1
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Min A, Schreiber J, Kundaje A, Noble WS. Predicting chromatin conformation contact maps. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589240. [PMID: 38645064 PMCID: PMC11030330 DOI: 10.1101/2024.04.12.589240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets-each represented as a 2D contact map- spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.
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Affiliation(s)
- Alan Min
- Department of Statistics, University of Washington
| | | | | | - William Stafford Noble
- Department of Genome Sciences, University of Washington
- Paul G. Allen School of Computer Science and Engineering, University of Washington
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2
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Gao Z, Liu Q, Zeng W, Jiang R, Wong WH. EpiGePT: a Pretrained Transformer model for epigenomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.15.549134. [PMID: 37502861 PMCID: PMC10370089 DOI: 10.1101/2023.07.15.549134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The inherent similarities between natural language and biological sequences have given rise to great interest in adapting the transformer-based large language models (LLMs) underlying recent breakthroughs in natural language processing (references), for applications in genomics. However, current LLMs for genomics suffer from several limitations such as the inability to include chromatin interactions in the training data, and the inability to make prediction in new cellular contexts not represented in the training data. To mitigate these problems, we propose EpiGePT, a transformer-based pretrained language model for predicting context-specific epigenomic signals and chromatin contacts. By taking the context-specific activities of transcription factors (TFs) and 3D genome interactions into consideration, EpiGePT offers wider applicability and deeper biological insights than models trained on DNA sequence only. In a series of experiments, EpiGePT demonstrates superior performance in a diverse set of epigenomic signals prediction tasks when compared to existing methods. In particular, our model enables cross-cell-type prediction of long-range interactions and offers insight on the functional impact of genetic variants under different cellular contexts. These new capabilities will enhance the usefulness of LLM in the study of gene regulatory mechanisms. We provide free online prediction service of EpiGePT through http://health.tsinghua.edu.cn/epigept/.
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Affiliation(s)
- Zijing Gao
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Qiao Liu
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - Wanwen Zeng
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wing Hung Wong
- Department of Statistics, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Bio-X Program, Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305, USA
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3
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Chen Y, Paramo MI, Zhang Y, Yao L, Shah SR, Jin Y, Zhang J, Pan X, Yu H. Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants. Annu Rev Genet 2023; 57:201-222. [PMID: 37562413 DOI: 10.1146/annurev-genet-030723-120717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.
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Affiliation(s)
- You Chen
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Mauricio I Paramo
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yingying Zhang
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Li Yao
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Sagar R Shah
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Yiyang Jin
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Junke Zhang
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
| | - Xiuqi Pan
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
| | - Haiyuan Yu
- Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, New York, USA;
- Department of Computational Biology, Cornell University, Ithaca, New York, USA
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4
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Li Y, Ju F, Chen Z, Qu Y, Xia H, He L, Wu L, Zhu J, Shao B, Deng P. CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms. Genome Biol 2023; 24:266. [PMID: 37996959 PMCID: PMC10666311 DOI: 10.1186/s13059-023-03103-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/03/2023] [Indexed: 11/25/2023] Open
Abstract
Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of only RNA-seq and ChIP-seq data, allows for the ready generalization of CREaTor to a broad range of cell types.
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Affiliation(s)
- Yongge Li
- Microsoft Research AI4Science, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
| | - Fusong Ju
- Microsoft Research AI4Science, Beijing, China
| | - Zhiyuan Chen
- Microsoft Research AI4Science, Beijing, China
- School of Computing, Australian National University, Canberra, Australia
| | - Yiming Qu
- Microsoft Research AI4Science, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | | | - Liang He
- Microsoft Research AI4Science, Beijing, China
| | - Lijun Wu
- Microsoft Research AI4Science, Beijing, China
| | - Jianwei Zhu
- Microsoft Research AI4Science, Beijing, China
| | - Bin Shao
- Microsoft Research AI4Science, Beijing, China
| | - Pan Deng
- Microsoft Research AI4Science, Beijing, China.
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5
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Klie A, Laub D, Talwar JV, Stites H, Jores T, Solvason JJ, Farley EK, Carter H. Predictive analyses of regulatory sequences with EUGENe. NATURE COMPUTATIONAL SCIENCE 2023; 3:946-956. [PMID: 38177592 PMCID: PMC10768637 DOI: 10.1038/s43588-023-00544-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/27/2023] [Indexed: 01/06/2024]
Abstract
Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to design software for deep-learning analyses in regulatory genomics that are findable, accessible, interoperable and reusable (FAIR) have fallen short of fully meeting these criteria. Here we present elucidating the utility of genomic elements with neural nets (EUGENe), a FAIR toolkit for the analysis of genomic sequences with deep learning. EUGENe consists of a set of modules and subpackages for executing the key functionality of a genomics deep learning workflow: (1) extracting, transforming and loading sequence data from many common file formats; (2) instantiating, initializing and training diverse model architectures; and (3) evaluating and interpreting model behavior. We designed EUGENe as a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses, and illustrate these principles through application of the toolkit to three predictive modeling tasks. We hope that EUGENe represents a springboard towards a collaborative ecosystem for deep-learning applications in genomics research.
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Affiliation(s)
- Adam Klie
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - David Laub
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - James V Talwar
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | | | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Joe J Solvason
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Molecular Biology, University of California San Diego, La Jolla, CA, USA
| | - Emma K Farley
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Molecular Biology, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
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6
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Mostafavi H, Spence JP, Naqvi S, Pritchard JK. Systematic differences in discovery of genetic effects on gene expression and complex traits. Nat Genet 2023; 55:1866-1875. [PMID: 37857933 DOI: 10.1038/s41588-023-01529-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/14/2023] [Indexed: 10/21/2023]
Abstract
Most signals in genome-wide association studies (GWAS) of complex traits implicate noncoding genetic variants with putative gene regulatory effects. However, currently identified regulatory variants, notably expression quantitative trait loci (eQTLs), explain only a small fraction of GWAS signals. Here, we show that GWAS and cis-eQTL hits are systematically different: eQTLs cluster strongly near transcription start sites, whereas GWAS hits do not. Genes near GWAS hits are enriched in key functional annotations, are under strong selective constraint and have complex regulatory landscapes across different tissue/cell types, whereas genes near eQTLs are depleted of most functional annotations, show relaxed constraint, and have simpler regulatory landscapes. We describe a model to understand these observations, including how natural selection on complex traits hinders discovery of functionally relevant eQTLs. Our results imply that GWAS and eQTL studies are systematically biased toward different types of variant, and support the use of complementary functional approaches alongside the next generation of eQTL studies.
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Affiliation(s)
| | | | - Sahin Naqvi
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, CA, USA.
- Department of Biology, Stanford University, Stanford, CA, USA.
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7
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Carbonetto P, Luo K, Sarkar A, Hung A, Tayeb K, Pott S, Stephens M. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. Genome Biol 2023; 24:236. [PMID: 37858253 PMCID: PMC10588049 DOI: 10.1186/s13059-023-03067-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/20/2023] [Indexed: 10/21/2023] Open
Abstract
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
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Affiliation(s)
- Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Research Computing Center, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Abhishek Sarkar
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Vesalius Therapeutics, Cambridge, MA, USA
| | - Anthony Hung
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Sebastian Pott
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Statistics, University of Chicago, Chicago, IL, USA.
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8
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Carbonetto P, Luo K, Sarkar A, Hung A, Tayeb K, Pott S, Stephens M. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.03.531029. [PMID: 36945441 PMCID: PMC10028846 DOI: 10.1101/2023.03.03.531029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
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Affiliation(s)
- Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Research Computing Center, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Abhishek Sarkar
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Vesalius Therapeutics, Cambridge, MA, USA
| | - Anthony Hung
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Sebastian Pott
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
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9
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Korsakova A, Phan AT. Prediction of G4 formation in live cells with epigenetic data: a deep learning approach. NAR Genom Bioinform 2023; 5:lqad071. [PMID: 37636021 PMCID: PMC10448861 DOI: 10.1093/nargab/lqad071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 05/25/2023] [Accepted: 07/26/2023] [Indexed: 08/29/2023] Open
Abstract
G-quadruplexes (G4s) are secondary structures abundant in DNA that may play regulatory roles in cells. Despite the ubiquity of the putative G-quadruplex-forming sequences (PQS) in the human genome, only a small fraction forms G4 structures in cells. Folded G4, histone methylation and chromatin accessibility are all parts of the complex cis regulatory landscape. We propose an approach for prediction of G4 formation in cells that incorporates epigenetic and chromatin accessibility data. The novel approach termed epiG4NN efficiently predicts cell-specific G4 formation in live cells based on a local epigenomic snapshot. Our results confirm the close relationship between H3K4me3 histone methylation, chromatin accessibility and G4 structure formation. Trained on A549 cell data, epiG4NN was then able to predict G4 formation in HEK293T and K562 cell lines. We observe the dependency of model performance with different epigenetic features on the underlying experimental condition of G4 detection. We expect that this approach will contribute to the systematic understanding of correlations between structural and epigenomic feature landscape.
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Affiliation(s)
- Anna Korsakova
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
| | - Anh Tuân Phan
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
- NTU Institute of Structural Biology, Nanyang Technological University, 636921, Singapore
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10
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Salvatore M, Horlacher M, Marsico A, Winther O, Andersson R. Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility. NAR Genom Bioinform 2023; 5:lqad026. [PMID: 37007588 PMCID: PMC10052367 DOI: 10.1093/nargab/lqad026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 04/03/2023] Open
Abstract
Dysfunction of regulatory elements through genetic variants is a central mechanism in the pathogenesis of disease. To better understand disease etiology, there is consequently a need to understand how DNA encodes regulatory activity. Deep learning methods show great promise for modeling of biomolecular data from DNA sequence but are limited to large input data for training. Here, we develop ChromTransfer, a transfer learning method that uses a pre-trained, cell-type agnostic model of open chromatin regions as a basis for fine-tuning on regulatory sequences. We demonstrate superior performances with ChromTransfer for learning cell-type specific chromatin accessibility from sequence compared to models not informed by a pre-trained model. Importantly, ChromTransfer enables fine-tuning on small input data with minimal decrease in accuracy. We show that ChromTransfer uses sequence features matching binding site sequences of key transcription factors for prediction. Together, these results demonstrate ChromTransfer as a promising tool for learning the regulatory code.
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Affiliation(s)
| | | | - Annalisa Marsico
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Ole Winther
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200, Copenhagen, Denmark
- Section for Cognitive Systems, DTU Compute, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
- Department of Genomic medicine, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Robin Andersson
- To whom correspondence should be addressed. Tel: +45 35330245;
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11
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Quan L, Chu X, Sun X, Wu T, Lyu Q. How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1594-1599. [PMID: 35471887 DOI: 10.1109/tcbb.2022.3170343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.
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12
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Zheng A, Shen Z, Glass CK, Gymrek M. Deep learning predicts the impact of regulatory variants on cell-type-specific enhancers in the brain. BIOINFORMATICS ADVANCES 2023; 3:vbad002. [PMID: 36726730 PMCID: PMC9887460 DOI: 10.1093/bioadv/vbad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/11/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Motivation Previous studies have shown that the heritability of multiple brain-related traits and disorders is highly enriched in transcriptional enhancer regions. However, these regions often contain many individual variants, while only a subset of them are likely to causally contribute to a trait. Statistical fine-mapping techniques can identify putative causal variants, but their resolution is often limited, especially in regions with multiple variants in high linkage disequilibrium. In these cases, alternative computational methods to estimate the impact of individual variants can aid in variant prioritization. Results Here, we develop a deep learning pipeline to predict cell-type-specific enhancer activity directly from genomic sequences and quantify the impact of individual genetic variants in these regions. We show that the variants highlighted by our deep learning models are targeted by purifying selection in the human population, likely indicating a functional role. We integrate our deep learning predictions with statistical fine-mapping results for 8 brain-related traits, identifying 63 distinct candidate causal variants predicted to contribute to these traits by modulating enhancer activity, representing 6% of all genome-wide association study signals analyzed. Overall, our study provides a valuable computational method that can prioritize individual variants based on their estimated regulatory impact, but also highlights the limitations of existing methods for variant prioritization and fine-mapping. Availability and implementation The data underlying this article, nucleotide-level importance scores, and code for running the deep learning pipeline are available at https://github.com/Pandaman-Ryan/AgentBind-brain. Contact mgymrek@ucsd.edu. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
| | | | - Christopher K Glass
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA,Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
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13
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Lan AY, Corces MR. Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases. Front Aging Neurosci 2022; 14:1027224. [PMID: 36466610 PMCID: PMC9716280 DOI: 10.3389/fnagi.2022.1027224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022] Open
Abstract
Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.
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Affiliation(s)
- Alexander Y. Lan
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - M. Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
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14
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Zhao Y, Shao J, Asmann YW. Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:899-911. [PMID: 35931322 PMCID: PMC10025763 DOI: 10.1016/j.gpb.2022.07.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 06/05/2022] [Accepted: 07/25/2022] [Indexed: 01/12/2023]
Abstract
Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.
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Affiliation(s)
- Yongbing Zhao
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA.
| | - Jinfeng Shao
- The Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20852, USA
| | - Yan W Asmann
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL 32224, USA.
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15
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Abstract
The tremendous amount of biological sequence data available, combined with the recent methodological breakthrough in deep learning in domains such as computer vision or natural language processing, is leading today to the transformation of bioinformatics through the emergence of deep genomics, the application of deep learning to genomic sequences. We review here the new applications that the use of deep learning enables in the field, focusing on three aspects: the functional annotation of genomes, the sequence determinants of the genome functions and the possibility to write synthetic genomic sequences.
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16
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Liu Q, Hua K, Zhang X, Wong WH, Jiang R. DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:496-507. [PMID: 35293310 PMCID: PMC9801045 DOI: 10.1016/j.gpb.2021.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/31/2021] [Accepted: 09/27/2021] [Indexed: 01/26/2023]
Abstract
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors (TFs) and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. To solve this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding statuses of TFs, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin accessible regions and then incorporates such features with expression levels and binding activities of human core TFs to predict novel chromatin accessible regions. In a series of systematic comparisons with existing methods, DeepCAGE exhibits superior performance in not only the classification but also the regression of chromatin accessibility signals. In a detailed analysis of TF activities, DeepCAGE successfully extracts novel binding motifs and measures the contribution of a TF to the regulation with respect to a specific locus in a certain cell type. When applied to whole-genome sequencing data analysis, our method successfully prioritizes putative deleterious variants underlying a human complex trait and thus provides insights into the understanding of disease-associated genetic variants. DeepCAGE can be downloaded from https://github.com/kimmo1019/DeepCAGE.
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Affiliation(s)
- Qiao Liu
- Ministry of Education Key Laboratory of Bioinformatics; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China,Department of Statistics, Stanford University, Stanford, CA 94305, USA
| | - Kui Hua
- Ministry of Education Key Laboratory of Bioinformatics; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wing Hung Wong
- Department of Statistics, Stanford University, Stanford, CA 94305, USA,Corresponding authors.
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China,Corresponding authors.
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17
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Prediction of histone post-translational modification patterns based on nascent transcription data. Nat Genet 2022; 54:295-305. [PMID: 35273399 PMCID: PMC9444190 DOI: 10.1038/s41588-022-01026-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 01/24/2022] [Indexed: 01/01/2023]
Abstract
The role of histone modifications in transcription remains incompletely understood. Here, we examine the relationship between histone modifications and transcription using experimental perturbations combined with sensitive machine-learning tools. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3, consistent with a role for RNA in recruiting PRC2. A subset of DNase-I-hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than an essential regulatory, role in transcription.
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18
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Zhao Y, Dong Y, Hong W, Jiang C, Yao K, Cheng C. Computational modeling of chromatin accessibility identified important epigenomic regulators. BMC Genomics 2022; 23:19. [PMID: 34996354 PMCID: PMC8742372 DOI: 10.1186/s12864-021-08234-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/03/2021] [Indexed: 11/28/2022] Open
Abstract
Chromatin accessibility is essential for transcriptional activation of genomic regions. It is well established that transcription factors (TFs) and histone modifications (HMs) play critical roles in chromatin accessibility regulation. However, there is a lack of studies that quantify these relationships. Here we constructed a two-layer model to predict chromatin accessibility by integrating DNA sequence, TF binding, and HM signals. By applying the model to two human cell lines (GM12878 and HepG2), we found that DNA sequences had limited power for accessibility prediction, while both TF binding and HM signals predicted chromatin accessibility with high accuracy. According to the HM model, HM features determined chromatin accessibility in a cell line shared manner, with the prediction power attributing to five core HM types. Results from the TF model indicated that chromatin accessibility was determined by a subset of informative TFs including both cell line-specific and generic TFs. The combined model of both TF and HM signals did not further improve the prediction accuracy, indicating that they provide redundant information in terms of chromatin accessibility prediction. The TFs and HM models can also distinguish the chromatin accessibility of proximal versus distal transcription start sites with high accuracy.
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Affiliation(s)
- Yanding Zhao
- Department of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yadong Dong
- Department of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Wei Hong
- Department of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Chongming Jiang
- Department of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kevin Yao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA
| | - Chao Cheng
- Department of Medicine, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA.
- The Institute for Clinical and Translational Research, Baylor College of Medicine, Room ICTR 100D, One Baylor Plaza, Baylor College of Medicine, Houston, TX, 77030, USA.
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19
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Krismer K, Hammelman J, Gifford DK. OUP accepted manuscript. Bioinformatics 2022; 38:2381-2388. [PMID: 35191481 PMCID: PMC9048673 DOI: 10.1093/bioinformatics/btac101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 01/26/2022] [Accepted: 02/18/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understanding of the biology of regulatory elements is often hindered by the complexity of the predictive model and thus poor interpretability of its decision boundaries. To address this, we introduce seqgra, a deep learning pipeline that incorporates the rule-based simulation of biological sequence data and the training and evaluation of models, whose decision boundaries mirror the rules from the simulation process. Results We show that seqgra can be used to (i) generate data under the assumption of a hypothesized model of genome regulation, (ii) identify neural network architectures capable of recovering the rules of said model and (iii) analyze a model’s predictive performance as a function of training set size and the complexity of the rules behind the simulated data. Availability and implementation The source code of the seqgra package is hosted on GitHub (https://github.com/gifford-lab/seqgra). seqgra is a pip-installable Python package. Extensive documentation can be found at https://kkrismer.github.io/seqgra. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Konstantin Krismer
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jennifer Hammelman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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20
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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: 294] [Impact Index Per Article: 98.0] [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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21
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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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22
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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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23
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Cofer EM, Raimundo J, Tadych A, Yamazaki Y, Wong AK, Theesfeld CL, Levine MS, Troyanskaya OG. Modeling transcriptional regulation of model species with deep learning. Genome Res 2021; 31:1097-1105. [PMID: 33888512 PMCID: PMC8168591 DOI: 10.1101/gr.266171.120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 04/19/2021] [Indexed: 12/11/2022]
Abstract
To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the cis-regulatory activities for four widely studied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.
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Affiliation(s)
- Evan M Cofer
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.,Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - João Raimundo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Alicja Tadych
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Yuji Yamazaki
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.,Yutaka Seino Distinguished Center for Diabetes Research, Kansai Electric Power Medical Research Institute, Kobe, 650-0047, Japan
| | - Aaron K Wong
- Flatiron Institute, Simons Foundation, New York, New York 10010, USA
| | - Chandra L Theesfeld
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA
| | - Michael S Levine
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.,Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.,Flatiron Institute, Simons Foundation, New York, New York 10010, USA.,Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
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24
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Cheng J, Çelik MH, Kundaje A, Gagneur J. MTSplice predicts effects of genetic variants on tissue-specific splicing. Genome Biol 2021; 22:94. [PMID: 33789710 PMCID: PMC8011109 DOI: 10.1186/s13059-021-02273-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 01/14/2021] [Indexed: 12/20/2022] Open
Abstract
We develop the free and open-source model Multi-tissue Splicing (MTSplice) to predict the effects of genetic variants on splicing of cassette exons in 56 human tissues. MTSplice combines MMSplice, which models constitutive regulatory sequences, with a new neural network that models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting tissue-specific variations associated with genetic variants in most tissues of the GTEx dataset, with largest improvements on brain tissues. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. We foresee that MTSplice will aid interpreting variants associated with tissue-specific disorders.
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Affiliation(s)
- Jun Cheng
- Department of Informatics, Technical University of Munich, Boltzmannstraße, Garching, 85748, Germany.
| | - Muhammed Hasan Çelik
- Department of Informatics, Technical University of Munich, Boltzmannstraße, Garching, 85748, Germany
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Julien Gagneur
- Department of Informatics, Technical University of Munich, Boltzmannstraße, Garching, 85748, Germany.
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute of Human Genetics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
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25
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Abstract
Motivation The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typically, such investigations have taken a ‘black box’ approach in which the internal structure of themodel used is set purely by machine learning considerations with little consideration of representing the internalstructure of the biological system by the mathematical structure of the DNN. DNNs have not yet been applied to thedetailed modeling of transcriptional control in which mRNA production is controlled by the binding of specific transcriptionfactors to DNA, in part because such models are in part formulated in terms of specific chemical equationsthat appear different in form from those used in neural networks. Results In this paper, we give an example of a DNN whichcan model the detailed control of transcription in a precise and predictive manner. Its internal structure is fully interpretableand is faithful to underlying chemistry of transcription factor binding to DNA. We derive our DNN from asystems biology model that was not previously recognized as having a DNN structure. Although we apply our DNNto data from the early embryo of the fruit fly Drosophila, this system serves as a test bed for analysis of much larger datasets obtained by systems biology studies on a genomic scale. . Availability and implementation The implementation and data for the models used in this paper are in a zip file in the supplementary material. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yi Liu
- Department of Statistics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Kenneth Barr
- Department of Human Genetics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - John Reinitz
- Departments of Statistics, Ecology and Evolution, Molecular Genetics & Cell Biology, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
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26
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Bartoszewicz JM, Seidel A, Renard BY. Interpretable detection of novel human viruses from genome sequencing data. NAR Genom Bioinform 2021; 3:lqab004. [PMID: 33554119 PMCID: PMC7849996 DOI: 10.1093/nargab/lqab004] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/04/2021] [Accepted: 01/15/2021] [Indexed: 01/21/2023] Open
Abstract
Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.
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Affiliation(s)
- Jakub M Bartoszewicz
- Bioinformatics (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
- Department of Mathematics and Computer Science, Free University of Berlin, 14195 Berlin, Germany
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, 14482 Potsdam, Brandenburg, Germany
- Digital Engineering Faculty, University of Postdam, 14482 Potsdam, Brandenburg, Germany
| | - Anja Seidel
- Bioinformatics (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
- Department of Mathematics and Computer Science, Free University of Berlin, 14195 Berlin, Germany
| | - Bernhard Y Renard
- Bioinformatics (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, 14482 Potsdam, Brandenburg, Germany
- Digital Engineering Faculty, University of Postdam, 14482 Potsdam, Brandenburg, Germany
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27
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MVP predicts the pathogenicity of missense variants by deep learning. Nat Commun 2021; 12:510. [PMID: 33479230 PMCID: PMC7820281 DOI: 10.1038/s41467-020-20847-0] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 12/14/2020] [Indexed: 12/15/2022] Open
Abstract
Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. Here, we describe MVP (Missense Variant Pathogenicity prediction), a new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors. We train the model separately in genes that are intolerant of loss of function variants and the ones that are tolerant in order to take account of potentially different genetic effect size and mode of action. We compile cancer mutation hotspots and de novo variants from developmental disorders for benchmarking. Overall, MVP achieves better performance in prioritizing pathogenic missense variants than previous methods, especially in genes tolerant of loss of function variants. Finally, using MVP, we estimate that de novo coding variants contribute to 7.8% of isolated congenital heart disease, nearly doubling previous estimates. Accurate prediction of variant pathogenicity is essential to understanding genetic risks in disease. Here, the authors present a deep neural network method for prediction of missense variant pathogenicity, MVP, and demonstrate its utility in prioritizing de novo variants contributing to developmental disorders.
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28
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Tao H, Li H, Xu K, Hong H, Jiang S, Du G, Wang J, Sun Y, Huang X, Ding Y, Li F, Zheng X, Chen H, Bo X. Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles. Brief Bioinform 2021; 22:6102668. [PMID: 33454752 PMCID: PMC8424394 DOI: 10.1093/bib/bbaa405] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 12/14/2022] Open
Abstract
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.
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Affiliation(s)
- Huan Tao
- Beijing Institute of Radiation Medicine
| | - Hao Li
- Beijing Institute of Radiation Medicine
| | - Kang Xu
- Beijing Institute of Radiation Medicine
| | - Hao Hong
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Shuai Jiang
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Guifang Du
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | | | - Yu Sun
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Xin Huang
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Yang Ding
- Beijing Institute of Radiation Medicine
| | - Fei Li
- Chinese Academy of Sciences, Department of Computer Network Information Center
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29
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Schreiber J, Singh R, Bilmes J, Noble WS. A pitfall for machine learning methods aiming to predict across cell types. Genome Biol 2020; 21:282. [PMID: 33213499 PMCID: PMC7678316 DOI: 10.1186/s13059-020-02177-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Accepted: 10/07/2020] [Indexed: 01/19/2023] Open
Abstract
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to perform well by effectively memorizing the average activity associated with each locus across the training cell types. We demonstrate this phenomenon in the context of predicting gene expression and chromatin domain boundaries, and we suggest methods to diagnose and avoid the pitfall. We anticipate that, as more data becomes available, future projects will increasingly risk suffering from this issue.
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Affiliation(s)
- Jacob Schreiber
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, USA
| | - Ritambhara Singh
- Department of Genome Science, University of Washington, Seattle, USA.,Current Affiliation: Department of Computer Science, and Center for Computational Molecular Biology, Brown University, Providence, 02906, RI, United States
| | - Jeffrey Bilmes
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, USA.,Department of Electrical & Computer Engineering, University of Washington, Seattle, USA
| | - William Stafford Noble
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, USA. .,Department of Genome Science, University of Washington, Seattle, USA.
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30
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Maslova A, Ramirez RN, Ma K, Schmutz H, Wang C, Fox C, Ng B, Benoist C, Mostafavi S. Deep learning of immune cell differentiation. Proc Natl Acad Sci U S A 2020; 117:25655-25666. [PMID: 32978299 PMCID: PMC7568267 DOI: 10.1073/pnas.2011795117] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular atlas of chromatin accessibility across 81 immune cell types, we asked if a convolutional neural network (CNN) could learn to infer cell type-specific chromatin accessibility solely from regulatory DNA sequences. With a tailored architecture and an ensemble approach to CNN parameter interpretation, we show that our trained network ("AI-TAC") does so by rediscovering ab initio the binding motifs for known regulators and some unknown ones. Motifs whose importance is learned virtually as functionally important overlap strikingly well with positions determined by chromatin immunoprecipitation for several TFs. AI-TAC establishes a hierarchy of TFs and their interactions that drives lineage specification and also identifies stage-specific interactions, like Pax5/Ebf1 vs. Pax5/Prdm1, or the role of different NF-κB dimers in different cell types. AI-TAC assigns Spi1/Cebp and Pax5/Ebf1 as the drivers necessary for myeloid and B lineage fates, respectively, but no factors seemed as dominantly required for T cell differentiation, which may represent a fall-back pathway. Mouse-trained AI-TAC can parse human DNA, revealing a strikingly similar ranking of influential TFs and providing additional support that AI-TAC is a generalizable regulatory sequence decoder. Thus, deep learning can reveal the regulatory syntax predictive of the full differentiative complexity of the immune system.
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Affiliation(s)
- Alexandra Maslova
- Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | | | - Ke Ma
- Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Hugo Schmutz
- Department of Immunology, Harvard Medical School, Boston, MA 02115
| | - Chendi Wang
- Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Curtis Fox
- Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Bernard Ng
- Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | | | - Sara Mostafavi
- Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Canadian Institute for Advanced Research, CIFAR AI, Toronto, ON M5G 1M1, Canada
- Vector Institute, Toronto, ON M5G 1M1, Canada
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31
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Environmental Epigenetics of Diesel Particulate Matter Toxicogenomics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17207386. [PMID: 33050454 PMCID: PMC7650680 DOI: 10.3390/ijerph17207386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 11/17/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disruptions in social communication and behavioral flexibility. Both genetic and environmental factors contribute to ASD risk. Epidemiologic studies indicate that roadway vehicle exhaust and in utero exposure to diesel particulate matter (DPM) are associated with ASD. Using the Comparative Toxicogenomics Database (CTD), we identified genes connected to DPM exposure and ASD, extracted the known enhancers/promoters of the identified genes, and integrated this with Assay for Transposase Accessible Chromatin (ATAC-seq) data from DPM-exposed human neural progenitor cells. Enhancer/promoter elements with significantly different chromosome accessibility revealed enriched DNA sequence motifs with transcription factor binding sites for EGR1. Variant extraction for linkage disequilibrium blocks of these regions followed by analysis through Genome Wide Association Studies (GWAS) revealed multiple neurological trait associations including exploratory eye movement and brain volume measurement. This approach highlights the effects of pollution on the regulatory regions of genes implicated in ASD by genetic studies, indicating convergence of genetic and environmental factors on molecular networks that contribute to ASD. Integration of publicly available data from the CTD, cell culture exposure studies, and phenotypic genetics synergize extensive evidence of chemical exposures on gene regulation for altered brain development.
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32
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He Y, Shen Z, Zhang Q, Wang S, Huang DS. A survey on deep learning in DNA/RNA motif mining. Brief Bioinform 2020; 22:5916939. [PMID: 33005921 PMCID: PMC8293829 DOI: 10.1093/bib/bbaa229] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/19/2020] [Accepted: 08/24/2020] [Indexed: 01/18/2023] Open
Abstract
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and management. For the past few decades, researchers have been working on designing new efficient and accurate algorithms for mining motif. These algorithms can be roughly divided into two categories: the enumeration approach and the probabilistic method. In recent years, machine learning methods had made great progress, especially the algorithm represented by deep learning had achieved good performance. Existing deep learning methods in motif mining can be roughly divided into three types of models: convolutional neural network (CNN) based models, recurrent neural network (RNN) based models, and hybrid CNN–RNN based models. We introduce the application of deep learning in the field of motif mining in terms of data preprocessing, features of existing deep learning architectures and comparing the differences between the basic deep learning models. Through the analysis and comparison of existing deep learning methods, we found that the more complex models tend to perform better than simple ones when data are sufficient, and the current methods are relatively simple compared with other fields such as computer vision, language processing (NLP), computer games, etc. Therefore, it is necessary to conduct a summary in motif mining by deep learning, which can help researchers understand this field.
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Affiliation(s)
- Ying He
- computer science and technology at Tongji University, China
| | - Zhen Shen
- computer science and technology at Tongji University, China
| | - Qinhu Zhang
- computer science and technology at Tongji University, China
| | - Siguo Wang
- computer science and technology at Tongji University, China
| | - De-Shuang Huang
- Institute of Machines Learning and Systems Biology, Tongji University
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33
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Hammelman J, Krismer K, Banerjee B, Gifford DK, Sherwood RI. Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay. Genome Res 2020; 30:1468-1480. [PMID: 32973041 PMCID: PMC7605270 DOI: 10.1101/gr.263228.120] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 08/26/2020] [Indexed: 12/20/2022]
Abstract
A key mechanism in cellular regulation is the ability of the transcriptional machinery to physically access DNA. Transcription factors interact with DNA to alter the accessibility of chromatin, which enables changes to gene expression during development or disease or as a response to environmental stimuli. However, the regulation of DNA accessibility via the recruitment of transcription factors is difficult to study in the context of the native genome because every genomic site is distinct in multiple ways. Here we introduce the multiplexed integrated accessibility assay (MIAA), an assay that measures chromatin accessibility of synthetic oligonucleotide sequence libraries integrated into a controlled genomic context with low native accessibility. We apply MIAA to measure the effects of sequence motifs on cell type-specific accessibility between mouse embryonic stem cells and embryonic stem cell-derived definitive endoderm cells, screening 7905 distinct DNA sequences. MIAA recapitulates differential accessibility patterns of 100-nt sequences derived from natively differential genomic regions, identifying E-box motifs common to epithelial-mesenchymal transition driver transcription factors in stem cell-specific accessible regions that become repressed in endoderm. We show that a single binding motif for a key regulatory transcription factor is sufficient to open chromatin, and classify sets of stem cell-specific, endoderm-specific, and shared accessibility-modifying transcription factor motifs. We also show that overexpression of two definitive endoderm transcription factors, T and Foxa2, results in changes to accessibility in DNA sequences containing their respective DNA-binding motifs and identify preferential motif arrangements that influence accessibility.
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Affiliation(s)
- Jennifer Hammelman
- Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Konstantin Krismer
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Budhaditya Banerjee
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
| | - David K Gifford
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Richard I Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA
- Hubrecht Institute, 3584 CT Utrecht, Netherlands
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34
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Xiao M, Zhuang Z, Pan W. Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks. Genes (Basel) 2019; 11:E41. [PMID: 31905774 PMCID: PMC7016741 DOI: 10.3390/genes11010041] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 12/23/2019] [Accepted: 12/26/2019] [Indexed: 12/13/2022] Open
Abstract
Enhancer-promoter interactions (EPIs) are crucial for transcriptional regulation. Mapping such interactions proves useful for understanding disease regulations and discovering risk genes in genome-wide association studies. Some previous studies showed that machine learning methods, as computational alternatives to costly experimental approaches, performed well in predicting EPIs from local sequence and/or local epigenomic data. In particular, deep learning methods were demonstrated to outperform traditional machine learning methods, and using DNA sequence data alone could perform either better than or almost as well as only utilizing epigenomic data. However, most, if not all, of these previous studies were based on randomly splitting enhancer-promoter pairs as training, tuning, and test data, which has recently been pointed out to be problematic; due to multiple and duplicating/overlapping enhancers (and promoters) in enhancer-promoter pairs in EPI data, such random splitting does not lead to independent training, tuning, and test data, thus resulting in model over-fitting and over-estimating predictive performance. Here, after correcting this design issue, we extensively studied the performance of various deep learning models with local sequence and epigenomic data around enhancer-promoter pairs. Our results confirmed much lower performance using either sequence or epigenomic data alone, or both, than reported previously. We also demonstrated that local epigenomic features were more informative than local sequence data. Our results were based on an extensive exploration of many convolutional neural network (CNN) and feed-forward neural network (FNN) structures, and of gradient boosting as a representative of traditional machine learning.
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Affiliation(s)
- Mengli Xiao
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Zhong Zhuang
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA;
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35
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Wnuk K, Sudol J, Givechian KB, Soon-Shiong P, Rabizadeh S, Szeto C, Vaske C. Deep Learning Implicitly Handles Tissue Specific Phenomena to Predict Tumor DNA Accessibility and Immune Activity. iScience 2019; 20:119-136. [PMID: 31563852 PMCID: PMC6823659 DOI: 10.1016/j.isci.2019.09.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/23/2019] [Accepted: 09/11/2019] [Indexed: 01/22/2023] Open
Abstract
DNA accessibility is a key dynamic feature of chromatin regulation that can potentiate transcriptional events and tumor progression. To gain insight into chromatin state across existing tumor data, we improved neural network models for predicting accessibility from DNA sequence and extended them to incorporate a global set of RNA sequencing gene expression inputs. Our expression-informed model expanded the application domain beyond specific tissue types to tissues not present in training and achieved consistently high accuracy in predicting DNA accessibility at promoter and promoter flank regions. We then leveraged our new tool by analyzing the DNA accessibility landscape of promoters across The Cancer Genome Atlas. We show that in lung adenocarcinoma the accessibility perspective uniquely highlights immune pathways inversely correlated with a more open chromatin state and that accessibility patterns learned from even a single tumor type can discriminate immune inflammation across many cancers, often with direct relation to patient prognosis.
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Affiliation(s)
- Kamil Wnuk
- ImmunityBio Inc., Culver City, CA 90232, USA.
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