Sequence Ontology

PubMed citation: Ontiveros-Palacios N, Cooke E, Nawrocki EP, Triebel S, Marz M, Rivas E, Griffiths-Jones S, Petrov AI, Bateman A, Sweeney B. Rfam 15: RNA families database in 2025. Nucleic Acids Res. 2025 Jan 6;53(D1):D258-D267. doi: 10.1093/nar/gkae1023. PMID: 39526405; PMCID: PMC11701678.

Authors: Nancy Ontiveros-Palacios¹; Emma Cooke²; Eric P Nawrocki³; Sandra Triebel^4,5; Manja Marz^4,5; Elena Rivas⁶; Sam Griffiths-Jones⁷; Anton I Petrov⁸; Alex Bateman¹; Blake Sweeney¹.

Affiliations:

1. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
2. SciBite Limited, BioData Innovation Centre, Wellcome Genome Campus, Hinxton, Cambridge CB10 1DR, UK.
3. National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
4. RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany.
5. European Virus Bioinformatics Center, Friedrich Schiller University Jena, 07743 Jena, Germany.
6. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA.
7. School of Biological Sciences, Faculty of Medicine, Biology and Health, Michael Smith Building, The University of Manchester, Dover St, Manchester M13 9NT, UK.
8. Riboscope Ltd, Cambridge CB1 1AH, UK.

DOI: 10.1093/nar/gkae1023 · PMID: 39526405 · PMCID: PMC11701678

Abstract

The Rfam database, a widely used repository of non-coding RNA families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26 106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology (GO) and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronization with miRBase, resulting in 1603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam's coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation and the development of machine learning models. Rfam is freely available at https://rfam.org.

Abstract reproduced from PubMed. © The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research. Distributed under CC BY 4.0.

PubMed citation: Riccio C, Jansen ML, Guo L, Ziegler A. Variant effect predictors: a systematic review and practical guide. Hum Genet. 2024 May;143(5):625-634. doi: 10.1007/s00439-024-02670-5. Epub 2024 Apr 4. PMID: 38573379; PMCID: PMC11098935.

Authors: Cristian Riccio^1,2; Max L Jansen^1,2; Linlin Guo^3,4; Andreas Ziegler^1,2,3,4,5.

Affiliations:

1. Cardio-CARE, Medizincampus Davos, Herman-Burchard-Str. 1, Davos Wolfgang, 7265, Davos, Switzerland.
2. Swiss Institute of Bioinformatics, Lausanne, Switzerland.
3. Center for Population Health Innovation (POINT), University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
4. University Center of Cardiovascular Science & Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
5. School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.

DOI: 10.1007/s00439-024-02670-5 · PMID: 38573379 · PMCID: PMC11098935

Abstract

Large-scale association analyses using whole-genome sequence data have become feasible, but understanding the functional impacts of these associations remains challenging. Although many tools are available to predict the functional impacts of genetic variants, it is unclear which tool should be used in practice. This work provides a practical guide to assist in selecting appropriate tools for variant annotation. We conducted a MEDLINE search up to November 10, 2023, and included tools that are applicable to a broad range of phenotypes, can be used locally, and have been recently updated. Tools were categorized based on the types of variants they accept and the functional impacts they predict. Sequence Ontology terms were used for standardization. We identified 118 databases and software packages, encompassing 36 variant types and 161 functional impacts. Combining only three tools, namely SnpEff, FAVOR, and SparkINFERNO, allows predicting 99 (61%) distinct functional impacts. Thirty-seven tools predict 89 functional impacts that are not supported by any other tool, while 75 tools predict pathogenicity and can be used within the ACMG/AMP guidelines in a clinical context. We launched a website allowing researchers to select tools based on desired variants and impacts. In summary, more than 100 tools are already available to predict approximately 160 functional impacts. About 60% of the functional impacts can be predicted by the combination of three tools. Unexpectedly, recent tools do not predict more impacts than older ones. Future research should allow predicting the functionality of so far unsupported variant types, such as gene fusions.URL: https://cardio-care.shinyapps.io/VEP_Finder/ .Registration: OSF Registries on November 10, 2023, https://osf.io/s2gct .

Abstract reproduced from PubMed. © 2024. The Author(s). Distributed under CC BY 4.0.

PubMed citation: Roberts AM, DiStefano MT, Riggs ER, Josephs KS, Alkuraya FS, Amberger J, Amin M, Berg JS, Cunningham F, Eilbeck K, Firth HV, Foreman J, Hamosh A, Hay E, Leigh S, Martin CL, McDonagh EM, Perrett D, Ramos EM, Robinson PN, Rath A, Sant DW, Stark Z, Whiffin N, Rehm HL, Ware JS. Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms. Genet Med. 2024 Feb;26(2):101029. doi: 10.1016/j.gim.2023.101029. Epub 2023 Nov 17. PMID: 37982373; PMCID: PMC11039201.

Authors: Angharad M Roberts^1,2; Marina T DiStefano³; Erin Rooney Riggs⁴; Katherine S Josephs^1,5; Fowzan S Alkuraya⁶; Joanna Amberger⁷; Mutaz Amin⁸; Jonathan S Berg⁹; Fiona Cunningham¹⁰; Karen Eilbeck¹¹; Helen V Firth^12,13; Julia Foreman^13,10; Ada Hamosh⁷; Eleanor Hay²; Sarah Leigh¹⁴; Christa L Martin¹⁵; Ellen M McDonagh^10,16; Daniel Perrett¹⁰; Erin M Ramos¹⁷; Peter N Robinson¹⁸; Ana Rath⁸; David W Sant¹¹; Zornitza Stark^19,20,21; Nicola Whiffin^3,22; Heidi L Rehm^3,23; James S Ware^1,3,5.

Affiliations:

1. National Heart and Lung Institute and MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
2. Dept of Medical Genetics, Great Ormond Street Hospital, Great Ormond Street, London, United Kingdom.
3. Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA.
4. Geisinger Autism and Developmental Medicine Institute, Danville, PA.
5. Royal Brompton and Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom.
6. Department of Translational Genomics, Center for Genomic Medicine, KFSHRC, Riyadh, Saudi Arabia.
7. Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
8. INSERM, US14-Orphanet, Paris, France.
9. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
10. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
11. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
12. Dept of Medical Genetics, Cambridge University Hospitals, Cambridge, United Kingdom.
13. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom.
14. Genomics England, Queen Mary University of London, Dawson Hall, London, United Kingdom.
15. Geisinger Health System, Danville, PA.
16. Open Targets, Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom.
17. National Human Genome Research Institute, National Institutes of Health, Bethesda, MD.
18. The Jackson Laboratory for Genomic Medicine, Farmington, CT.
19. Australian Genomics, Melbourne 3052, Australia.
20. Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne 3052, Australia.
21. University of Melbourne, Melbourne 3052, Australia.
22. Big Data Institute and Wellcome Centre for Human Genetics, University of Oxford, United Kingdom.
23. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA.

DOI: 10.1016/j.gim.2023.101029 · PMID: 37982373 · PMCID: PMC11039201

Abstract

Purpose: The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework.

Methods: Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated.

Results: We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources.

Conclusion: The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Abstract reproduced from PubMed. Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved. Distributed under CC BY 4.0.

PubMed citation: Roberts AM, DiStefano MT, Riggs ER, Josephs KS, Alkuraya FS, Amberger J, Amin M, Berg JS, Cunningham F, Eilbeck K, Firth HV, Foreman J, Hamosh A, Hay E, Leigh S, Martin CL, McDonagh EM, Perrett D, Ramos EM, Robinson PN, Rath A, van Sant D, Stark Z, Whiffin N, Rehm HL, Ware JS. Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease-gene relationships - allelic requirement, inheritance modes and disease mechanisms. medRxiv [Preprint]. 2023 Apr 3:2023.03.30.23287948. doi: 10.1101/2023.03.30.23287948. PMID: 37066232; PMCID: PMC10104222.

Authors: Angharad M Roberts^1,2; Marina T DiStefano³; Erin Rooney Riggs⁴; Katherine S Josephs^1,5; Fowzan S Alkuraya⁶; Joanna Amberger⁷; Mutaz Amin⁸; Jonathan S Berg⁹; Fiona Cunningham¹⁰; Karen Eilbeck¹¹; Helen V Firth^12,13; Julia Foreman^13,10; Ada Hamosh⁷; Eleanor Hay²; Sarah Leigh¹⁴; Christa L Martin¹⁵; Ellen M McDonagh^10,16; Daniel Perrett¹⁰; Erin M Ramos¹⁷; Peter N Robinson¹⁸; Ana Rath⁸; David van Sant¹¹; Zornitza Stark^19,20,21; Nicola Whiffin^3,22; Heidi L Rehm^3,23; James S Ware^1,3,10.

Affiliations:

1. National Heart & Lung Institute & MRC London Institute of Medical Sciences, Imperial College London, London, UK.
2. Dept of Medical Genetics, Great Ormond Street Hospital, Great Ormond Street, London. WC1N 3JH, UK.
3. Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
4. Geisinger Autism & Developmental Medicine Institute, Danville, PA, USA.
5. Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK.
6. Department of Translational Genomics, Center for Genomic Medicine, KFSHRC, Riyadh, Saudi Arabia.
7. Online Mendelian Inheritance in Man (OMIM), Johns Hopkins University School of Medicine, USA.
8. INSERM, US14-Orphanet, Paris, France.
9. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill NC, 27599.
10. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, United Kingdom.
11. Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah.
12. Dept of Medical Genetics, Cambridge University Hospitals, Cambridge CB2 0QQ, UK.
13. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK.
14. Genomics England, Queen Mary University of London, Dawson Hall, London, EC1M 6BQ, UK.
15. Clinical Genome Resource (ClinGen), USA.
16. Open Targets, Cambridge, UK.
17. National Human Genome Research Institute, National Institutes of Health, USA.
18. The Jackson Laboratory for Genomic Medicine, Farmington CT 06032, USA.
19. Australian Genomics, Melbourne 3052, Australia.
20. Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne 3052, Australia.
21. University of Melbourne, Melbourne 3052, Australia.
22. Big Data Institute and Wellcome Centre for Human Genetics, University of Oxford, UK.
23. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.

DOI: 10.1101/2023.03.30.23287948 · PMID: 37066232 · PMCID: PMC10104222

Abstract

Purpose: The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation, and to support variant classification within the ACMG/AMP framework.

Methods: Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition (GenCC) members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated.

Results: We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both sequence ontology (SO) and human phenotype ontology (HPO) ontologies. GenCC member groups intend to use or map to these terms in their respective resources.

Conclusion: The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Abstract reproduced from PubMed. Distributed under CC BY 4.0.

PubMed citation: An HJ, Kim JH, Yoon S, Choi J, Koo J, Lee S. Genome-Wide Association Study Identifies Genetic Variants Associated with Rotator Cuff Tear-A Pilot Study. Diagnostics (Basel). 2022 Oct 15;12(10):2497. doi: 10.3390/diagnostics12102497. PMID: 36292186; PMCID: PMC9601242.

Authors: Hyun-Ju An¹; Jae-Hwa Kim¹; Siyeong Yoon¹; Junwon Choi²; Jeongmo Koo¹; Soonchul Lee¹.

Affiliations:

1. Department of Orthopaedic Surgery, CHA Bundang Medical Center, CHA University School of Medicine, 335 Pangyo-ro, Bundang-gu, Seongnam-si 13488, Gyeonggi-do, Korea.
2. Department of Molecular Science and Technology, Ajou University, 206 World cup-ro, Yeongtong-gu, Suwon-si 16499, Gyeonggi-do, Korea.

DOI: 10.3390/diagnostics12102497 · PMID: 36292186 · PMCID: PMC9601242

Abstract

A rotator cuff is a muscle and tendon surrounding the shoulder joint, and a rotator cuff tear can be caused by overuse or injury, which leads to great pain in affected individuals. However, rotator cuff tear is a multifactorial process whose underlying mechanism is still unclear. Many previous studies have suggested an important role of genetic predisposition, such as single-nucleotide polymorphisms (SNPs), in explaining the genesis of tendinopathy. This study aimed to identify specific genes or genetic variants associated with rotator cuff tears by performing a genome-wide association study (GWAS) using an independent case of rotator cuff tears. GWAS was performed using data from CHA Bundang Medical Center with 20 cases of rotator cuff tears, and 20 cases of healthy controls genotyped on the Illumina HiSeq 2500. Tests of association were performed using the Burrows−Wheeler Aligner (BWA) software at 284,246 SNPs. Data were filtered based on sequence ontology, minor allele frequency, and Hardy−Weinberg equilibrium values, and SNPs were considered significant if the p-value was <0.05. The tests of association revealed more than 20 significantly associated SNPs. SNPs showing the highest significance occurred in candidate genes, including LAIR2 (rs2287828, OR 9.116, p-value 5.49 × 10−4) on chromosome 19 and CRIPAK (rs9328733, OR 6, p-value 1.11 × 10−3) and REST (rs2228991, OR 8.222, p-value 1.20 × 10−3) on chromosome 4. This study attempted to identify genetic variants influencing rotator cuff tears through a genome-wide association study using a dense set of SNPs. More than 20 SNPs were significantly associated with rotator cuff tears. The major limitation of this study is that it was conducted on a small study group and requires further validation. Nevertheless, the identification of potential genetic variants related to rotator cuff injury would aid in the early detection of individuals at risk for the development of tendinopathy and will provide insight into future gene therapies.

Abstract reproduced from PubMed. © 2022 by the authors. Distributed under CC BY 4.0.

PubMed citation: Perry MN, Smith CL. Murine allele and transgene symbols: ensuring unique, concise, and informative nomenclature. Mamm Genome. 2022 Mar;33(1):108-119. doi: 10.1007/s00335-021-09902-3. Epub 2021 Aug 14. PMID: 34389871; PMCID: PMC8913455.

Authors: M N Perry¹; C L Smith¹.

Affiliations:

1. Mouse Genome Informatics (MGI), Jackcon Laboratory, Bar Harbor, ME, USA.

DOI: 10.1007/s00335-021-09902-3 · PMID: 34389871 · PMCID: PMC8913455

Abstract

In addition to naturally occurring sequence variation and spontaneous mutations, a wide array of technologies exist for modifying the mouse genome. Standardized nomenclature, including allele, transgene, and other mutation nomenclature, as well as persistent unique identifiers (PUID) are critical for effective scientific communication, comparison of results, and integration of data into knowledgebases such as Mouse Genome Informatics (MGI), Alliance for Genome Resources, and International Mouse Strain Resource (IMSR). As well as being the authoritative source for mouse gene, allele, and strain nomenclature, MGI integrates published and unpublished genomic, phenotypic, and expression data while linking to other online resources for a complete view of the mouse as a valuable model organism. The International Committee on Standardized Genetic Nomenclature for Mice has developed allele nomenclature rules and guidelines that take into account the number of genes impacted, the method of allele generation, and the nature of the sequence alteration. To capture details that cannot be included in allele symbols, MGI has further developed allele to gene relationships using sequence ontology (SO) definitions for mutations that provide links between alleles and the genes affected. MGI is also using (HGVS) variant nomenclature for variants associated with alleles that will enhance searching for mutations and will improve cross-species comparison. With the ability to assign unique and informative symbols as well as to link alleles with more than one gene, allele and transgene nomenclature rules and guidelines provide an unambiguous way to represent alterations in the mouse genome and facilitate data integration among multiple resources such the Alliance of Genome Resources and International Mouse Strain Resource.

Abstract reproduced from PubMed. © 2021. The Author(s). Distributed under CC BY 4.0.

PubMed citation: Gao Y, Liu S, Baldwin Vi RL, Connor EE, Cole JB, Ma L, Fang L, Li CJ, Liu GE. Functional annotation of regulatory elements in cattle genome reveals the roles of extracellular interaction and dynamic change of chromatin states in rumen development during weaning. Genomics. 2022 Mar;114(2):110296. doi: 10.1016/j.ygeno.2022.110296. Epub 2022 Feb 8. PMID: 35143887.

Authors: Yahui Gao^1,2; Shuli Liu^1,3; Ransom L Baldwin Vi¹; Erin E Connor⁴; John B Cole¹; Li Ma²; Lingzhao Fang⁵; Cong-Jun Li¹; George E Liu¹.

Affiliations:

1. Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD 20705, USA.
2. Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20742, USA.
3. College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
4. Department of Animal and Food Sciences, University of Delaware, Newark, DE 19716, USA.
5. MRC Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, United Kingdom.

DOI: 10.1016/j.ygeno.2022.110296 · PMID: 35143887

Abstract

We profiled landscapes of bovine regulatory elements and explored dynamic changes of chromatin states in rumen development during weaning. The regulatory elements (15 chromatin states) and their coordinated activities in cattle were defined through genome-wide profiling of four histone modifications, CTCF-binding, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial tissues. Each chromatin state presented specific enrichment for sequence ontology, methylation, trait-associated variants, transcription, gene expression-associated variants, selection signatures, and evolutionarily conserved elements. During weaning, weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states and occurred in tandem with significant variations in gene expression and DNA methylation, significantly associated with stature, production, and reproduction economic traits. By comparing with in vitro cultured epithelial cells and in vivo rumen tissues, we showed the commonness and uniqueness of these results, especially the roles of cell interactions and mitochondrial activities in tissue development.

Abstract reproduced from PubMed. Copyright © 2022. Published by Elsevier Inc.

PubMed citation: Shi S, Zhang Z, Li B, Zhang S, Fang L. Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models. Methods Mol Biol. 2022;2467:329-340. doi: 10.1007/978-1-0716-2205-6_11. PMID: 35451781.

Authors: Shaolei Shi¹; Zhe Zhang²; Bingjie Li³; Shengli Zhang¹; Lingzhao Fang⁴.

Affiliations:

1. College of Animal Science and Technology, China Agricultural University, Beijing, China.
2. Department of Animal Breeding and genetics, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
3. The Roslin Institute Building, Scotland's Rural College, Edinburgh, UK.
4. MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.

DOI: 10.1007/978-1-0716-2205-6_11 · PMID: 35451781

Abstract

Due to the rapid development of high-throughput sequencing technology, we can easily obtain not only the genetic variants at the whole-genome sequence level (e.g., from 1000 Genomes project and 1000 Bull Genomes project), but also a wide range of functional annotations (e.g., enhancers and promoters from ENCODE, FAANG, and FarmGTEx projects) across a wide range of tissues, cell types, developmental stages, and environmental conditions. This huge amount of information leads to a revolution in studying genetics and genomics of complex traits in humans, livestock, and plant species. In this chapter, we focused on and reviewed the genomic prediction methods that incorporate external biological information into genomic prediction, such as sequence ontology, linkage disequilibrium (LD) of SNPs, quantitative trait loci (QTL), and multi-layer omics data (e.g., transcriptome, epigenome, and microbiome).

Abstract reproduced from PubMed. © 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

PubMed citation: Juanes Cortés B, Vera-Ramos JA, Lovering RC, Gaudet P, Laegreid A, Logie C, Schulz S, Roldán-García MDM, Kuiper M, Fernández-Breis JT. Formalization of gene regulation knowledge using ontologies and gene ontology causal activity models. Biochim Biophys Acta Gene Regul Mech. 2021 Nov-Dec;1864(11-12):194766. doi: 10.1016/j.bbagrm.2021.194766. Epub 2021 Oct 25. PMID: 34710644.

Authors: Belén Juanes Cortés¹; José Antonio Vera-Ramos²; Ruth C Lovering³; Pascale Gaudet⁴; Astrid Laegreid⁵; Colin Logie⁶; Stefan Schulz²; María Del Mar Roldán-García^7,8,9; Martin Kuiper¹⁰; Jesualdo Tomás Fernández-Breis¹.

Affiliations:

1. Departamento de Informatica y Sistemas, University of Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, Campus de Espinardo, 30100 Murcia, Spain.
2. Institute of Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerpl. 2, Graz, Austria.
3. Institute of Cardiovascular Science, Faculty of Pop Health Sciences, University College London, Rayne Building, 5 University Street, London WC1E 6JF, United Kingdom.
4. Swiss Institute of Bioinformatics, 1, rue Michel Servet, 1211 Geneva 4, Switzerland.
5. Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Gastrosenteret, 431.03.046, Øya, Prinsesse Kristinas gate 1, Trondheim, Norway.
6. Faculty of Science, Radboud Institute for Molecular Life Sciences, Geert Grooteplein Zuid 28, 6525, GA, Nijmegen, the Netherlands.
7. Departamento de Lenguajes y Ciencias de la Computación, University of Málaga,Bulevard Louis Pasteur 35, 29071 Málaga, Spain.
8. ITIS Software, University of Málaga, Calle Arquitecto Francisco Peñalosa s/n, 29071 Málaga,Spain.
9. Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Calle Doctor Miguel Díaz Recio, 28, 29010 Málaga, Spain.
10. Department of Biology, Norwegian University of Science and Technology, Realfagbygget, Høgskoleringen 5, 7034 Trondheim, Norway.

DOI: 10.1016/j.bbagrm.2021.194766 · PMID: 34710644

Abstract

Gene regulation computational research requires handling and integrating large amounts of heterogeneous data. The Gene Ontology has demonstrated that ontologies play a fundamental role in biological data interoperability and integration. Ontologies help to express data and knowledge in a machine processable way, which enables complex querying and advanced exploitation of distributed data. Contributing to improve data interoperability in gene regulation is a major objective of the GREEKC Consortium, which aims to develop a standardized gene regulation knowledge commons. GREEKC proposes the use of ontologies and semantic tools for developing interoperable gene regulation knowledge models, which should support data annotation. In this work, we study how such knowledge models can be generated from cartoons of gene regulation scenarios. The proposed method consists of generating descriptions in natural language of the cartoons; extracting the entities from the texts; finding those entities in existing ontologies to reuse as much content as possible, especially from well known and maintained ontologies such as the Gene Ontology, the Sequence Ontology, the Relations Ontology and ChEBI; and implementation of the knowledge models. The models have been implemented using Protégé, a general ontology editor, and Noctua, the tool developed by the Gene Ontology Consortium for the development of causal activity models to capture more comprehensive annotations of genes and link their activities in a causal framework for Gene Ontology Annotations. We applied the method to two gene regulation scenarios and illustrate how to apply the models generated to support the annotation of data from research articles.

Abstract reproduced from PubMed. Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

PubMed citation: Sant DW, Sinclair M, Mungall CJ, Schulz S, Zerbino D, Lovering RC, Logie C, Eilbeck K. Sequence Ontology terminology for gene regulation. Biochim Biophys Acta Gene Regul Mech. 2021 Oct;1864(10):194745. doi: 10.1016/j.bbagrm.2021.194745. Epub 2021 Aug 11. PMID: 34389511.

Authors: David W Sant^1,2; Michael Sinclair¹; Christopher J Mungall³; Stefan Schulz⁴; Daniel Zerbino⁵; Ruth C Lovering⁶; Colin Logie⁷; Karen Eilbeck¹.

Affiliations:

1. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
2. Department of Biomedical Sciences, Noorda College of Osteopathic Medicine, Provo, UT, USA.
3. Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
4. Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
5. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
6. Functional Gene Annotation, Preclinical and Fundamental Science, UCL Institute of Cardiovascular Science, University College London, London, UK.
7. Radboud Institute for Molecular Life Sciences, Geert Grooteplein Zuid 28, 6525 GA Nijmegen, Netherlands.

DOI: 10.1016/j.bbagrm.2021.194745 · PMID: 34389511

Abstract

The Sequence Ontology (SO) is a structured, controlled vocabulary that provides terms and definitions for genomic annotation. The Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) initiative has gathered input from many groups of researchers, including the SO, the Gene Ontology (GO), and gene regulation experts, with the goal of curating information about how gene expression is regulated at the molecular level. Here we discuss recent updates to the SO reflecting current knowledge. We have developed more accurate human-readable terms (also known as classes), including new definitions, and relationships related to the expression of genes. New findings continue to give us insight into the biology of gene regulation, including the order of events, and participants in those events. These updates to the SO support logical reasoning with the current understanding of gene expression regulation at the molecular level.

Abstract reproduced from PubMed. Copyright © 2021 Elsevier B.V. All rights reserved.

PubMed citation: RNAcentral Consortium. RNAcentral 2021: secondary structure integration, improved sequence search and new member databases. Nucleic Acids Res. 2021 Jan 8;49(D1):D212-D220. doi: 10.1093/nar/gkaa921. PMID: 33106848; PMCID: PMC7779037.

Authors: RNAcentral Consortium.

Consortium contributors: Blake A Sweeney; Anton I Petrov; Carlos E Ribas; Robert D Finn; Alex Bateman; Maciej Szymanski; Wojciech M Karlowski; Stefan E Seemann; Jan Gorodkin; Jamie J Cannone; Robin R Gutell; Simon Kay; Steven Marygold; Gil dos Santos; Adam Frankish; Jonathan M Mudge; Ruth Barshir; Simon Fishilevich; Patricia P Chan; Todd M Lowe; Ruth Seal; Elspeth Bruford; Simona Panni; Pablo Porras; Dimitra Karagkouni; Artemis G Hatzigeorgiou; Lina Ma; Zhang Zhang; Pieter-Jan Volders; Pieter Mestdagh; Sam Griffiths-Jones; Bastian Fromm; Kevin J Peterson; Ioanna Kalvari; Eric P Nawrocki; Anton S Petrov; Shuai Weng; Philia Bouchard-Bourelle; Michelle Scott; Lauren M Lui; David Hoksza; Ruth C Lovering; Barbara Kramarz; Prita Mani; Sridhar Ramachandran; Zasha Weinberg.

Affiliations:

1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
2. Center for non-coding RNA in Technology and Health, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, DK-1871, Denmark.
3. Department of Integrative Biology, University of Texas at Austin, Austin, Texas 78712, USA.
4. Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK.
5. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel.
6. Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.
7. Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0AW, UK.
8. Università della Calabria, Dipartimento di Biologia, Ecologia e Scienze della Terra, Via Pietro Bucci Cubo 6/C, Rende, CS, 87036, Italy.
9. DIANA-LAB, Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece & Hellenic Pasteur Institute, 351 31, Greece.
10. China National Center for Bioinformation & National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, 100101, China.
11. Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium.
12. Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium.
13. Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
14. Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, S-10691, Sweden.
15. Department of Biological Sciences, Dartmouth College, Hanover, NH, 03755, USA.
16. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA.
17. Center for the Origins of Life, School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA 30032, USA.
18. Department of Genetics, Stanford University, Palo Alto, CA 94303, USA.
19. Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QCJ1H 5N4, Canada.
20. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
21. Department of Software Engineering, Faculty of Mathematics and Physics, Charles Univesity, Prague, 11800 Praha 1, Czech Republic.
22. Functional Gene Annotation, Preclinical and Fundamental Science, UCL Institute of Cardiovascular Science, University College London, London, WC1E 6BT, UK.
23. The Institute of Neuroscience, University of Oregon, Eugene, OR 97403-1254, USA.
24. Bioinformatics Group, Department of Computer Science and Interdisciplinary Centre for Bioinformatics, Leipzig University, Härtelstraße 16–18, 04107, Leipzig, Germany.
25. Department of Computational Biology, Adam Mickiewicz University in Poznan, Poznan, 61-614, Poland.
26. The Biological Laboratories, Harvard University, 16 Divinity Avenue, Cambridge, MA 02138, USA.

DOI: 10.1093/nar/gkaa921 · PMID: 33106848 · PMCID: PMC7779037

Abstract

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world's largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.

Abstract reproduced from PubMed. © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. Distributed under CC BY 4.0.

PubMed citation: Yasser N, Karam A. GAD: A Python Script for Dividing Genome Annotation Files into Feature-Based Files. Interdiscip Sci. 2020 Sep;12(3):377-381. doi: 10.1007/s12539-020-00378-4. Epub 2020 Jun 10. PMID: 32524530.

Authors: Norhan Yasser¹; Ahmed Karam¹.

Affiliations:

1. Agricultural Genetic Engineering Research Institute (AGERI), Agricultural Research Center (ARC), Giza, Egypt.

DOI: 10.1007/s12539-020-00378-4 · PMID: 32524530

Abstract

Nowadays, the manipulation and analysis of genomic data stored in publicly accessible repositories have become a daily task in genomics and bioinformatics laboratories. Due to the enormous advancement in the field of genome sequencing and the emergence of many projects, bioinformaticians have pushed for the creation of a variety of programs and pipelines that will automatically analyze such big data, in particular the pipelines of gene annotation. Dealing with annotation files using easy and simple programs is very important, particularly for non-developers, enhancing the genomic data analysis acceleration. One of the first tasks required to work with genomic annotation files is to extract different features. In this regard, we have developed GAD ( https://github.com/bio-projects/GAD ) using Python to be a fast, easy, and controlled script that has a high ability to handle annotation files such as GFF3 and GTF. GAD is a cross-platform graphical interface tool used to extract genome features such as intergenic regions, upstream, and downstream genes. Besides, GAD finds all names of ambiguous sequence ontology, and either extracts them or considers them as genes or transcripts. The results are produced in a variety of file formats, such as BED, GTF, GFF3, and FASTA, supported by other bioinformatics programs. The GAD can handle large sizes of different genomes and an infinite number of files with minimal user effort. Therefore, our script could be integrated into various pipelines in all genomic laboratories to accelerate data analysis.

Abstract reproduced from PubMed.

PubMed citation: Fang L, Liu S, Liu M, Kang X, Lin S, Li B, Connor EE, Baldwin RL, Tenesa A, Ma L, Liu GE, Li CJ. Functional annotation of the cattle genome through systematic discovery and characterization of chromatin states and butyrate-induced variations. BMC Biol. 2019 Aug 16;17(1):68. doi: 10.1186/s12915-019-0687-8. PMID: 31419979; PMCID: PMC6698049.

Authors: Lingzhao Fang^1,2; Shuli Liu^1,3; Mei Liu^1,4; Xiaolong Kang^1,5; Shudai Lin^1,6; Bingjie Li¹; Erin E Connor¹; Ransom L Baldwin 6th¹; Albert Tenesa⁷; Li Ma²; George E Liu¹; Cong-Jun Li¹.

Affiliations:

1. Animal Genomics and Improvement Laboratory, BARC, Agricultural Research Service, USDA, Beltsville, MD, 20705, USA.
2. Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
3. College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
4. College of Animal Science and Technology, Shaanxi Key Laboratory of Agricultural Molecular Biology, Northwest A&F University, Yangling, 712100, Shaanxi, China.
5. College of Agriculture, Ningxia University, Yinchuan, 750021, China.
6. Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science of South China Agricultural University, Guangzhou, 510642, China.
7. The Roslin Institute, University of Edinburgh, Edinburgh, EH4 2XU, UK.

DOI: 10.1186/s12915-019-0687-8 · PMID: 31419979 · PMCID: PMC6698049

Abstract

Background: The functional annotation of genomes, including chromatin accessibility and modifications, is important for understanding and effectively utilizing the increased amount of genome sequences reported. However, while such annotation has been well explored in a diverse set of tissues and cell types in human and model organisms, relatively little data are available for livestock genomes, hindering our understanding of complex trait variation, domestication, and adaptive evolution. Here, we present the first complete global landscape of regulatory elements in cattle and explore the dynamics of chromatin states in rumen epithelial cells induced by the rumen developmental regulator-butyrate.

Results: We established the first global map of regulatory elements (15 chromatin states) and defined their coordinated activities in cattle, through genome-wide profiling for six histone modifications, RNA polymerase II, CTCF-binding sites, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial primary cells (REPC), rumen tissues, and Madin-Darby bovine kidney epithelial cells (MDBK). We demonstrated that each chromatin state exhibited specific enrichment for sequence ontology, transcription, methylation, trait-associated variants, gene expression-associated variants, selection signatures, and evolutionarily conserved elements, implying distinct biological functions. After butyrate treatments, we observed that the weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states, occurred concomitantly with significant alterations in gene expression and DNA methylation, which was significantly associated with heifer conception rate and stature economic traits.

Conclusion: Our results demonstrate the crucial role of functional genome annotation for understanding genome regulation, complex trait variation, and adaptive evolution in livestock. Using butyrate to induce the dynamics of the epigenomic landscape, we were able to establish the correlation among nutritional elements, chromatin states, gene activities, and phenotypic outcomes.

Abstract reproduced from PubMed. © The Author(s). 2019 Distributed under CC BY 4.0.

PubMed citation: El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, Qureshi M, Richardson LJ, Salazar GA, Smart A, Sonnhammer ELL, Hirsh L, Paladin L, Piovesan D, Tosatto SCE, Finn RD. The Pfam protein families database in 2019. Nucleic Acids Res. 2019 Jan 8;47(D1):D427-D432. doi: 10.1093/nar/gky995. PMID: 30357350; PMCID: PMC6324024.

Authors: Sara El-Gebali¹; Jaina Mistry¹; Alex Bateman¹; Sean R Eddy²; Aurélien Luciani¹; Simon C Potter¹; Matloob Qureshi¹; Lorna J Richardson¹; Gustavo A Salazar¹; Alfredo Smart¹; Erik L L Sonnhammer³; Layla Hirsh^4,5; Lisanna Paladin⁴; Damiano Piovesan⁴; Silvio C E Tosatto⁴; Robert D Finn¹.

Affiliations:

1. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
2. HHMI, Harvard University, 16 Divinity Ave Cambridge, MA 02138 USA.
3. Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17121 Solna, Sweden.
4. Department of Biomedical Sciences, University of Padua, 35131 Padova, Italy.
5. Dept. of Engineering, Pontificia Universidad Católica del Perú 1801, San Miguel 15088, Lima, Perú.

DOI: 10.1093/nar/gky995 · PMID: 30357350 · PMCID: PMC6324024

Abstract

The last few years have witnessed significant changes in Pfam (https://pfam.xfam.org). The number of families has grown substantially to a total of 17,929 in release 32.0. New additions have been coupled with efforts to improve existing families, including refinement of domain boundaries, their classification into Pfam clans, as well as their functional annotation. We recently began to collaborate with the RepeatsDB resource to improve the definition of tandem repeat families within Pfam. We carried out a significant comparison to the structural classification database, namely the Evolutionary Classification of Protein Domains (ECOD) that led to the creation of 825 new families based on their set of uncharacterized families (EUFs). Furthermore, we also connected Pfam entries to the Sequence Ontology (SO) through mapping of the Pfam type definitions to SO terms. Since Pfam has many community contributors, we recently enabled the linking between authorship of all Pfam entries with the corresponding authors' ORCID identifiers. This effectively permits authors to claim credit for their Pfam curation and link them to their ORCID record.

Abstract reproduced from PubMed. © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research. Distributed under CC BY 4.0.

Conference citation: Sinclair M, Bada M, Eilbeck K. Intelligently Designed Ontology Alignment: A Case Study from the Sequence Ontology. ISMB 2018, Bio-ontologies track. Presented July 10, 2018.

Authors: Michael Sinclair¹; Michael Bada²; Karen Eilbeck¹.

Affiliations:

1. Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
2. University of Colorado Anschutz Medical Campus, Department of Pharmacology, Aurora, CO, USA.

Conference: The International Society for Computational Biology's 26th Conference on Intelligent Systems for Molecular Biology (ISMB)

Conference location: Chicago, Illinois, USA

Conference dates: July 6-10, 2018

Presentation track: Community of Special Interest (COSI) Track Presentations, Bio-Ontologies track

Presentation date: Tuesday, July 10, 2018

Links: Conference home page · Conference proceedings · Recorded presentation · Conference paper (PDF)

Abstract

As the number and size of biomedical ontologies continue to grow, the problem of coordinating them becomes increasingly urgent. This is particularly problematic for ontologies with semantically overlapping or analogous content but based on different underlying conceptualizations. Ontology matching methods have been developed to align existing ontologies for semantic interoperability, but this is a laborious and inefficient process compared to designing related ontologies to be aligned from the outset. We present a method for designing companion ontologies to be largely semantically and structurally aligned as they evolve. We have applied this method to the specific case of the coordinated management of two ontologies focused on the representation of analogous types of biological sequences: the Sequence Ontology and the Molecular Sequence Ontology. We derived general principles that any group can apply to their own use cases: 1) Create inter-ontology logical definitions for those portions of the ontologies to be aligned, 2) use class annotations to automatically manage those portions of the ontologies not to be aligned, and 3) use the taxonomy of one ontology to classify the other with an OWL reasoner. We propose this as an efficient way of designing companion ontologies to be aligned throughout their development life cycle.

PubMed citation: Murphy SN, Avillach P, Bellazzi R, Phillips L, Gabetta M, Eran A, McDuffie MT, Kohane IS. Combining clinical and genomics queries using i2b2 - Three methods. PLoS One. 2017;12(4):e0172187. doi: 10.1371/journal.pone.0172187. PMID: 28388645; PMCID: PMC5384666.

Authors: Shawn N Murphy^1,2,3; Paul Avillach^2,4; Riccardo Bellazzi^5,6,7; Lori Phillips¹; Matteo Gabetta^5,8; Alal Eran^2,4; Michael T McDuffie^2,4; Isaac S Kohane^2,4.

Affiliations:

1. Research IS and Computing, Partners HealthCare, Charlestown, Massachusetts, United States of America.
2. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America.
3. Laboratory of Computer Science, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
4. Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
5. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
6. IRCCS Fondazione S. Maugeri, Pavia, Italy.
7. Centre for Health Technologies, University of Pavia, Pavia, Italy.
8. Biomeris s.r.l, Via Ferrata, Pavia, Italy.

DOI: 10.1371/journal.pone.0172187 · PMID: 28388645 · PMCID: PMC5384666

Abstract

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical "Big Data" challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.

Abstract reproduced from PubMed. © 2017 Murphy et al Distributed under CC BY 4.0.

PubMed citation: Arguello Casteleiro M, Klein J, Stevens R. The Proteasix Ontology. J Biomed Semantics. 2016 Jun 4;7(1):33. doi: 10.1186/s13326-016-0078-9. PMID: 27259807; PMCID: PMC4893253.

Authors: Mercedes Arguello Casteleiro¹; Julie Klein²; Robert Stevens¹.

Affiliations:

1. School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
2. Institut National de la Sante et de la Recherche Medicale (INSERM), U1048, Toulouse, 24105, France.

DOI: 10.1186/s13326-016-0078-9 · PMID: 27259807 · PMCID: PMC4893253

Abstract

Background: The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides)

Methods: The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis.

Results: The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results.

Availability: The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone.

Abstract reproduced from PubMed. © Arguello Casteleiro et al. 2016 Distributed under CC BY 4.0.

PubMed citation: Cunningham F, Moore B, Ruiz-Schultz N, Ritchie GR, Eilbeck K. Improving the Sequence Ontology terminology for genomic variant annotation. J Biomed Semantics. 2015 Jul 31;6:32. doi: 10.1186/s13326-015-0030-4. PMID: 26229585; PMCID: PMC4520272.

Authors: Fiona Cunningham¹; Barry Moore²; Nicole Ruiz-Schultz³; Graham RS Ritchie^1,4; Karen Eilbeck³.

Affiliations:

1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD UK.
2. Department of Human Genetics, University of Utah, Salt Lake City, UT USA.
3. Department of Biomedical Informatics, University of Utah, Salt Lake City, UT USA.
4. Wellcome Trust Sanger Institute, Hinxton, Cambridge UK.

DOI: 10.1186/s13326-015-0030-4 · PMID: 26229585 · PMCID: PMC4520272

Abstract

Background: The Genome Variant Format (GVF) uses the Sequence Ontology (SO) to enable detailed annotation of sequence variation. The annotation includes SO terms for the type of sequence alteration, the genomic features that are changed and the effect of the alteration. The SO maintains and updates the specification and provides the underlying ontologicial structure.

Methods: A requirements analysis was undertaken to gather terms missing in the SO release at the time, but needed to adequately describe the effects of sequence alteration on a set of variant genomic annotations. We have extended and remodeled the SO to include and define all terms that describe the effect of variation upon reference genomic features in the Ensembl variation databases.

Results: The new terminology was used to annotate the human reference genome with a set of variants from both COSMIC and dbSNP. A GVF file containing 170,853 sequence alterations was generated using the SO terminology to annotate the kinds of alteration, the effect of the alteration and the reference feature changed. There are four kinds of alteration and 24 kinds of effect seen in this dataset. (Ensembl Variation annotates 34 different SO consequence terms: http://www.ensembl.org/info/docs/variation/predicted_data.html).

Conclusions: We explain the updates to the Sequence Ontology to describe the effect of variation on existing reference features. We have provided a set of annotations using this terminology, and the well defined GVF specification. We have also provided a provisional exploration of this large annotation dataset.

Abstract reproduced from the linked PubMed record under CC BY 4.0.

PubMed citation: Crosby MA, Gramates LS, Dos Santos G, Matthews BB, St Pierre SE, Zhou P, Schroeder AJ, Falls K, Emmert DB, Russo SM, Gelbart WM; FlyBase Consortium. Gene Model Annotations for Drosophila melanogaster: The Rule-Benders. G3 (Bethesda). 2015 Jun 24;5(8):1737-49. doi: 10.1534/g3.115.018937. PMID: 26109356; PMCID: PMC4528330.

Authors: Madeline A Crosby¹; L Sian Gramates¹; Gilberto Dos Santos¹; Beverley B Matthews¹; Susan E St Pierre¹; Pinglei Zhou¹; Andrew J Schroeder¹; Kathleen Falls¹; David B Emmert¹; Susan M Russo¹; William M Gelbart¹; FlyBase Consortium^1,2,3,4.

Affiliations:

1. Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138.
2. Department of Genetics, University of Cambridge, Cambridge CB2 3EH, United Kingdom.
3. Department of Biology, Indiana University, Bloomington, Indiana 47405.
4. Department of Biology, University of New Mexico, Albuquerque, New Mexico 87131.

DOI: 10.1534/g3.115.018937 · PMID: 26109356 · PMCID: PMC4528330

Abstract

In the context of the FlyBase annotated gene models in Drosophila melanogaster, we describe the many exceptional cases we have curated from the literature or identified in the course of FlyBase analysis. These range from atypical but common examples such as dicistronic and polycistronic transcripts, noncanonical splices, trans-spliced transcripts, noncanonical translation starts, and stop-codon readthroughs, to single exceptional cases such as ribosomal frameshifting and HAC1-type intron processing. In FlyBase, exceptional genes and transcripts are flagged with Sequence Ontology terms and/or standardized comments. Because some of the rule-benders create problems for handlers of high-throughput data, we discuss plans for flagging these cases in bulk data downloads.

Abstract reproduced from PubMed. Copyright © 2015 Crosby et al. Distributed under CC BY 4.0.

PubMed citation: Legaz-García Mdel C, Miñarro-Giménez JA, Madrid M, Menárguez-Tortosa M, Torres Martínez S, Fernández-Breis JT. Linking genome annotation projects with genetic disorders using ontologies. J Med Syst. 2012 Nov;36 Suppl 1:S11-23. doi: 10.1007/s10916-012-9890-7. PMID: 23149630.

Authors: María del Carmen Legaz-García¹; José Antonio Miñarro-Giménez¹; Marisa Madrid²; Marcos Menárguez-Tortosa¹; Santiago Torres Martínez²; Jesualdo Tomás Fernández-Breis¹.

Affiliations:

1. Facultad de Informática, Universidad de Murcia, Murcia, 30071, Spain.
2. Facultad de Biología, Universidad de Murcia, Murcia, 30071, Spain.

DOI: 10.1007/s10916-012-9890-7 · PMID: 23149630

Abstract

Genome sequencing projects generate vast amounts of data of a wide variety of types and complexities, and at a growing pace. Traditionally, the annotation of such sequences was difficult to share with other researchers. Despite the fact that this has improved with the development and application of biological ontologies, such annotation efforts remain isolated since the amount of information that can be used from other annotation projects is limited. In addition to this, they do not benefit from the translational information available for the genomic sequences. In this paper, we describe a system that supports genome annotation processes by providing useful information about orthologous genes and the genetic disorders which can be associated with a gene identified in a sequence. The seamless integration of such data will be facilitated by an ontological infrastructure which, following best practices in ontology engineering, will reuse existing biological ontologies like Sequence Ontology or Ontological Gene Orthology.

Abstract reproduced from PubMed.

Conference citation: Bada M, Eilbeck K. Efforts toward a More Consistent and Interoperable Sequence Ontology. 3rd International Conference on Biomedical Ontology (ICBO 2012), KR-MED Series. Presented July 24, 2012.

Authors: Michael Bada¹; Karen Eilbeck².

Affiliations:

1. University of Colorado Anschutz Medical Campus, Department of Pharmacology, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6400A, P.O. Box 6511, Aurora, CO 80045 USA.
2. Department of Biomedical Informatics, Health Sciences Education Bldg, University of Utah, 26 South 2000 East, Suite 5700, Salt Lake City, UT 84112-5750 USA.

Conference: 3rd International Conference on Biomedical Ontology (ICBO 2012)

Conference location: Graz, Austria

Conference dates: July 21-25, 2012

Presentation track: KR-MED Series

Presentation date: Tuesday, July 24, 2012

Links: Conference proceedings · Conference paper (PDF)

Abstract

The Sequence Ontology (SO), a member of the Open Biomedical Ontologies (OBOs) library, was developed with the goals of standardizing the vocabulary and semantics of biological-sequence annotation with the goal of increased interoperability for software developers and users of genomic sequences. Here we present our recent developmental approaches to address three issues of import for the SO: (1) representation of molecular sequences versus abstract sequences; (2) integration with the ChEBI ontology, the Protein Ontology, the RNA Ontology, the Gene Ontology, the Chemical Information Ontology, and the Information Artifact Ontology; and (3) consistent representation of DNA, RNA, and peptide sequences and harmonizing their use toward annotation in sequence databases. We anticipate that these efforts will result in a representation of biological sequences that is more consistent not only internally but also with respect to its use in annotations in sequence databases. We further envision increased interoperability of the SO with other OBOs, which would benefit applications beyond sequence annotations.

PubMed citation: Bada M, Eckert M, Evans D, Garcia K, Shipley K, Sitnikov D, Baumgartner WA, Cohen KB, Verspoor K, Blake JA, Hunter LE. Concept annotation in the CRAFT corpus. BMC Bioinformatics. 2012 Jul 9;13:161. doi: 10.1186/1471-2105-13-161. PMID: 22776079; PMCID: PMC3476437.

Authors: Michael Bada¹; Miriam Eckert²; Donald Evans¹; Kristin Garcia¹; Krista Shipley¹; Dmitry Sitnikov³; William A Baumgartner Jr¹; K Bretonnel Cohen¹; Karin Verspoor^1,4; Judith A Blake³; Lawrence E Hunter¹.

Affiliations:

1. Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
2. Department of Linguistics, University of Colorado Boulder, Boulder, CO, USA.
3. Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, ME, USA.
4. Victoria Research Lab, National ICT Australia, Melbourne, VIC, 3010, Australia.

DOI: 10.1186/1471-2105-13-161 · PMID: 22776079 · PMCID: PMC3476437

Abstract

Background: Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text.

Results: This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement.

Conclusions: As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.

Abstract reproduced from PubMed. Copyright ©2012 Bada et al.; licensee BioMed Central Ltd. Distributed under CC BY 2.0.

PubMed citation: Pannarale P, Catalano D, De Caro G, Grillo G, Leo P, Pappadà G, Rubino F, Scioscia G, Licciulli F. GIDL: a rule based expert system for GenBank Intelligent Data Loading into the Molecular Biodiversity Database. BMC Bioinformatics. 2012 Mar 28;13 Suppl 4:S4. doi: 10.1186/1471-2105-13-S4-S4. PMID: 22536971; PMCID: PMC3303717.

Authors: Paolo Pannarale³; Domenico Catalano²; Giorgio De Caro¹; Giorgio Grillo¹; Pietro Leo³; Graziano Pappadà⁴; Francesco Rubino³; Gaetano Scioscia³; Flavio Licciulli¹.

Affiliations:

1. Institute for Biomedical Technologies (ITB), National Research Council (CNR), Bari, 70100, Italy.
2. Institute of Plant Genetics (IGV), National Research Council (CNR), Bari, 70100, Italy.
3. IBM GBS BAO Advanced Analytics Services and MBLab, Bari, 70100, Italy.
4. Exhicon srl, Trani, 70059, Italy.

DOI: 10.1186/1471-2105-13-S4-S4 · PMID: 22536971 · PMCID: PMC3303717

Abstract

Background: In the scientific biodiversity community, it is increasingly perceived the need to build a bridge between molecular and traditional biodiversity studies. We believe that the information technology could have a preeminent role in integrating the information generated by these studies with the large amount of molecular data we can find in bioinformatics public databases. This work is primarily aimed at building a bioinformatic infrastructure for the integration of public and private biodiversity data through the development of GIDL, an Intelligent Data Loader coupled with the Molecular Biodiversity Database. The system presented here organizes in an ontological way and locally stores the sequence and annotation data contained in the GenBank primary database.

Methods: The GIDL architecture consists of a relational database and of an intelligent data loader software. The relational database schema is designed to manage biodiversity information (Molecular Biodiversity Database) and it is organized in four areas: MolecularData, Experiment, Collection and Taxonomy. The MolecularData area is inspired to an established standard in Generic Model Organism Databases, the Chado relational schema. The peculiarity of Chado, and also its strength, is the adoption of an ontological schema which makes use of the Sequence Ontology. The Intelligent Data Loader (IDL) component of GIDL is an Extract, Transform and Load software able to parse data, to discover hidden information in the GenBank entries and to populate the Molecular Biodiversity Database. The IDL is composed by three main modules: the Parser, able to parse GenBank flat files; the Reasoner, which automatically builds CLIPS facts mapping the biological knowledge expressed by the Sequence Ontology; the DBFiller, which translates the CLIPS facts into ordered SQL statements used to populate the database. In GIDL Semantic Web technologies have been adopted due to their advantages in data representation, integration and processing.

Results and conclusions: Entries coming from Virus (814,122), Plant (1,365,360) and Invertebrate (959,065) divisions of GenBank rel.180 have been loaded in the Molecular Biodiversity Database by GIDL. Our system, combining the Sequence Ontology and the Chado schema, allows a more powerful query expressiveness compared with the most commonly used sequence retrieval systems like Entrez or SRS.

Abstract reproduced from PubMed. Copyright ©2012 Pannarale et al.; licensee BioMed Central Ltd. Distributed under CC BY 2.0.

PubMed citation: Mungall CJ, Batchelor C, Eilbeck K. Evolution of the Sequence Ontology terms and relationships. J Biomed Inform. 2011 Feb;44(1):87-93. doi: 10.1016/j.jbi.2010.03.002. Epub 2010 Mar 10. PMID: 20226267; PMCID: PMC3052763.

Authors: Christopher J. Mungall¹; Colin Batchelor²; Karen Eilbeck³.

Affiliations:

1. Lawrence Berkeley National Laboratory, Mail Stop 64R0121, Berkeley, CA 94720, USA.
2. Royal Society of Chemistry, Thomas Graham House, Cambridge, UK CB4 0WF.
3. Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA.

DOI: 10.1016/j.jbi.2010.03.002 · PMID: 20226267 · PMCID: PMC3052763

Abstract

The Sequence Ontology is an established ontology, with a large user community, for the purpose of genomic annotation. We are reforming the ontology to provide better terms and relationships to describe the features of biological sequence, for both genomic and derived sequence. The SO is working within the guidelines of the OBO Foundry to provide interoperability between SO and the other related OBO ontologies. Here, we report changes and improvements made to SO including new relationships to better define the mereological, spatial and temporal aspects of biological sequence.

Abstract reproduced from PubMed. Copyright © 2010 Elsevier Inc. All rights reserved.

Conference citation: Bada M, Eilbeck K. Toward a richer representation of sequence variation in the Sequence Ontology. 9th European Conference on Computational Biology (ECCB 2010), Workshop on Annotation, Interpretation and Management of Mutations (AIMM-2010). Presented September 26, 2010.

Authors: Michael Bada¹; Karen Eilbeck².

Affiliations:

1. University of Colorado Anschutz Medical Campus, Department of Pharmacology, MS 8303, RC-1 South, 12801 East 17th Avenue, L18-6400A, P.O. Box 6511, Aurora, CO 80045 USA.
2. Department of Biomedical Informatics, Health Sciences Education Bldg, University of Utah, 26 South 2000 East, Suite 5700, Salt Lake City, UT 84112-5750 USA.

Conference: 9th European Conference on Computational Biology (ECCB 2010)

Conference location: Ghent, Belgium

Conference dates: September 26th, 2010

Presentation track: Workshop on Annotation, Interpretation and Management of Mutations (AIMM-2010)

Presentation date: Sunday, September 26th, 2010

Links: Conference proceedings · Conference paper (PDF)

Abstract

The Sequence Ontology (SO) is a member ontology of the Open Biomedical Ontologies (OBO) library that is charged with formally representing types of biomacromolecular sequences and their associated attributes as well as the interrelationships among these. By providing a common vocabulary and set of definitions, it is widely used to facilitate accurate storage, processing, and exchange of sequence data. While some parts of the SO are quite mature, particularly the independently defined sequence features, the current representation of sequence variation is more problematic in that the representation of corresponding variations, sequences containing these variations, and processes resulting in these variations and their interrelationships is incomplete. Additionally, corresponding variations in DNAs, RNAs, and polypeptides and their interrelationships are not represented. We report here on our progress in more completely and precisely representing these concepts, which will allow for more consistent annotation of variant sequence data. Furthermore, formally linking and defining these sequence-variation classes within the OBO framework will enable powerful, logically sound reasoning with various types of variant data as well as with other types of annotated biological data.

PubMed citation: Reese MG, Moore B, Batchelor C, Salas F, Cunningham F, Marth GT, Stein L, Flicek P, Yandell M, Eilbeck K. A standard variation file format for human genome sequences. Genome Biol. 2010;11(8):R88. doi: 10.1186/gb-2010-11-8-r88. Epub 2010 Aug 26. PMID: 20796305; PMCID: PMC2945790.

Authors: Martin G Reese¹; Barry Moore²; Colin Batchelor³; Fidel Salas¹; Fiona Cunningham⁴; Gabor T Marth⁵; Lincoln Stein⁶; Paul Flicek⁴; Mark Yandell²; Karen Eilbeck⁷.

Affiliations:

1. Omicia, 2200 Powell Street, Suite 525, Emeryville, CA 94608, USA.
2. Department of Human Genetics and Eccles Institute of Human Genetics, 15 North 2030 East, University of Utah, Salt Lake City, UT 84108, USA.
3. Royal Society of Chemistry, Thomas Graham House, Cambridge, CB4 0WF, UK.
4. EMBL Outstation - Hinxton, European Bioinformatics Institute, Wellcome Trust, Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
5. Department of Biology, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA.
6. Ontario Institute for Cancer Research, 101 College St, Suite 800, Toronto, ON M5G0A3, Canada.
7. Department of Biomedical Informatics, Health Sciences Education Building, Suite 5700, 26 South 2000 East, University of Utah, Salt Lake City, UT 84112, USA.

DOI: 10.1186/gb-2010-11-8-r88 · PMID: 20796305 · PMCID: PMC2945790

Abstract

Here we describe the Genome Variation Format (GVF) and the 10Gen dataset. GVF, an extension of Generic Feature Format version 3 (GFF3), is a simple tab-delimited format for DNA variant files, which uses Sequence Ontology to describe genome variation data. The 10Gen dataset, ten human genomes in GVF format, is freely available for community analysis from the Sequence Ontology website and from an Amazon elastic block storage (EBS) snapshot for use in Amazon's EC2 cloud computing environment.

Abstract reproduced from the linked PubMed record under CC BY 2.0.

PubMed citation: Moore B, Fan G, Eilbeck K. SOBA: sequence ontology bioinformatics analysis. Nucleic Acids Res. 2010 Jul;38(Web Server issue):W161-4. doi: 10.1093/nar/gkq426. Epub 2010 May 21. PMID: 20494974; PMCID: PMC2896117.

Authors: Barry Moore¹; Guozhen Fan¹; Karen Eilbeck¹.

Affiliations:

1. Department of Human Genetics, University of Utah, Salt Lake City, Utah, UT 84112, USA.

DOI: 10.1093/nar/gkq426 · PMID: 20494974 · PMCID: PMC2896117

Abstract

The advent of cheaper, faster sequencing technologies has pushed the task of sequence annotation from the exclusive domain of large-scale multi-national sequencing projects to that of research laboratories and small consortia. The bioinformatics burden placed on these laboratories, some with very little programming experience can be daunting. Fortunately, there exist software libraries and pipelines designed with these groups in mind, to ease the transition from an assembled genome to an annotated and accessible genome resource. We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.

Abstract reproduced from the linked PubMed record under CC BY-NC 2.5.

PubMed citation: Holford ME, Khurana E, Cheung KH, Gerstein M. Using semantic web rules to reason on an ontology of pseudogenes. Bioinformatics. 2010 Jun 15;26(12):i71-8. doi: 10.1093/bioinformatics/btq173. PMID: 20529940; PMCID: PMC2881358.

Authors: Matthew E Holford¹; Ekta Khurana²; Kei-Hoi Cheung^1,3,4,5; Mark Gerstein^1,2,3.

Affiliations:

1. Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
2. Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
3. Department of Computer Science, Yale University, New Haven, CT 06520, USA.
4. Center for Medical Informatics, Yale University, New Haven, CT 06520, USA.
5. Department of Genetics, Yale University, New Haven, CT 06520, USA.

DOI: 10.1093/bioinformatics/btq173 · PMID: 20529940 · PMCID: PMC2881358

Abstract

Motivation: Recent years have seen the development of a wide range of biomedical ontologies. Notable among these is Sequence Ontology (SO) which offers a rich hierarchy of terms and relationships that can be used to annotate genomic data. Well-designed formal ontologies allow data to be reasoned upon in a consistent and logically sound way and can lead to the discovery of new relationships. The Semantic Web Rules Language (SWRL) augments the capabilities of a reasoner by allowing the creation of conditional rules. To date, however, formal reasoning, especially the use of SWRL rules, has not been widely used in biomedicine.

Results: We have built a knowledge base of human pseudogenes, extending the existing SO framework to incorporate additional attributes. In particular, we have defined the relationships between pseudogenes and segmental duplications. We then created a series of logical rules using SWRL to answer research questions and to annotate our pseudogenes appropriately. Finally, we were left with a knowledge base which could be queried to discover information about human pseudogene evolution.

Availability: The fully populated knowledge base described in this document is available for download from http://ontology.pseudogene.org. A SPARQL endpoint from which to query the dataset is also available at this location.

Abstract reproduced from PubMed. © The Author(s) 2010. Published by Oxford University Press. Distributed under CC BY-NC 2.0 UK.

PubMed citation: Hoehndorf R, Kelso J, Herre H. The ontology of biological sequences. BMC Bioinformatics. 2009 Nov 18;10:377. doi: 10.1186/1471-2105-10-377. PMID: 19919720; PMCID: PMC2785798.

Authors: Robert Hoehndorf^1,2,3; Janet Kelso²; Heinrich Herre^1,3.

Affiliations:

1. Research Group Ontologies in Medicine, Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany.
2. Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
3. Department of Computer Science, University of Leipzig, Johannesgasse 26, 04103 Leipzig, Germany.

DOI: 10.1186/1471-2105-10-377 · PMID: 19919720 · PMCID: PMC2785798

Abstract

Background: Biological sequences play a major role in molecular and computational biology. They are studied as information-bearing entities that make up DNA, RNA or proteins. The Sequence Ontology, which is part of the OBO Foundry, contains descriptions and definitions of sequences and their properties. Yet the most basic question about sequences remains unanswered: what kind of entity is a biological sequence? An answer to this question benefits formal ontologies that use the notion of biological sequences and analyses in computational biology alike.

Results: We provide both an ontological analysis of biological sequences and a formal representation that can be used in knowledge-based applications and other ontologies. We distinguish three distinct kinds of entities that can be referred to as "biological sequence": chains of molecules, syntactic representations such as those in biological databases, and the abstract information-bearing entities. For use in knowledge-based applications and inclusion in biomedical ontologies, we implemented the developed axiom system for use in automated theorem proving.

Conclusion: Axioms are necessary to achieve the main goal of ontologies: to formally specify the meaning of terms used within a domain. The axiom system for the ontology of biological sequences is the first elaborate axiom system for an OBO Foundry ontology and can serve as starting point for the development of more formal ontologies and ultimately of knowledge-based applications.

Abstract reproduced from PubMed. Copyright ©2009 Hoehndorf et al; licensee BioMed Central Ltd. Distributed under CC BY 2.0.

PubMed citation: Szpakowski S, McCusker J, Krauthammer M. Using semantic web technologies to annotate and align microarray designs. Cancer Inform. 2009;8:65-73. doi: 10.4137/cin.s2335. PMID: 24904201; PMCID: PMC4042255.

Authors: Sebastian Szpakowski^1,2; James McCusker²; Michael Krauthammer².

Affiliations:

1. Program for Computational Biology and Bioinformatics (CBB), Yale University School of Medicine, New Haven, CT.
2. Department of Pathology, Yale University School of Medicine, New Haven, CT.

DOI: 10.4137/cin.s2335 · PMID: 24904201 · PMCID: PMC4042255

Abstract

In this paper, we annotate and align two different gene expression microarray designs using the Genomic ELement Ontology (GELO). GELO is a new ontology that leverages an existing community resource, Sequence Ontology (SO), to create views of genomically-aligned data in a semantic web environment. We start the process by mapping array probes to genomic coordinates. The coordinates represent an implicit link between the probes and multiple genomic elements, such as genes, transcripts, miRNA, and repetitive elements, which are represented using concepts in SO. We then use the RDF Query Language (SPARQL) to create explicit links between the probes and the elements. We show how the approach allows us to easily determine the element coverage and genomic overlap of the two array designs. We believe that the method will ultimately be useful for integration of cancer data across multiple omic studies. The ontology and other materials described in this paper are available at http://krauthammerlab.med.yale.edu/wiki/Gelo.

Abstract reproduced from PubMed. © 2009 Copyright in this article, its metadata, and any supplementary data is held by its author or authors Distributed under CC BY 3.0.

PubMed citation: Eilbeck K, Moore B, Holt C, Yandell M. Quantitative measures for the management and comparison of annotated genomes. BMC Bioinformatics. 2009 Feb 23;10:67. doi: 10.1186/1471-2105-10-67. PMID: 19236712; PMCID: PMC2653490.

Authors: Karen Eilbeck¹; Barry Moore¹; Carson Holt¹; Mark Yandell¹.

Affiliations:

1. Department of Human genetics, Eccles Institute of Human Genetics, University of Utah and School of Medicine, Salt Lake City, Utah, USA.

DOI: 10.1186/1471-2105-10-67 · PMID: 19236712 · PMCID: PMC2653490

Abstract

Background: The ever-increasing number of sequenced and annotated genomes has made management of their annotations a significant undertaking, especially for large eukaryotic genomes containing many thousands of genes. Typically, changes in gene and transcript numbers are used to summarize changes from release to release, but these measures say nothing about changes to individual annotations, nor do they provide any means to identify annotations in need of manual review.

Results: In response, we have developed a suite of quantitative measures to better characterize changes to a genome's annotations between releases, and to prioritize problematic annotations for manual review. We have applied these measures to the annotations of five eukaryotic genomes over multiple releases -- H. sapiens, M. musculus, D. melanogaster, A. gambiae, and C. elegans.

Conclusion: Our results provide the first detailed, historical overview of how these genomes' annotations have changed over the years, and demonstrate the usefulness of these measures for genome annotation management.

Abstract reproduced from the linked PubMed record under CC BY 2.0.

PubMed citation: Reeves GA, Eilbeck K, Magrane M, O'Donovan C, Montecchi-Palazzi L, Harris MA, Orchard S, Jimenez RC, Prlic A, Hubbard TJP, Hermjakob H, Thornton JM. The Protein Feature Ontology: a tool for the unification of protein feature annotations. Bioinformatics. 2008 Dec 1;24(23):2767-72. doi: 10.1093/bioinformatics/btn528. Epub 2008 Oct 20. PMID: 18936051; PMCID: PMC2912506.

Authors: Gabrielle A. Reeves¹; Karen Eilbeck²; Michele Magrane¹; Claire O'Donovan¹; Luisa Montecchi-Palazzi¹; Midori A. Harris¹; Sandra Orchard¹; Rafael C. Jimenez³; Andreas Prlic⁴; Tim J. P. Hubbard⁴; Henning Hermjakob¹; Janet M. Thornton¹.

Affiliations:

1. EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
2. Eccles Institute of Human Genetics, 15 North, 2030 East, University of Utah, Salt Lake City, UT 84112, USA.
3. Bioinformatics and Genomics Department, Centro de Investigacion Principe Felipe (CIPF), Valencia, Spain.
4. The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.

DOI: 10.1093/bioinformatics/btn528 · PMID: 18936051 · PMCID: PMC2912506

Abstract

Motivation: The advent of sequencing and structural genomics projects has provided a dramatic boost in the number of uncharacterized protein structures and sequences. Consequently, many computational tools have been developed to help elucidate protein function. However, such services are spread throughout the world, often with standalone web pages. Integration of these methods is needed and so far this has not been possible as there was no common vocabulary available that could be used as a standard language.

Results: The Protein Feature Ontology has been developed to provide a structured controlled vocabulary for features on a protein sequence or structure and comprises approximately 100 positional terms, now integrated into the Sequence Ontology (SO) and 40 non-positional terms which describe features relating to the whole-protein sequence. In addition, post-translational modifications are described by using a pre-existing ontology, the Protein Modification Ontology (MOD). This ontology is being used to integrate over 150 distinct annotations provided by the BioSapiens Network of Excellence, a consortium comprising 19 partner sites in Europe.

Availability: The Protein Feature Ontology can be browsed by accessing the ontology lookup service at the European Bioinformatics Institute (http://www.ebi.ac.uk/ontology-lookup/browse.do?ontName=BS).

Abstract reproduced from PubMed. © The Author 2008. Published by Oxford University Press. All rights reserved.

PubMed citation: Mungall CJ, Emmert DB; FlyBase Consortium. A Chado case study: an ontology-based modular schema for representing genome-associated biological information. Bioinformatics. 2007 Jul 1;23(13):i337-46. doi: 10.1093/bioinformatics/btm189. PMID: 17646315.

Authors: Christopher J. Mungall¹; David B. Emmert²; The FlyBase Consortium.

Affiliations:

1. Lawrence Berkeley National Laboratory, Lawrence Berkeley National Lab, Mail Stop 64R0121, Berkeley, CA 94720, USA.
2. Harvard University, Molecular and Cell Biology: FlyBase, 16 Divinity Avenue, Cambridge, MA 02138, USA.

DOI: 10.1093/bioinformatics/btm189 · PMID: 17646315

Abstract

Motivation: A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused.

Results: Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences.

Availability: GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).

Abstract reproduced from PubMed. © 2007 The Author(s). Distributed under CC BY-NC 2.0 UK.

PubMed citation: Vlachos A, Gasperin C, Lewin I, Briscoe T. Bootstrapping the recognition and anaphoric linking of named entities in Drosophila articles. Pac Symp Biocomput. 2006:100-11. PMID: 17094231.

Authors: Andreas Vlachos¹; Caroline Gasperin¹; Ian Lewin¹; Ted Briscoe¹.

Affiliations:

1. Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, CB3 0FD, UK.

PMID: 17094231

Abstract

This paper demonstrates how Drosophila gene name recognition and anaphoric linking of gene names and their products can be achieved using existing information in FlyBase and the Sequence Ontology. Extending an extant approach to gene name recognition we achieved a F-score of 0.8559, and we report a preliminary experiment using a baseline anaphora resolution algorithm. We also present guidelines for annotation of gene mentions in texts and outline how the resulting system is used to aid FlyBase curation.

Abstract reproduced from PubMed.

PubMed citation: Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol. 2005;6(5):R44. doi: 10.1186/gb-2005-6-5-r44. Epub 2005 Apr 29. PMID: 15892872; PMCID: PMC1175956.

Authors: Karen Eilbeck¹; Suzanna E Lewis¹; Christopher J Mungall²; Mark Yandell²; Lincoln Stein³; Richard Durbin⁴; Michael Ashburner⁵.

Affiliations:

1. Department of Molecular and Cellular Biology, Life Sciences Addition, University of California, Berkeley, CA 94729-3200, USA.
2. Howard Hughes Memorial Institute, Department of Molecular and Cellular Biology, Life Sciences Addition, University of California, Berkeley, CA 94729-3200, USA.
3. Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, New York 11724, USA.
4. Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SA, UK.
5. Department of Genetics, University of Cambridge, Downing Street, Cambridge, CB2 3EH, UK.

DOI: 10.1186/gb-2005-6-5-r44 · PMID: 15892872 · PMCID: PMC1175956

Abstract

The Sequence Ontology (SO) is a structured controlled vocabulary for the parts of a genomic annotation. SO provides a common set of terms and definitions that will facilitate the exchange, analysis and management of genomic data. Because SO treats part-whole relationships rigorously, data described with it can become substrates for automated reasoning, and instances of sequence features described by the SO can be subjected to a group of logical operations termed extensional mereology operators.

Abstract reproduced from the linked PubMed record under CC BY 2.0.

PubMed citation: Eilbeck K, Lewis SE. Sequence ontology annotation guide. Comp Funct Genomics. 2004;5(8):642-7. doi: 10.1002/cfg.446. PMID: 18629179; PMCID: PMC2447471.

Authors: Karen Eilbeck¹; Suzanna E. Lewis¹.

Affiliations:

1. Department of Molecular and Cellular Biology, Life Sciences Addition, University of California, Berkeley, California 94729-3200, USA.

DOI: 10.1002/cfg.446 · PMID: 18629179 · PMCID: PMC2447471

Abstract

This Sequence Ontology (SO) [13] aims to unify the way in which we describe sequence annotations, by providing a controlled vocabulary of terms and the relationships between them. Using SO terms to label the parts of sequence annotations greatly facilitates downstream analyses of their contents, as it ensures that annotations produced by different groups conform to a single standard. This greatly facilitates analyses of annotation contents and characteristics, e.g. comparisons of UTRs, alternative splicing, etc. Because SO also specifies the relationships between features, e.g. part_of, kind_of, annotations described with SO terms are also better substrates for validation and visualization software. This document provides a step-by-step guide to producing a SO compliant file describing a sequence annotation. We illustrate this by using an annotated gene as an example. First we show where the terms needed to describe the gene's features are located in SO and their relationships to one another. We then show line by line how to format the file to construct a SO compliant annotation of this gene.

Abstract reproduced from the linked PubMed record under Creative Commons Attribution.