Department of Pathology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Paolo Ferragina
- Department of Computer Science, University of Pisa, Pisa 56128, Italy
| | - Yakir Reshef
- Department of Computer Science, Harvard University, Cambridge, MA 02138, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guillaume Lettre
- Faculty of Medicine, Université de Montréal, Montreal, Quebec H3C3J7, Canada
- Montreal Heart Institute, Montreal, Quebec H1T1C8, Canada
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Luca Pinello
- Department of Pathology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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230
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Ullah F, Ben-Hur A. A self-attention model for inferring cooperativity between regulatory features. Nucleic Acids Res 2021; 49:e77. [PMID: 33950192 PMCID: PMC8287919 DOI: 10.1093/nar/gkab349] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 04/15/2021] [Accepted: 04/20/2021] [Indexed: 11/14/2022] Open
Abstract
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between regulatory elements in a sequence. A comprehensive evaluation demonstrates the ability of SATORI to identify numerous statistically significant TF-TF interactions, many of which have been previously reported. Our method is able to detect higher numbers of experimentally verified TF-TF interactions than existing methods, and has the advantage of not requiring a computationally expensive post-processing step. Finally, SATORI can be used for detection of any type of feature interaction in models that use a similar attention mechanism, and is not limited to the detection of TF-TF interactions.
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Affiliation(s)
- Fahad Ullah
- Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA
| | - Asa Ben-Hur
- Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA
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231
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Ji Y, Zhou Z, Liu H, Davuluri RV. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. Bioinformatics 2021; 37:2112-2120. [PMID: 33538820 PMCID: PMC11025658 DOI: 10.1093/bioinformatics/btab083] [Citation(s) in RCA: 202] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/31/2020] [Accepted: 02/01/2021] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. RESULTS To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. AVAILABILITY AND IMPLEMENTATION The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanrong Ji
- Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Zhihan Zhou
- Department of Computer Science, Northwestern University, Evanston, IL 60208, USA
| | - Han Liu
- Department of Computer Science, Northwestern University, Evanston, IL 60208, USA
| | - Ramana V Davuluri
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA
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232
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Cazier AP, Blazeck J. Advances in promoter engineering: novel applications and predefined transcriptional control. Biotechnol J 2021; 16:e2100239. [PMID: 34351706 DOI: 10.1002/biot.202100239] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/30/2021] [Accepted: 08/03/2021] [Indexed: 11/08/2022]
Abstract
Synthetic biology continues to progress by relying on more robust tools for transcriptional control, of which promoters are the most fundamental component. Numerous studies have sought to characterize promoter function, determine principles to guide their engineering, and create promoters with stronger expression or tailored inducible control. In this review, we will summarize promoter architecture and highlight recent advances in the field, focusing on the novel applications of inducible promoter design and engineering towards metabolic engineering and cellular therapeutic development. Additionally, we will highlight how the expansion of new, machine learning techniques for modeling and engineering promoter sequences are enabling more accurate prediction of promoter characteristics. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Andrew P Cazier
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst St. NW, Atlanta, Georgia, 30332, USA
| | - John Blazeck
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst St. NW, Atlanta, Georgia, 30332, USA
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233
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Abstract
Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.
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234
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Vaz JM, Balaji S. Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics. Mol Divers 2021; 25:1569-1584. [PMID: 34031788 PMCID: PMC8342355 DOI: 10.1007/s11030-021-10225-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 04/21/2021] [Indexed: 12/17/2022]
Abstract
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing issues previously faced by other computational methods. With the rising attention for personalized and precision medicine, scientists and clinicians have now turned to artificial intelligence systems to provide them with solutions for therapeutics development. CNNs have already provided valuable insights into biological data transformation. Due to the rise of interest in precision and personalized medicine, in this review, we have provided a brief overview of the possibilities of implementing CNNs as an effective tool for analyzing one-dimensional biological data, such as nucleotide and protein sequences, as well as small molecular data, e.g., simplified molecular-input line-entry specification, InChI, binary fingerprints, etc., to categorize the models based on their objective and also highlight various challenges. The review is organized into specific research domains that participate in pharmacogenomics for a more comprehensive understanding. Furthermore, the future intentions of deep learning are outlined.
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Affiliation(s)
- Joel Markus Vaz
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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235
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Hammelman J, Gifford DK. Discovering differential genome sequence activity with interpretable and efficient deep learning. PLoS Comput Biol 2021; 17:e1009282. [PMID: 34370721 PMCID: PMC8376110 DOI: 10.1371/journal.pcbi.1009282] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 08/19/2021] [Accepted: 07/16/2021] [Indexed: 11/23/2022] Open
Abstract
Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/.
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Affiliation(s)
- Jennifer Hammelman
- Computational and Systems Biology, MIT, Cambridge, Massachusetts, United States of America
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, United States of America
| | - David K. Gifford
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts, United States of America
- Department of Electrical Engineering & Computer Science, MIT, Cambridge, Massachusetts, United States of America
- Department of Biological Engineering, MIT, Cambridge, Massachusetts, United States of America
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236
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Zhao H, Tu Z, Liu Y, Zong Z, Li J, Liu H, Xiong F, Zhan J, Hu X, Xie W. PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants. Nucleic Acids Res 2021; 49:W523-W529. [PMID: 34037796 PMCID: PMC8262748 DOI: 10.1093/nar/gkab383] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 04/09/2021] [Accepted: 04/28/2021] [Indexed: 12/13/2022] Open
Abstract
Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs 'in silico saturated mutagenesis' analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.
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Affiliation(s)
- Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zhuo Tu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yinmeng Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zhanxiang Zong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jiacheng Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Hao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Feng Xiong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jinling Zhan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xuehai Hu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Weibo Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
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237
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Huang D, Song B, Wei J, Su J, Coenen F, Meng J. Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. Bioinformatics 2021; 37:i222-i230. [PMID: 34252943 PMCID: PMC8336446 DOI: 10.1093/bioinformatics/btab278] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, UK
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jingjue Wei
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Frans Coenen
- Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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238
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Hallal M, Awad M, Khoueiry P. TempoMAGE: a deep learning framework that exploits the causal dependency between time-series data to predict histone marks in open chromatin regions at time-points with missing ChIP-seq datasets. Bioinformatics 2021; 37:4336-4342. [PMID: 34255822 DOI: 10.1093/bioinformatics/btab513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 07/05/2021] [Accepted: 07/09/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information. Although several machine learning methods were developed to predict histone marks, none exploited the dependence that exists in time-series experiments between data generated at specific time-points to extrapolate these findings to time-points where data cannot be generated for lack or scarcity of materials (i.e., early developmental stages). RESULTS Here, we train a deep learning model named TempoMAGE, to predict the presence or absence of H3K27ac in open chromatin regions by integrating information from sequence, gene expression, chromatin accessibility and the estimated change in H3K27ac state from a reference time-point. We show that adding reference time-point information systematically improves the overall model's performance. Additionally, sequence signatures extracted from our method were exclusive to the training dataset indicating that our model learned data-specific features. As an application, TempoMAGE was able to predict the activity of enhancers from pre-validated in-vivo dataset highlighting its ability to be used for functional annotation of putative enhancers. AVAILABILITY TempoMAGE is freely available through GitHub at https://github.com/pkhoueiry/TempoMAGE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mohammad Hallal
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Lebanon.,Biomedical Engineering Program, American University of Beirut, Lebanon
| | - Mariette Awad
- Department of Electrical and Computer Engineering, American University of Beirut, Lebanon
| | - Pierre Khoueiry
- Department of Biochemistry and Molecular Genetics, Faculty of Medicine, American University of Beirut, Lebanon.,Pillar Genomics Institute, Faculty of Medicine, American University of Beirut, Lebanon
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239
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Li JY, Jin S, Tu XM, Ding Y, Gao G. Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network. Brief Bioinform 2021; 22:6312656. [PMID: 34219140 DOI: 10.1093/bib/bbab233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/25/2021] [Accepted: 05/28/2021] [Indexed: 01/10/2023] Open
Abstract
Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif identification in high-throughput omics data by learning kernel length from data adaptively. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. Meanwhile, vConv could be readily integrated into multi-layer neural networks as an 'in-place replacement' of canonical convolutional layer. All source codes are freely available on GitHub for academic usage.
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Affiliation(s)
- Jing-Yi Li
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
| | - Shen Jin
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Xin-Ming Tu
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
| | - Yang Ding
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
| | - Ge Gao
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
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240
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Pei G, Hu R, Jia P, Zhao Z. DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner. Nucleic Acids Res 2021; 49:W131-W139. [PMID: 34048560 PMCID: PMC8262726 DOI: 10.1093/nar/gkab429] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/26/2021] [Accepted: 05/04/2021] [Indexed: 12/29/2022] Open
Abstract
More than 90% of the genetic variants identified from genome-wide association studies (GWAS) are located in non-coding regions of the human genome. Here, we present a user-friendly web server, DeepFun (https://bioinfo.uth.edu/deepfun/), to assess the functional activity of non-coding genetic variants. This new server is built on a convolutional neural network (CNN) framework that has been extensively evaluated. Specifically, we collected chromatin profiles from ENCODE and Roadmap projects to construct the feature space, including 1548 DNase I accessibility, 1536 histone mark, and 4795 transcription factor binding profiles covering 225 tissues or cell types. With such comprehensive epigenomics annotations, DeepFun expands the functionality of existing non-coding variant prioritizing tools to provide a more specific functional assessment on non-coding variants in a tissue- and cell type-specific manner. By using the datasets from various GWAS studies, we conducted independent validations and demonstrated the functions of the DeepFun web server in predicting the effect of a non-coding variant in a specific tissue or cell type, as well as visualizing the potential motifs in the region around variants. We expect our server will be widely used in genetics, functional genomics, and disease studies.
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Affiliation(s)
- Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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241
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Dasari CM, Bhukya R. Explainable deep neural networks for novel viral genome prediction. APPL INTELL 2021; 52:3002-3017. [PMID: 34764607 PMCID: PMC8232563 DOI: 10.1007/s10489-021-02572-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/26/2021] [Indexed: 11/27/2022]
Abstract
Viral infection causes a wide variety of human diseases including cancer and COVID-19. Viruses invade host cells and associate with host molecules, potentially disrupting the normal function of hosts that leads to fatal diseases. Novel viral genome prediction is crucial for understanding the complex viral diseases like AIDS and Ebola. While most existing computational techniques classify viral genomes, the efficiency of the classification depends solely on the structural features extracted. The state-of-the-art DNN models achieved excellent performance by automatic extraction of classification features, but the degree of model explainability is relatively poor. During model training for viral prediction, proposed CNN, CNN-LSTM based methods (EdeepVPP, EdeepVPP-hybrid) automatically extracts features. EdeepVPP also performs model interpretability in order to extract the most important patterns that cause viral genomes through learned filters. It is an interpretable CNN model that extracts vital biologically relevant patterns (features) from feature maps of viral sequences. The EdeepVPP-hybrid predictor outperforms all the existing methods by achieving 0.992 mean AUC-ROC and 0.990 AUC-PR on 19 human metagenomic contig experiment datasets using 10-fold cross-validation. We evaluate the ability of CNN filters to detect patterns across high average activation values. To further asses the robustness of EdeepVPP model, we perform leave-one-experiment-out cross-validation. It can work as a recommendation system to further analyze the raw sequences labeled as ‘unknown’ by alignment-based methods. We show that our interpretable model can extract patterns that are considered to be the most important features for predicting virus sequences through learned filters.
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Affiliation(s)
| | - Raju Bhukya
- National Institute of Technology, Warangal, Telangana 506004 India
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242
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Zeng X, Park SJ, Nakai K. Characterizing Promoter and Enhancer Sequences by a Deep Learning Method. Front Genet 2021; 12:681259. [PMID: 34211503 PMCID: PMC8239401 DOI: 10.3389/fgene.2021.681259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/20/2021] [Indexed: 11/21/2022] Open
Abstract
Promoters and enhancers are well-known regulatory elements modulating gene expression. As confirmed by high-throughput sequencing technologies, these regulatory elements are bidirectionally transcribed. That is, promoters produce stable mRNA in the sense direction and unstable RNA in the antisense direction, while enhancers transcribe unstable RNA in both directions. Although it is thought that enhancers and promoters share a similar architecture of transcription start sites (TSSs), how the transcriptional machinery distinctly uses these genomic regions as promoters or enhancers remains unclear. To address this issue, we developed a deep learning (DL) method by utilizing a convolutional neural network (CNN) and the saliency algorithm. In comparison with other classifiers, our CNN presented higher predictive performance, suggesting the overarching importance of the high-order sequence features, captured by the CNN. Moreover, our method revealed that there are substantial sequence differences between the enhancers and promoters. Remarkably, the 20–120 bp downstream regions from the center of bidirectional TSSs seemed to contribute to the RNA stability. These regions in promoters tend to have a larger number of guanines and cytosines compared to those in enhancers, and this feature contributed to the classification of the regulatory elements. Our CNN-based method can capture the complex TSS architectures. We found that the genomic regions around TSSs for promoters and enhancers contribute to RNA stability and show GC-biased characteristics as a critical determinant for promoter TSSs.
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Affiliation(s)
- Xin Zeng
- Department of Computational Biology and Medical Science, The University of Tokyo, Kashiwa, Japan
| | - Sung-Joon Park
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Science, The University of Tokyo, Kashiwa, Japan.,Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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243
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Neural Network Methodology for the Identification and Classification of Lipopeptides Based on SMILES Annotation. COMPUTERS 2021. [DOI: 10.3390/computers10060074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Artificial Neural Networks can be applied for the identification and classification of prospective drug candidates such as complex compounds, including lipopeptide, based on their SMILES string representation. The training of neural networks is done with SMILES strings, which are predictive of structural identification; the ANNs are efficient of correctly classifying all compounds, substructures and their analogues distinguishing the drugs based upon atomic organization to obtain lead optimization in drug discovery. The proficiency of the trained ANN models in recognizing and classifying the analogous compounds was tested for analysis of similar compounds, which were not taken previously for training and achieved results with correct classification in the validation set. The best result was achieved with 10 numbers of hidden layers. The R2 value for training is 0.90586; the R2 value for testing is 0.99508; the R2 value after validation is 0.94151; the final value of R2 for total sets is 0.89456. The graphs are plotted between 21 epochs and mean square error (MSE) to report the performance of the model. The value of 798.1735 for the gradient of the curve after 21 iterations and 6 validation checks was obtained. A successful model was developed for the identification and classification of lipopeptides from their SMILES annotation that efficiently classifies similar compounds and supports in decision making for analogue-based drug discovery. This will help in appropriate lead optimization studies for the prediction of potential anticancer and antimicrobial lipopeptide-based therapeutics.
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244
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Zrimec J, Buric F, Kokina M, Garcia V, Zelezniak A. Learning the Regulatory Code of Gene Expression. Front Mol Biosci 2021; 8:673363. [PMID: 34179082 PMCID: PMC8223075 DOI: 10.3389/fmolb.2021.673363] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology.
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Affiliation(s)
- Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mariia Kokina
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Victor Garcia
- School of Life Sciences and Facility Management, Zurich University of Applied Sciences, Wädenswil, Switzerland
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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245
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A deep learning approach to identify gene targets of a therapeutic for human splicing disorders. Nat Commun 2021; 12:3332. [PMID: 34099697 PMCID: PMC8185002 DOI: 10.1038/s41467-021-23663-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 05/07/2021] [Indexed: 01/16/2023] Open
Abstract
Pre-mRNA splicing is a key controller of human gene expression. Disturbances in splicing due to mutation lead to dysregulated protein expression and contribute to a substantial fraction of human disease. Several classes of splicing modulator compounds (SMCs) have been recently identified and establish that pre-mRNA splicing represents a target for therapy. We describe herein the identification of BPN-15477, a SMC that restores correct splicing of ELP1 exon 20. Using transcriptome sequencing from treated fibroblast cells and a machine learning approach, we identify BPN-15477 responsive sequence signatures. We then leverage this model to discover 155 human disease genes harboring ClinVar mutations predicted to alter pre-mRNA splicing as targets for BPN-15477. Splicing assays confirm successful correction of splicing defects caused by mutations in CFTR, LIPA, MLH1 and MAPT. Subsequent validations in two disease-relevant cellular models demonstrate that BPN-15477 increases functional protein, confirming the clinical potential of our predictions.
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246
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Schreiber J, Singh R. Machine learning for profile prediction in genomics. Curr Opin Chem Biol 2021; 65:35-41. [PMID: 34107341 DOI: 10.1016/j.cbpa.2021.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/21/2021] [Accepted: 04/24/2021] [Indexed: 02/08/2023]
Abstract
A recent deluge of publicly available multi-omics data has fueled the development of machine learning methods aimed at investigating important questions in genomics. Although the motivations for these methods vary, a task that is commonly adopted is that of profile prediction, where predictions are made for one or more forms of biochemical activity along the genome, for example, histone modification, chromatin accessibility, or protein binding. In this review, we give an overview of the research works performing profile prediction, define two broad categories of profile prediction tasks, and discuss the types of scientific questions that can be answered in each.
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Affiliation(s)
| | - Ritambhara Singh
- Department of Computer Science, Center for Computational Molecular Biology, Brown University, United States.
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247
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Ge W, Meier M, Roth C, Söding J. Bayesian Markov models improve the prediction of binding motifs beyond first order. NAR Genom Bioinform 2021; 3:lqab026. [PMID: 33928244 PMCID: PMC8057495 DOI: 10.1093/nargab/lqab026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 03/11/2021] [Accepted: 03/30/2021] [Indexed: 12/13/2022] Open
Abstract
Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy. Models that can learn dependencies between positions, for instance, induced by DNA structure preferences, have yielded markedly improved predictions for most TFs on in vivo data. However, they are more prone to overfit the data and to learn patterns merely correlated with rather than directly involved in TF binding. We present an improved, faster version of our Bayesian Markov model software, BaMMmotif2. We tested it with state-of-the-art motif discovery tools on a large collection of ChIP-seq and HT-SELEX datasets. BaMMmotif2 models of fifth-order achieved a median false-discovery-rate-averaged recall 13.6% and 12.2% higher than the next best tool on 427 ChIP-seq datasets and 164 HT-SELEX datasets, respectively, while being 8 to 1000 times faster. BaMMmotif2 models showed no signs of overtraining in cross-cell line and cross-platform tests, with similar improvements on the next-best tool. These results demonstrate that dependencies beyond first order clearly improve binding models for most TFs.
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Affiliation(s)
- Wanwan Ge
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
| | - Markus Meier
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
| | - Christian Roth
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
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248
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Agarwal V, Shendure J. Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks. Cell Rep 2021; 31:107663. [PMID: 32433972 DOI: 10.1016/j.celrep.2020.107663] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 06/11/2019] [Accepted: 04/28/2020] [Indexed: 01/06/2023] Open
Abstract
Algorithms that accurately predict gene structure from primary sequence alone were transformative for annotating the human genome. Can we also predict the expression levels of genes based solely on genome sequence? Here, we sought to apply deep convolutional neural networks toward that goal. Surprisingly, a model that includes only promoter sequences and features associated with mRNA stability explains 59% and 71% of variation in steady-state mRNA levels in human and mouse, respectively. This model, termed Xpresso, more than doubles the accuracy of alternative sequence-based models and isolates rules as predictive as models relying on chromatic immunoprecipitation sequencing (ChIP-seq) data. Xpresso recapitulates genome-wide patterns of transcriptional activity, and its residuals can be used to quantify the influence of enhancers, heterochromatic domains, and microRNAs. Model interpretation reveals that promoter-proximal CpG dinucleotides strongly predict transcriptional activity. Looking forward, we propose cell-type-specific gene-expression predictions based solely on primary sequences as a grand challenge for the field.
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Affiliation(s)
- Vikram Agarwal
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Calico Life Sciences LLC, South San Francisco, CA 94080, USA.
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA; Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
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249
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Talukder A, Barham C, Li X, Hu H. Interpretation of deep learning in genomics and epigenomics. Brief Bioinform 2021; 22:bbaa177. [PMID: 34020542 PMCID: PMC8138893 DOI: 10.1093/bib/bbaa177] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/26/2020] [Accepted: 07/10/2020] [Indexed: 12/17/2022] Open
Abstract
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.
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Affiliation(s)
- Amlan Talukder
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Clayton Barham
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, University of Central Florida, Orlando, FL 32816, USA
| | - Haiyan Hu
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
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Xu H, Jia P, Zhao Z. DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:2004958. [PMID: 33977077 PMCID: PMC8097320 DOI: 10.1002/advs.202004958] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Indexed: 05/08/2023]
Abstract
Approximately 15% of human cancers are estimated to be attributed to viruses. Virus sequences can be integrated into the host genome, leading to genomic instability and carcinogenesis. Here, a new deep convolutional neural network (CNN) model is developed with attention architecture, namely DeepVISP, for accurately predicting oncogenic virus integration sites (VISs) in the human genome. Using the curated benchmark integration data of three viruses, hepatitis B virus (HBV), human herpesvirus (HPV), and Epstein-Barr virus (EBV), DeepVISP achieves high accuracy and robust performance for all three viruses through automatically learning informative features and essential genomic positions only from the DNA sequences. In comparison, DeepVISP outperforms conventional machine learning methods by 8.43-34.33% measured by area under curve (AUC) value enhancement in three viruses. Moreover, DeepVISP can decode cis-regulatory factors that are potentially involved in virus integration and tumorigenesis, such as HOXB7, IKZF1, and LHX6. These findings are supported by multiple lines of evidence in literature. The clustering analysis of the informative motifs reveales that the representative k-mers in clusters could help guide virus recognition of the host genes. A user-friendly web server is developed for predicting putative oncogenic VISs in the human genome using DeepVISP.
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Affiliation(s)
- Haodong Xu
- Center for Precision HealthSchool of Biomedical InformaticsThe University of Texas Health Science Center at Houston (UTHealth)HoustonTX77030USA
| | - Peilin Jia
- Center for Precision HealthSchool of Biomedical InformaticsThe University of Texas Health Science Center at Houston (UTHealth)HoustonTX77030USA
| | - Zhongming Zhao
- Center for Precision HealthSchool of Biomedical InformaticsThe University of Texas Health Science Center at Houston (UTHealth)HoustonTX77030USA
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical SciencesHoustonTX77030USA
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTN37203USA
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