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Rodriguez-Esteban R. New reasons for biologists to write with a formal language. Database (Oxford) 2022; 2022:6600538. [PMID: 35657112 PMCID: PMC9216469 DOI: 10.1093/database/baac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/18/2022] [Accepted: 05/17/2022] [Indexed: 12/03/2022]
Abstract
Current biological writing is afflicted by the use of ambiguous names, convoluted sentences, vague statements and narrative-fitted storylines. This represents a challenge for biological research in general and in particular for fields such as biological database curation and text mining, which have been tasked to cope with exponentially growing content. Improving the quality of biological writing by encouraging unambiguity and precision would foster expository discipline and machine reasoning. More specifically, the routine inclusion of formal languages in biological writing would improve our ability to describe, compile and model biology.
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Affiliation(s)
- Raul Rodriguez-Esteban
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124 , Basel 4070, Switzerland
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2
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Rodriguez-Esteban R, Duarte J, Teixeira PC, Richard F, Koltsova S, So WV. Prediction of standard cell types and functional markers from textual descriptions of flow cytometry gating definitions using machine learning. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2022; 102:220-227. [PMID: 35253974 DOI: 10.1002/cyto.b.22065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 02/02/2022] [Accepted: 02/28/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND A key step in clinical flow cytometry data analysis is gating, which involves the identification of cell populations. The process of gating produces a set of reportable results, which are typically described by gating definitions. The non-standardized, non-interpreted nature of gating definitions represents a hurdle for data interpretation and data sharing across and within organizations. Interpreting and standardizing gating definitions for subsequent analysis of gating results requires a curation effort from experts. Machine learning approaches have the potential to help in this process by predicting expert annotations associated with gating definitions. METHODS We created a gold-standard dataset by manually annotating thousands of gating definitions with cell type and functional marker annotations. We used this dataset to train and test a machine learning pipeline able to predict standard cell types and functional marker genes associated with gating definitions. RESULTS The machine learning pipeline predicted annotations with high accuracy for both cell types and functional marker genes. Accuracy was lower for gating definitions from assays belonging to laboratories from which limited or no prior data was available in the training. Manual error review ensured that resulting predicted annotations could be reused subsequently as additional gold-standard training data. CONCLUSIONS Machine learning methods are able to consistently predict annotations associated with gating definitions from flow cytometry assays. However, a hybrid automatic and manual annotation workflow would be recommended to achieve optimal results.
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Affiliation(s)
- Raul Rodriguez-Esteban
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - José Duarte
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Priscila C Teixeira
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Fabien Richard
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Svetlana Koltsova
- Curation Department, Rancho BioSciences LLC, San Diego, California, USA
| | - W Venus So
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center New York, New York, USA
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3
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Lucía Schmidt A, Rodriguez-Esteban R, Gottowik J, Leddin M. Applications of quantitative social media listening to patient-centric drug development. Drug Discov Today 2022; 27:1523-1530. [PMID: 35114364 DOI: 10.1016/j.drudis.2022.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/13/2021] [Accepted: 01/26/2022] [Indexed: 11/27/2022]
Abstract
Social media listening has been increasingly acknowledged as a tool with applications in many stages of the drug development process. These applications were created to meet the need for patient-centric therapies that are fit-for-purpose and meaningful to patients. Such applications, however, require the leverage of new quantitative approaches and analytical methods that draw from developments in artificial intelligence and real-world data (RWD) analysis. Here, we review the state-of-the-art in quantitative social media listening (QSML) methods applied to drug discovery from the perspective of the pharmaceutical industry.
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Affiliation(s)
- Ana Lucía Schmidt
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Raul Rodriguez-Esteban
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland.
| | - Juergen Gottowik
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Mathias Leddin
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
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Rodriguez-Esteban R. Biomedical articles share annotations with their citation neighbors. BMC Bioinformatics 2021; 22:95. [PMID: 33637047 PMCID: PMC7912518 DOI: 10.1186/s12859-021-04044-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/16/2021] [Indexed: 11/24/2022] Open
Abstract
Background Numerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale. Results With the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood’s most common annotations are likely to be those appearing in the article. Conclusions These findings posit that an article’s citation neighborhood defines to a large extent the article’s annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles.
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Affiliation(s)
- Raul Rodriguez-Esteban
- Roche Innovation Center Basel, Roche Pharmaceutical Research and Early Development, 4070, Basel, Switzerland.
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Desmet S, Brouckaert M, Boerjan W, Morreel K. Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks. Comput Struct Biotechnol J 2020; 19:72-85. [PMID: 33384856 PMCID: PMC7753198 DOI: 10.1016/j.csbj.2020.11.050] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/26/2020] [Accepted: 11/28/2020] [Indexed: 02/06/2023] Open
Abstract
Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.
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Affiliation(s)
- Sandrien Desmet
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Marlies Brouckaert
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Wout Boerjan
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
| | - Kris Morreel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
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Humayun F, Domingo-Fernández D, Paul George AA, Hopp MT, Syllwasschy BF, Detzel MS, Hoyt CT, Hofmann-Apitius M, Imhof D. A Computational Approach for Mapping Heme Biology in the Context of Hemolytic Disorders. Front Bioeng Biotechnol 2020; 8:74. [PMID: 32211383 PMCID: PMC7069124 DOI: 10.3389/fbioe.2020.00074] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/28/2020] [Indexed: 01/07/2023] Open
Abstract
Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme's role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme's interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.
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Affiliation(s)
- Farah Humayun
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Ajay Abisheck Paul George
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Marie-Thérèse Hopp
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Benjamin F. Syllwasschy
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Milena S. Detzel
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Diana Imhof
- Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, University of Bonn, Bonn, Germany
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Golriz Khatami S, Robinson C, Birkenbihl C, Domingo-Fernández D, Hoyt CT, Hofmann-Apitius M. Challenges of Integrative Disease Modeling in Alzheimer's Disease. Front Mol Biosci 2020; 6:158. [PMID: 31993440 PMCID: PMC6971060 DOI: 10.3389/fmolb.2019.00158] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 12/15/2022] Open
Abstract
Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Christine Robinson
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Hoyt CT, Domingo-Fernández D, Aldisi R, Xu L, Kolpeja K, Spalek S, Wollert E, Bachman J, Gyori BM, Greene P, Hofmann-Apitius M. Re-curation and rational enrichment of knowledge graphs in Biological Expression Language. Database (Oxford) 2019; 2019:baz068. [PMID: 31225582 PMCID: PMC6587072 DOI: 10.1093/database/baz068] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/03/2019] [Accepted: 04/29/2019] [Indexed: 12/23/2022]
Abstract
The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.
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Affiliation(s)
- Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Rana Aldisi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Lingling Xu
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Kristian Kolpeja
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Sandra Spalek
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Esther Wollert
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - John Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Patrick Greene
- Laboratory of Systems Pharmacology, Harvard Medical School, 200 Longwood Ave, Boston, MA, USA
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Naithani S, Gupta P, Preece J, Garg P, Fraser V, Padgitt-Cobb LK, Martin M, Vining K, Jaiswal P. Involving community in genes and pathway curation. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5289625. [PMID: 30649295 PMCID: PMC6334007 DOI: 10.1093/database/bay146] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Accepted: 12/11/2018] [Indexed: 12/25/2022]
Abstract
Biocuration plays a crucial role in building databases and complex systems-level platforms required for processing, annotating and analyzing ‘Big Data’ in biology. However, biocuration efforts cannot keep pace with a dramatic increase in the production of omics data; this presents one of the bottlenecks in genomics. In two pathway curation jamborees, Plant Reactome curators tested strategies for introducing researchers to pathway curation tools, harnessing biologists’ expertise in curating plant pathways and developing a network of community biocurators. We summarize the strategy, workflow and outcomes of these exercises, and discuss the role of community biocuration in advancing databases and genomic resources.
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Affiliation(s)
- Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Parul Gupta
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Justin Preece
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Priyanka Garg
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Valerie Fraser
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA.,Molecular and Cellular Biology Graduate Program, Oregon State University, Corvallis, OR, USA
| | | | - Matthew Martin
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Kelly Vining
- Department of Horticulture, Oregon State University, Corvallis, OR, USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
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Wei CH, Phan L, Feltz J, Maiti R, Hefferon T, Lu Z. tmVar 2.0: integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine. Bioinformatics 2018; 34:80-87. [PMID: 28968638 DOI: 10.1093/bioinformatics/btx541] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 08/31/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Despite significant efforts in expert curation, clinical relevance about most of the 154 million dbSNP reference variants (RS) remains unknown. However, a wealth of knowledge about the variant biological function/disease impact is buried in unstructured literature data. Previous studies have attempted to harvest and unlock such information with text-mining techniques but are of limited use because their mutation extraction results are not standardized or integrated with curated data. Results We propose an automatic method to extract and normalize variant mentions to unique identifiers (dbSNP RSIDs). Our method, in benchmarking results, demonstrates a high F-measure of ∼90% and compared favorably to the state of the art. Next, we applied our approach to the entire PubMed and validated the results by verifying that each extracted variant-gene pair matched the dbSNP annotation based on mapped genomic position, and by analyzing variants curated in ClinVar. We then determined which text-mined variants and genes constituted novel discoveries. Our analysis reveals 41 889 RS numbers (associated with 9151 genes) not found in ClinVar. Moreover, we obtained a rich set worth further review: 12 462 rare variants (MAF ≤ 0.01) in 3849 genes which are presumed to be deleterious and not frequently found in the general population. To our knowledge, this is the first large-scale study to analyze and integrate text-mined variant data with curated knowledge in existing databases. Our results suggest that databases can be significantly enriched by text mining and that the combined information can greatly assist human efforts in evaluating/prioritizing variants in genomic research. Availability and implementation The tmVar 2.0 source code and corpus are freely available at https://www.ncbi.nlm.nih.gov/research/bionlp/Tools/tmvar/. Contact zhiyong.lu@nih.gov.
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Affiliation(s)
- Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA
| | - Lon Phan
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA
| | - Juliana Feltz
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA
| | - Rama Maiti
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA
| | - Tim Hefferon
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), Bethesda, MD 20894, USA
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