Relevant bibliographies by topics / Genomics – methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Genomics – methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "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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Dissertations / Theses on the topic "Genomics – methods"

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Eriksen, Niklas. "Combinatorial methods in comparative genomics." Doctoral thesis, KTH, Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3508.

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Lo, Chi Ho. "Statistical methods for high throughput genomics." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/13762.

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The advancement of biotechnologies has led to indispensable high-throughput techniques for biological and medical research. Microarray is applied to monitor the expression levels of thousands of genes simultaneously, while flow cytometry (FCM) offers rapid quantification of multi-parametric properties for millions of cells. In this thesis, we develop approaches based on mixture modeling to deal with the statistical issues arising from both high-throughput biological data sources. Inference about differential expression is a typical objective in analysis of gene expression data. The use of Bayesian hierarchical gamma-gamma and lognormal-normal models is popular for this type of problem. Some unrealistic assumptions, however, have been made in these frameworks. In view of this, we propose flexible forms of mixture models based on an empirical Bayes approach to extend both frameworks so as to release the unrealistic assumptions, and develop EM-type algorithms for parameter estimation. The extended frameworks have been shown to significantly reduce the false positive rate whilst maintaining a high sensitivity, and are more robust to model misspecification. FCM analysis currently relies on the sequential application of a series of manually defined 1D or 2D data filters to identify cell populations of interest. This process is time-consuming and ignores the high-dimensionality of FCM data. We reframe this as a clustering problem, and propose a robust model-based clustering approach based on t mixture models with the Box-Cox transformation for identifying cell populations. We describe an EM algorithm to simultaneously handle parameter estimation along with transformation selection and outlier identification, issues of mutual influence. Empirical studies have shown that this approach is well adapted to FCM data, in which a high abundance of outliers and asymmetric cell populations are frequently observed. Finally, in recognition of concern for an efficient automated FCM analysis platform, we have developed an R package called flowClust to automate the gating analysis with the proposed methodology. Focus during package development has been put on the computational efficiency and convenience of use at users' end. The package offers a wealth of tools to summarize and visualize features of the clustering results, and is well integrated with other FCM packages.
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Fuxelius, Hans-Henrik. "Methods and Applications in Comparative Bacterial Genomics." Doctoral thesis, Uppsala universitet, Molekylär evolution, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8398.

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Comparative studies of bacterial genomes, now counting in the hundreds, generate massive amounts of information. In order to support a systematic and efficient approach to genomic analyses, a database driven system with graphic visualization of genomic properties was developed - GenComp. The software was applied to studies of obligate intracellular bacteria. In all studies, ORFs were extracted and grouped into ORF-families. Based on gene order synteny, orthologous clusters of core genes and variable spacer ORFs were identified and extracted for alignments and computation of substitution frequencies. The software was applied to the genomes of six Chlamydia trachomatis strains to identify the most rapidly evolving genes. Five genes were chosen for genotyping, and close to a 3-fold higher discrimination capacity was achieved than that of serotypes. With GenComp as the backbone, a massive comparative analysis were performed on the variable gene set in the Rickettsiaceae, which includes Rickettsia prowazekii and Orientia tsutsugamushi, the agents of epidemic and scrub typhus, respectively. O. tsutsugamushi has the most exceptional bacterial genome identified to date; the 2.2 Mb genome is 200-fold more repeated than the 1.1 Mb R. prowazekii genome due to an extensive proliferation of conjugative type IV secretion systems and associated genes. GenComp identified 688 core genes that are conserved across 7 closely related Rickettsia genomes along with a set of 469 variably present genes with homologs in other species. The analysis indicates that up to 70% of the extensively degraded and variably present genes represent mobile genetic elements and genes putatively acquired by horizontal gene transfer. This explains the paradox of the high pseudogene load in the small Rickettsia genomes. This study demonstrates that GenComp provides an efficient system for pseudogene identification and may help distinguish genes from spurious ORFs in the many pan-genome sequencing projects going on worldwide.
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Li, Yang. "Statistical Methods for Large-Scale Integrative Genomics." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:33493551.

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In the past 20 years, we have witnessed a significant advance of high-throughput genetic and genomic technologies. With the massively generated genomics data, there is a pressing need for statistical methods that can utilize them to make quantitative inference on substantive scientific questions. My research has been focusing on statistical methods for large-scale integrative genomics. The human genome encodes more than 20,000 genes, while the functions of about 50% (>10,000) genes remains unknown up to date. The determination of the functions of the poorly characterized genes is crucial for understanding biological processes and human diseases. In the era of Big Data, the availability of massive genomic data provides us unprecedented opportunity to identify the association between genes and predict their biological functions. Genome sequencing data and mRNA expression data are the two most important classes of genomic data. This thesis presents three research projects in self-contained chapters: (1) a statistical framework for inferring evolutionary history of human genes and identifying gene modules with shared evolutionary history from genome sequencing data, (2) a statistical method to predict frequent and specific gene co-expression by integrating a large number of mRNA expression datasets, and (3) robust variable and interaction selection for high-dimensional classification problem under the discriminant analysis and logistic regression model. Chapter 1. Human has more than 20,000 genes but till now most of their functions are uncharacterized. Determination of the function for poorly characterized genes is crucial for understanding biological processes and study of human diseases. Functionally associated genes tend to gain and lose simultaneously during evolution, therefore identifying co-evolution of genes predicts gene-gene associations. In this chapter, we propose a mixture of tree-structured hidden Markov models for gene evolution process, and a Bayesian model-based clustering algorithm to detect gene modules with shared evolutionary history (named as evolutionary conserved modules, ECM). Dirichlet process prior is adopted for estimation of number of gene clusters and an efficient Gibbs sampler is developed for posterior distribution computation. By simulation study and benchmarks on real data sets, we show that our algorithm outperforms traditional methods that use simple metrics (e.g. Hamming distance, Pearson correlation) to measure the similarity between genes presence/absence patterns. We apply our methods on 1,025 canonical human pathways gene sets, and found a large portion of the detected gene associations are substantiated by other sources of evidence. The rest of genes have predicted functions of high priority to be verified by further biological experiments. Chapter 2. The availability of gene expression measurements across thousands of experimental conditions provides the opportunity to predict gene function based on shared mRNA expression. While many biological complexes and pathways are coordinately expressed, their genes may be organized into co-expression modules with distinct patterns in certain tissues or conditions, which can provide insight into pathway organization and function. We developed the algorithm CLIC (clustering by inferred co-expression, www.gene-clic.org) that clusters a set of functionally-related genes into co-expressed modules, highlights the most relevant datasets, and predicts additional co-expressed genes. Using a statistical Bayesian partition model, CLIC simultaneously partitions the input gene set into disjoint co-expression modules and weights the most relevant datasets for each module. CLIC then expands each module with additional members that co-express with the module’s genes more than the background model in the weighted datasets. We applied CLIC to (i) model the background correlation in each of 3,662 mouse and human microarray datasets from the Gene Expression Omnibus (GEO), (ii) partition each of 900 annotated complexes/pathways into co-expression modules, and (iii) expand each co-expression module with additional genes showing frequent and specific co-expression over multiple GEO datasets. CLIC provided very strong functional predictions for many completely uncharacterized genes, including a link between protein C7orf55 and the mitochondrial ATP synthase complex that we experimentally validated via CRISPR knock-out. CLIC software is freely available and should become increasingly powerful with the growing wealth of transcriptomic datasets. Chapter 3. Discriminant analysis and logistic regression are fundamental tools for classification problems. Quadratic discriminant analysis has the ability to exploit interaction effects of predictors, but the selection of interaction terms is non-trivial and the Gaussian assumption is often too restrictive for many real problems. Under the logistic regression framework, we propose a forward-backward method, SODA, for variable selection with both main and quadratic interaction terms, where in the forward stage, a stepwise procedure is conducted to screen for important predictors with both main and interaction effects, and in the backward stage SODA remove insignificant terms so as to optimize the extended BIC (EBIC) criterion. Compared with existing methods on quadratic discriminant analysis variable selection (e.g., (Murphy et al., 2010), (Zhang and Wang, 2011) and (Maugis et al., 2011)), SODA can deal with high-dimensional data with the number of predictors much larger than the sample size and does not require the joint normality assumption on predictors, leading to much enhanced robustness. Theoretical analysis establishes the consistency of SODA under high-dimensional setting. Empirical performance of SODA is assessed on both simulated and real data and is found to be superior to all existing methods we have tested. For all the three real datasets we have studied, SODA selected more parsimonious models achieving higher classification accuracies compared to other tested methods.
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Ausmees, Kristiina. "Efficient computational methods for applications in genomics." Licentiate thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396409.

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During the last two decades, advances in molecular technology have facilitated the sequencing and analysis of ancient DNA recovered from archaeological finds, contributing to novel insights into human evolutionary history. As more ancient genetic information has become available, the need for specialized methods of analysis has also increased. In this thesis, we investigate statistical and computational models for analysis of genetic data, with a particular focus on the context of ancient DNA. The main focus is on imputation, or the inference of missing genotypes based on observed sequence data. We present results from a systematic evaluation of a common imputation pipeline on empirical ancient samples, and show that imputed data can constitute a realistic option for population-genetic analyses. We also discuss preliminary results from a simulation study comparing two methods of phasing and imputation, which suggest that the parametric Li and Stephens framework may be more robust to extremely low levels of sparsity than the parsimonious Browning and Browning model. An evaluation of methods to handle missing data in the application of PCA for dimensionality reduction of genotype data is also presented. We illustrate that non-overlapping sequence data can lead to artifacts in projected scores, and evaluate different methods for handling unobserved genotypes. In genomics, as in other fields of research, increasing sizes of data sets are placing larger demands on efficient data management and compute infrastructures. The last part of this thesis addresses the use of cloud resources for facilitating such analysis. We present two different cloud-based solutions, and exemplify them on applications from genomics.
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Pappalardo, Elisa. "Combinatorial optimization methods for problems in genomics." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1029.

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I recenti progressi in genomica hanno sollevato una miriade di problemi estremamente stimolanti dal punto di vista computazionale; in particolare, per molti di essi e' stata provata l'appartenenza alla classe dei problemi NP-hard. Sulla base di questi risultati, grande attenzione e' stata posta allo sviluppo di algoritmi che fornissero soluzioni soddisfacenti con uno sforzo computazionale contenuto; in tale contesto, i metodi di ottimizzazione rappresentano un valido approccio in quanto molti problemi richiedono l'individuazione di soluzioni caratterizzati da costo minimo. Questo lavoro di tesi introduce nuovi metodi di ottimizzazione combinatoria per l'analisi e il design di sequenze nucleotidiche. In particolare, la tesi e' focalizzata su metodi effi cienti per la risoluzione del Non-Unique Probe Selection Problem e del Closest String Problem. I risultati sperimentali hanno evidenziato che i nuovi approcci introdotti rappresentano metodi e fficienti e competitivi con lo stato dell'arte e, in molti casi, essi sono in grado di individuare soluzioni migliori rispetto a quelle note in letteratura.
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Ericsson, Ulrika. "A structural genomics pilot project : methods and applications /." Stockholm : Department of Biochemistry and Biophysics, Stockholm University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1060.

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Tchitchek, Nicolas. "Novel statistical and geometrical methods for integrative genomics." Paris 7, 2011. http://www.theses.fr/2011PA077207.

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Durant les trois années de mon projet de doctorat, j'ai développé plusieurs méthodes complémentaires pour l'analyse de données de type -omique, dont: (i) un modèle pour la génomique intégrative dans lequel toutes les sortes d'informations qui peuvent être obtenues sur un génome sont modélisées d'une manière probabiliste unifiée, permettant ainsi d'analyser les corrélations entre des données hétérogènes à l'échelle du génome, (ii) un test statistique ayant pour critère l'amplification de l'expression pour l'identification de gènes différentiellement et similairement exprimés entre deux conditions biologiques, et permettant la détermination d'intervalles de confiance concernant l'amplification, (iii) de nouvelles méthodes de réduction de dimensionnalité qui surpassent les autres méthodes existantes et produisant des représentations géométriques plus facilement interprétables dans le contexte de grands ensembles de données. Ces méthodes ont été appliquées à plusieurs nalyses et études biologiques dans le cadre de collaborations scientifiques: (i) afin d'identifier des domaines fonctionnels dans les régions promotrices de gènes candidats impliqués dans le pseudohypoaldostéronisme. (ii) pour découvrir les réponses transcriptionnelles qui sous-tendent les différences entre les virus pulmonaires faiblement et fortement pathogènes basé sur un ensemble de réponses transcriptomiques. (iii) afin d'étudier la progression du virus de l'hépatite C chez des patients infectés ayant subi une transplantation hépatique (iv) afin d'analyser une banque de marqueur de séquences exprimées obtenues à partir de cellules de sang périphérique de singes verts africains infectés ou non par le SIV
During the three years of my Ph. D. Project, I developed several complementary methods and frameworks for the analysis of -omics data, such as: (i) a framework for integrative genomics in which every kind of information that can be obtained about the genomic processes and features are modeled in a common probabilistic manner, allowing then to analyze the correlations among the heterogeneous genome-wide information, (ii) a fold-change based statistical test for the identification of differentially and similarly expressed genes between two biological conditions, allowing also the determination of confidence intervals of specific confidence levels for the fold-change. (iii) novel dimensionality reduction methods that outperform other related existing methods and provide more interpretable geometrical representations in the context of large dataset of-omics data. These methods have been applied to several biological analyses and studies as part of different scientific collaborations: (i) to identify functional Glucocorticoid Response Elements in the promoter regions of specific candidate genes involved in Type 1 Pseudohypoaldosteronism. (ii) to uncover the host transcriptional responses underlying differences between low- and high- pathogenic pulmonary viruses based on a compendium of host transcription responses of infected cells from mouse lungs. (iii) to study the progression of the hepatitis C virus in infected patients who underwent orthotopic liver transplantation, based on a cohort of transcriptome profiles for liver biopsy specimens, (iv) to analyze an Expression Sequence Tag library obtained from PBMC of African green monkeys infected or not by the SIV
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Ming, Jingsi. "Statistical methods for integrative analysis of genomic data." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/545.

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Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association studies (GWAS). However, there are still several challenges towards deepening our understanding of the genetic architectures of complex phenotypes. First, the majority of GWAS hits are in non-coding region and their biological interpretation is still unclear. Second, most complex traits are suggested to be highly polygenic, i.e., they are affected by a vast number of risk variants with individually small or moderate effects, whereas a large proportion of risk variants with small effects remain unknown. Third, accumulating evidence from GWAS suggests the pervasiveness of pleiotropy, a phenomenon that some genetic variants can be associated with multiple traits, but there is a lack of unified framework which is scalable to reveal relationship among a large number of traits and prioritize genetic variants simultaneously with functional annotations integrated. In this thesis, we propose two statistical methods to address these challenges using integrative analysis of summary statistics from GWASs and functional annotations. In the first part, we propose a latent sparse mixed model (LSMM) to integrate functional annotations with GWAS data. Not only does it increase the statistical power of identifying risk variants, but also offers more biological insights by detecting relevant functional annotations. To allow LSMM scalable to millions of variants and hundreds of functional annotations, we developed an efficient variational expectation-maximization (EM) algorithm for model parameter estimation and statistical inference. We first conducted comprehensive simulation studies to evaluate the performance of LSMM. Then we applied it to analyze 30 GWASs of complex phenotypes integrated with nine genic category annotations and 127 cell-type specific functional annotations from the Roadmap project. The results demonstrate that our method possesses more statistical power than conventional methods, and can help researchers achieve deeper understanding of genetic architecture of these complex phenotypes. In the second part, we propose a latent probit model (LPM) which combines summary statistics from multiple GWASs and functional annotations, to characterize relationship and increase statistical power to identify risk variants. LPM can also perform hypothesis testing for pleiotropy and annotations enrichment. To enable the scalability of LPM as the number of GWASs increases, we developed an efficient parameter-expanded EM (PX-EM) algorithm which can execute parallelly. We first validated the performance of LPM through comprehensive simulations, then applied it to analyze 44 GWASs with nine genic category annotations. The results demonstrate the benefits of LPM and can offer new insights of disease etiology.
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Sofer, Tamar. "Statistical Methods for High Dimensional Data in Environmental Genomics." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10403.

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In this dissertation, we propose methodology to analyze high dimensional genomics data, in which the observations have large number of outcome variables, in addition to exposure variables. In the Chapter 1, we investigate methods for genetic pathway analysis, where we have a small number of exposure variables. We propose two Canonical Correlation Analysis based methods, that select outcomes either sequentially or by screening, and show that the performance of the proposed methods depend on the correlation between the genes in the pathway. We also propose and investigate criterion for fixing the number of outcomes, and a powerful test for the exposure effect on the pathway. The methodology is applied to show that air pollution exposure affects gene methylation of a few genes from the asthma pathway. In Chapter 2, we study penalized multivariate regression as an efficient and flexible method to study the relationship between large number of covariates and multiple outcomes. We use penalized likelihood to shrink model parameters to zero and to select only the important effects. We use the Bayesian Information Criterion (BIC) to select tuning parameters for the employed penalty and show that it chooses the right tuning parameter with high probability. These are combined in the “two-stage procedure”, and asymptotic results show that it yields consistent, sparse and asymptotically normal estimator of the regression parameters. The method is illustrated on gene expression data in normal and diabetic patients. In Chapter 3 we propose a method for estimation of covariates-dependent principal components analysis (PCA) and covariance matrices. Covariates, such as smoking habits, can affect the variation in a set of gene methylation values. We develop a penalized regression method that incorporates covariates in the estimation of principal components. We show that the parameter estimates are consistent and sparse, and show that using the BIC to select the tuning parameter for the penalty functions yields good models. We also propose the scree plot residual variance criterion for selecting the number of principal components. The proposed procedure is implemented to show that the first three principal components of genes methylation in the asthma pathway are different in people who did not smoke, and people who did.
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Books on the topic "Genomics – methods"

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Henry, Robert J., and Agnelo Furtado. Cereal genomics: Methods and protocols. New York: Humana Press, 2014.

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name, No. Functional genomics: Methods and protocols. Totowa, NJ: Humana Press, 2003.

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Rose, Ray J. Legume genomics: Methods and protocols. New York: Humana Press, 2013.

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J, Brownstein Michael, and Khodursky Arkady B, eds. Functional genomics: Methods and protocols. Totowa, N.J: Humana Press, 2003.

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Claudia, Klinger, ed. Functional genomics: Methods and protocols. 2nd ed. New York: Springer, 2012.

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J, Somers Daryl, Langridge Peter, and Gustafson J. P, eds. Plant genomics: Methods and protocols. New York , NY: Humana Press, 2009.

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J, Read Simon, and Virley David, eds. Stroke genomics: Methods and reviews. Totowa, N.J: Humana Press, 2005.

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Alonso, Jose M., and Anna N. Stepanova. Plant functional genomics: Methods and protocols. New York: Humana Press, 2015.

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Hicks, Glenn R., and Stéphanie Robert. Plant chemical genomics: Methods and protocols. New York: Humana Press, 2014.

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H, Bergman Nicholas, ed. Comparative genomics. Totowa, NJ: Humana Press, 2007.

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Book chapters on the topic "Genomics – methods"

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Cheng, Jan-Fang, James R. Priest, and Len A. Pennacchio. "Comparative Genomics." In Methods in Molecular Biology, 229–51. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-030-0_13.

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Brousseau, Louise, Pauline Garnier-Géré, and Charles R. Clement. "Historical Genomics." In Methods in Historical Ecology, 104–11. Abingdon, Oxon ; New York, NY : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9780429060175-15.

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Del Giacco, Luca, and Cristina Cattaneo. "Introduction to Genomics." In Methods in Molecular Biology, 79–88. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-60327-216-2_6.

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Savaris, Ricardo Francalacci, and Linda C. Giudice. "Genomics Analysis: Endometrium." In Methods in Molecular Biology, 91–113. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-378-7_6.

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Dutheil, Julien Y., and Asger Hobolth. "Ancestral Population Genomics." In Methods in Molecular Biology, 555–89. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9074-0_18.

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Dutheil, Julien Y., and Asger Hobolth. "Ancestral Population Genomics." In Methods in Molecular Biology, 293–313. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-585-5_12.

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Ouyang, Shu, Françoise Thibaud-Nissen, Kevin L. Childs, Wei Zhu, and C. Robin Buell. "Plant Genome Annotation Methods." In Plant Genomics, 263–82. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-427-8_14.

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Parab, Sushant, Davide Corà, and Federico Bussolino. "Comparative Genomics of Actinobacteria." In Methods in Actinobacteriology, 229–35. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1728-1_31.

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Rampitsch, Christof, and Natalia V. Bykova. "Methods for Functional Proteomic Analyses." In Plant Genomics, 93–110. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-427-8_6.

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Schulz, Tizian, Luca Parmigiani, Andreas Rempel, and Jens Stoye. "Methods for Pangenomic Core Detection." In Comparative Genomics, 73–106. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3838-5_4.

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Conference papers on the topic "Genomics – methods"

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Maksimchuk, Pavel, and Anna Ilunina. "ON METHODS OF MEASURING THE IMPACT OF DRAUGHT ON PINUS SYLVESTRIS." In Manager of the Year. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2022. http://dx.doi.org/10.34220/my2021_177-179.

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Drought periods can be responsible for substantial damage in forests. Different studies have used empirical methods to measure the impact of drought on trees. More recently, huge advances in genomics have allowed finding potential genetic markers involved in drought resistance or tolerance. In this paper we review some empirical and genomic approaches that have been discussed by the scientists of Technische Universität of München (Germany) and National Institute of Agricultural Research (France). We suggest that a combination of these two types of approaches allows a better understanding of the mechanisms leading to drought resistance or tolerance.
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"High-performance Computing Methods for Computational Genomics." In 2007 IEEE International Parallel and Distributed Processing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/ipdps.2007.370209.

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"Molecular genetic methods for assessing drought resistance of spring barley." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Novosibirsk ICG SB RAS 2021, 2021. http://dx.doi.org/10.18699/plantgen2021-142.

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"Methods of computer vision to extract the quantitative characteristics of the wheat spike." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-060.

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Aluru, Srinivas, David Bader, and Ananth Kalyanaraman. "M11---High-performance computing methods for computational genomics." In the 2006 ACM/IEEE conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1188455.1188689.

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Mitchell, Julie C., Stefanella Boatto, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Symposium on Mathematical Methods in Biophysics and Genomics." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3498079.

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Mitchell, Julie C., Stefanella Boatto, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Symposium on Mathematical Methods in Biophysics and Genomics." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2009: Volume 1 and Volume 2. AIP, 2009. http://dx.doi.org/10.1063/1.3241318.

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Penenko, Alexey, Ulyana Zubairova, Alexander Bobrovskikh, and Alexey Doroshkov. "Adjoint Ensemble Methods for the Inverse Modeling of Biological Processes." In 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB). IEEE, 2020. http://dx.doi.org/10.1109/csgb51356.2020.9214652.

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Bogomolov, Anton, Tatyana Karamysheva, and Nikolay Rubtsov. "Computer methods for visualization chromosome-specific DNA sequences in FISH images." In 2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB). IEEE, 2020. http://dx.doi.org/10.1109/csgb51356.2020.9214751.

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"Development of new methods for obtaining hybrid forms of spring and winter wheat with the involvement of the gene pool of wheatgrass and soybean and confirmation of the applicability of these methods in practical breeding." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-147.

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Reports on the topic "Genomics – methods"

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Ron, Eliora, and Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, March 2009. http://dx.doi.org/10.32747/2009.7695860.bard.

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The aim of this study was to carry out a global functional genomics analysis of plant cell transformation by Agrobacterium in order to define and characterize the physiology of Agrobacterium in the acidic environment of a wounded plant. We planed to study the proteome and transcriptome of Agrobacterium in response to a change in pH, from 7.2 to 5.5 and identify genes and circuits directly involved in this change. Bacteria-plant interactions involve a large number of global regulatory systems, which are essential for protection against new stressful conditions. The interaction of bacteria with their hosts has been previously studied by genetic-physiological methods. We wanted to make use of the new capabilities to study these interactions on a global scale, using transcription analysis (transcriptomics, microarrays) and proteomics (2D gel electrophoresis and mass spectrometry). The results provided extensive data on the functional genomics under conditions that partially mimic plant infection and – in addition - revealed some surprising and significant data. Thus, we identified the genes whose expression is modulated when Agrobacterium is grown under the acidic conditions found in the rhizosphere (pH 5.5), an essential environmental factor in Agrobacterium – plant interactions essential for induction of the virulence program by plant signal molecules. Among the 45 genes whose expression was significantly elevated, of special interest is the two-component chromosomally encoded system, ChvG/I which is involved in regulating acid inducible genes. A second exciting system under acid and ChvG/Icontrol is a secretion system for proteins, T6SS, encoded by 14 genes which appears to be important for Rhizobium leguminosarum nodule formation and nitrogen fixation and for virulence of Agrobacterium. The proteome analysis revealed that gamma aminobutyric acid (GABA), a metabolite secreted by wounded plants, induces the synthesis of an Agrobacterium lactonase which degrades the quorum sensing signal, N-acyl homoserine lactone (AHL), resulting in attenuation of virulence. In addition, through a transcriptomic analysis of Agrobacterium growing at the pH of the rhizosphere (pH=5.5), we demonstrated that salicylic acid (SA) a well-studied plant signal molecule important in plant defense, attenuates Agrobacterium virulence in two distinct ways - by down regulating the synthesis of the virulence (vir) genes required for the processing and transfer of the T-DNA and by inducing the same lactonase, which in turn degrades the AHL. Thus, GABA and SA with different molecular structures, induce the expression of these same genes. The identification of genes whose expression is modulated by conditions that mimic plant infection, as well as the identification of regulatory molecules that help control the early stages of infection, advance our understanding of this complex bacterial-plant interaction and has immediate potential applications to modify it. We expect that the data generated by our research will be used to develop novel strategies for the control of crown gall disease. Moreover, these results will also provide the basis for future biotechnological approaches that will use genetic manipulations to improve bacterial-plant interactions, leading to more efficient DNA transfer to recalcitrant plants and robust symbiosis. These advances will, in turn, contribute to plant protection by introducing genes for resistance against other bacteria, pests and environmental stress.
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Breiman, Adina, Jan Dvorak, Abraham Korol, and Eduard Akhunov. Population Genomics and Association Mapping of Disease Resistance Genes in Israeli Populations of Wild Relatives of Wheat, Triticum dicoccoides and Aegilops speltoides. United States Department of Agriculture, December 2011. http://dx.doi.org/10.32747/2011.7697121.bard.

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Wheat is the most widely grown crop on earth, together with rice it is second to maize in total global tonnage. One of the emerging threats to wheat is stripe (yellow) rust, especially in North Africa, West and Central Asia and North America. The most efficient way to control plant diseases is to introduce disease resistant genes. However, the pathogens can overcome rapidly the effectiveness of these genes when they are wildly used. Therefore, there is a constant need to find new resistance genes to replace the non-effective genes. The resistance gene pool in the cultivated wheat is depleted and there is a need to find new genes in the wild relative of wheat. Wild emmer (Triticum dicoccoides) the progenitor of the cultivated wheat can serve as valuable gene pool for breeding for disease resistance. Transferring of novel genes into elite cultivars is highly facilitated by the availability of information of their chromosomal location. Therefore, our goals in this study was to find stripe rust resistant and susceptible genotypes in Israeli T. dicoccoides population, genotype them using state of the art genotyping methods and to find association between genetic markers and stripe rust resistance. We have screened 129 accessions from our collection of wild emmer wheat for resistance to three isolates of stripe rust. About 30% of the accessions were resistant to one or more isolates, 50% susceptible, and the rest displayed intermediate response. The accessions were genotyped with Illumina'sInfinium assay which consists of 9K single nucleotide polymorphism (SNP) markers. About 13% (1179) of the SNPs were polymorphic in the wild emmer population. Cluster analysis based on SNP diversity has shown that there are two main groups in the wild population. A big cluster probably belongs to the Horanum ssp. and a small cluster of the Judaicum ssp. In order to avoid population structure bias, the Judaicum spp. was removed from the association analysis. In the remaining group of genotypes, linkage disequilibrium (LD) measured along the chromosomes decayed rapidly within one centimorgan. This is the first time when such analysis is conducted on a genome wide level in wild emmer. Such a rapid decay in LD level, quite unexpected for a selfer, was not observed in cultivated wheat collection. It indicates that wild emmer populations are highly suitable for association studies yielding a better resolution than association studies in cultivated wheat or genetic mapping in bi-parental populations. Significant association was found between an SNP marker located in the distal region of chromosome arm 1BL and resistance to one of the isolates. This region is not known in the literature to bear a stripe rust resistance gene. Therefore, there may be a new stripe rust resistance gene in this locus. With the current fast increase of wheat genome sequence data, genome wide association analysis becomes a feasible task and efficient strategy for searching novel genes in wild emmer wheat. In this study, we have shown that the wild emmer gene pool is a valuable source for new stripe rust resistance genes that can protect the cultivated wheat.
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Hulata, Gideon, Thomas D. Kocher, Micha Ron, and Eyal Seroussi. Molecular Mechanisms of Sex Determination in Cultured Tilapias. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7697106.bard.

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Tilapias are among the most important aquaculture commodities worldwide. Commercial production of tilapia is based on monosex culture of males. Current methods for producing all-male fingerlings, including hormone treatments and genetic manipulations, are not entirely reliable, in part because of the genetic complexity of sex determination and sexual differentiation in tilapias. The goals of this project are to map QTL and identify genes regulating sex determination in commonly cultured tilapia species, in order to provide a rational basis for designing reliable genetic approaches for producing all-male fingerlings. The original objectives for this research were: 1) to identify the gene underlying the QTL on LG1 through positional cloning and gene expression analysis; 2) to fine map the QTL on LG 3 and 23; and 3) to characterize the patterns of dominance and epistasis among QTL alleles influencing sex determination. The brain aromatase gene Cyp19b, a possible candidate for the genetic or environmental SD, was mapped to LG7 using our F2 mapping population. This region has not been identified before as affecting SD in tilapias. The QTL affecting SD on LG 1 and 23 have been fine-mapped down to 1 and 4 cM, respectively, but the key regulators for SD have not been found yet. Nevertheless, a very strong association with gender was found on LG23 for marker UNH898. Allele 276 was found almost exclusively in males, and we hypothesized that this allele is a male-associated allele (MAA). Mating of males homozygous for MAA with normal females is underway for production of all-male populations. The first progeny reaching size allowing accurate sexing had 43 males and no females. During the course of the project it became apparent that in order to achieve those objectives there is a need to develop genomic infrastructures that were lacking. Efforts have been devoted to the development of genomic resources: a database consisting of nearly 117k ESTs representing 16 tissues from tilapia were obtained; a web tool based on the RepeatMasker software was designed to assist tilapia genomics; collaboration has been established with a sequencing company to sequence the tilapia genome; steps have been taken toward constructing a microarray to enable comparative analysis of the entire transcriptome that is required in order to detect genes that are differentially expressed between genders in early developmental stages. Genomic resources developed will be invaluable for studies of cichlid physiology, evolution and development, and will hopefully lead to identification of the key regulators of SD. Thus, they will have both scientific and agricultural implications in the coming years.
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Weller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, March 2015. http://dx.doi.org/10.32747/2015.7594404.bard.

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The main objectives of this research was to detect the specific polymorphisms responsible for observed quantitative trait loci and develop optimal strategies for genomic evaluations and selection for moderate (Israel) and large (US) dairy cattle populations. A joint evaluation using all phenotypic, pedigree, and genomic data is the optimal strategy. The specific objectives were: 1) to apply strategies for determination of the causative polymorphisms based on the “a posteriori granddaughter design” (APGD), 2) to develop methods to derive unbiased estimates of gene effects derived from SNP chips analyses, 3) to derive optimal single-stage methods to estimate breeding values of animals based on marker, phenotypic and pedigree data, 4) to extend these methods to multi-trait genetic evaluations and 5) to evaluate the results of long-term genomic selection, as compared to traditional selection. Nearly all of these objectives were met. The major achievements were: The APGD and the modified granddaughter designs were applied to the US Holstein population, and regions harboring segregating quantitative trait loci (QTL) were identified for all economic traits of interest. The APGD was able to find segregating QTL for all the economic traits analyzed, and confidence intervals for QTL location ranged from ~5 to 35 million base pairs. Genomic estimated breeding values (GEBV) for milk production traits in the Israeli Holstein population were computed by the single-step method and compared to results for the two-step method. The single-step method was extended to derive GEBV for multi-parity evaluation. Long-term analysis of genomic selection demonstrated that inclusion of pedigree data from previous generations may result in less accurate GEBV. Major conclusions are: Predictions using single-step genomic best linear unbiased prediction (GBLUP) were the least biased, and that method appears to be the best tool for genomic evaluation of a small population, as it automatically accounts for parental index and allows for inclusion of female genomic information without additional steps. None of the methods applied to the Israeli Holstein population were able to derive GEBV for young bulls that were significantly better than parent averages. Thus we confirm previous studies that the main limiting factor for the accuracy of GEBV is the number of bulls with genotypes and progeny tests. Although 36 of the grandsires included in the APGD were genotyped for the BovineHDBeadChip, which includes 777,000 SNPs, we were not able to determine the causative polymorphism for any of the detected QTL. The number of valid unique markers on the BovineHDBeadChip is not sufficient for a reasonable probability to find the causative polymorphisms. Complete resequencing of the genome of approximately 50 bulls will be required, but this could not be accomplished within the framework of the current project due to funding constraints. Inclusion of pedigree data from older generations in the derivation of GEBV may result is less accurate evaluations.
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Sun, Xiaochen, David Habier, Rohan L. Fernando, Dorian J. Garrick, and Jack C. M. Dekkers. Genomic Prediction and QTL Mapping Using Bayesian Methods. Ames (Iowa): Iowa State University, January 2011. http://dx.doi.org/10.31274/ans_air-180814-959.

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Brown, Martin, and James A. Brown. A Novel Yeast Genomics Method for Identifying New Breast Cancer Susceptibility. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada435292.

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Harvey, Maria E., and Ricardo F. Rosenbusch. Determining Preferred Methods of Detection and Genomic Fingerprinting for Mycoplasma ovipnemoniae. Ames (Iowa): Iowa State University, January 2006. http://dx.doi.org/10.31274/ans_air-180814-934.

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Brown, J. M., and James A. Brown. A Novel Yeast Genomics Method for Identifying New Breast Cancer Susceptibility Genes. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada471490.

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Seroussi, Eyal, and George Liu. Genome-Wide Association Study of Copy Number Variation and QTL for Economic Traits in Holstein Cattle. United States Department of Agriculture, September 2010. http://dx.doi.org/10.32747/2010.7593397.bard.

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Copy number variation (CNV) has been recently identified in human and other mammalian genomes and increasing awareness that CNV might be a major source for heritable variation in complex traits has emerged. Despite this, little has been published on CNVs in Holsteins. In order to fill this knowledge-gap, we proposed a genome-wide association study between quantitative trait loci (QTL) for economic traits and CNV in the Holstein cattle. The approved feasibility study was aimed at the genome-wide characterization of CNVs in Holstein cattle and at the demonstrating of their possible association with economic traits by performing the activities of preparation of DNA samples, Comparative Genomic Hybridization (CGH), initial association study between CNVs and production traits and characterization of CNVSNP associations. For both countries, 40 genomic DNA samples of bulls representing the extreme sub-populations for economically important traits were CGH analyzed using the same reference genome on a NimbleGen tiling array. We designed this array based on the latest build of the bovine genome (UMD3) with average probe spacing of 1150 bases (total number of probes was 2,166,672). Two CNV gene clusters, PLA2G2D on BTA2 and KIAA1683 on BTA7 revealed significant association with milk percentage and cow fertility, respectively, and were chosen for further characterization and verification in a larger sample using other methodologies including sequencing, tag SNPs and real time PCR (qPCR). Comparison between these four methods indicated that there is under estimation of the number of CNV loci in Holstein cattle and their complexity. The variation in sequence between different copies seemed to affect their functionality and thus the hybridization based methods were less informative than the methods that are based on sequencing. We thus conclude that large scale sequencing effort complemented by array CGH should be considered to better detect and characterize CNVs in order to effectively employ them in marker-assisted selection.
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Vuyisich, Momchilo. Resequencing and assembly of bacterial genomes using new NEBNext library prep methods. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1055317.

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