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Garda S, Schwarz JM, Schuelke M, Leser U, Seelow D. Public data sources for regulatory genomic features. MED GENET-BERLIN 2021; 33:167-177. [PMID: 38836022 PMCID: PMC11113004 DOI: 10.1515/medgen-2021-2075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 06/24/2021] [Indexed: 06/06/2024]
Abstract
High-throughput technologies have led to a continuously growing amount of information about regulatory features in the genome. A wealth of data generated by large international research consortia is available from online databases. Disease-driven studies provide details on specific DNA elements or epigenetic modifications regulating gene expression in specific cellular and developmental contexts, but these results are usually only published in scientific articles. All this information can be helpful in interpreting variants in the regulatory genome. This review describes a selection of high-profile data sources providing information on the non-coding genome, as well as pitfalls and techniques to search and capture information from the literature.
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Affiliation(s)
- Samuele Garda
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Jana Marie Schwarz
- Department of Neuropediatrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Markus Schuelke
- Department of Neuropediatrics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulf Leser
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Dominik Seelow
- BIH-Bioinformatics and Translational Genetics, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Institute for Medical and Human Genetics, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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2
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Sänger M, Leser U. Large-scale entity representation learning for biomedical relationship extraction. Bioinformatics 2021; 37:236-242. [PMID: 32726411 DOI: 10.1093/bioinformatics/btaa674] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 07/14/2020] [Accepted: 07/21/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The automatic extraction of published relationships between molecular entities has important applications in many biomedical fields, ranging from Systems Biology to Personalized Medicine. Existing works focused on extracting relationships described in single articles or in single sentences. However, a single record is rarely sufficient to judge upon the biological correctness of a relation, as experimental evidence might be weak or only valid in a certain context. Furthermore, statements may be more speculative than confirmative, and different articles often contradict each other. Experts therefore always take the complete literature into account to take a reliable decision upon a relationship. It is an open research question how to do this effectively in an automatic manner. RESULTS We propose two novel relation extraction approaches which use recent representation learning techniques to create comprehensive models of biomedical entities or entity-pairs, respectively. These representations are learned by considering all publications from PubMed mentioning an entity or a pair. They are used as input for a neural network for classifying relations globally, i.e. the derived predictions are corpus-based, not sentence- or article based as in prior art. Experiments on the extraction of mutation-disease, drug-disease and drug-drug relationships show that the learned embeddings indeed capture semantic information of the entities under study and outperform traditional methods by 4-29% regarding F1 score. AVAILABILITY AND IMPLEMENTATION Source codes are available at: https://github.com/mariosaenger/bio-re-with-entity-embeddings. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mario Sänger
- Computer Science Department, Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Ulf Leser
- Computer Science Department, Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
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3
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Casper J, Zweig AS, Villarreal C, Tyner C, Speir ML, Rosenbloom KR, Raney BJ, Lee CM, Lee BT, Karolchik D, Hinrichs AS, Haeussler M, Guruvadoo L, Navarro Gonzalez J, Gibson D, Fiddes IT, Eisenhart C, Diekhans M, Clawson H, Barber GP, Armstrong J, Haussler D, Kuhn RM, Kent WJ. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res 2019; 46:D762-D769. [PMID: 29106570 PMCID: PMC5753355 DOI: 10.1093/nar/gkx1020] [Citation(s) in RCA: 338] [Impact Index Per Article: 67.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/18/2017] [Indexed: 12/14/2022] Open
Abstract
The UCSC Genome Browser (https://genome.ucsc.edu) provides a web interface for exploring annotated genome assemblies. The assemblies and annotation tracks are updated on an ongoing basis—12 assemblies and more than 28 tracks were added in the past year. Two recent additions are a display of CRISPR/Cas9 guide sequences and an interactive navigator for gene interactions. Other upgrades from the past year include a command-line version of the Variant Annotation Integrator, support for Human Genome Variation Society variant nomenclature input and output, and a revised highlighting tool that now supports multiple simultaneous regions and colors.
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Affiliation(s)
- Jonathan Casper
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ann S Zweig
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Chris Villarreal
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Cath Tyner
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Matthew L Speir
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Kate R Rosenbloom
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Brian J Raney
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Christopher M Lee
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Brian T Lee
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Donna Karolchik
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angie S Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Maximilian Haeussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Luvina Guruvadoo
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - David Gibson
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ian T Fiddes
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - Mark Diekhans
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Hiram Clawson
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Galt P Barber
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Joel Armstrong
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - David Haussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA.,Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Robert M Kuhn
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - W James Kent
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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4
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Estimation of Transcription Factor Activity in Knockdown Studies. Sci Rep 2019; 9:9593. [PMID: 31270369 PMCID: PMC6610105 DOI: 10.1038/s41598-019-46053-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 06/20/2019] [Indexed: 11/24/2022] Open
Abstract
Numerous methods have been developed trying to infer actual regulatory events in a sample. A prominent class of methods model genome-wide gene expression as linear equations derived from a transcription factor (TF) – gene network and optimizes parameters to fit the measured expression intensities. We apply four such methods on experiments with a TF-knockdown (KD) in human and E. coli. The transcriptome data provides clear expression signals and thus represents an extremely favorable test setting. The methods estimate activity changes of all TFs, which we expect to be highest in the KD TF. However, only in 15 out of 54 cases, the KD TFs ranked in the top 5%. We show that this poor overall performance cannot be attributed to a low effectiveness of the knockdown or the specific regulatory network provided as background knowledge. Further, the ranks of regulators related to the KD TF by the network or pathway are not significantly different from a random selection. In general, the result overlaps of different methods are small, indicating that they draw very different conclusions when presented with the same, presumably simple, inference problem. These results show that the investigated methods cannot yield robust TF activity estimates in knockdown schemes.
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5
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Reverse engineering gene regulatory networks by modular response analysis - a benchmark. Essays Biochem 2018; 62:535-547. [PMID: 30315094 DOI: 10.1042/ebc20180012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 08/13/2018] [Accepted: 08/24/2018] [Indexed: 11/17/2022]
Abstract
Gene regulatory networks control the cellular phenotype by changing the RNA and protein composition. Despite its importance, the gene regulatory network in higher organisms is only partly mapped out. Here, we investigate the potential of reverse engineering methods to unravel the structure of these networks. Particularly, we focus on modular response analysis (MRA), a method that can disentangle networks from perturbation data. We benchmark a version of MRA that was previously successfully applied to reconstruct a signalling-driven genetic network, termed MLMSMRA, to test cases mimicking various aspects of gene regulatory networks. We then investigate the performance in comparison with other MRA realisations and related methods. The benchmark shows that MRA has the potential to predict functional interactions, but also shows that successful application of MRA is restricted to small sparse networks and to data with a low signal-to-noise ratio.
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Lichtblau Y, Zimmermann K, Haldemann B, Lenze D, Hummel M, Leser U. Comparative assessment of differential network analysis methods. Brief Bioinform 2017; 18:837-850. [PMID: 27473063 DOI: 10.1093/bib/bbw061] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Indexed: 12/31/2022] Open
Abstract
Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.
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Roy S, Yun D, Madahian B, Berry MW, Deng LY, Goldowitz D, Homayouni R. Navigating the Functional Landscape of Transcription Factors via Non-Negative Tensor Factorization Analysis of MEDLINE Abstracts. Front Bioeng Biotechnol 2017; 5:48. [PMID: 28894735 PMCID: PMC5581332 DOI: 10.3389/fbioe.2017.00048] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 07/31/2017] [Indexed: 01/09/2023] Open
Abstract
In this study, we developed and evaluated a novel text-mining approach, using non-negative tensor factorization (NTF), to simultaneously extract and functionally annotate transcriptional modules consisting of sets of genes, transcription factors (TFs), and terms from MEDLINE abstracts. A sparse 3-mode term × gene × TF tensor was constructed that contained weighted frequencies of 106,895 terms in 26,781 abstracts shared among 7,695 genes and 994 TFs. The tensor was decomposed into sub-tensors using non-negative tensor factorization (NTF) across 16 different approximation ranks. Dominant entries of each of 2,861 sub-tensors were extracted to form term–gene–TF annotated transcriptional modules (ATMs). More than 94% of the ATMs were found to be enriched in at least one KEGG pathway or GO category, suggesting that the ATMs are functionally relevant. One advantage of this method is that it can discover potentially new gene–TF associations from the literature. Using a set of microarray and ChIP-Seq datasets as gold standard, we show that the precision of our method for predicting gene–TF associations is significantly higher than chance. In addition, we demonstrate that the terms in each ATM can be used to suggest new GO classifications to genes and TFs. Taken together, our results indicate that NTF is useful for simultaneous extraction and functional annotation of transcriptional regulatory networks from unstructured text, as well as for literature based discovery. A web tool called Transcriptional Regulatory Modules Extracted from Literature (TREMEL), available at http://binf1.memphis.edu/tremel, was built to enable browsing and searching of ATMs.
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Affiliation(s)
- Sujoy Roy
- Bioinformatics Program, University of Memphis, Memphis, TN, United States.,Center for Translational Informatics, University of Memphis, Memphis, TN, United States
| | - Daqing Yun
- Computer and Information Sciences Program, Harrisburg University of Science and Technology, Harrisburg, PA, United States
| | - Behrouz Madahian
- Department of Mathematical Sciences, University of Memphis, Memphis, TN, United States
| | - Michael W Berry
- Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN, United States
| | - Lih-Yuan Deng
- Department of Mathematical Sciences, University of Memphis, Memphis, TN, United States
| | - Daniel Goldowitz
- Center for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Ramin Homayouni
- Bioinformatics Program, University of Memphis, Memphis, TN, United States.,Center for Translational Informatics, University of Memphis, Memphis, TN, United States.,Department of Biological Sciences, University of Memphis, Memphis, TN, United States
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8
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Trescher S, Münchmeyer J, Leser U. Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization. BMC SYSTEMS BIOLOGY 2017; 11:41. [PMID: 28347313 PMCID: PMC5369021 DOI: 10.1186/s12918-017-0419-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2016] [Accepted: 03/08/2017] [Indexed: 12/28/2022]
Abstract
Background Gene regulation is one of the most important cellular processes, indispensable for the adaptability of organisms and closely interlinked with several classes of pathogenesis and their progression. Elucidation of regulatory mechanisms can be approached by a multitude of experimental methods, yet integration of the resulting heterogeneous, large, and noisy data sets into comprehensive and tissue or disease-specific cellular models requires rigorous computational methods. Recently, several algorithms have been proposed which model genome-wide gene regulation as sets of (linear) equations over the activity and relationships of transcription factors, genes and other factors. Subsequent optimization finds those parameters that minimize the divergence of predicted and measured expression intensities. In various settings, these methods produced promising results in terms of estimating transcription factor activity and identifying key biomarkers for specific phenotypes. However, despite their common root in mathematical optimization, they vastly differ in the types of experimental data being integrated, the background knowledge necessary for their application, the granularity of their regulatory model, the concrete paradigm used for solving the optimization problem and the data sets used for evaluation. Results Here, we review five recent methods of this class in detail and compare them with respect to several key properties. Furthermore, we quantitatively compare the results of four of the presented methods based on publicly available data sets. Conclusions The results show that all methods seem to find biologically relevant information. However, we also observe that the mutual result overlaps are very low, which contradicts biological intuition. Our aim is to raise further awareness of the power of these methods, yet also to identify common shortcomings and necessary extensions enabling focused research on the critical points. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0419-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Saskia Trescher
- Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany.
| | - Jannes Münchmeyer
- Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
| | - Ulf Leser
- Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany
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