1
|
Goto A, Rodriguez-Esteban R, Scharf SH, Morris GM. Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature. Sci Rep 2022; 12:14476. [PMID: 36008431 PMCID: PMC9403226 DOI: 10.1038/s41598-022-17746-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 07/30/2022] [Indexed: 11/16/2022] Open
Abstract
Drug resistance caused by mutations is a public health threat for existing and emerging viral diseases. A wealth of evidence about these mutations and their clinically associated phenotypes is scattered across the literature, but a comprehensive perspective is usually lacking. This work aimed to produce a clinically relevant view for the case of Hepatitis B virus (HBV) mutations by combining a chronic HBV clinical study with a compendium of genetic mutations systematically gathered from the scientific literature. We enriched clinical mutation data by systematically mining 2,472,725 scientific articles from PubMed Central in order to gather information about the HBV mutational landscape. By performing this analysis, we were able to identify mutational hotspots for each HBV genotype (A-E) and gene (C, X, P, S), as well as the location of disulfide bonds associated with these mutations. Through a modelling study, we also identified a mutation position common in both the clinical data and the literature that is located at the binding pocket for a known anti-HBV drug, namely entecavir. The results of this novel approach show the potential of integrated analyses to assist in the development of new drugs for viral diseases that are more robust to resistance. Such analyses should be of particular interest due to the increasing importance of viral resistance in established and emerging viruses, such as for newly developed drugs against SARS-CoV-2.
Collapse
Affiliation(s)
- An Goto
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK
| | | | | | - Garrett M Morris
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', Oxford, OX1 3LB, UK.
| |
Collapse
|
2
|
Wang X, Song N, Zhou H, Cheng H. The representation of argumentation in scientific papers: A comparative analysis of two research areas. J Assoc Inf Sci Technol 2021. [DOI: 10.1002/asi.24590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Xiaoguang Wang
- School of Information Management Wuhan University Wuhan China
| | - Ningyuan Song
- School of Information Management Nanjing University Nanjing China
| | - Huimin Zhou
- School of Information Management Wuhan University Wuhan China
| | - Hanghang Cheng
- School of Information Management Wuhan University Wuhan China
| |
Collapse
|
3
|
Becker TE, Jakobsson E. ResidueFinder: extracting individual residue mentions from protein literature. J Biomed Semantics 2021; 12:14. [PMID: 34289903 PMCID: PMC8293528 DOI: 10.1186/s13326-021-00243-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 05/07/2021] [Indexed: 11/10/2022] Open
Abstract
Background The revolution in molecular biology has shown how protein function and structure are based on specific sequences of amino acids. Thus, an important feature in many papers is the mention of the significance of individual amino acids in the context of the entire sequence of the protein. MutationFinder is a widely used program for finding mentions of specific mutations in texts. We report on augmenting the positive attributes of MutationFinder with a more inclusive regular expression list to create ResidueFinder, which finds mentions of native amino acids as well as mutations. We also consider parameter options for both ResidueFinder and MutationFinder to explore trade-offs between precision, recall, and computational efficiency. We test our methods and software in full text as well as abstracts. Results We find there is much more variety of formats for mentioning residues in the entire text of papers than in abstracts alone. Failure to take these multiple formats into account results in many false negatives in the program. Since MutationFinder, like several other programs, was primarily tested on abstracts, we found it necessary to build an expanded regular expression list to achieve acceptable recall in full text searches. We also discovered a number of artifacts arising from PDF to text conversion, which we wrote elements in the regular expression library to address. Taking into account those factors resulted in high recall on randomly selected primary research articles. We also developed a streamlined regular expression (called “cut”) which enables a several hundredfold speedup in both MutationFinder and ResidueFinder with only a modest compromise of recall. All regular expressions were tested using expanded F-measure statistics, i.e., we compute Fβ for various values of where the larger the value of β the more recall is weighted, the smaller the value of β the more precision is weighted. Conclusions ResidueFinder is a simple, effective, and efficient program for finding individual residue mentions in primary literature starting with text files, implemented in Python, and available in SourceForge.net. The most computationally efficient versions of ResidueFinder could enable creation and maintenance of a database of residue mentions encompassing all articles in PubMed. Supplementary Information The online version contains supplementary material available at 10.1186/s13326-021-00243-3.
Collapse
Affiliation(s)
- Ton E Becker
- Department of Molecular and Integrative Physiology, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois, 61801, Urbana, USA
| | - Eric Jakobsson
- Department of Molecular and Integrative Physiology, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois, 61801, Urbana, USA. .,Department of Biochemistry, Program in Biophysics and Computational Biology, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Illinois, 61801, Urbana, USA.
| |
Collapse
|
4
|
Lee K, Wei CH, Lu Z. Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature. Brief Bioinform 2021; 22:bbaa142. [PMID: 32770181 PMCID: PMC8138883 DOI: 10.1093/bib/bbaa142] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/07/2020] [Accepted: 06/25/2020] [Indexed: 12/28/2022] Open
Abstract
MOTIVATION To obtain key information for personalized medicine and cancer research, clinicians and researchers in the biomedical field are in great need of searching genomic variant information from the biomedical literature now than ever before. Due to the various written forms of genomic variants, however, it is difficult to locate the right information from the literature when using a general literature search system. To address the difficulty of locating genomic variant information from the literature, researchers have suggested various solutions based on automated literature-mining techniques. There is, however, no study for summarizing and comparing existing tools for genomic variant literature mining in terms of how to search easily for information in the literature on genomic variants. RESULTS In this article, we systematically compared currently available genomic variant recognition and normalization tools as well as the literature search engines that adopted these literature-mining techniques. First, we explain the problems that are caused by the use of non-standard formats of genomic variants in the PubMed literature by considering examples from the literature and show the prevalence of the problem. Second, we review literature-mining tools that address the problem by recognizing and normalizing the various forms of genomic variants in the literature and systematically compare them. Third, we present and compare existing literature search engines that are designed for a genomic variant search by using the literature-mining techniques. We expect this work to be helpful for researchers who seek information about genomic variants from the literature, developers who integrate genomic variant information from the literature and beyond.
Collapse
Affiliation(s)
- Kyubum Lee
- National Center for Biotechnology Information
| | | | - Zhiyong Lu
- National Center for Biotechnology Information
| |
Collapse
|
5
|
Stourac J, Dubrava J, Musil M, Horackova J, Damborsky J, Mazurenko S, Bednar D. FireProtDB: database of manually curated protein stability data. Nucleic Acids Res 2021; 49:D319-D324. [PMID: 33166383 PMCID: PMC7778887 DOI: 10.1093/nar/gkaa981] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/18/2020] [Accepted: 10/12/2020] [Indexed: 01/13/2023] Open
Abstract
The majority of naturally occurring proteins have evolved to function under mild conditions inside the living organisms. One of the critical obstacles for the use of proteins in biotechnological applications is their insufficient stability at elevated temperatures or in the presence of salts. Since experimental screening for stabilizing mutations is typically laborious and expensive, in silico predictors are often used for narrowing down the mutational landscape. The recent advances in machine learning and artificial intelligence further facilitate the development of such computational tools. However, the accuracy of these predictors strongly depends on the quality and amount of data used for training and testing, which have often been reported as the current bottleneck of the approach. To address this problem, we present a novel database of experimental thermostability data for single-point mutants FireProtDB. The database combines the published datasets, data extracted manually from the recent literature, and the data collected in our laboratory. Its user interface is designed to facilitate both types of the expected use: (i) the interactive explorations of individual entries on the level of a protein or mutation and (ii) the construction of highly customized and machine learning-friendly datasets using advanced searching and filtering. The database is freely available at https://loschmidt.chemi.muni.cz/fireprotdb.
Collapse
Affiliation(s)
- Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Juraj Dubrava
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic.,Department of Information Systems, Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
| | - Jana Horackova
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Masaryk University, Brno, Czech Republic.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| |
Collapse
|
6
|
Alag S. Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology. PLoS One 2020; 15:e0233438. [PMID: 32459809 PMCID: PMC7252633 DOI: 10.1371/journal.pone.0233438] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Accepted: 05/05/2020] [Indexed: 01/31/2023] Open
Abstract
Researchers and clinicians face a significant challenge in keeping up-to-date with the rapid rate of new associations between genetic mutations and diseases. To remedy this problem, this research mined the ClinicalTrials.gov corpus to extract relevant biological insights, produce unique reports to summarize findings, and make the meta-data available via APIs. An automated text-analysis pipeline performed the following features: parsing the ClinicalTrials.gov files, extracting and analyzing mutations from the corpus, mapping clinical trials to Human Phenotype Ontology (HPO), and finding associations between clinical trials and HPO nodes. Unique reports were created for each mutation (SNPs and protein mutations) mentioned in the corpus, as well as for each clinical trial that references a mutation. These reports, which have been run over multiple time points, along with APIs to access meta-data, are freely available at http://snpminertrials.com. Additionally, HPO was used to normalize disease terms and associate clinical trials with relevant genes. The creation of the pipeline and reports, the association of clinical trials with HPO terms, and the insights, public repository, and APIs produced are all novel in this work. The freely-available resources present relevant biological information and novel insights between biomedical entities in a robust and accessible manner, mitigating the challenge of being informed about new associations between mutations, genes, and diseases.
Collapse
Affiliation(s)
- Shray Alag
- The Harker School, San Jose, CA, United States of America
- * E-mail:
| |
Collapse
|
7
|
Tawfik NS, Spruit MR. The SNPcurator: literature mining of enriched SNP-disease associations. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4925332. [PMID: 29688369 PMCID: PMC5844215 DOI: 10.1093/database/bay020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2017] [Accepted: 02/05/2018] [Indexed: 01/08/2023]
Abstract
The uniqueness of each human genetic structure motivated the shift from the current practice of medicine to a more tailored one. This personalized medicine revolution would not be possible today without the genetics data collected from genome-wide association studies (GWASs) that investigate the relation between different phenotypic traits and single-nucleotide polymorphisms (SNPs). The huge increase in the literature publication space imposes a challenge on the conventional manual curation process which is becoming more and more expensive. This research aims at automatically extracting SNP associations of any given disease and its reported statistical significance (P-value) and odd ratio as well as cohort information such as size and ethnicity. Our evaluation illustrates that SNPcurator was able to replicate a large number of SNP-disease associations that were also reported in the NHGRI-EBI Catalog of published GWASs. SNPcurator was also tested by eight external genetics experts, who queried the system to examine diseases of their choice, and was found to be efficient and satisfactory. We conclude that the text-mining-based system has a great potential for helping researchers and scientists, especially in their preliminary genetics research. SNPcurator is publicly available at http://snpcurator.science.uu.nl/. Database URL: http://snpcurator.science.uu.nl/
Collapse
Affiliation(s)
- Noha S Tawfik
- Computer Engineering Department, College of Engineering, Arab Academy for Science, Technology, and Maritime Transport (AAST), Abukir,1029 Alexandria, Egypt.,Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| | - Marco R Spruit
- Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
Affiliation(s)
- Nancy L. Green
- University of North Carolina Greensboro, Greensboro, NC 27402, USA. Tel.: ; Fax: ; E-mail:
| |
Collapse
|
10
|
Bokharaeian B, Diaz A, Taghizadeh N, Chitsaz H, Chavoshinejad R. SNPPhenA: a corpus for extracting ranked associations of single-nucleotide polymorphisms and phenotypes from literature. J Biomed Semantics 2017; 8:14. [PMID: 28388928 PMCID: PMC5383945 DOI: 10.1186/s13326-017-0116-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 01/13/2017] [Indexed: 11/17/2022] Open
Abstract
Background Single Nucleotide Polymorphisms (SNPs) are among the most important types of genetic variations influencing common diseases and phenotypes. Recently, some corpora and methods have been developed with the purpose of extracting mutations and diseases from texts. However, there is no available corpus, for extracting associations from texts, that is annotated with linguistic-based negation, modality markers, neutral candidates, and confidence level of associations. Method In this research, different steps were presented so as to produce the SNPPhenA corpus. They include automatic Named Entity Recognition (NER) followed by the manual annotation of SNP and phenotype names, annotation of the SNP-phenotype associations and their level of confidence, as well as modality markers. Moreover, the produced corpus was annotated with negation scopes and cues as well as neutral candidates that play crucial role as far as negation and the modality phenomenon in relation to extraction tasks. Result The agreement between annotators was measured by Cohen’s Kappa coefficient where the resulting scores indicated the reliability of the corpus. The Kappa score was 0.79 for annotating the associations and 0.80 for the confidence degree of associations. Further presented were the basic statistics of the annotated features of the corpus in addition to the results of our first experiments related to the extraction of ranked SNP-Phenotype associations. The prepared guideline documents render the corpus more convenient and facile to use. The corpus, guidelines and inter-annotator agreement analysis are available on the website of the corpus: http://nil.fdi.ucm.es/?q=node/639. Conclusion Specifying the confidence degree of SNP-phenotype associations from articles helps identify the strength of associations that could in turn assist genomics scientists in determining phenotypic plasticity and the importance of environmental factors. What is more, our first experiments with the corpus show that linguistic-based confidence alongside other non-linguistic features can be utilized in order to estimate the strength of the observed SNP-phenotype associations. Trial Registration: Not Applicable Electronic supplementary material The online version of this article (doi:10.1186/s13326-017-0116-2) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Behrouz Bokharaeian
- Facultad informatica, Complutense University of Madrid, Calle Profesor José García Santesmases, 9, 28040, Madrid, Spain.
| | - Alberto Diaz
- Facultad informatica, Complutense University of Madrid, Calle Profesor José García Santesmases, 9, 28040, Madrid, Spain
| | - Nasrin Taghizadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamidreza Chitsaz
- Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA
| | - Ramyar Chavoshinejad
- External Collaborator, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, Tehran, Iran
| |
Collapse
|
11
|
Munk C, Harpsøe K, Hauser AS, Isberg V, Gloriam DE. Integrating structural and mutagenesis data to elucidate GPCR ligand binding. Curr Opin Pharmacol 2016; 30:51-58. [PMID: 27475047 DOI: 10.1016/j.coph.2016.07.003] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 07/03/2016] [Accepted: 07/04/2016] [Indexed: 12/22/2022]
Abstract
G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins, as well as drug targets. A recent boom in GPCR structural biology has provided detailed images of receptor ligand binding sites and interactions on the molecular level. An ever-increasing number of ligands is reported that exhibit activity through multiple receptors, binding in allosteric sites, and bias towards different intracellular signalling pathways. Furthermore, a wealth of single point mutants has accumulated in literature and public databases. Integrating these structural and mutagenesis data will help elucidate new GPCR ligand binding sites, and ultimately design drugs with tailored pharmacological activity.
Collapse
Affiliation(s)
- Christian Munk
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Jagtvej 162, 2100 Copenhagen, Denmark
| | - Kasper Harpsøe
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Jagtvej 162, 2100 Copenhagen, Denmark
| | - Alexander S Hauser
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Jagtvej 162, 2100 Copenhagen, Denmark
| | - Vignir Isberg
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Jagtvej 162, 2100 Copenhagen, Denmark
| | - David E Gloriam
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Jagtvej 162, 2100 Copenhagen, Denmark.
| |
Collapse
|
12
|
Thomas P, Rocktäschel T, Hakenberg J, Lichtblau Y, Leser U. SETH detects and normalizes genetic variants in text. Bioinformatics 2016; 32:2883-5. [PMID: 27256315 DOI: 10.1093/bioinformatics/btw234] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 04/18/2016] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED : Descriptions of genetic variations and their effect are widely spread across the biomedical literature. However, finding all mentions of a specific variation, or all mentions of variations in a specific gene, is difficult to achieve due to the many ways such variations are described. Here, we describe SETH, a tool for the recognition of variations from text and their subsequent normalization to dbSNP or UniProt. SETH achieves high precision and recall on several evaluation corpora of PubMed abstracts. It is freely available and encompasses stand-alone scripts for isolated application and evaluation as well as a thorough documentation for integration into other applications. AVAILABILITY AND IMPLEMENTATION SETH is released under the Apache 2.0 license and can be downloaded from http://rockt.github.io/SETH/ CONTACT: thomas@informatik.hu-berlin.de or leser@informatik.hu-berlin.de.
Collapse
Affiliation(s)
- Philippe Thomas
- Language Technology Lab, DFKI Berlin, Germany Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany
| | | | - Jörg Hakenberg
- Illumina, Inc, 451 El Camino Real, Santa Clara, CA 95050, USA
| | - Yvonne Lichtblau
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany
| | - Ulf Leser
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität Zu Berlin, Unter Den Linden 6, Berlin 10099, Germany
| |
Collapse
|
13
|
Mahmood ASMA, Wu TJ, Mazumder R, Vijay-Shanker K. DiMeX: A Text Mining System for Mutation-Disease Association Extraction. PLoS One 2016; 11:e0152725. [PMID: 27073839 PMCID: PMC4830514 DOI: 10.1371/journal.pone.0152725] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 03/19/2016] [Indexed: 11/22/2022] Open
Abstract
The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.
Collapse
Affiliation(s)
- A. S. M. Ashique Mahmood
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
| | - Tsung-Jung Wu
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, District of Columbia, United States of America
| | - Raja Mazumder
- Department of Biochemistry and Molecular Medicine, George Washington University, Washington, District of Columbia, United States of America
- McCormick Genomic and Proteomic Center, George Washington University, Washington, District of Columbia, United States of America
| | - K. Vijay-Shanker
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America
| |
Collapse
|
14
|
Badal VD, Kundrotas PJ, Vakser IA. Text Mining for Protein Docking. PLoS Comput Biol 2015; 11:e1004630. [PMID: 26650466 PMCID: PMC4674139 DOI: 10.1371/journal.pcbi.1004630] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 10/29/2015] [Indexed: 11/18/2022] Open
Abstract
The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. Protein interactions are central for many cellular processes. Physical characterization of these interactions is essential for understanding of life processes and applications in biology and medicine. Because of the inherent limitations of experimental techniques and rapid development of computational power and methodology, computer modeling is a tool of choice in many studies. Publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for modeling of proteins and protein complexes. A major paradigm shift in modeling of protein complexes is emerging due to the rapidly expanding amount of such information, which can be used as modeling constraints. Text mining has been widely used in recreating networks of protein interactions, as well as in detecting small molecule binding sites on proteins. Combining and expanding these two well-developed areas of research, we applied the text mining to physical modeling of protein complexes (protein docking). Our procedure retrieves published abstracts on a protein-protein interaction and extracts the relevant information. The results show that correct information on binding can be obtained for about half of protein complexes. The extracted constraints were incorporated in a modeling procedure, significantly improving its performance.
Collapse
Affiliation(s)
- Varsha D. Badal
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America
| | - Petras J. Kundrotas
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (PJK)
| | - Ilya A. Vakser
- Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America
- * E-mail: (IAV); (PJK)
| |
Collapse
|
15
|
Ravikumar KE, Wagholikar KB, Li D, Kocher JP, Liu H. Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature. BMC Bioinformatics 2015; 16:185. [PMID: 26047637 PMCID: PMC4457984 DOI: 10.1186/s12859-015-0609-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Accepted: 04/30/2015] [Indexed: 12/03/2022] Open
Abstract
Background Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains ‘locked’ in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. Results We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3 % for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10 % in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5 %. Conclusions Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.
Collapse
Affiliation(s)
- Komandur Elayavilli Ravikumar
- Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.
| | - Kavishwar B Wagholikar
- Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.
| | - Dingcheng Li
- Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.
| | - Jean-Pierre Kocher
- Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St SW, Harvick 3rd, Rochester, MN, 55905, USA.
| |
Collapse
|
16
|
A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2015; 2015:142-6. [PMID: 26306257 PMCID: PMC4525254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.
Collapse
|
17
|
Burger JD, Doughty E, Khare R, Wei CH, Mishra R, Aberdeen J, Tresner-Kirsch D, Wellner B, Kann MG, Lu Z, Hirschman L. Hybrid curation of gene-mutation relations combining automated extraction and crowdsourcing. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau094. [PMID: 25246425 PMCID: PMC4170591 DOI: 10.1093/database/bau094] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background: This article describes capture of biological information using a hybrid approach that combines natural language processing to extract biological entities and crowdsourcing with annotators recruited via Amazon Mechanical Turk to judge correctness of candidate biological relations. These techniques were applied to extract gene– mutation relations from biomedical abstracts with the goal of supporting production scale capture of gene–mutation–disease findings as an open source resource for personalized medicine. Results: The hybrid system could be configured to provide good performance for gene–mutation extraction (precision ∼82%; recall ∼70% against an expert-generated gold standard) at a cost of $0.76 per abstract. This demonstrates that crowd labor platforms such as Amazon Mechanical Turk can be used to recruit quality annotators, even in an application requiring subject matter expertise; aggregated Turker judgments for gene–mutation relations exceeded 90% accuracy. Over half of the precision errors were due to mismatches against the gold standard hidden from annotator view (e.g. incorrect EntrezGene identifier or incorrect mutation position extracted), or incomplete task instructions (e.g. the need to exclude nonhuman mutations). Conclusions: The hybrid curation model provides a readily scalable cost-effective approach to curation, particularly if coupled with expert human review to filter precision errors. We plan to generalize the framework and make it available as open source software. Database URL:http://www.mitre.org/publications/technical-papers/hybrid-curation-of-gene-mutation-relations-combining-automated
Collapse
Affiliation(s)
- John D Burger
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Emily Doughty
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Ritu Khare
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Chih-Hsuan Wei
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Rajashree Mishra
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - John Aberdeen
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - David Tresner-Kirsch
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Ben Wellner
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Maricel G Kann
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Zhiyong Lu
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| | - Lynette Hirschman
- The MITRE Corporation, Bedford, MA 01730, USA, Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA and The University of Maryland, Baltimore County, Baltimore MD 21250, USA
| |
Collapse
|
18
|
Klein A, Riazanov A, Hindle MM, Baker CJO. Benchmarking infrastructure for mutation text mining. J Biomed Semantics 2014; 5:11. [PMID: 24568600 PMCID: PMC3939821 DOI: 10.1186/2041-1480-5-11] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2013] [Accepted: 02/05/2014] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. RESULTS We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. CONCLUSION We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption.
Collapse
Affiliation(s)
- Artjom Klein
- Computer Science And Applied Statistics Department, University of New Brunswick, Saint John, Canada
| | | | - Matthew M Hindle
- Synthetic and Systems Biology, Edinburgh University, Edinburgh, UK
| | - Christopher JO Baker
- Computer Science And Applied Statistics Department, University of New Brunswick, Saint John, Canada
| |
Collapse
|
19
|
Jimeno Yepes A, Verspoor K. Literature mining of genetic variants for curation: quantifying the importance of supplementary material. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau003. [PMID: 24520105 PMCID: PMC3920087 DOI: 10.1093/database/bau003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
A major focus of modern biological research is the understanding of how genomic variation relates to disease. Although there are significant ongoing efforts to capture this understanding in curated resources, much of the information remains locked in unstructured sources, in particular, the scientific literature. Thus, there have been several text mining systems developed to target extraction of mutations and other genetic variation from the literature. We have performed the first study of the use of text mining for the recovery of genetic variants curated directly from the literature. We consider two curated databases, COSMIC (Catalogue Of Somatic Mutations In Cancer) and InSiGHT (International Society for Gastro-intestinal Hereditary Tumours), that contain explicit links to the source literature for each included mutation. Our analysis shows that the recall of the mutations catalogued in the databases using a text mining tool is very low, despite the well-established good performance of the tool and even when the full text of the associated article is available for processing. We demonstrate that this discrepancy can be explained by considering the supplementary material linked to the published articles, not previously considered by text mining tools. Although it is anecdotally known that supplementary material contains 'all of the information', and some researchers have speculated about the role of supplementary material (Schenck et al. Extraction of genetic mutations associated with cancer from public literature. J Health Med Inform 2012;S2:2.), our analysis substantiates the significant extent to which this material is critical. Our results highlight the need for literature mining tools to consider not only the narrative content of a publication but also the full set of material related to a publication.
Collapse
Affiliation(s)
- Antonio Jimeno Yepes
- National ICT Australia, Victoria Research Laboratory, Melbourne, Australia and Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
| | | |
Collapse
|
20
|
Jimeno Yepes A, Verspoor K. Mutation extraction tools can be combined for robust recognition of genetic variants in the literature. F1000Res 2014; 3:18. [PMID: 25285203 PMCID: PMC4176422 DOI: 10.12688/f1000research.3-18.v2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/27/2014] [Indexed: 11/20/2022] Open
Abstract
As the cost of genomic sequencing continues to fall, the amount of data being collected and studied for the purpose of understanding the genetic basis of disease is increasing dramatically. Much of the source information relevant to such efforts is available only from unstructured sources such as the scientific literature, and significant resources are expended in manually curating and structuring the information in the literature. As such, there have been a number of systems developed to target automatic extraction of mutations and other genetic variation from the literature using text mining tools. We have performed a broad survey of the existing publicly available tools for extraction of genetic variants from the scientific literature. We consider not just one tool but a number of different tools, individually and in combination, and apply the tools in two scenarios. First, they are compared in an intrinsic evaluation context, where the tools are tested for their ability to identify specific mentions of genetic variants in a corpus of manually annotated papers, the Variome corpus. Second, they are compared in an extrinsic evaluation context based on our previous study of text mining support for curation of the COSMIC and InSiGHT databases. Our results demonstrate that no single tool covers the full range of genetic variants mentioned in the literature. Rather, several tools have complementary coverage and can be used together effectively. In the intrinsic evaluation on the Variome corpus, the combined performance is above 0.95 in F-measure, while in the extrinsic evaluation the combined recall performance is above 0.71 for COSMIC and above 0.62 for InSiGHT, a substantial improvement over the performance of any individual tool. Based on the analysis of these results, we suggest several directions for the improvement of text mining tools for genetic variant extraction from the literature.
Collapse
Affiliation(s)
- Antonio Jimeno Yepes
- National ICT Australia, Victoria Research Laboratory, Melbourne, Australia ; Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
| | - Karin Verspoor
- National ICT Australia, Victoria Research Laboratory, Melbourne, Australia ; Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia
| |
Collapse
|
21
|
Peterson TA, Doughty E, Kann MG. Towards precision medicine: advances in computational approaches for the analysis of human variants. J Mol Biol 2013; 425:4047-63. [PMID: 23962656 PMCID: PMC3807015 DOI: 10.1016/j.jmb.2013.08.008] [Citation(s) in RCA: 93] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 08/07/2013] [Accepted: 08/08/2013] [Indexed: 12/26/2022]
Abstract
Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.
Collapse
Affiliation(s)
- Thomas A Peterson
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Emily Doughty
- Biomedical Informatics Program, Stanford University, Stanford, CA 94305, USA
| | - Maricel G Kann
- Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| |
Collapse
|
22
|
Verspoor K, Jimeno Yepes A, Cavedon L, McIntosh T, Herten-Crabb A, Thomas Z, Plazzer JP. Annotating the biomedical literature for the human variome. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat019. [PMID: 23584833 PMCID: PMC3676157 DOI: 10.1093/database/bat019] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This article introduces the Variome Annotation Schema, a schema that
aims to capture the core concepts and relations relevant to cataloguing and interpreting
human genetic variation and its relationship to disease, as described in the published
literature. The schema was inspired by the needs of the database curators of the
International Society for Gastrointestinal Hereditary Tumours (InSiGHT) database, but is
intended to have application to genetic variation information in a range of diseases. The
schema has been applied to a small corpus of full text journal publications on the subject
of inherited colorectal cancer. We show that the inter-annotator agreement on annotation
of this corpus ranges from 0.78 to 0.95 F-score across different entity
types when exact matching is measured, and improves to a minimum F-score
of 0.87 when boundary matching is relaxed. Relations show more variability in agreement,
but several are reliable, with the highest, cohort-has-size, reaching
0.90 F-score. We also explore the relevance of the schema to the InSiGHT
database curation process. The schema and the corpus represent an important new resource
for the development of text mining solutions that address relationships among patient
cohorts, disease and genetic variation, and therefore, we also discuss the role text
mining might play in the curation of information related to the human variome. The corpus
is available at http://opennicta.com/home/health/variome.
Collapse
Affiliation(s)
- Karin Verspoor
- National ICT Australia (NICTA), Victoria Research Laboratory, Level 2, Building 193, The University of Melbourne, Parkville VIC 3010, Australia.
| | | | | | | | | | | | | |
Collapse
|
23
|
Wei CH, Harris BR, Kao HY, Lu Z. tmVar: a text mining approach for extracting sequence variants in biomedical literature. Bioinformatics 2013; 29:1433-9. [PMID: 23564842 DOI: 10.1093/bioinformatics/btt156] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Text-mining mutation information from the literature becomes a critical part of the bioinformatics approach for the analysis and interpretation of sequence variations in complex diseases in the post-genomic era. It has also been used for assisting the creation of disease-related mutation databases. Most of existing approaches are rule-based and focus on limited types of sequence variations, such as protein point mutations. Thus, extending their extraction scope requires significant manual efforts in examining new instances and developing corresponding rules. As such, new automatic approaches are greatly needed for extracting different kinds of mutations with high accuracy. RESULTS Here, we report tmVar, a text-mining approach based on conditional random field (CRF) for extracting a wide range of sequence variants described at protein, DNA and RNA levels according to a standard nomenclature developed by the Human Genome Variation Society. By doing so, we cover several important types of mutations that were not considered in past studies. Using a novel CRF label model and feature set, our method achieves higher performance than a state-of-the-art method on both our corpus (91.4 versus 78.1% in F-measure) and their own gold standard (93.9 versus 89.4% in F-measure). These results suggest that tmVar is a high-performance method for mutation extraction from biomedical literature. AVAILABILITY tmVar software and its corpus of 500 manually curated abstracts are available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/pub/tmVar
Collapse
Affiliation(s)
- Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), 8600 Rockville Pike, Bethesda, MD 20894, USA
| | | | | | | |
Collapse
|
24
|
Verspoor K, Mackinlay A, Cohn JD, Wall ME. Detection of protein catalytic sites in the biomedical literature. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2013:433-444. [PMID: 23424147 PMCID: PMC3664919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
This paper explores the application of text mining to the problem of detecting protein functional sites in the biomedical literature, and specifically considers the task of identifying catalytic sites in that literature. We provide strong evidence for the need for text mining techniques that address residue-level protein function annotation through an analysis of two corpora in terms of their coverage of curated data sources. We also explore the viability of building a text-based classifier for identifying protein functional sites, identifying the low coverage of curated data sources and the potential ambiguity of information about protein functional sites as challenges that must be addressed. Nevertheless we produce a simple classifier that achieves a reasonable ∼69% F-score on our full text silver corpus on the first attempt to address this classification task. The work has application in computational prediction of the functional significance of protein sites as well as in curation workflows for databases that capture this information.
Collapse
Affiliation(s)
- Karin Verspoor
- National ICT Australia, Victoria Research Lab, Parkville, VIC 3010 Australia
| | - Andrew Mackinlay
- National ICT Australia, Victoria Research Lab, Parkville, VIC 3010 Australia
| | - Judith D. Cohn
- Computer and Computational Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545 USA
| | - Michael E. Wall
- Computer and Computational Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545 USA
| |
Collapse
|
25
|
Bromberg Y, Capriotti E. SNP-SIG Meeting 2011: identification and annotation of SNPs in the context of structure, function, and disease. BMC Genomics 2012; 13 Suppl 4:S1. [PMID: 22759647 PMCID: PMC3395891 DOI: 10.1186/1471-2164-13-s4-s1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Yana Bromberg
- Department of Biochemistry and Microbiology, School of Environmental and Biological Sciences, Rutgers University, 76 Lipman Drive, New Brunswick, NJ 08901, USA.
| | | |
Collapse
|