1
|
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
|
2
|
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
|
3
|
Pons T, Vazquez M, Matey-Hernandez ML, Brunak S, Valencia A, Izarzugaza JM. KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily. BMC Genomics 2016; 17 Suppl 2:396. [PMID: 27357839 PMCID: PMC4928150 DOI: 10.1186/s12864-016-2723-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Background The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease. Results KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified. A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2. Conclusions KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2723-1) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Tirso Pons
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain
| | - Miguel Vazquez
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain
| | - María Luisa Matey-Hernandez
- Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kemitorvet, Building 208, 2800 Kgs., Lyngby, Denmark
| | - Søren Brunak
- Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kemitorvet, Building 208, 2800 Kgs., Lyngby, Denmark.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
| | - Alfonso Valencia
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain
| | - Jose Mg Izarzugaza
- Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kemitorvet, Building 208, 2800 Kgs., Lyngby, Denmark.
| |
Collapse
|
4
|
Vazquez M, Pons T, Brunak S, Valencia A, Izarzugaza JMG. wKinMut-2: Identification and Interpretation of Pathogenic Variants in Human Protein Kinases. Hum Mutat 2015; 37:36-42. [PMID: 26443060 DOI: 10.1002/humu.22914] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Accepted: 09/22/2015] [Indexed: 12/31/2022]
Abstract
Most genomic alterations are tolerated while only a minor fraction disrupts molecular function sufficiently to drive disease. Protein kinases play a central biological function and the functional consequences of their variants are abundantly characterized. However, this heterogeneous information is often scattered across different sources, which makes the integrative analysis complex and laborious. wKinMut-2 constitutes a solution to facilitate the interpretation of the consequences of human protein kinase variation. Nine methods predict their pathogenicity, including a kinase-specific random forest approach. To understand the biological mechanisms causative of human diseases and cancer, information from pertinent reference knowledge bases and the literature is automatically mined, digested, and homogenized. Variants are visualized in their structural contexts and residues affecting catalytic and drug binding are identified. Known protein-protein interactions are reported. Altogether, this information is intended to assist the generation of new working hypothesis to be corroborated with ulterior experimental work. The wKinMut-2 system, along with a user manual and examples, is freely accessible at http://kinmut2.bioinfo.cnio.es, the code for local installations can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.
Collapse
Affiliation(s)
- Miguel Vazquez
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Tirso Pons
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen 2200, Denmark.,Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kongens Lyngby 2800, Denmark
| | - Alfonso Valencia
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, 28029, Spain
| | - Jose M G Izarzugaza
- Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kongens Lyngby 2800, Denmark
| |
Collapse
|
5
|
Ernst P, Siu A, Weikum G. KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences. BMC Bioinformatics 2015; 16:157. [PMID: 25971816 PMCID: PMC4448285 DOI: 10.1186/s12859-015-0549-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 03/25/2015] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Biomedical knowledge bases (KB's) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. RESULTS We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB's based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors. CONCLUSION KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from different text genres such as scientific publications, health portals, and online communities. Thus, it has the potential to serve as one-stop portal for a wide range of relations and use cases. To showcase the breadth and usefulness, we make the KnowLife KB accessible through the health portal (http://knowlife.mpi-inf.mpg.de).
Collapse
Affiliation(s)
- Patrick Ernst
- Max-Planck-Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany.
| | - Amy Siu
- Max-Planck-Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany.
| | - Gerhard Weikum
- Max-Planck-Institute for Informatics, Campus E1 4, Saarbrücken, 66123, Germany.
| |
Collapse
|
6
|
Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, Valencia A. CHEMDNER: The drugs and chemical names extraction challenge. J Cheminform 2015; 7:S1. [PMID: 25810766 PMCID: PMC4331685 DOI: 10.1186/1758-2946-7-s1-s1] [Citation(s) in RCA: 128] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties.
Collapse
Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain
| | - Florian Leitner
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politecnica de Madrid, Calle Ramiro de Maeztu, 7, Madrid, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Center for Applied Medical Research (CIMA), University of Navarra, Avenida de Pio XII, 55, Pamplona, Spain
| | - Miguel Vazquez
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Center for Applied Medical Research (CIMA), University of Navarra, Avenida de Pio XII, 55, Pamplona, Spain
| | - Alfonso Valencia
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Calle Melchor Fernndez Almagro, 3, Madrid, Spain
| |
Collapse
|
7
|
Krallinger M, Rabal O, Leitner F, Vazquez M, Salgado D, Lu Z, Leaman R, Lu Y, Ji D, Lowe DM, Sayle RA, Batista-Navarro RT, Rak R, Huber T, Rocktäschel T, Matos S, Campos D, Tang B, Xu H, Munkhdalai T, Ryu KH, Ramanan SV, Nathan S, Žitnik S, Bajec M, Weber L, Irmer M, Akhondi SA, Kors JA, Xu S, An X, Sikdar UK, Ekbal A, Yoshioka M, Dieb TM, Choi M, Verspoor K, Khabsa M, Giles CL, Liu H, Ravikumar KE, Lamurias A, Couto FM, Dai HJ, Tsai RTH, Ata C, Can T, Usié A, Alves R, Segura-Bedmar I, Martínez P, Oyarzabal J, Valencia A. The CHEMDNER corpus of chemicals and drugs and its annotation principles. J Cheminform 2015; 7:S2. [PMID: 25810773 PMCID: PMC4331692 DOI: 10.1186/1758-2946-7-s1-s2] [Citation(s) in RCA: 112] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.
Collapse
Affiliation(s)
- Martin Krallinger
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Obdulia Rabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Florian Leitner
- Computational Intelligence Group, Department of Artificial Intelligence, Universidad Politecnica de Madrid, Madrid, Spain
| | - Miguel Vazquez
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - David Salgado
- Faculte de Medecine La Timone, Marseille, Marseille, France
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), National Institutes of Health, Bethesda, USA
| | - Robert Leaman
- National Center for Biotechnology Information (NCBI), National Institutes of Health, Bethesda, USA
| | - Yanan Lu
- Natural Language Processing Lab, Wuhan University, Wuhan, Hubei, PR China
| | - Donghong Ji
- Natural Language Processing Lab, Wuhan University, Wuhan, Hubei, PR China
| | - Daniel M Lowe
- NextMove Software Ltd, Innovation Centre, Unit 23, Science Park, Milton Road, Cambridge, UK
| | - Roger A Sayle
- NextMove Software Ltd, Innovation Centre, Unit 23, Science Park, Milton Road, Cambridge, UK
| | | | - Rafal Rak
- National Centre for Text Mining, Manchester Institute of Biotechnology, Manchester, UK
| | - Torsten Huber
- Humboldt-Universität zu Berlin, Knowledge Management in Bioinformatics, Berlin, Germany
| | - Tim Rocktäschel
- Department of Computer Science, University College London, London, UK
| | - Sérgio Matos
- IEETA/DETI, University of Aveiro, Campus Universitario de Santiago, Aveiro, Portugal
| | - David Campos
- IEETA/DETI, University of Aveiro, Campus Universitario de Santiago, Aveiro, Portugal
| | - Buzhou Tang
- Department of Computer Science, Harbin Institute of Technology, Shenzhen Graduate School Shenzhen, GuangDong, PR China
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, USA
| | - Tsendsuren Munkhdalai
- Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - Keun Ho Ryu
- Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea
| | - SV Ramanan
- RelAgent Pvt Ltd, IIT Madras Research Park, Taramani, Chennai, India
| | - Senthil Nathan
- RelAgent Pvt Ltd, IIT Madras Research Park, Taramani, Chennai, India
| | - Slavko Žitnik
- Faculty of computer and information science, University of Ljubljana, Ljubljana, Slovenia
| | - Marko Bajec
- Faculty of computer and information science, University of Ljubljana, Ljubljana, Slovenia
| | | | | | - Saber A Akhondi
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Shuo Xu
- Information Technology Supporting Center, Institute of Scientific and Technical Information of China, Beijing, PR China
| | - Xin An
- School of Economics and Management, Beijing Forestry University, Beijing, PR China
| | - Utpal Kumar Sikdar
- Department of Computer Science and Engineering Indian institute of Technology, Patna, Bihar, India
| | - Asif Ekbal
- Department of Computer Science and Engineering Indian institute of Technology, Patna, Bihar, India
| | - Masaharu Yoshioka
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Thaer M Dieb
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Miji Choi
- Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia
- National ICT Australia Victoria Research Laboratory, West Melbourne, Australia
| | - Madian Khabsa
- Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, USA
| | - C Lee Giles
- Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, USA
- Information Sciences and Technology, The Pennsylvania State University, Pennsylvania, USA
| | - Hongfang Liu
- Department of Health Sciences Research, Mayo College of Medicine, Rochester, USA
| | | | - Andre Lamurias
- LaSIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Francisco M Couto
- LaSIGE, Department of Informatics, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Hong-Jie Dai
- Graduate Institute of BioMedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Richard Tzong-Han Tsai
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Caglar Ata
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tolga Can
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Anabel Usié
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida, Lleida, Spain
- Departament d'Informatica i Enginyeria Industrial, Univesitat de Lleida, Lleida, Spain
| | - Rui Alves
- Departament Ciències Mèdiques Bàsiques, Universitat de Lleida, Lleida, Spain
| | | | - Paloma Martínez
- Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain
| | - Julen Oyarzabal
- Small Molecule Discovery Platform, Molecular Therapeutics Program, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
| | - Alfonso Valencia
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| |
Collapse
|
8
|
The HIV mutation browser: a resource for human immunodeficiency virus mutagenesis and polymorphism data. PLoS Comput Biol 2014; 10:e1003951. [PMID: 25474213 PMCID: PMC4256008 DOI: 10.1371/journal.pcbi.1003951] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 09/29/2014] [Indexed: 01/19/2023] Open
Abstract
Huge research effort has been invested over many years to determine the phenotypes of natural or artificial mutations in HIV proteins--interpretation of mutation phenotypes is an invaluable source of new knowledge. The results of this research effort are recorded in the scientific literature, but it is difficult for virologists to rapidly find it. Manually locating data on phenotypic variation within the approximately 270,000 available HIV-related research articles, or the further 1,500 articles that are published each month is a daunting task. Accordingly, the HIV research community would benefit from a resource cataloguing the available HIV mutation literature. We have applied computational text-mining techniques to parse and map mutagenesis and polymorphism information from the HIV literature, have enriched the data with ancillary information and have developed a public, web-based interface through which it can be intuitively explored: the HIV mutation browser. The current release of the HIV mutation browser describes the phenotypes of 7,608 unique mutations at 2,520 sites in the HIV proteome, resulting from the analysis of 120,899 papers. The mutation information for each protein is organised in a residue-centric manner and each residue is linked to the relevant experimental literature. The importance of HIV as a global health burden advocates extensive effort to maximise the efficiency of HIV research. The HIV mutation browser provides a valuable new resource for the research community. The HIV mutation browser is available at: http://hivmut.org.
Collapse
|
9
|
Macintyre G, Jimeno Yepes A, Ong CS, Verspoor K. Associating disease-related genetic variants in intergenic regions to the genes they impact. PeerJ 2014; 2:e639. [PMID: 25374782 PMCID: PMC4217187 DOI: 10.7717/peerj.639] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Accepted: 10/07/2014] [Indexed: 11/20/2022] Open
Abstract
We present a method to assist in interpretation of the functional impact of intergenic disease-associated SNPs that is not limited to search strategies proximal to the SNP. The method builds on two sources of external knowledge: the growing understanding of three-dimensional spatial relationships in the genome, and the substantial repository of information about relationships among genetic variants, genes, and diseases captured in the published biomedical literature. We integrate chromatin conformation capture data (HiC) with literature support to rank putative target genes of intergenic disease-associated SNPs. We demonstrate that this hybrid method outperforms a genomic distance baseline on a small test set of expression quantitative trait loci, as well as either method individually. In addition, we show the potential for this method to uncover relationships between intergenic SNPs and target genes across chromosomes. With more extensive chromatin conformation capture data becoming readily available, this method provides a way forward towards functional interpretation of SNPs in the context of the three dimensional structure of the genome in the nucleus.
Collapse
Affiliation(s)
- Geoff Macintyre
- Department of Computing and Information Systems, The University of Melbourne, VIC, Australia
- Centre for Neural Engineering, The University of Melbourne, VIC, Australia
| | - Antonio Jimeno Yepes
- Department of Computing and Information Systems, The University of Melbourne, VIC, Australia
| | - Cheng Soon Ong
- Department of Electrical and Electronic Engineering, The University of Melbourne, VIC, Australia
- Machine Learning Group, NICTA Canberra Research Laboratory, Australia
- Research School of Computer Science, Australian National University, Australia
| | - Karin Verspoor
- Department of Computing and Information Systems, The University of Melbourne, VIC, Australia
- Health and Biomedical Informatics Centre, The University of Melbourne, VIC, Australia
| |
Collapse
|
10
|
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
|
11
|
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
|
12
|
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
|
13
|
Izarzugaza JMG, Vazquez M, del Pozo A, Valencia A. wKinMut: an integrated tool for the analysis and interpretation of mutations in human protein kinases. BMC Bioinformatics 2013; 14:345. [PMID: 24289158 PMCID: PMC3879071 DOI: 10.1186/1471-2105-14-345] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2012] [Accepted: 05/30/2013] [Indexed: 11/13/2022] Open
Abstract
Background Protein kinases are involved in relevant physiological functions and a broad number of mutations in this superfamily have been reported in the literature to affect protein function and stability. Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation remains a considerable challenge. Results The wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including our recently developed prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det. This predictor yields interesting results that compare favourably with other methods in the field when applied to protein kinases. Together with the predictions, wKinMut offers a number of integrated services for the analysis of mutations. These include: the classification of the kinase, information about associations of the kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures, pathogenicity records from a number of databases and the classification of mutations in large-scale cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of mutations directly from the literature, and therefore increases the possibilities of finding interesting functional information associated to the studied mutations. Conclusions wKinMut facilitates the exploration of the information available about individual mutations by integrating prediction approaches with the automatic extraction of information from the literature (text mining) and several state-of-the-art databases. wKinMut has been used during the last year for the analysis of the consequences of mutations in the context of a number of cancer genome projects, including the recent analysis of Chronic Lymphocytic Leukemia cases and is publicly available at
http://wkinmut.bioinfo.cnio.es.
Collapse
Affiliation(s)
- Jose M G Izarzugaza
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro, 3, E-28029 Madrid, Spain.
| | | | | | | |
Collapse
|
14
|
Blaschke C, Valencia A. The Functional Genomics Network in the evolution of biological text mining over the past decade. N Biotechnol 2012. [PMID: 23202358 DOI: 10.1016/j.nbt.2012.11.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental.
Collapse
Affiliation(s)
- Christian Blaschke
- Spanish National Cancer Research Centre, C/Melchor Fernández Almagro, 3, E-28029 Madrid, Spain.
| | | |
Collapse
|
15
|
Pandey KR, Maden N, Poudel B, Pradhananga S, Sharma AK. The curation of genetic variants: difficulties and possible solutions. GENOMICS PROTEOMICS & BIOINFORMATICS 2012; 10:317-25. [PMID: 23317699 PMCID: PMC5054708 DOI: 10.1016/j.gpb.2012.06.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/05/2012] [Revised: 05/27/2012] [Accepted: 06/20/2012] [Indexed: 11/15/2022]
Abstract
The curation of genetic variants from biomedical articles is required for various clinical and research purposes. Nowadays, establishment of variant databases that include overall information about variants is becoming quite popular. These databases have immense utility, serving as a user-friendly information storehouse of variants for information seekers. While manual curation is the gold standard method for curation of variants, it can turn out to be time-consuming on a large scale thus necessitating the need for automation. Curation of variants described in biomedical literature may not be straightforward mainly due to various nomenclature and expression issues. Though current trends in paper writing on variants is inclined to the standard nomenclature such that variants can easily be retrieved, we have a massive store of variants in the literature that are present as non-standard names and the online search engines that are predominantly used may not be capable of finding them. For effective curation of variants, knowledge about the overall process of curation, nature and types of difficulties in curation, and ways to tackle the difficulties during the task are crucial. Only by effective curation, can variants be correctly interpreted. This paper presents the process and difficulties of curation of genetic variants with possible solutions and suggestions from our work experience in the field including literature support. The paper also highlights aspects of interpretation of genetic variants and the importance of writing papers on variants following standard and retrievable methods.
Collapse
Affiliation(s)
- Kapil Raj Pandey
- Deerwalk Services, Kathmandu 44602, Nepal
- Department of Microbiology, Bangalore University, Bangalore 560001, India
| | - Narendra Maden
- Deerwalk Services, Kathmandu 44602, Nepal
- Central Department of Microbiology, Tribhuvan University, Kathmandu 44613, Nepal
- Corresponding author.
| | - Barsha Poudel
- Department of Bioinformatics, Wageningen University, Wageningen 6700 AA-6799 ZZ, Netherlands
| | - Sailendra Pradhananga
- Deerwalk Services, Kathmandu 44602, Nepal
- Department of Biotechnology, Kathmandu University, Kavrepalanchok 45200, Nepal
| | - Amit Kumar Sharma
- Deerwalk Services, Kathmandu 44602, Nepal
- Department of Biotechnology, Kathmandu University, Kavrepalanchok 45200, Nepal
| |
Collapse
|
16
|
Izarzugaza JMG, Krallinger M, Valencia A. Interpretation of the consequences of mutations in protein kinases: combined use of bioinformatics and text mining. Front Physiol 2012; 3:323. [PMID: 23055974 PMCID: PMC3449330 DOI: 10.3389/fphys.2012.00323] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2012] [Accepted: 07/23/2012] [Indexed: 11/30/2022] Open
Abstract
Protein kinases play a crucial role in a plethora of significant physiological functions and a number of mutations in this superfamily have been reported in the literature to disrupt protein structure and/or function. Computational and experimental research aims to discover the mechanistic connection between mutations in protein kinases and disease with the final aim of predicting the consequences of mutations on protein function and the subsequent phenotypic alterations. In this article, we will review the possibilities and limitations of current computational methods for the prediction of the pathogenicity of mutations in the protein kinase superfamily. In particular we will focus on the problem of benchmarking the predictions with independent gold standard datasets. We will propose a pipeline for the curation of mutations automatically extracted from the literature. Since many of these mutations are not included in the databases that are commonly used to train the computational methods to predict the pathogenicity of protein kinase mutations we propose them to build a valuable gold standard dataset in the benchmarking of a number of these predictors. Finally, we will discuss how text mining approaches constitute a powerful tool for the interpretation of the consequences of mutations in the context of disease genome analysis with particular focus on cancer.
Collapse
Affiliation(s)
- Jose M G Izarzugaza
- Structural Computational Biology Group, Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre Madrid, Spain
| | | | | |
Collapse
|
17
|
Valencia A, Hidalgo M. Getting personalized cancer genome analysis into the clinic: the challenges in bioinformatics. Genome Med 2012; 4:61. [PMID: 22839973 PMCID: PMC3580417 DOI: 10.1186/gm362] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.
Collapse
Affiliation(s)
- Alfonso Valencia
- Spanish National Cancer Research Centre (CNIO), Calle Melchor Fernández Almagro, 3, E-28029 Madrid, Spain
| | - Manuel Hidalgo
- Spanish National Cancer Research Centre (CNIO), Calle Melchor Fernández Almagro, 3, E-28029 Madrid, Spain
| |
Collapse
|
18
|
Gyimesi G, Borsodi D, Sarankó H, Tordai H, Sarkadi B, Hegedűs T. ABCMdb: a database for the comparative analysis of protein mutations in ABC transporters, and a potential framework for a general application. Hum Mutat 2012; 33:1547-56. [PMID: 22693078 DOI: 10.1002/humu.22138] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Accepted: 05/29/2012] [Indexed: 11/08/2022]
Abstract
To overcome the pathological phenomena caused by altered function of ABC (ATP Binding Cassette) proteins, their mechanisms of action are extensively investigated, often involving the design of mutant constructs for experiments. Designing mutagenetic constructs, interpreting the result of mutagenetic experiments, and finding individual genetic variants require an extensive knowledge of previously published mutations. To aid the recapitulation of mutations described in the literature, we set up a database of ABC protein mutations (ABCMdb) extracted from full-text papers using an automatic mining approach. We have also developed a Web application interface to compare mutations in different ABC proteins using sequence alignments and to interactively map the mutations to 3D structural models. Currently our database contains protein mutations published for ABCB1, ABCB11, ABCC1, ABCC6, ABCC7, and the proteins of the ABCG subfamily. The database will be extended to include other members and subfamilies, and to provide information on whether or not a mutation is disease causing, represents a high-incidence polymorphism, or was generated only in vitro. The ABCMdb database should already help to compare the effects of mutations at homologous positions in different ABC proteins, and its interactive tools aim to advance the design of experiments for a wider range of proteins.
Collapse
Affiliation(s)
- Gergely Gyimesi
- Membrane Research Group, Hungarian Academy of Sciences, Budapest, Hungary
| | | | | | | | | | | |
Collapse
|
19
|
Izarzugaza JMG, del Pozo A, Vazquez M, Valencia A. Prioritization of pathogenic mutations in the protein kinase superfamily. BMC Genomics 2012; 13 Suppl 4:S3. [PMID: 22759651 PMCID: PMC3303724 DOI: 10.1186/1471-2164-13-s4-s3] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Most of the many mutations described in human protein kinases are tolerated without significant disruption of the corresponding structures or molecular functions, while some of them have been associated to a variety of human diseases, including cancer. In the last decade, a plethora of computational methods to predict the effect of missense single-nucleotide variants (SNVs) have been developed. Still, current high-throughput sequencing efforts and the concomitant need for massive interpretation of protein sequence variants will demand for more efficient and/or accurate computational methods in the forthcoming years. RESULTS We present KinMut, a support vector machine (SVM) approach, to identify pathogenic mutations in the protein kinase superfamily. KinMut relays on a combination of sequence-derived features that describe mutations at different levels: (1) Gene level: membership to a specific group in Kinbase and the annotation with GO terms; (2) Domain level: annotated PFAM domains; and (3) Residue level: physicochemical features of amino acids, specificity determining positions, and functional annotations from SwissProt and FireDB. The system has been trained with the set of 3492 human kinase mutations in UniProt for which experimental validation of their pathogenic or neutral character exists. In addition, we discuss the relative importance of these independent properties and their combination for the development of a kinase-specific predictor. Finally, we compare KinMut with other state-of-the-art prediction methods. CONCLUSIONS Family-specific features appear among the most discriminative information sources, which allow us to produce accurate results in a reliable and very simple way with minimal supervision. Our study aims to broaden the knowledge on the mechanisms by which mutations in the human kinome contribute to disease with a particular focus in cancer. The classifier as well as further documentation is available at http://kinmut.bioinfo.cnio.es/.
Collapse
Affiliation(s)
- Jose M G Izarzugaza
- Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.
| | | | | | | |
Collapse
|
20
|
Söhngen C, Chang A, Schomburg D. Development of a classification scheme for disease-related enzyme information. BMC Bioinformatics 2011; 12:329. [PMID: 21827651 PMCID: PMC3166944 DOI: 10.1186/1471-2105-12-329] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2011] [Accepted: 08/09/2011] [Indexed: 11/24/2022] Open
Abstract
Background BRENDA (BRaunschweig ENzyme DAtabase, http://www.brenda-enzymes.org) is a major resource for enzyme related information. First and foremost, it provides data which are manually curated from the primary literature. DRENDA (Disease RElated ENzyme information DAtabase) complements BRENDA with a focus on the automatic search and categorization of enzyme and disease related information from title and abstracts of primary publications. In a two-step procedure DRENDA makes use of text mining and machine learning methods. Results Currently enzyme and disease related references are biannually updated as part of the standard BRENDA update. 910,897 relations of EC-numbers and diseases were extracted from titles or abstracts and are included in the second release in 2010. The enzyme and disease entity recognition has been successfully enhanced by a further relation classification via machine learning. The classification step has been evaluated by a 5-fold cross validation and achieves an F1 score between 0.802 ± 0.032 and 0.738 ± 0.033 depending on the categories and pre-processing procedures. In the eventual DRENDA content every category reaches a classification specificity of at least 96.7% and a precision that ranges from 86-98% in the highest confidence level, and 64-83% for the smallest confidence level associated with higher recall. Conclusions The DRENDA processing chain analyses PubMed, locates references with disease-related information on enzymes and categorises their focus according to the categories causal interaction, therapeutic application, diagnostic usage and ongoing research. The categorisation gives an impression on the focus of the located references. Thus, the relation categorisation can facilitate orientation within the rapidly growing number of references with impact on diseases and enzymes. The DRENDA information is available as additional information in BRENDA.
Collapse
Affiliation(s)
- Carola Söhngen
- Technische Universität Braunschweig, Department of Bioinformatics and Biochemistry Langer Kamp 19 B, 38106 Braunschweig, Germany
| | | | | |
Collapse
|
21
|
Thomas PE, Klinger R, Furlong LI, Hofmann-Apitius M, Friedrich CM. Challenges in the association of human single nucleotide polymorphism mentions with unique database identifiers. BMC Bioinformatics 2011; 12 Suppl 4:S4. [PMID: 21992066 PMCID: PMC3194196 DOI: 10.1186/1471-2105-12-s4-s4] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Most information on genomic variations and their associations with phenotypes are covered exclusively in scientific publications rather than in structured databases. These texts commonly describe variations using natural language; database identifiers are seldom mentioned. This complicates the retrieval of variations, associated articles, as well as information extraction, e. g. the search for biological implications. To overcome these challenges, procedures to map textual mentions of variations to database identifiers need to be developed. RESULTS This article describes a workflow for normalization of variation mentions, i.e. the association of them to unique database identifiers. Common pitfalls in the interpretation of single nucleotide polymorphism (SNP) mentions are highlighted and discussed. The developed normalization procedure achieves a precision of 98.1 % and a recall of 67.5% for unambiguous association of variation mentions with dbSNP identifiers on a text corpus based on 296 MEDLINE abstracts containing 527 mentions of SNPs. The annotated corpus is freely available at http://www.scai.fraunhofer.de/snp-normalization-corpus.html. CONCLUSIONS Comparable approaches usually focus on variations mentioned on the protein sequence and neglect problems for other SNP mentions. The results presented here indicate that normalizing SNPs described on DNA level is more difficult than the normalization of SNPs described on protein level. The challenges associated with normalization are exemplified with ambiguities and errors, which occur in this corpus.
Collapse
Affiliation(s)
- Philippe E Thomas
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
- Knowledge Management in Bioinformatics, Humboldt-University Berlin, Unter den Linden 6, 10099 Berlin, Germany
| | - Roman Klinger
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Laura I Furlong
- Research Unit on Biomedical Informatics (GRIB), IMIM-Hospital del Mar, UPF, PRBB, c/Dr. Aiguader 88, E-08003 Barcelona, Spain
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
| | - Christoph M Friedrich
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Department of Bioinformatics, Schloss Birlinghoven, 53754 Sankt Augustin, Germany
- University of Applied Science and Arts Dortmund, Department of Computer Science, Emil-Figge-Str. 42, 44227 Dortmund, Germany
| |
Collapse
|
22
|
Izarzugaza JMG, Hopcroft LEM, Baresic A, Orengo CA, Martin ACR, Valencia A. Characterization of pathogenic germline mutations in human protein kinases. BMC Bioinformatics 2011; 12 Suppl 4:S1. [PMID: 21992016 PMCID: PMC3194193 DOI: 10.1186/1471-2105-12-s4-s1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites. RESULTS Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families. CONCLUSIONS Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.
Collapse
Affiliation(s)
- Jose M G Izarzugaza
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), C/Melchor Fernandez Almagro 3, E28029 Madrid, Spain
| | | | | | | | | | | |
Collapse
|
23
|
Stenson PD, Cooper DN. Prospects for the automated extraction of mutation data from the scientific literature. Hum Genomics 2011; 5:1-4. [PMID: 21106485 PMCID: PMC3500153 DOI: 10.1186/1479-7364-5-1-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
|
24
|
Laurila JB, Naderi N, Witte R, Riazanov A, Kouznetsov A, Baker CJO. Algorithms and semantic infrastructure for mutation impact extraction and grounding. BMC Genomics 2010; 11 Suppl 4:S24. [PMID: 21143808 PMCID: PMC3005927 DOI: 10.1186/1471-2164-11-s4-s24] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Background Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases. Results We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework. Conclusion We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.
Collapse
Affiliation(s)
- Jonas B Laurila
- Department of Computer Science & Applied Statistics, University of New Brunswick, Saint John, New Brunswick, Canada.
| | | | | | | | | | | |
Collapse
|
25
|
Dixit A, Yi L, Gowthaman R, Torkamani A, Schork NJ, Verkhivker GM. Sequence and structure signatures of cancer mutation hotspots in protein kinases. PLoS One 2009; 4:e7485. [PMID: 19834613 PMCID: PMC2759519 DOI: 10.1371/journal.pone.0007485] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2009] [Accepted: 09/25/2009] [Indexed: 11/18/2022] Open
Abstract
Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.
Collapse
Affiliation(s)
- Anshuman Dixit
- Graduate Program for Bioinformatics, Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Pharmaceutical Chemistry, School of Pharmacy, The University of Kansas, Lawrence, Kansas, United States of America
| | - Lin Yi
- Graduate Program for Bioinformatics, Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
| | - Ragul Gowthaman
- Graduate Program for Bioinformatics, Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
| | - Ali Torkamani
- Scripps Genomic Medicine, Department of Molecular and Experimental Medicine, Scripps Health and The Scripps Research Institute, La Jolla, California, United States of America
| | - Nicholas J. Schork
- Scripps Genomic Medicine, Department of Molecular and Experimental Medicine, Scripps Health and The Scripps Research Institute, La Jolla, California, United States of America
| | - Gennady M. Verkhivker
- Graduate Program for Bioinformatics, Center for Bioinformatics, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Pharmaceutical Chemistry, School of Pharmacy, The University of Kansas, Lawrence, Kansas, United States of America
- Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America
- * E-mail:
| |
Collapse
|
26
|
Baker CJO, Rebholz-Schuhmann D. Between proteins and phenotypes: annotation and interpretation of mutations. BMC Bioinformatics 2009; 10 Suppl 8:I1. [PMID: 19758463 PMCID: PMC2745581 DOI: 10.1186/1471-2105-10-s8-i1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|