Journal articles: 'Genomics – methods'

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Blaškovičová, Jana, and Ján Labuda. "Analytical methods in herpesvirus genomics." Acta Chimica Slovaca 7, no. 2 (October 1, 2014): 109–18. http://dx.doi.org/10.2478/acs-2014-0019.

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Abstract Genomics is a branch of bioanalytical chemistry characterized as the study of the genome structure and function. Genome represents the complete set of chromosomal and extrachromosomal genes of an organism, a cell, an organelle or a virus. There are at least five from eight species of herpesviruses commonly widespread among humans, Herpes simplex virus type 1 and 2, Varicella zoster virus, Epstein-Barr virus and Cytomegalovirus. Human gammaherpesviruses can cause serious diseases including B-cell lymphoma and Kaposi’s sarcoma. Diagnostics and study of the herpesviruses is directly dependent on the development of modern analytical methods able to detect and determine the presence and evolution of herpesviral particles/ genomes. Diagnostics and genomic characterization of human herpesvirus species is based on bioanalytical methods such as polymerase chain reaction (PCR), DNA sequencing, gel electrophoresis, blotting and others. The progress in analytical approaches in the herpesvirus genomics is reviewed in this article.

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Richardson, Sylvia, George C. Tseng, and Wei Sun. "Statistical Methods in Integrative Genomics." Annual Review of Statistics and Its Application 3, no. 1 (June 2016): 181–209. http://dx.doi.org/10.1146/annurev-statistics-041715-033506.

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Satagopan, Jaya M., and Alex D. Smith. "Statistical Methods in Genomics Research." Heart Drug 3, no. 1 (2003): 48–60. http://dx.doi.org/10.1159/000070907.

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Kuwabara, P. E. "Functional Genomics: Methods and Protocols." Briefings in Functional Genomics and Proteomics 2, no. 3 (January 1, 2003): 268–69. http://dx.doi.org/10.1093/bfgp/2.3.268.

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Byrne, Stephen, and Toni Wendt. "Plant genomics. Methods and protocols." Annals of Botany 107, no. 4 (April 2011): vii. http://dx.doi.org/10.1093/aob/mcr052.

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Nagy, László G., Zsolt Merényi, Botond Hegedüs, and Balázs Bálint. "Novel phylogenetic methods are needed for understanding gene function in the era of mega-scale genome sequencing." Nucleic Acids Research 48, no. 5 (January 16, 2020): 2209–19. http://dx.doi.org/10.1093/nar/gkz1241.

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Abstract Ongoing large-scale genome sequencing projects are forecasting a data deluge that will almost certainly overwhelm current analytical capabilities of evolutionary genomics. In contrast to population genomics, there are no standardized methods in evolutionary genomics for extracting evolutionary and functional (e.g. gene-trait association) signal from genomic data. Here, we examine how current practices of multi-species comparative genomics perform in this aspect and point out that many genomic datasets are under-utilized due to the lack of powerful methodologies. As a result, many current analyses emphasize gene families for which some functional data is already available, resulting in a growing gap between functionally well-characterized genes/organisms and the universe of unknowns. This leaves unknown genes on the ‘dark side’ of genomes, a problem that will not be mitigated by sequencing more and more genomes, unless we develop tools to infer functional hypotheses for unknown genes in a systematic manner. We provide an inventory of recently developed methods capable of predicting gene-gene and gene-trait associations based on comparative data, then argue that realizing the full potential of whole genome datasets requires the integration of phylogenetic comparative methods into genomics, a rich but underutilized toolbox for looking into the past.

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Lee, Hyunhwa, Jessica Gill, Taura Barr, Sijung Yun, and Hyungsuk Kim. "Primer in Genetics and Genomics, Article 2—Advancing Nursing Research With Genomic Approaches." Biological Research For Nursing 19, no. 2 (January 30, 2017): 229–39. http://dx.doi.org/10.1177/1099800416689822.

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Purpose: Nurses investigate reasons for variable patient symptoms and responses to treatments to inform how best to improve outcomes. Genomics has the potential to guide nursing research exploring contributions to individual variability. This article is meant to serve as an introduction to the novel methods available through genomics for addressing this critical issue and includes a review of methodological considerations for selected genomic approaches. Approach: This review presents essential concepts in genetics and genomics that will allow readers to identify upcoming trends in genomics nursing research and improve research practice. It introduces general principles of genomic research and provides an overview of the research process. It also highlights selected nursing studies that serve as clinical examples of the use of genomic technologies. Finally, the authors provide suggestions about how to apply genomic technology in nursing research along with directions for future research. Conclusions: Using genomic approaches in nursing research can advance the understanding of the complex pathophysiology of disease susceptibility and different patient responses to interventions. Nurses should be incorporating genomics into education, clinical practice, and research as the influence of genomics in health-care research and practice continues to grow. Nurses are also well placed to translate genomic discoveries into improved methods for patient assessment and intervention.

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Jayanthi, K., and C. Mahesh. "A Study on machine learning methods and applications in genetics and genomics." International Journal of Engineering & Technology 7, no. 1.7 (February 5, 2018): 201. http://dx.doi.org/10.14419/ijet.v7i1.7.10653.

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Machine learning enables computers to help humans in analysing knowledge from large, complex data sets. One of the complex data is genetics and genomic data which needs to analyse various set of functions automatically by the computers. Hope this machine learning methods can provide more useful for making these data for further usage like gene prediction, gene expression, gene ontology, gene finding, gene editing and etc. The purpose of this study is to explore some machine learning applications and algorithms to genetic and genomic data. At the end of this study we conclude the following topics classifications of machine learning problems: supervised, unsupervised and semi supervised, which type of method is suitable for various problems in genomics, applications of machine learning and future views of machine learning in genomics.

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Dougherty, Edward, Jianping Hua, and Michael Bittner. "Validation of Computational Methods in Genomics." Current Genomics 8, no. 1 (March 1, 2007): 1–19. http://dx.doi.org/10.2174/138920207780076956.

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Wold, Barbara, and Richard M. Myers. "Sequence census methods for functional genomics." Nature Methods 5, no. 1 (December 19, 2007): 19–21. http://dx.doi.org/10.1038/nmeth1157.

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Khan, Nida Tabassum, Namra Jameel, and Maham Jamil Khan. "Functional Genomics–Linking Genotype with Phenotype on Genome-wide Scale." INTERNATIONAL JOURNAL OF APPLIED PHARMACEUTICAL SCIENCES AND RESEARCH 4, no. 01 (January 1, 2019): 4–12. http://dx.doi.org/10.21477/ijapsr.4.1.2.

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Functional genomics manipulates genomic data to study genes and its expression on a genome wide scale involving high-throughput methods. The keyobjective of Functional genomics is to exploit the data acquired from transcriptomic and genomic studies to explain the functions and interfaces of a genome and its corresponding phenotype.

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Orlov, Yuriy L., Ancha V. Baranova, and Tatiana V. Tatarinova. "Bioinformatics Methods in Medical Genetics and Genomics." International Journal of Molecular Sciences 21, no. 17 (August 28, 2020): 6224. http://dx.doi.org/10.3390/ijms21176224.

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Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools.

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Mehta, Divya, and Anita Sathyanarayanan. "NOVEL GENOMICS METHODS TO PREDICT LITHIUM RESPONSE." European Neuropsychopharmacology 51 (October 2021): e36. http://dx.doi.org/10.1016/j.euroneuro.2021.07.084.

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Goldsmith-Fischman, Sharon, and Barry Honig. "Structural genomics: Computational methods for structure analysis." Protein Science 12, no. 9 (September 2003): 1813–21. http://dx.doi.org/10.1110/ps.0242903.

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CLAYTON, DAVID F. "Songbird Genomics: Methods, Mechanisms, Opportunities, and Pitfalls." Annals of the New York Academy of Sciences 1016, no. 1 (June 2004): 45–60. http://dx.doi.org/10.1196/annals.1298.028.

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Sibley, Carol Hopkins. "Parasite Genomics Protocols (Methods in Molecular Biology)." American Journal of Tropical Medicine and Hygiene 72, no. 2 (February 1, 2005): 227. http://dx.doi.org/10.4269/ajtmh.2005.72.227.

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Gilchrist, Michael J., Ken W. Y. Cho, and Gert Jan C. Veenstra. "Genomics Methods for Xenopus Embryos and Tissues." Cold Spring Harbor Protocols 2020, no. 5 (March 2, 2020): pdb.top097915. http://dx.doi.org/10.1101/pdb.top097915.

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Cain, Aurora A., Robert Kosara, and Cynthia J. Gibas. "GenoSets: Visual Analytic Methods for Comparative Genomics." PLoS ONE 7, no. 10 (October 3, 2012): e46401. http://dx.doi.org/10.1371/journal.pone.0046401.

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Taylor, Natalie, Stephanie Best, Melissa Martyn, Janet C. Long, Kathryn N. North, Jeffrey Braithwaite, and Clara Gaff. "A transformative translational change programme to introduce genomics into healthcare: a complexity and implementation science study protocol." BMJ Open 9, no. 3 (March 2019): e024681. http://dx.doi.org/10.1136/bmjopen-2018-024681.

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IntroductionTranslating scientific advances in genomic medicine into evidence-based clinical practice is challenging. Studying the natural translation of genomics into ‘early-adopting’ health system sectors is essential. We will (a) examine 29 health systems (Australian and Melbourne Genomics Health Alliance flagships) integrating genomics into practice and (b) combine this learning to co-design and test an evidence-based generalisable toolkit for translating genomics into healthcare.Methods and analysisTwenty-nine flagships integrating genomics into clinical settings are studied as complex adaptive systems to understand emergent and self-organising behaviours among inter-related actors and processes. The Effectiveness–Implementation Hybrid approach is applied to gather information on the delivery and potential for real-world implementation. Stages ‘1’ and ‘2a’ (representing hybrid model 1) are the focus of this protocol. The Translation Science to Population Impact (TSci Impact) framework is used to study policy decisions and service provision, and the Theoretical Domains Framework (TDF) is used to understand individual level behavioural change; both frameworks are applied across stages 1 and 2a. Stage 1 synthesises interview data from 32 participants involved in developing the genomics clinical practice systems and approaches across five ‘demonstration-phase’ (early adopter) flagships. In stage 2a, stakeholders are providing quantitative and qualitative data on process mapping, clinical audits, uptake and sustainability (TSci Impact), and psychosocial and environmental determinants of change (TDF). Findings will be synthesised before codesigning an intervention toolkit to facilitate implementation of genomic testing. Study methods to simultaneously test the comparative effectiveness of genomic testing and the implementation toolkit (stage 2b), and the refined implementation toolkit while simply observing the genomics intervention (stage 3) are summarised.Ethics and disseminationEthical approval has been granted. The results will be disseminated in academic forums and used to refine interventions to translate genomics evidence into healthcare. Non-traditional academic dissemination methods (eg, change in guidelines or government policy) will also be employed.

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Mercado, Gabriela, Ainsley J. Newson, David Espinoza, Anne E. Cust, and Amelia K. Smit. "Motivations and Barriers to Participation in a Randomized Trial on Melanoma Genomic Risk: A Mixed-Methods Analysis." Journal of Personalized Medicine 12, no. 10 (October 12, 2022): 1704. http://dx.doi.org/10.3390/jpm12101704.

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The evolution of polygenic scores for use in for disease prevention and control compels the development of guidelines to optimize their effectiveness and promote equitable use. Understanding the motivations and barriers to participation in genomics research can assist in drafting these standards. We investigated these in a community-based randomized controlled trial that examined the health behavioral impact of receiving personalized melanoma genomic risk information. We examined participant responses in a baseline questionnaire and conducted interviews post-trial participation. Motivations differed in two ways: (1) by gender, with those identifying as women placing greater importance on learning about their personal risk or familial risk, and how to reduce risk; and (2) by age in relation to learning about personal risk, and fear of developing melanoma. A barrier to participation was distrust in the handling of genomic data. Our findings provide new insights into the motivations for participating in genomics research and highlight the need to better target population subgroups including younger men, which will aid in tailoring recruitment for future genomic studies.

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Vavekanand, Raja. "Data Security and Privacy in Genomics Research: A Comparative Analysis to Protect Confidentiality." Studies in Medical and Health Sciences 1, no. 1 (May 11, 2024): 23–31. http://dx.doi.org/10.48185/smhs.v1i1.1158.

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The quick progress of genomics examination has driven a surge in the creation of significantly fragile genomic data, making ensuring its security essential. This data contains sensitive information roughly an individual's prosperity, family history, and defencelessness to ailments. Unauthorized access or mishandling can lead to isolation, stigmatization, and mystery breaches. The potential threats to genomic data affirmation are multifaceted, checking the chance of re-identification and extended defense lessness to data breaches, hacking events, and unauthorized get to by harmful actors. To address these challenges, a multifaceted approach is required, tallying solid privacy-preserving methods, securing data capacity, and transmission sharpens, and getting to controls. Encryption techniques, differential security methods, and secure multiparty computation offer promising streets for securing genomic data while progressing collaborative ask approximately. Establishing clear authority frameworks and rules for data management, capacity, and sharing is essential to reduce security threats in genomics research. Collaboration between researchers, policymakers, industry partners, and support groups is essential for developing comprehensive methods to protect genomic data security. By prioritizing security concerns and executing effective safeguards, the community can uphold individuals' rights, maintain open acceptance, and drive advancements in genomics research for the betterment of society.

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Puddester, Rebecca, April Pike, Joy Maddigan, and Alison Farrell. "Nurses’ Knowledge, Attitudes, Confidence, and Practices with Genetics and Genomics: A Theory-Informed Integrative Review Protocol." Journal of Personalized Medicine 12, no. 9 (August 24, 2022): 1358. http://dx.doi.org/10.3390/jpm12091358.

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Introduction: As key healthcare providers, nurses require genomic competency to fulfil their professional obligations in the genomic era. Prior research suggests that nurses have limited competency with genomics-informed practice. Concepts in the Rogers’ Diffusion of Innovation (DOI) theory (i.e., knowledge, attitudes, and attributes of innovation adopters) provide a framework to understand the process of adoption of innovations, such as genomics, across organizations. We aim to synthesize what is known about the adoption of genomics across nursing within the DOI framework to identify gaps and opportunities to enact sustained adoption of genomics in nursing. Methods and analysis: An integrative literature review, following Whittemore and Knafl’s five steps, will be conducted to evaluate qualitative, quantitative, and mixed-method primary studies that meet inclusion and exclusion criteria. The MEDLINE, PsychINFO, CINAHL, Cochrane, and Sociological Abstracts electronic databases will be searched in addition to the ancestry search method. Two researchers will perform independent screening of studies, quality appraisal using the Mixed-Methods Appraisal Tool, and data analysis using the narrative synthesis method. Disagreements will be resolved by a third reviewer. Findings in this review could be used to develop theory- and evidence-informed strategies to support the sustained adoption of genomics in nursing.

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Yue, Tianwei, Yuanxin Wang, Longxiang Zhang, Chunming Gu, Haoru Xue, Wenping Wang, Qi Lyu, and Yujie Dun. "Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models." International Journal of Molecular Sciences 24, no. 21 (November 1, 2023): 15858. http://dx.doi.org/10.3390/ijms242115858.

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The data explosion driven by advancements in genomic research, such as high-throughput sequencing techniques, is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in various fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning, since we expect a superhuman intelligence that explores beyond our knowledge to interpret the genome from deep learning. A powerful deep learning model should rely on the insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with proper deep learning-based architecture, and we remark on practical considerations of developing deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research and point out current challenges and potential research directions for future genomics applications. We believe the collaborative use of ever-growing diverse data and the fast iteration of deep learning models will continue to contribute to the future of genomics.

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Akhila, Jana, and Manoj Kumar Pandey. "Genomic Advancement in Wheat (Triticum aestivum L.): Harnessing Technological Breakthroughs for Future Strategies." Journal of Advances in Biology & Biotechnology 27, no. 6 (May 9, 2024): 186–98. http://dx.doi.org/10.9734/jabb/2024/v27i6878.

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Wheat can be greatly improved by identifying genes that are significant to agronomy. Although progress in wheat genetics and genomics has been limited by the genome’s vast size and complexity. Wheat has a high-quality genome sequence in light of recent developments in genome sequencing and sequence assembly. Here, we propose that wheat biology can benefit from the same approaches that have been used to define biological systems in model species, such as the generation and characterization of mutants, methods for gene cloning, and enhanced transgenic technology. These tactics will encourage the establishment of wheat breeding programs and hasten the field of wheat biology. We also summarize current developments in functional genomics of wheat. In order to contribute to global food security, we conclude by talking about the future of wheat functional genomics and the sensible design-based molecular breeding of new wheat varieties. We suggest that researchers studying wheat should embrace the methods utilized for functional genomic analysis in other model species. One method for doing this is using gene cloning to find the gene causing an intriguing phenotype, deciphering the biological role of genes through the analysis of their corresponding mutants, and creating a related wheat mutant from the proposed wild-type gene to confirm the target gene’s functionality.

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Woeste, Steven. "Review of Methods in Molecular Biology, Functional Genomics Methods and Protocols." Journal of Chemical Neuroanatomy 27, no. 3 (June 2004): 215. http://dx.doi.org/10.1016/j.jchemneu.2004.03.001.

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KM, Remyamol. "Machine Learning Techniques and Hybrid Feature Selection Methods for Efficient Prediction of Cancer." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34874.

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The high-dimensional genomic data presents significant challenges, and traditional analytical methods often struggle to capture the complex, non-linear relationships within these datasets. This study elaborates into the application of machine learning methods for dimensionality reduction and predictive modeling of binary phenotypes using gene expression data. Various dimensionality reduction techniques are explored, including t-distributed stochastic neighbor embedding (t-SNE), Non-negative matrix factorization (NMF), Principal component analysis (PCA), and manifold learning methods. Additionally, various algorithms such as logistic regression, random forests, support vector machines (SVMs)cand naive Bayes models are evaluated for predicting phenotypes. The study employs rigorous cross-validation, permutation testing, and evaluation metrics like the Matthews Correlation Coefficient (MCC) to assess model performance. The study rigorously assesses current genomics strategies, pinpointing their drawbacks and suggesting areas for future investigation, while delving into the potential of machine learning to overcome these hurdles and offer valuable insights in genomics. Keywords- Gene Expression , Dimensionality Reduction, Biomarkers, Cancer Prediction, Epigenetics, Machine Learning, Feature extraction.

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Tan, Kenneth Lee Shean, and Saharuddin Bin Mohamad. "CFPG: Creating a Common Fungal Pathogenic Genes Database through Data Mining." Chiang Mai Journal of Science 51, no. 3 (May 31, 2024): 1–11. http://dx.doi.org/10.12982/cmjs.2024.038.

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Fu ngal pathogenicity is one of the most vigorously tackled ecological and medicinal issues facing many scientists. Comparative genomics is an extremely important methodology and tool used to understand fungal pathogenicity, and it allows the development of early diagnostic tools for fungal-inflicted diseases across different host organisms. However, comparative genomics depends heavily on readily available fungal pathogenic gene databases to enable downstream genomics study and the development of new diagnosis and detection methods. Here, we have developed the Common Fungal Pathogenic Genes Database through comparative genomic analysis using 86 publicly available fungal genomic data against fungal pathogenicity-related databases, such as Pathogenic-Host Interaction Database (PHI-base), Carbohydrate-Active enZymes Database (CAZy), and Database of Fungal Virulence Factory (DFVF).

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Laudadio, Jennifer, Jeffrey L. McNeal, Scott D. Boyd, Long Phi Le, Christina Lockwood, Cindy B. McCloskey, Gaurav Sharma, Karl V. Voelkerding, and Richard L. Haspel. "Design of a Genomics Curriculum: Competencies for Practicing Pathologists." Archives of Pathology & Laboratory Medicine 139, no. 7 (July 1, 2015): 894–900. http://dx.doi.org/10.5858/arpa.2014-0253-cp.

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Context The field of genomics is rapidly impacting medical care across specialties. To help guide test utilization and interpretation, pathologists must be knowledgeable about genomic techniques and their clinical utility. The technology allowing timely generation of genomic data is relatively new to patient care and the clinical laboratory, and therefore, many currently practicing pathologists have been trained without any molecular or genomics exposure. Furthermore, the exposure that current and recent trainees receive in this field remains inconsistent. Objective To assess pathologists' learning needs in genomics and to develop a curriculum to address these educational needs. Design A working group formed by the College of American Pathologists developed an initial list of genomics competencies (knowledge and skills statements) that a practicing pathologist needs to be successful. Experts in genomics were then surveyed to rate the importance of each competency. These data were used to create a final list of prioritized competencies. A subset of the working group defined subtopics and tasks for each competency. Appropriate delivery methods for the educational material were also proposed. Results A final list of 32 genomics competency statements was developed. A prioritized curriculum was created with designated subtopics and tasks associated with each competency. Conclusions We present a genomics curriculum designed as a first step toward providing practicing pathologists with the competencies needed to practice successfully.

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Su, Xinzhuan, Rachel V. Stadler, Fangzheng Xu, and Jian Wu. "Malaria Genomics, Vaccine Development, and Microbiome." Pathogens 12, no. 8 (August 18, 2023): 1061. http://dx.doi.org/10.3390/pathogens12081061.

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Recent advances in malaria genetics and genomics have transformed many aspects of malaria research in areas of molecular evolution, epidemiology, transmission, host–parasite interaction, drug resistance, pathogenicity, and vaccine development. Here, in addition to introducing some background information on malaria parasite biology, parasite genetics/genomics, and genotyping methods, we discuss some applications of genetic and genomic approaches in vaccine development and in studying interactions with microbiota. Genetic and genomic data can be used to search for novel vaccine targets, design an effective vaccine strategy, identify protective antigens in a whole-organism vaccine, and evaluate the efficacy of a vaccine. Microbiota has been shown to influence disease outcomes and vaccine efficacy; studying the effects of microbiota in pathogenicity and immunity may provide information for disease control. Malaria genetics and genomics will continue to contribute greatly to many fields of malaria research.

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Bunker, Grant, Nicholas Dimakis, and Gocha Khelashvili. "New methods for EXAFS analysis in structural genomics." Journal of Synchrotron Radiation 12, no. 1 (December 23, 2004): 53–56. http://dx.doi.org/10.1107/s090904950402881x.

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Cámara, Pablo G. "Topological methods for genomics: Present and future directions." Current Opinion in Systems Biology 1 (February 2017): 95–101. http://dx.doi.org/10.1016/j.coisb.2016.12.007.

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Hamblin, Martha T., Edward S. Buckler, and Jean-Luc Jannink. "Population genetics of genomics-based crop improvement methods." Trends in Genetics 27, no. 3 (March 2011): 98–106. http://dx.doi.org/10.1016/j.tig.2010.12.003.

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Montelione, Gaetano T., Cheryl Arrowsmith, Mark E. Girvin, Michael A. Kennedy, John L. Markley, Robert Powers, James H. Prestegard, and Thomas Szyperski. "Unique opportunities for NMR methods in structural genomics." Journal of Structural and Functional Genomics 10, no. 2 (March 15, 2009): 101–6. http://dx.doi.org/10.1007/s10969-009-9064-0.

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Saxena, Shivani. "Digital Signal Processing Approaches in the field of Genomics: A Recent Trend." Journal of Medical Science and clinical Research 12, no. 02 (February 28, 2024): 66–77. http://dx.doi.org/10.18535/jmscr/v12i02.10.

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Digital signal processing (DSP) techniques have emerged as powerful tools in the field of genomics, enabling researchers to extract valuable insights from complex genetic data. This research paper presents a comprehensive analysis of the recent trends and advance- ments in applying DSP approaches to genomics. The objective is to provide an overview of the transformative role of DSP in genomic data analysis, variant calling, and interpretation. By leveraging DSP methods such as filtering, feature extraction, time-frequency analysis, and machine learning algorithms, researchers can enhance the quality of genetic signals, identify genetic variants, and gain a deeper understanding of genomic processes. The paper highlights key applications of DSP in genomics, including DNA sequence analysis, RNA expression pro- filing, epigenetics, and genome-wide association studies. Additionally, the challenges associated with applying DSP techniques in genomics, such as signal noise, data in- tegration, and computational complexity, are discussed. This research paper serves as a valuable resource for researchers, bioinformaticians, and geneticists seeking to harness the power of DSP in genomics, advancing our knowledge of genetic diseases and paving the way for personalized medicine and precision healthcare. Keywords: Digital signal processing, Genome analysis, Feature extraction, DNA sequence analysis, RNA expression profiling.

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de Campos, Cassio P., and Paola M. V. Rancoita. "Introduction to the special issue on statistical and computational methods for genomics and integrative genomics." International Journal of Approximate Reasoning 90 (November 2017): 272–73. http://dx.doi.org/10.1016/j.ijar.2017.08.002.

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Mboowa, Gerald, Savannah Mwesigwa, Eric Katagirya, Gaone Retshabile, Busisiwe C. Mlotshwa, Lesedi Williams, Adeodata Kekitiinwa, et al. "The Collaborative African Genomics Network (CAfGEN): Applying Genomic technologies to probe host factors important to the progression of HIV and HIV-tuberculosis infection in sub-Saharan Africa." AAS Open Research 1 (April 18, 2018): 3. http://dx.doi.org/10.12688/aasopenres.12832.1.

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Background: The Human Heredity and Health in Africa consortium (H3Africa) was conceived to facilitate the application of genomics technologies to improve health across Africa. Here, we describe how the Collaborative African Genomics Network (CAfGEN) of the H3Africa consortium is using genomics to probe host genetic factors important to the progression of HIV and HIV-tuberculosis (TB) coinfection in sub-Saharan Africa. Methods: CAfGEN is an H3Africa collaborative centre comprising expertise from the University of Botswana; Makerere University; Baylor College of Medicine Children’s Clinical Centers of Excellence (COEs) in Botswana, Uganda, and Swaziland; as well as Baylor College of Medicine, Texas. The COEs provide clinical expertise for community engagement, participant recruitment and sample collection while the three University settings facilitate processing and management of genomic samples and provide infrastructure and training opportunities to sustain genomics research. Results: The project has focused on utilizing whole-exome sequencing to identify genetic variants contributing to extreme HIV disease progression phenotypes in children, as well as RNA sequencing and integrated genomics to identify host genetic factors associated with TB disease progression among HIV-positive children. These cohorts, developed using the COEs’ electronic medical records, are exceptionally well-phenotyped and present an unprecedented opportunity to assess genetic factors in individuals whose HIV was acquired by a different route than their adult counterparts in the context of a unique clinical course and disease pathophysiology. Conclusions: Our approach offers the prospect of developing a critical mass of well-trained, highly-skilled, continent-based African genomic scientists. To ensure long term genomics research sustainability in Africa, CAfGEN contributes to a wide range of genomics capacity and infrastructure development on the continent, has laid a foundation for genomics graduate programs at its institutions, and continues to actively promote genomics research through innovative forms of community engagement brokered by partnerships with governments and academia to support genomics policy formulation.

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Mboowa, Gerald, Savannah Mwesigwa, Eric Katagirya, Gaone Retshabile, Busisiwe C. Mlotshwa, Lesedi Williams, Adeodata Kekitiinwa, et al. "The Collaborative African Genomics Network (CAfGEN): Applying Genomic technologies to probe host factors important to the progression of HIV and HIV-tuberculosis infection in sub-Saharan Africa." AAS Open Research 1 (June 21, 2018): 3. http://dx.doi.org/10.12688/aasopenres.12832.2.

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Background: Here, we describe how the Collaborative African Genomics Network (CAfGEN) of the Human Heredity and Health in Africa (H3Africa) consortium is using genomics to probe host genetic factors important to the progression of HIV and HIV-tuberculosis (TB) coinfection in sub-Saharan Africa. The H3Africa was conceived to facilitate the application of genomics technologies to improve health across Africa.. Methods: CAfGEN is an H3Africa collaborative centre comprising expertise from the University of Botswana; Makerere University; Baylor College of Medicine Children’s Clinical Centers of Excellence (COEs) in Botswana, Uganda, and Swaziland; as well as Baylor College of Medicine, Texas. The COEs provide clinical expertise for community engagement, participant recruitment and sample collection while the three University settings facilitate processing and management of genomic samples and provide infrastructure and training opportunities to sustain genomics research. Results: The project has focused on utilizing whole-exome sequencing to identify genetic variants contributing to extreme HIV disease progression phenotypes in children, as well as RNA sequencing and integrated genomics to identify host genetic factors associated with TB disease progression among HIV-positive children. These cohorts, developed using the COEs’ electronic medical records, are exceptionally well-phenotyped and present an unprecedented opportunity to assess genetic factors in individuals whose HIV was acquired by a different route than their adult counterparts in the context of a unique clinical course and disease pathophysiology. Conclusions: Our approach offers the prospect of developing a critical mass of well-trained, highly-skilled, continent-based African genomic scientists. To ensure long term genomics research sustainability in Africa, CAfGEN contributes to a wide range of genomics capacity and infrastructure development on the continent, has laid a foundation for genomics graduate programs at its institutions, and continues to actively promote genomics research through innovative forms of community engagement brokered by partnerships with governments and academia to support genomics policy formulation.

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Kansal, Arushi, Catherine Quinlan, Zornitza Stark, Peter G. Kerr, Andrew J. Mallett, Chandni Lakshmanan, Stephanie Best, and Kushani Jayasinghe. "Theory Designed Strategies to Support Implementation of Genomics in Nephrology." Genes 13, no. 10 (October 21, 2022): 1919. http://dx.doi.org/10.3390/genes13101919.

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(1) Background: Genomic testing is increasingly utilized as a clinical tool; however, its integration into nephrology remains limited. The purpose of this study was to identify barriers and prioritize interventions for the widespread implementation of genomics in nephrology. (2) Methods: Qualitative, semi-structured interviews were conducted with 25 Australian adult nephrologists to determine their perspectives on interventions and models of care to support implementation of genomics in nephrology. Interviews were guided by a validated theoretical framework for the implementation of genomic medicine—the Consolidated Framework of Implementation Research (CFIR). (3) Results: Nephrologists were from 18 hospitals, with 7 having a dedicated multidisciplinary kidney genetics service. Most practiced in the public healthcare system (n = 24), a large number were early-career (n = 13), and few had genomics experience (n = 4). The top three preferred interventions were increased funding, access to genomics champions, and education and training. Where interventions to barriers were not reported, we used the CFIR/Expert Recommendations for Implementing Change matching tool to generate theory-informed approaches. The preferred model of service delivery was a multidisciplinary kidney genetics clinic. (4) Conclusions: This study identified surmountable barriers and practical interventions for the implementation of genomics in nephrology, with multidisciplinary kidney genetics clinics identified as the preferred model of care. The integration of genomics education into nephrology training, secure funding for testing, and counselling along with the identification of genomics champions should be pursued by health services more broadly.

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Alterovitz, Gil, Jeremy Warner, Peijin Zhang, Yishen Chen, Mollie Ullman-Cullere, David Kreda, and Isaac S. Kohane. "SMART on FHIR Genomics: facilitating standardized clinico-genomic apps." Journal of the American Medical Informatics Association 22, no. 6 (July 21, 2015): 1173–78. http://dx.doi.org/10.1093/jamia/ocv045.

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Abstract Background Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patient’s electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. Methods A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. Results Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. Conclusion This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine.

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Rafiq, Muhammad, and Stefania Boccia. "Application of the GRADE Approach in the Development of Guidelines and Recommendations in Genomic Medicine." Genomics Insights 11 (January 1, 2018): 117863101775336. http://dx.doi.org/10.1177/1178631017753360.

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A great deal of ambiguity exists in the development of guidelines for genomic applications used in clinical practice. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach has the potential to be applied in the guidelines and recommendations development process in genomics. Here, we discuss whether and how GRADE can be applied to address the challenges posed by the evidence-based guidelines and recommendations development process in genomics. To see how GRADE can complement to the current guidelines development in genomics, we compare and contrast GRADE with other approaches. GRADE differed from other methods by incorporating patient values and preferences and balance of consequences. We conclude that the groups trying to implement genomics into practice may gleam more information from applying the GRADE framework. However, it is not clear yet whether GRADE can address the issue of timeliness in terms of the differences between the time required for guidelines development and the rapid pace of genomics.

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Green, Eric D. "Genomics in 2K10: Fulfilling the Promise of a Sequenced Human Genome." Blood 116, no. 21 (November 19, 2010): SCI—16—SCI—16. http://dx.doi.org/10.1182/blood.v116.21.sci-16.sci-16.

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Abstract Abstract SCI-16 The Human Genome Project's completion of the human genome sequence in 2003 was a landmark scientific achievement of historic significance. It also signified a critical transition for the field of genomics, as the new foundation of genomic knowledge started to be used in powerful ways by researchers and clinicians to tackle increasingly complex problems in biomedicine. To exploit the opportunities provided by the human genome sequence and to ensure the productive growth of genomics as one of the most vital biomedical disciplines of the 21st century, the National Human Genome Research Institute (NHGRI) is pursuing a broad vision for genomics research beyond the Human Genome Project. This vision includes facilitating and supporting the highest-priority research areas that interconnect genomics to biology, to health, and to society.Current efforts in genomics research are focused on using genomic data, technologies, and insights to acquire a deeper understanding of biology and to uncover the genetic basis of human disease. Some of the most profound advances are being catalyzed by revolutionary new DNA sequencing technologies; these methods are already producing prodigious amounts of DNA sequence data, including from large numbers of individual patients. Such a capability, coupled with better associations between genetic diseases and specific regions of the human genome, are accelerating our understanding of the genetic basis for complex genetic disorders and for drug response. Together, these developments will usher in the era of genomic medicine. Disclosures: No relevant conflicts of interest to declare.

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Coyle, Heather Miller. "Functional Genomics and Eye Color Prediction." International Journal of Forensic Sciences 8, no. 4 (2023): 1–7. http://dx.doi.org/10.23880/ijfsc-16000334.

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The ability to reconstruct a recognizable face from skeletal remains is a useful investigative tool for human identification. Functional genomics plays a role in facial reconstruction through the identification of the regulation of pigmentation pathways. Functional genomics here refers to the study of how genes and intergenic sequences contribute to metabolic pathways and work together to produce a particular phenotype. This form of genetic information adds and confirms the physical traits that help with a visual identification of a person by allowing for the biological predictions of pigmentation for eye, hair, and skin color in facial reconstruction. The principle of SNP testing for eye color prediction from human tooth DNA using both destructive and nondestructive DNA extraction methods is presented using the IrisPlex eye color prediction software. This IrisPlex eye color prediction software is not one hundred percent accurate, and we have explored the classification issues behind those discrepancies by examining DNA from blue, brown, and intermediate eye color donors as well as those donors exhibiting heterochromia (mixed eye colors). The importance of correct prediction of eye color for facial reconstruction is to aid in correct identification of skeletal remains through forensic phenotyping investigation. Irisplex is useful for the correct prediction of blue and brown eye individuals but is less able to distinguish between the subcategories of grey, green, heterochromia and hazel for the intermediate category.

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Nichols, Heather L., Ning Zhang, and Xuejun Wen. "Proteomics and genomics of microgravity." Physiological Genomics 26, no. 3 (August 2006): 163–71. http://dx.doi.org/10.1152/physiolgenomics.00323.2005.

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Many serious adverse physiological changes occur during spaceflight. In the search for underlying mechanisms and possible new countermeasures, many experimental tools and methods have been developed to study microgravity caused physiological changes, ranging from in vitro bioreactor studies to spaceflight investigations. Recently, genomic and proteomic approaches have gained a lot of attention; after major scientific breakthroughs in the fields of genomics and proteomics, they are now widely accepted and used to understand biological processes. Understanding gene and/or protein expression is the key to unfolding the mechanisms behind microgravity-induced problems and, ultimately, finding effective countermeasures to spaceflight-induced alterations. Significant progress has been made in identifying the genes/proteins responsible for these changes. Although many of these genes and/or proteins were observed to be either upregulated or downregulated, however, no large-scale genomics and proteomics studies have been published so far. This review aims at summarizing the current status of microgravity-related genomics and proteomics studies and stimulating large-scale proteomics and genomics research activities.

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Carey, Vincent J., Marcel Ramos, Benjamin J. Stubbs, Shweta Gopaulakrishnan, Sehyun Oh, Nitesh Turaga, Levi Waldron, and Martin Morgan. "Global Alliance for Genomics and Health Meets Bioconductor: Toward Reproducible and Agile Cancer Genomics at Cloud Scale." JCO Clinical Cancer Informatics, no. 4 (September 2020): 472–79. http://dx.doi.org/10.1200/cci.19.00111.

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PURPOSE Institutional efforts toward the democratization of cloud-scale data and analysis methods for cancer genomics are proceeding rapidly. As part of this effort, we bridge two major bioinformatic initiatives: the Global Alliance for Genomics and Health (GA4GH) and Bioconductor. METHODS We describe in detail a use case in pancancer transcriptomics conducted by blending implementations of the GA4GH Workflow Execution Services and Tool Registry Service concepts with the Bioconductor curatedTCGAData and BiocOncoTK packages. RESULTS We carried out the analysis with a formally archived workflow and container at dockstore.org and a workspace and notebook at app.terra.bio. The analysis identified relationships between microsatellite instability and biomarkers of immune dysregulation at a finer level of granularity than previously reported. Our use of standard approaches to containerization and workflow programming allows this analysis to be replicated and extended. CONCLUSION Experimental use of dockstore.org and app.terra.bio in concert with Bioconductor enabled novel statistical analysis of large genomic projects without the need for local supercomputing resources but involved challenges related to container design, script archiving, and unit testing. Best practices and cost/benefit metrics for the management and analysis of globally federated genomic data and annotation are evolving. The creation and execution of use cases like the one reported here will be helpful in the development and comparison of approaches to federated data/analysis systems in cancer genomics.

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Marçais, Guillaume, Brad Solomon, Rob Patro, and Carl Kingsford. "Sketching and Sublinear Data Structures in Genomics." Annual Review of Biomedical Data Science 2, no. 1 (July 20, 2019): 93–118. http://dx.doi.org/10.1146/annurev-biodatasci-072018-021156.

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Large-scale genomics demands computational methods that scale sublinearly with the growth of data. We review several data structures and sketching techniques that have been used in genomic analysis methods. Specifically, we focus on four key ideas that take different approaches to achieve sublinear space usage and processing time: compressed full-text indices, approximate membership query data structures, locality-sensitive hashing, and minimizers schemes. We describe these techniques at a high level and give several representative applications of each.

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Du, Xingjie, Yu Sun, Tong Fu, Tengyun Gao, and Tianliu Zhang. "Research Progress and Applications of Bovine Genome in the Tribe Bovini." Genes 15, no. 4 (April 18, 2024): 509. http://dx.doi.org/10.3390/genes15040509.

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Various bovine species have been domesticated and bred for thousands of years, and they provide adequate animal-derived products, including meat, milk, and leather, to meet human requirements. Despite the review studies on economic traits in cattle, the genetic basis of traits has only been partially explained by phenotype and pedigree breeding methods, due to the complexity of genomic regulation during animal development and growth. With the advent of next-generation sequencing technology, genomics projects, such as the 1000 Bull Genomes Project, Functional Annotation of Animal Genomes project, and Bovine Pangenome Consortium, have advanced bovine genomic research. These large-scale genomics projects gave us a comprehensive concept, technology, and public resources. In this review, we summarize the genomics research progress of the main bovine species during the past decade, including cattle (Bos taurus), yak (Bos grunniens), water buffalo (Bubalus bubalis), zebu (Bos indicus), and gayal (Bos frontalis). We mainly discuss the development of genome sequencing and functional annotation, focusing on how genomic analysis reveals genetic variation and its impact on phenotypes in several bovine species.

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Wickens, H. J., S. Simpson, A. Pope, and J. Allen. "Pharmacy and Genomic Medicine: A UK-wide survey of pharmacy staff assessing their prior education, confidence and educational needs." International Journal of Pharmacy Practice 31, Supplement_2 (November 30, 2023): ii53. http://dx.doi.org/10.1093/ijpp/riad074.066.

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Abstract Introduction Pharmacy teams are key in helping patients to get the most from genomic medicine.1,2 However, genomics has only recently been included in undergraduate curricula, and it has been suggested that all healthcare professionals could benefit from education in pharmacogenomics2. We surveyed pharmacy staff to gather information on previous education, current practice and future educational needs in genomics and pharmacogenomics. Aim This survey aimed to establish existing levels of education and confidence in genomics and pharmacogenomics in pharmacy staff working in any role, in any sector, across the UK, and to investigate respondents’ preferences in delivery of genomic education. Methods The survey was based on a 2021 survey of genomic knowledge among medical staff by Health Education England (HEE)3, and amended to reflect pharmacy roles and practice following discussion with pharmacy leads from the 7 NHS Genomic Medicine Service Alliances in England, and from Scotland, Wales and Northern Ireland. SmartSurvey software was used to host the survey, with data held securely. The survey was open between 1st March and 16th May 2022, and was publicised via pharmacy groups including the Royal Pharmaceutical Society, National Pharmacy Association, Local Pharmaceutical Committees, chief pharmacists networks in primary and secondary care, and social media. This work was assessed using the NHS Health Research Authority Research screening tool and judged as ‘not research’; therefore ethical approval was not required. Results 1,552 responses were received from pharmacists, pharmacy technicians, dispensers and other pharmacy staff across the UK; 68% of responses were from England, 13% from Scotland, 10% from Northern Ireland and 9% from Wales. The majority of responses (69%) were from Pharmacists, with 24% from Pharmacy Technicians and 4% from Pharmacy support workers. Only 13% of respondents had received any formal training in genomics. Most respondents felt unprepared to use genomic testing in their practice; just 8% of pharmacists (including trainees), and 1% of pharmacy technicians (including trainees) felt prepared. However, 65% of respondents thought that genomics would change their practice within the next 5 years, and over 70% of pharmacists, and 56% of pharmacy technicians, could envisage ordering, advising on, or counselling patients on genomic testing in the future after appropriate training. 29% of respondents (mainly pharmacy managers) did not currently see patients and therefore might not train personally in genomics. Discussion/Conclusion This work suggests that pharmacy teams are likely to require educational support to embrace the opportunities of genomic medicine. High survey engagement suggested that respondents were keen to make their voices heard. Pharmacists appeared more confident in their ability to advise patients on genomics than Technicians, however both groups seemed keen to receive training. One limitation is that respondents were likely interested in genomics; those with no interest may not have completed the survey. Additionally, pharmacy managers who do not see patients might not train personally in genomics, but may influence strategy for pharmacy genomics service development and delivery. National bodies should capitalise on enthusiasm across the sector to help drive pharmacy genomics services forward through education and training. References 1. Royal College of Physicians and British Pharmacological Society. Personalised prescribing: using pharmacogenomics to improve patient outcomes. Report of a working party. London: RCP and BPS, 2022. https://www.rcp.ac.uk/projects/outputs/personalised-prescribing-using-pharmacogenomics-improve-patient-outcomes last accessed 1/6/23 2. Royal Pharmaceutical Society. Collaborative Position statement for Pharmacy professionals and Genomic Medicine. London: RPS, 2023 https://www.rpharms.com/development/pharmacogenomics/genomic-statement last accessed 1/6/23 3. Health Education England. Genomics in your practice: a health and social care survey. Birmingham, Health Education England, 2023 https://www.genomicseducation.hee.nhs.uk/documents/genomics-in-your-practice-a-health-and-social-care-survey/ last accessed 1/6/23

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Saada, Bacem, Tianchi Zhang, Estevao Siga, Jing Zhang, and Maria Malane Magalhães Muniz. "Whole-Genome Alignment: Methods, Challenges, and Future Directions." Applied Sciences 14, no. 11 (June 3, 2024): 4837. http://dx.doi.org/10.3390/app14114837.

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Whole-genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, hash-based, anchor-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. This paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explore these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects.

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KUZNETSOV, VLADIMIR A., and CHARLES AUFFRAY. "PREFACE: INTEGRATIVE STATISTICAL AND COMPUTATIONAL METHODS FOR GENOMICS STUDIES." Journal of Biological Systems 10, no. 04 (December 2002): 281–83. http://dx.doi.org/10.1142/s0218339002000755.

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Murphy, Shawn N., Paul Avillach, Riccardo Bellazzi, Lori Phillips, Matteo Gabetta, Alal Eran, Michael T. McDuffie, and Isaac S. Kohane. "Combining clinical and genomics queries using i2b2 – Three methods." PLOS ONE 12, no. 4 (April 7, 2017): e0172187. http://dx.doi.org/10.1371/journal.pone.0172187.

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Journal articles on the topic 'Genomics – methods'

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