TAR cloning: insights into gene function, long-range haplotypes and genome structure and evolution

2006 ◽  
Vol 7 (10) ◽  
pp. 805-812 ◽  
Author(s):  
Natalay Kouprina ◽  
Vladimir Larionov
Keyword(s):  
Long Range ◽  
Gene Function ◽  
Genome Structure ◽  
Tar Cloning

scholarly journals An Empirical Bayes approach for the identification of long-range chromosomal interaction from Hi-C data

2021 ◽  
Vol 20 (1) ◽  
pp. 1-15
Author(s):  
Qi Zhang ◽  
Zheng Xu ◽  
Yutong Lai
Keyword(s):  
Long Range ◽  
Latent Variables ◽  
Empirical Bayes ◽  
Genome Structure ◽  
Peak Detection ◽  
Data Matrix ◽  
Dispersion Modeling ◽  
Empirical Bayes Approach ◽  
Over Dispersion ◽  
Bayes Approach

Abstract Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the “true” interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a Smoothed Expectation Maximization algorithm, and the empirical null based on the zero assumption. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can identify better peaks in terms of accuracy, biological interpretability, and the consistency across biological replicates. The source code is available on Github (https://github.com/QiZhangStat/EBHiC).

scholarly journals Long range genome structure around the human α-globin complex analysed by PFGE

1987 ◽  
Vol 15 (15) ◽  
pp. 6197-6207 ◽  
Author(s):  
N. Fischel-Ghodsian ◽  
R.D. Nicholls ◽  
D.R. Higgs
Keyword(s):  
Long Range ◽  
Genome Structure

Geminiviruses: Genome Structure and Gene Function

1992 ◽  
Vol 11 (4) ◽  
pp. 327-327 ◽  
Author(s):  
S. G. Lazarowitz
Keyword(s):  
Gene Function ◽  
Genome Structure

Geminiviruses: Genome structure and gene function

1992 ◽  
Vol 11 (4) ◽  
pp. 327-349 ◽  
Author(s):  
Sondra G. Lazarowitz ◽  
R. J. Shepherd
Keyword(s):  
Gene Function ◽  
Genome Structure

scholarly journals An Empirical Bayes Approach for the Identification of Long-range Chromosomal Interaction from Hi-C Data

2018 ◽  
Author(s):  
Qi Zhang ◽  
Zheng Xu ◽  
Yutong Lai
Keyword(s):  
Long Range ◽  
Latent Variables ◽  
Empirical Bayes ◽  
Genome Structure ◽  
Peak Detection ◽  
Data Matrix ◽  
Dispersion Modeling ◽  
Empirical Bayes Approach ◽  
Over Dispersion ◽  
Bayes Approach

Hi-C experiments have become very popular for studying the 3D genome structure in recent years. Identification of long-range chromosomal interaction, i.e., peak detection, is crucial for Hi-C data analysis. But it remains a challenging task due to the inherent high dimensionality, sparsity and the over-dispersion of the Hi-C count data matrix. We propose EBHiC, an empirical Bayes approach for peak detection from Hi-C data. The proposed framework provides flexible over-dispersion modeling by explicitly including the 'true' interaction intensities as latent variables. To implement the proposed peak identification method (via the empirical Bayes test), we estimate the overall distributions of the observed counts semiparametrically using a smoothed EM algorithm, and the empirical null by discrete curve fitting. We conducted extensive simulations to validate and evaluate the performance of our proposed approach and applied it to real datasets. Our results suggest that EBHiC can better identify peaks than Fit-Hi-C in terms of accuracy, biological interpretability, and the consistency across biological replicates.

scholarly journals A first genetic map of date palm (Phoenix dactylifera) reveals long-range genome structure conservation in the palms

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 285 ◽  
Author(s):  
Lisa S Mathew ◽  
Manuel Spannagl ◽  
Ameena Al-Malki ◽  
Binu George ◽  
Maria F Torres ◽  
...  
Keyword(s):  
Long Range ◽  
Genetic Map ◽  
Date Palm ◽  
Genome Structure ◽  
Phoenix Dactylifera ◽  
Structure Conservation
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