14,546 research outputs found
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
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
Systems approaches to modelling pathways and networks.
Peer reviewedPreprin
Current advances in systems and integrative biology
Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal
Method for finding metabolic properties based on the general growth law. Liver examples. A General framework for biological modeling
We propose a method for finding metabolic parameters of cells, organs and
whole organisms, which is based on the earlier discovered general growth law.
Based on the obtained results and analysis of available biological models, we
propose a general framework for modeling biological phenomena and discuss how
it can be used in Virtual Liver Network project. The foundational idea of the
study is that growth of cells, organs, systems and whole organisms, besides
biomolecular machinery, is influenced by biophysical mechanisms acting at
different scale levels. In particular, the general growth law uniquely defines
distribution of nutritional resources between maintenance needs and biomass
synthesis at each phase of growth and at each scale level. We exemplify the
approach considering metabolic properties of growing human and dog livers and
liver transplants. A procedure for verification of obtained results has been
introduced too. We found that two examined dogs have high metabolic rates
consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per
day, and verified this using the proposed verification procedure. We also
evaluated consumption rate of nutrients in human livers, determining it to be
about 0.088 gram of nutrients per cubic centimeter of liver per day for males,
and about 0.098 for females. This noticeable difference can be explained by
evolutionary development, which required females to have greater liver
processing capacity to support pregnancy. We also found how much nutrients go
to biomass synthesis and maintenance at each phase of liver and liver
transplant growth. Obtained results demonstrate that the proposed approach can
be used for finding metabolic characteristics of cells, organs, and whole
organisms, which can further serve as important inputs for many applications in
biology (protein expression), biotechnology (synthesis of substances), and
medicine.Comment: 20 pages, 6 figures, 4 table
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