Relevant bibliographies by topics / Genome-wide analysis

Contents

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

Academic literature on the topic 'Genome-wide analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Genome-wide analysis"

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Wu, Weihuai, Kexian Yi, Xing Huang, Thomas Gbokie Jr, and Baohui Liu. "Genome-wide analysis of defensin-like genes in Coffea arabica." SDRP Journal of Plant Science 3, no. 1 (2019): 1–6. http://dx.doi.org/10.25177/jps.3.1.ra.499.

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Weerasekara, Vajira Samanthi. "Genome-wide haplotype analysis." Sri Lanka Journal of Bio-Medical Informatics 3, no. 1 (January 8, 2013): 20. http://dx.doi.org/10.4038/sljbmi.v3i1.2564.

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Zhang, Jianzhi. "Epistasis Analysis Goes Genome-Wide." PLOS Genetics 13, no. 2 (February 16, 2017): e1006558. http://dx.doi.org/10.1371/journal.pgen.1006558.

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Krapohl, E., J. Euesden, D. Zabaneh, J.-B. Pingault, K. Rimfeld, S. von Stumm, P. S. Dale, G. Breen, P. F. O'Reilly, and R. Plomin. "Phenome-wide analysis of genome-wide polygenic scores." Molecular Psychiatry 21, no. 9 (August 25, 2015): 1188–93. http://dx.doi.org/10.1038/mp.2015.126.

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Lee, Young Ho, and Gwan Gyu Song. "Genome-wide pathway analysis of a genome-wide association study on Alzheimer’s disease." Neurological Sciences 36, no. 1 (July 19, 2014): 53–59. http://dx.doi.org/10.1007/s10072-014-1885-3.

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Song, Gwan Gyu, Sung Jae Choi, Jong Dae Ji, and Young Ho Lee. "Genome-wide pathway analysis of a genome-wide association study on multiple sclerosis." Molecular Biology Reports 40, no. 3 (December 14, 2012): 2557–64. http://dx.doi.org/10.1007/s11033-012-2341-1.

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Zhu, Lei, Yanman Li, Jintao Li, Yong Wang, Zhenli Zhang, Yanjiao Wang, Zanlin Wang, Jianbin Hu, Luming Yang, and Shouru Sun. "Genome-wide identification and analysis of the MLO gene families in three Cucurbita species." Czech Journal of Genetics and Plant Breeding 57, No. 3 (July 14, 2021): 119–23. http://dx.doi.org/10.17221/99/2020-cjgpb.

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Powdery mildew (PM) is a major fungal disease in the Cucurbita species in the world, which can cause significant yield loss. The Mildew Locus O (MLO) family genes play important roles in the PM stress response. In this paper, twenty, twenty-one, and eighteen candidate MLO genes in Cucurbita moschata, Cucurbita maxima and Cucurbita pepo, respectively, were identified and designated as CmoMLO, CmaMLO and CpeMLO, respectively. The phylogenetic analysis indicated that these MLOs were divided into five clades and the number of MLOs belonging to clade V in the Cucurbita species was more than that in other crops. Furthermore, the expression analysis in the susceptibility (S) and resistance (R) lines showed that CpeMLO1, CpeMLO2 and CpeMLO5 might be involved in the susceptibility response. CpeMLO4 and CpeMLO6 showing opposite expression patterns in the R/S lines might be involved in the resistance response. All these data would be beneficial for future functional analysis of MLOs in the Cucurbita species.
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Anusha.B.N, Anusha B. N., Shambu M. G. Shambu.M.G, and Kusum Paul. "Genome Wide Transcriptional Analysis of Gene Expression Signatures and Pathways on Neoplastic Pancreatic Cancer." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 43–44. http://dx.doi.org/10.15373/22778179/aug2013/15.

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Tang, Lin. "Genome-wide analysis of structural variation." Nature Methods 18, no. 5 (May 2021): 448. http://dx.doi.org/10.1038/s41592-021-01161-z.

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Sonoyama, M. "Genome-wide analysis of membrane proteins." Seibutsu Butsuri 41, supplement (2001): S9. http://dx.doi.org/10.2142/biophys.41.s9_1.

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Dissertations / Theses on the topic "Genome-wide analysis"

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Jordan, Barbara M. (Barbara Marie) 1975. "Genome complexity reduction for genome-wide single nucleotide polymorphism analysis." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8319.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2002.
Vita.
Includes bibliographical references.
Millions of single nucleotide polymorphisms (SNPs) have been identified in the human genome, and more are cataloged every day. The challenge now is to use these SNPs to discover the genetic risk factors underlying common and complex diseases. Efficient, large-scale genotyping methods are one necessary component of this endeavor. Current SNP genotyping techniques all rely on an initial PCR amplification of each SNP locus. Individual or low-level multiplexed PCR reactions are sufficient for genotyping a few to a few hundred different SNPs, but genome-wide linkage and association studies in humans will require thousands to tens of thousands of different SNPs, each typed on a few thousand individuals. To efficiently reach this goal, PCR techniques capable of amplifying a few hundred loci per reaction are needed. To meet this need we investigated the use of PCR-based genome complexity reduction methods for SNP genotyping. We discovered that degenerate oligonucleotide primed PCR (DOP-PCR) is capable of amplifying a specific fraction of a genome in a highly reproducible manner. The genomic sequences amplified are determined by the oligonucleotide primer's nondegenerate, 8-12 nucleotide, 3' end sequence. The amplified complexity can be varied from one to over 10,000 loci by changing the DOP-PCR primer's length and specific sequence. We collected SNPs from a human DOP-PCR that amplifies roughly 600 loci, and demonstrated that about half of the SNPs tested could be genotyped directly from the DOP-PCR product mixture, using the allele specific oligonucleotide hybridization genotyping technique.
(cont.) We investigated using the human genome sequence to electronically predict, based on DOP-PCR primer 3' end sequence, the products of DOP-PCRs. We successfully demonstrated that approximately 80% of such predicted products were in fact amplified in DOP-PCRs done with human genomic DNA. Electronic prediction of DOP-PCR products, and the SNPs contained in them from SNP databases, could provide a method to compile a set of DOP-PCRs that amplify tens of thousands of SNP loci for genome-wide scans. We also tested SNP genotyping from a mouse DOP-PCR amplifying about 200 loci, and from several Arabidopsis thaliana DOP-PCRs that amplify about 100 loci each. Half of the SNPs collected in these DOP-PCRs were also amenable to genotyping, directly from the DOP-PCR product mixtures. We identified SNPs in these DOP-PCRs by resequencing, but as more species' genomes are sequenced and more SNPs are contributed to public databases, DOP-PCR will become easier to implement in these and other model organisms. Currently, we are developing a genome-wide set of SNPs amplified in 32 DOP-PCRs for the mouse.
by Barbara M. Jordan.
Ph.D.
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Simonson, Matthew A. "Polygenic analysis of genome-wide SNP data." Thesis, University of Colorado at Boulder, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3562047.

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One of the central motivators behind genetic research is to understand how genetic variation relates to human health and disease. Recently, there has been a large-scale effort to find common genetic variants associated with many forms of disease and disorder using single nucleotide polymorphisms (SNPs). Several genome-wide association (GWAS) studies have successfully identified SNPs associated with phenotypes. However, the effect sizes attributed to individual variants is generally small, explaining only a very small amount of the genetic risk and heritability expected based on the estimates of family and twin studies. Several explanations exist for the inability of GWAS to find the "missing heritability."

The results of recent research appear to confirm the prediction made by population genetics theory that most complex phenotypes are highly polygenic, occasionally influenced by a few alleles of relatively large effect, and usually by several of small effect. Studies have also confirmed that common variants are only part of what contributes to the total genetic variance for most traits, indicating rare-variants may play a significant role.

This research addresses some of the most glaring weaknesses of the traditional GWAS approach through the application of methods of polygenic analysis. We apply several methods, including those that investigate the net effects of large sets of SNPs, more sophisticated approaches informed by biology rather than the purely statistical approach of GWAS, as well as methods that infer the effects of recessive rare variants.

Our results indicate that traditional GWAS is well complemented and improved upon by methods of polygenic analysis. We demonstrate that polygenic approaches can be used to significantly predict individual risk for disease, provide an unbiased estimate of a substantial proportion of the heritability for multiple phenotypes, identify sets of genes grouped into biological pathways that are enriched for associations, and finally, detect the significant influence of recessive rare variants.

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Kung, Johnny Tsun-Yi. "Genome-wide Analysis of Ctcf-RNA Interactions." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11618.

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Ctcf is a "master regulator" of the genome that plays a role in a variety of gene regulatory functions as well as in genome architecture. Evidence from studying the epigenetic process of X-chromosome inactivation suggests that, in certain cases, Ctcf might carry out its functions through interacting with RNA. Using mouse embryonic stem (ES) cells and a modified protocol for UV-crosslinking and immunoprecipitation followed by high-throughput sequencing (CLIP-seq), Ctcf is found to interact with a multitude of transcripts genome-wide, both protein-coding mRNA (or noncoding transcripts therein) as well as many long-noncoding RNA (lncRNA). Examples of the latter include both well-characterized species from imprinted loci and previously unannotated transcripts from intergenic space. RNA binding targets of Ctcf are validated by a variety of biochemical methods, and Ctcf is found to interact with RNA through its C-terminal domain, distinct from its DNA-binding zinc-finger domain. Ctcf chromatin immunoprecipitation (ChIP)-seq done in parallel reveals distinct but correlated binding of Ctcf to DNA and RNA. In addition, allelic analysis of Ctcf ChIP pattern reveals significant differences between Ctcf binding to the presumptive inactive and active X chromosomes. Together, the current work reveals a further layer of complexity to Ctcf biology by implicating a role for Ctcf-RNA interactions in its recruitment to genomic binding sites.
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Chen, Stacy Yen-chun. "Genome-wide analysis of yeast meiotic recombination landscape." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3390037.

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Barrera, Leah Ortiz-Luis. "Genome-wide mapping and analysis of mammalian promoters." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3258393.

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Thesis (Ph. D.)--University of California, San Diego, 2007.
Title from first page of PDF file (viewed June 1, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 151-169).
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Barrett, Jeffrey C. "Design and analysis of genome-wide association studies." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:45790b5c-e50c-406a-bb3c-a96868b11a28.

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Despite many years of effort, linkage and candidate gene association studies have yielded disappointingly few risk loci for common human diseases such as diabetes, auto-immune disorders and cancers. Large sample sizes, increased understanding of the patterns of correlation in genetic variation, and plunging genotyping costs have enabled genome-wide association studies, which have good power to detect common risk alleles of modest effect. I present an evaluation of SNP choice in study design and show that overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first generation platforms all offer good levels of genome coverage (∼ 70%). I next describe the largest such project undertaken to date, the Wellcome Trust Case Control Consortium, which consisted of 2000 cases from each of seven common diseases and 3000 shared controls. It identified nearly two dozen new associations. I demonstrate the importance of careful data quality control, including both standard and unorthodox analyses. I next focus on the association results therein for Crohn’s disease. I present a replication experiment in over 1000 additional Crohn’s patients which unambiguously confirmed six previously published loci and four new loci. Next I describe, in a general context, several issues impeding the combination of genome-wide scans, including data annotation, population structure and differences in genotyping platform. Each of these problems is shown to be tractable with available methods, provided that these methods are applied prudently. I present the results of a meta-analysis of three genome-wide scans for Crohn’s disease. The data showed a striking excess of significant associations, and a replication experiment involving over 4000 independent Crohn’s patients verified twenty new risk loci. Finally, I discuss the early success of genome-wide association and its consequences for further understanding the biology of human disease.
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Nilsson, Emil. "Genome wide methylation analysis and obesity related traits." Doctoral thesis, Uppsala universitet, Institutionen för neurovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-248685.

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The most studied form of epigenetics is DNA methylation and several studies have investigated the link between the methylome and body weight. In paper I we analyzed the methylation profile of whole blood in 46 subjects measured with Illumina 27K chip. We provide evidence that obesity influences age driven epigenetic changes. These identified markers may prove to be valuable biomarkers for the understanding of the molecular basis of aging, obesity and associated diseases. In paper II we studied the effect of bariatric surgery, and subsequent weight loss, on methylation and relating this to normal weight controls. In paper II we found 115 promoters had altered methylation after surgery. Among these promoters, an enrichment for genes involved in metabolic processes was found (n=36, p<0.05). In addition, these 51 promoters was more similar after surgery to that of normal-weight controls, than it had been at baseline (p<0.0001). One of the major comorbidities of severe obesity is obstructive sleep apnea and lack of sleep is highly correlated with obesity. Paper III shows how acute sleep deprivation increases portion size and affects food choice in 16 young men. In paper VI, whole genome DNA methylation profiles of whole blood was assessed following both conditions by the Illumina 450K methylation in the same trial as in paper III. This paper shows how sleep deprivation affects DNA methylation profiles of whole blood in a manner both dependent and independent on monocyte subpopulations. Hypothesis free genome wide analysis revealed differential methylation in ING5, a gene previously known to be differentially expressed in sleep deprivation.
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Lin, Yu-fei. "Genome-wide analysis of Propionibacterium acnes gene regulation." Thesis, University of Leeds, 2013. http://etheses.whiterose.ac.uk/15231/.

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Sequencing of the genome of Propionibacterium acnes produced a catalogue of genes many of which enable this organism to colonise sites in human skin and survive a range of environmental challenges. However as yet, there is little understanding of the relationships and interactions between genes that give rise to an organism, which has major impact on human health and wellbeing as an opportunistic pathogen that causes infections beyond the skin. To provide a platform for better understanding gene regulation in P. acnes, this thesis shows using microarrays, reproducible genetic responses to external changes relevant to the skin environment in P. acnes can be studied using batch cultures. It then goes on to describe the generation of nucleotide-resolution maps of the primary and secondary transcriptome. The maps were produced by combining differential and global RNA sequencing approaches. Sites of transcriptional initiation, stable RNA processing and mRNA cleavage as well as riboswitches, small non-coding RNAs, vegetative promoters, and previously undetected genes were identified across the genome. In addition, evidence was obtained for the widespread use of leaderless mRNAs, which may be translated by specialised ribosomes. Preliminary evidence for the existence of the latter, in the form of particular ribosomal RNA processing, was obtained. The study also provided statistically robust evidence for pervasive transcription that is associated with both the sense and antisense strands of coding regions. Continuing annotation of the primary and secondary transcriptomes of pathogens will assist comparative and functional genomics approaches and may also aid the modelling of the disease process and therapeutic development.
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Yazdani, Akram. "Statistical Approaches in Genome-Wide Association Studies." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423743.

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Genome-wide association studies, GWAS, typically contain hundreds of thousands single nucleotide polymorphisms, SNPs, genotyped for few numbers of samples. The aim of these studies is to identify regions harboring SNPs or to predict the outcomes of interest. Since the number of predictors in the GWAS far exceeds the number of samples, it is impossible to analyze the data with classical statistical methods. In the current GWAS, the widely applied methods are based on single marker analysis that does assess association of each SNP with the complex traits independently. Because of the low power of this analysis for detecting true association, simultaneous analysis has recently received more attention. The new statistical methods for simultaneous analysis in high dimensional settings have a limitation of disparity between the number of predictors and the number of samples. Therefore, reducing the dimensionality of the set of SNPs is required. This thesis reviews single marker analysis and simultaneous analysis with a focus on Bayesian methods. It addresses the weaknesses of these approaches with reference to recent literature and illustrating simulation studies. To bypass these problems, we first attempt to reduce dimension of the set of SNPs with random projection technique. Since this method does not improve the predictive performance of the model, we present a new two-stage approach that is a hybrid method of single and simultaneous analyses. This full Bayesian approach selects the most promising SNPs in the first stage by evaluating the impact of each marker independently. In the second stage, we develop a hierarchical Bayesian model to analyze the impact of selected markers simultaneously. The model that accounts for related samples places the local-global shrinkage prior on marker effects in order to shrink small effects to zero while keeping large effects relatively large. The prior specification on marker effects, which is hierarchical representation of generalized double Pareto, improves the predictive performance. Finally, we represent the result of real SNP-data analysis through single-maker study and the new two-stage approach.
Lo Studio di Associazione Genome-Wide, GWAS, tipicamente comprende centinaia di migliaia di polimorfismi a singolo nucleotide, SNPs, genotipizzati per pochi campioni. L'obiettivo di tale studio consiste nell'individuare le regioni cruciali SNPs e prevedere gli esiti di una variabile risposta. Dal momento che il numero di predittori è di gran lunga superiore al numero di campioni, non è possibile condurre l'analisi dei dati con metodi statistici classici. GWAS attuali, i metodi negli maggiormente utilizzati si basano sull'analisi a marcatore unico, che valuta indipendentemente l'associazione di ogni SNP con i tratti complessi. A causa della bassa potenza dell'analisi a marcatore unico nel rilevamento delle associazioni reali, l'analisi simultanea ha recentemente ottenuto più attenzione. I recenti metodi per l'analisi simultanea nel multidimensionale hanno una limitazione sulla disparità tra il numero di predittori e il numero di campioni. Pertanto, è necessario ridurre la dimensionalità dell'insieme di SNPs. Questa tesi fornisce una panoramica dell'analisi a marcatore singolo e dell'analisi simultanea, focalizzandosi su metodi Bayesiani. Vengono discussi i limiti di tali approcci in relazione ai GWAS, con riferimento alla letteratura recente e utilizzando studi di simulazione. Per superare tali problemi, si è cercato di ridurre la dimensione dell'insieme di SNPs con una tecnica a proiezione casuale. Poiché questo approccio non comporta miglioramenti nella accuratezza predittiva del modello, viene quindi proposto un approccio in due fasi, che risulta essere un metodo ibrido di analisi singola e simultanea. Tale approccio, completamente Bayesiano, seleziona gli SNPs più promettenti nella prima fase valutando l'impatto di ogni marcatore indipendentemente. Nella seconda fase, viene sviluppato un modello gerarchico Bayesiano per analizzare contemporaneamente l'impatto degli indicatori selezionati. Il modello che considera i campioni correlati pone una priori locale-globale ristretta sugli effetti dei marcatori. Tale prior riduce a zero gli effetti piccoli, mentre mantiene gli effetti più grandi relativamente grandi. Le priori specificate sugli effetti dei marcatori sono rappresentazioni gerarchiche della distribuzione Pareto doppia; queste a priori migliorano le prestazioni predittive del modello. Infine, nella tesi vengono riportati i risultati dell'analisi su dati reali di SNP basate sullo studio a marcatore singolo e sul nuovo approccio a due stadi.
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Schleiermacher, Chris. "Algorithmic support for PCR and genome wide repeat analysis." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963799495.

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Books on the topic "Genome-wide analysis"

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Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9443-0.

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Muley, Vijaykumar Yogesh, and Vishal Acharya. Genome-Wide Prediction and Analysis of Protein-Protein Functional Linkages in Bacteria. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4705-4.

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Bioinformatics: The impact of accurate quantification on proteomic and genetic analysis and research. Toronto: Apple Academic Press, 2014.

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Kung, Johnny Tsun-Yi. Genome-wide Analysis of Ctcf-RNA Interactions. 2014.

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Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer, 2016.

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Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer, 2013.

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Ma, Jun. Genome-wide analysis of human peripheral leukocyte gene expression. 2003.

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Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer London, Limited, 2013.

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Stram, Daniel O. Design, Analysis, and Interpretation of Genome-Wide Association Scans. Springer, 2013.

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Yang, Sheng, Shiquan Sun, Xiang Zhou, and Yang Zhao, eds. Integrative Analysis of Genome-Wide Association Studies and Single-Cell Sequencing Studies. Frontier Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-467-4.

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Book chapters on the topic "Genome-wide analysis"

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Unterberger, Alexander, Adrian M. Dubuc, and Michael D. Taylor. "Genome-Wide Methylation Analysis." In Methods in Molecular Biology, 303–17. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-612-8_19.

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Lysenko, Artem, Keith A. Boroevich, and Tatsuhiko Tsunoda. "Genotyping and Statistical Analysis." In Genome-Wide Association Studies, 1–20. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8177-5_1.

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Zheng, Gang, Yaning Yang, Xiaofeng Zhu, and Robert C. Elston. "Genome-Wide Association Studies." In Analysis of Genetic Association Studies, 337–49. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2245-7_12.

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Sutphin, George L., Brady A. Olsen, Brian K. Kennedy, and Matt Kaeberlein. "Genome-Wide Analysis of Yeast Aging." In Aging Research in Yeast, 251–89. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2561-4_12.

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Larsson, Ola, and Peter B. Bitterman. "Genome-Wide Analysis of Translational Control." In mTOR Pathway and mTOR Inhibitors in Cancer Therapy, 217–36. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-271-1_11.

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Pelizzola, Mattia, and Annette Molinaro. "Methylated DNA Immunoprecipitation Genome-Wide Analysis." In Methods in Molecular Biology, 113–23. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-316-5_9.

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McRae, Allan F. "Analysis of Genome-Wide Association Data." In Methods in Molecular Biology, 161–73. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6613-4_9.

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Isidro-Sánchez, Julio, Deniz Akdemir, and Gracia Montilla-Bascón. "Genome-Wide Association Analysis Using R." In Methods in Molecular Biology, 189–207. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6682-0_14.

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Guruceaga, Elizabeth, Victor Segura, Fernando J. Corrales, and Angel Rubio. "Genome-Wide Proximal Promoter Analysis and Interpretation." In Methods in Molecular Biology, 157–74. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-194-3_8.

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Kim, Kyu-Tae, and Woong-Yang Park. "Genome-Wide Analysis of THz-Bio Interaction." In Convergence of Terahertz Sciences in Biomedical Systems, 257–79. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3965-9_15.

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Conference papers on the topic "Genome-wide analysis"

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DUBCHAK, INNA, LIOR PACHTER, and LIPING WEI. "GENOME-WIDE ANALYSIS AND COMPARATIVE GENOMICS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2001. http://dx.doi.org/10.1142/9789812799623_0011.

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Wang, Zhaoxi, Yang Zhao, Lin Li, Li Su, Xihong Lin, Mark M. Wurfel, and David C. Christiani. "Genome-Wide Association Analysis Of Sepsis." In American Thoracic Society 2012 International Conference, May 18-23, 2012 • San Francisco, California. American Thoracic Society, 2012. http://dx.doi.org/10.1164/ajrccm-conference.2012.185.1_meetingabstracts.a6817.

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Xuan Si, Quhuan Li, Jianwei Wang, and Zhenyang Li. "A platform for genome-wide analysis." In 2011 International Symposium on Information Technology in Medicine and Education (ITME 2011). IEEE, 2011. http://dx.doi.org/10.1109/itime.2011.6130917.

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Chen, Kuanchung, and Yuh-Jyh Hu. "Bicluster Analysis of Genome-Wide Gene Expression." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330994.

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ESKIN, ELEAZAR, URI KEICH, MIKHAIL S. GELFAND, and PAVEL A. PEVZNER. "GENOME-WIDE ANALYSIS OF BACTERIAL PROMOTER REGIONS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776303_0004.

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Liu, Yang, Jin Zhou, Zhiping Liu, Luonan Chen, and Michael K. Ng. "Construction and analysis of genome-wide SNP networks." In 2012 IEEE 6th International Conference on Systems Biology (ISB). IEEE, 2012. http://dx.doi.org/10.1109/isb.2012.6314158.

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Bhattacharya, Soumyaroop, Jody L. Gascon, Heidi L. Huyck, Sorachai Srisuma, Leon A. Metlay, Sivakumar Solleti, Thomas J. Mariani, and Gloria S. Pryhuber. "Genome-Wide Expression Analysis Of Human Bronchopulmonary Dysplasia." In American Thoracic Society 2010 International Conference, May 14-19, 2010 • New Orleans. American Thoracic Society, 2010. http://dx.doi.org/10.1164/ajrccm-conference.2010.181.1_meetingabstracts.a1859.

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Huo, Jinlong, Wenmin Chen, Xiaowei Wu, Kuan Yang, Weirong Pan, Liqing Zhang, and Yangzhi Zeng. "Analysis of whole genome sequence and genome-wide SNPs in highly inbred pigs." In 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2017. http://dx.doi.org/10.1109/iccabs.2017.8114286.

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Beretta, Stefano, Lucia Morganti, Elena Corni, Andrea Ferraro, Daniele Cesini, Daniele D'Agostino, Luciano Milanesi, and Ivan Merelli. "Low-Power Architectures for miRNA-Target Genome Wide Analysis." In 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE, 2017. http://dx.doi.org/10.1109/pdp.2017.88.

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Eppstein, Margaret J., and Paul Haake. "Very large scale ReliefF for genome-wide association analysis." In 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2008). IEEE, 2008. http://dx.doi.org/10.1109/cibcb.2008.4675767.

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Reports on the topic "Genome-wide analysis"

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Baumbach, Lisa B. Genome Wide Expression Analysis of Breast Cancer in African Americans. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada430386.

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Qian, Shu-Bing. Genome-Wide Analysis of Translational Control in Tuberous Sclerosis Complex. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada576362.

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Qian, Shu-Bing. Genome-Wide Analysis of Translational Control in Tuberous Sclerosis Complex. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada584195.

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Xu, Jianfeng, Siqun L. Zheng, Bao-Li Chang, Wennuan Liu, and Jielin Sun. Genome-Wide Analysis of Germline Cnps and Snps in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2008. http://dx.doi.org/10.21236/ada492476.

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Xu, Jianfeng. Genome-wide Analysis of Germline CNPs and SNPs in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada534038.

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Xu, Jianfeng, Siqun L. Zheng, Wennuan Liu, and Jielin Sun. Genome-Wide Analysis of Germline CNPs and SNPs in Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada505051.

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Ahn, Jiyoung. Integrative Analysis of Genome-wide Gene Expression for Prostate Cancer Prognosis. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada552228.

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Green, Pamela J. Genome-Wide Analysis of miRNA targets in Brachypodium and Biomass Energy Crops. Office of Scientific and Technical Information (OSTI), August 2015. http://dx.doi.org/10.2172/1209217.

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Elbeltagy, Ahmed R., Eui-Soo Kim, Barbara Rischkowsky, Adel M. Aboul-naga, Joram M. Mwacharo, and Max F. Rothschild. Genome-wide Analysis of Small Ruminant Tolerance to Grazing Stress Under Arid Desert. Ames (Iowa): Iowa State University, January 2016. http://dx.doi.org/10.31274/ans_air-180814-236.

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Mitchell, S. C., D. Bocskai, and Y. Cao. Construction of genome-wide physical BAC contigs using mapped cDNA as probes: Toward an integrated BAC library resource for genome sequencing and analysis. Annual report, July 1995--January 1997. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/639708.

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