NIASGBdb: NIAS Genebank databases for genetic resources and plant disease information

The National Institute of Agrobiological Sciences (NIAS) is implementing the NIAS Genebank Project for conservation and promotion of agrobiological genetic resources to contribute to the development and utilization of agriculture and agricultural products. The project’s databases (NIASGBdb; http://www.gene.affrc.go.jp/databases_en.php) consist of a genetic resource database and a plant diseases database, linked by a web retrieval database. The genetic resources database has plant and microorganism search systems to provide information on research materials, including passport and evaluation data for genetic resources with the desired properties. To facilitate genetic diversity research, several NIAS Core Collections have been developed. The NIAS Rice (Oryza sativa) Core Collection of Japanese Landraces contains information on simple sequence repeat (SSR) polymorphisms. SSR marker information for azuki bean (Vigna angularis) and black gram (V. mungo) and DNA sequence data from some selected Japanese strains of the genus Fusarium are also available. A database of plant diseases in Japan has been developed based on the listing of common names of plant diseases compiled by the Phytopathological Society of Japan. Relevant plant and microorganism genetic resources are associated with the plant disease names by the web retrieval database and can be obtained from the NIAS Genebank for research or educational purposes

db4DNASeq: An object-oriented DNA database model associated with sequence search method

DNA database consists of many nucleotide sequences, it is not only supporting typical database queries, but it also needs to facilitate sequence search and alignment. In this paper, we present an object-oriented nucleotide database which is designed not only for the convenience of executing normal database operations such as insertion, modification or data querying in a fast manner, but it also supports a fast search method on database sequences with reasonable tradeoff between time and memory usage

Using conceptual modeling to improve genome data management

[EN] With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context.Spanish State Research Agency and the Generalitat Valenciana under the projects TIN2016-80811-P and PROMETEO/2018/176; ERDF.Pastor López, O.; León-Palacio, A.; Reyes Román, JF.; García-Simón, A.; Casamayor Rodenas, JC. (2020). Using conceptual modeling to improve genome data management. Briefings in Bioinformatics. 22(1):45-54. https://doi.org/10.1093/bib/bbaa100S4554221McCombie, W. R., McPherson, J. D., & Mardis, E. R. (2018). Next-Generation Sequencing Technologies. Cold Spring Harbor Perspectives in Medicine, 9(11), a036798. doi:10.1101/cshperspect.a036798Condit, C. M., Achter, P. J., Lauer, I., & Sefcovic, E. (2001). The changing meanings of ?mutation:? A contextualized study of public discourse. Human Mutation, 19(1), 69-75. doi:10.1002/humu.10023Karki, R., Pandya, D., Elston, R. C., & Ferlini, C. (2015). Defining «mutation» and «polymorphism» in the era of personal genomics. BMC Medical Genomics, 8(1). doi:10.1186/s12920-015-0115-zHamid, J. S., Hu, P., Roslin, N. M., Ling, V., Greenwood, C. M. T., & Beyene, J. (2009). Data Integration in Genetics and Genomics: Methods and Challenges. Human Genomics and Proteomics, 1(1). doi:10.4061/2009/869093Baudhuin, L. M., Biesecker, L. G., Burke, W., Green, E. D., & Green, R. C. (2019). Predictive and Precision Medicine with Genomic Data. Clinical Chemistry, 66(1), 33-41. doi:10.1373/clinchem.2019.304345Amaral, G., & Guizzardi, G. (2019). On the Application of Ontological Patterns for Conceptual Modeling in Multidimensional Models. Lecture Notes in Computer Science, 215-231. doi:10.1007/978-3-030-28730-6_14Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., … Sherlock, G. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25(1), 25-29. doi:10.1038/75556Eilbeck, K., Lewis, S. E., Mungall, C. J., Yandell, M., Stein, L., Durbin, R., & Ashburner, M. (2005). Genome Biology, 6(5), R44. doi:10.1186/gb-2005-6-5-r44Vihinen, M. (2013). Variation Ontology for annotation of variation effects and mechanisms. Genome Research, 24(2), 356-364. doi:10.1101/gr.157495.113Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J.-P., … McMurry, J. A. (2018). Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research, 47(D1), D1018-D1027. doi:10.1093/nar/gky1105Proceedings of the Eleventh International Conference on Data Engineering. (1995). Proceedings of the Eleventh International Conference on Data Engineering. doi:10.1109/icde.1995.380416Okayama, T., Tamura, T., Gojobori, T., Tateno, Y., Ikeo, K., Miyazaki, S., … Sugawara, H. (1998). Formal design and implementation of an improved DDBJ DNA database with a new schema and object-oriented library. Bioinformatics, 14(6), 472-478. doi:10.1093/bioinformatics/14.6.472Medigue, C., Rechenmann, F., Danchin, A., & Viari, A. (1999). Imagene: an integrated computer environment for sequence annotation and analysis. Bioinformatics, 15(1), 2-15. doi:10.1093/bioinformatics/15.1.2Paton, N. W., Khan, S. A., Hayes, A., Moussouni, F., Brass, A., Eilbeck, K., … Oliver, S. G. (2000). Conceptual modelling of genomic information. Bioinformatics, 16(6), 548-557. doi:10.1093/bioinformatics/16.6.548Vihinen, M., Hancock, J. M., Maglott, D. R., Landrum, M. J., Schaafsma, G. C. P., & Taschner, P. (2016). Human Variome Project Quality Assessment Criteria for Variation Databases. Human Mutation, 37(6), 549-558. doi:10.1002/humu.22976Fleuren, W. W. M., & Alkema, W. (2015). Application of text mining in the biomedical domain. Methods, 74, 97-106. doi:10.1016/j.ymeth.2015.01.015Salzberg, S. L. (2007). Genome re-annotation: a wiki solution? Genome Biology, 8(1). doi:10.1186/gb-2007-8-1-102Rigden, D. J., & Fernández, X. M. (2018). The 26th annual Nucleic Acids Research database issue and Molecular Biology Database Collection. Nucleic Acids Research, 47(D1), D1-D7. doi:10.1093/nar/gky1267Reyes Román, J. F., García, A., Rueda, U., & Pastor, Ó. (2019). GenesLove.Me 2.0: Improving the Prioritization of Genetic Variations. Evaluation of Novel Approaches to Software Engineering, 314-333. doi:10.1007/978-3-030-22559-9_14Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., … Rehm, H. L. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine, 17(5), 405-423. doi:10.1038/gim.2015.30Kelly, M. A., Caleshu, C., Morales, A., Buchan, J., Wolf, Z., … Funke, B. (2018). Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen’s Inherited Cardiomyopathy Expert Panel. Genetics in Medicine, 20(3), 351-359. doi:10.1038/gim.2017.21

Applied Bioinformatics in Saccharomyces cerevisiae: Data storage, integration and analysis

The massive amount of biological data has had a significant effect on the field of bioinformatics. This growth of data has not only lead to the growing number of biological databases but has also imposed the needs for additional and more sophisticated computational techniques to proficiently manage, store and retrieve these data, as well as to competently help gaining biological insights and contribute to novel discoveries. This thesis presents results from applying several bioinformatics approaches on yeast datasets. Three yeast databases were developed using different technologies. Each database emphasizes on a specific aspect. yApoptosis collects and structurally organizes vital information specifically for yeast cell death pathway, apoptosis. It includes predicted protein complexes and clustered motifs from the incorporation of apoptosis genes and interaction data. yStreX highlights exploitation of transcriptome data generated by studies of stress responses and ageing in yeast. It contains a compilation of results from gene expression analyses in different contexts making it an integrated resource to facilitate data query and data comparison between different experiments. A yeast data repository is a centralized database encompassing with multiple kinds of yeast data. The database is applied on a dedicated database system that was developed addressing data integration issue in managing heterogeneous datasets. Data analysis was performed in parallel using several methods and software packages such as Limma, Piano and metaMA. Particularly the gene expressions of chronologically ageing yeast were analyzed in the integrative fashion to gain a more thorough picture of the condition such as gene expression patterns, biological processes, transcriptional regulations, metabolic pathways and interactions of active components. This study demonstrates extensive applications of bioinformatics in the domains of data storage, data sharing, data integration and data analysis on various data from yeast S.cerevisiae in order to gain biological insights. Numerous methodologies and technologies were selectively applied in different contexts depended upon characteristics of the data and the goal of the specific biological question

Conceptual Modeling Applied to Genomics: Challenges Faced in Data Loading

Todays genomic domain evolves around insecurity: too many imprecise concepts, too much information to be properly managed. Considering that conceptualization is the most exclusive human characteristic, it makes full sense to try to conceptualize the principles that guide the essence of why humans are as we are. This question can of course be generalized to any species, but we are especially interested in this work in showing how conceptual modeling is strictly required to understand the ''execution model'' that human beings ''implement''. The main issue is to defend the idea that only by having an in-depth knowledge of the Conceptual Model that is associated to the Human Genome, can this Human Genome properly be understood. This kind of Model-Driven perspective of the Human Genome opens challenging possibilities, by looking at the individuals as implementation of that Conceptual Model, where different values associated to different modeling primitives will explain the diversity among individuals and the potential, unexpected variations together with their unwanted effects in terms of illnesses. This work focuses on the challenges faced in loading data from conventional resources into Information Systems created according to the above mentioned conceptual modeling approach. The work reports on various loading efforts, problems encountered and the solutions to these problems. Also, a strong argument is made about why conventional methods to solve the so called `data chaos¿ problems associated to the genomics domain so often fail to meet the demands.Van Der Kroon ., M. (2011). Conceptual Modeling Applied to Genomics: Challenges Faced in Data Loading. http://hdl.handle.net/10251/16993Archivo delegad

Altered Heparan Sulfate in Ageing and Dementia: a Potential Axis for the Dysregulation of BACE-1 in Alzheimer's disease

Alzheimer’s disease (AD) is characterised by amyloid plaques composed of amyloid-beta (Aβ), the cleavage product of the amyloid precursor protein (APP) by the protease beta-secretase (BACE- 1). Heparan sulfate (HS) inhibits BACE-1 and holds potential as a new drug discovery target; in vivo HS may act as a brake on the generation of Aß via regulation of BACE-1. Previous work has identified the sulfate moieties in HS as key determinants in the efficacy of BACE-1 inhibition. Structural changes in HS are known to occur with ageing and we hypothesised that these changes could result in reduced BACE-1 inhibition and ultimately elevated production of Aβ. Strong anion exchange chromatography was used to assess disaccharide composition of HS from AD (n=20) and age-matched control (n=15) brain tissue. TaqMan® array profiling of HS-related genes was also carried out to explore expression levels of HS-related genes that may be responsible for downstream HS structural changes. HS purified from AD and age-matched control samples was assessed for its ability to inhibit BACE-1 using FRET-based BACE-1 activity assays and finally, manipulation of endogenous HS in HEKSweAPP cells with RNAi was carried out to explore the possibility of modulating generation of the toxic Aβ species. HS from AD tissue was found to carry a significantly decreased proportion of the di-sulfated ΔUA-GlcNS(6S) disaccharide vs. controls (p<0.01) and increased levels of the lesser-sulfated ΔUAGlcNAc( 6S) unit vs. controls (p<0.05). Furthermore, significantly more total HS was present within control brain tissue (122.3μg/100mg) vs. AD (78.6μg/100mg) (p<0.01). TaqMan® array analysis revealed significant alteration in expression of HS biosynthetic genes with AD including upregulation of HS6ST1 (p<0.05) and a strong trend for down regulation of HS6ST3, coupled with up regulation of SULF1. These changes may go some way to explain changes in the level of sulfation of HS particularly, 6-O sulfation, as observed by structural analysis. Most noticeably, BACE-1 activity assays revealed a significant reduction of BACE-1 inhibition efficacy by HS from AD patients (p<0.05). In addition, knockdown of SULF1 in HEKSweAPP cells, which would be expected to elevate 6-O sulfation, generated a significant reduction in Aß. Our observation that AD brain HS contains fewer di-sulfated ΔUA-GlcNS(6S) disaccharides, alongside observed upstream gene expression changes, would be consistent with a less sulfated HS chain with reduced ability to inhibit BACE-1 thus generating more Aß as observed in AD. The observed reduction in BACE-1 inhibition efficacy by HS with AD confirms our hypothesis that structural changes in HS may contribute to modulating AD pathogenesis in patients. Finally, these studies support the idea that HS-based therapeutics might provide the basis for novel disease modifying drugs that could prove beneficial in future efforts to treat an underlying cause of AD