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You searched for +publisher:"Rutgers University" +contributor:("Xing, Jinchuan"). Showing records 1 – 6 of 6 total matches.

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Rutgers University

1. Wang, Nan, 1988-. Exome sequencing to identify the genetic bases for lysosomal storage diseases of unknown etiology.

Degree: MS, Microbiology and Molecular Genetics, 2014, Rutgers University

 Lysosomes are membrane-bound, acidic eukaryotic cellular organelles. As an enzyme container, they play important roles in the degradation of macromolecules. Monogenic mutations resulting in the… (more)

Subjects/Keywords: Lysosomal storage diseases; Genomes – Analysis

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APA (6th Edition):

Wang, Nan, 1. (2014). Exome sequencing to identify the genetic bases for lysosomal storage diseases of unknown etiology. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/45559/

Chicago Manual of Style (16th Edition):

Wang, Nan, 1988-. “Exome sequencing to identify the genetic bases for lysosomal storage diseases of unknown etiology.” 2014. Masters Thesis, Rutgers University. Accessed November 30, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/45559/.

MLA Handbook (7th Edition):

Wang, Nan, 1988-. “Exome sequencing to identify the genetic bases for lysosomal storage diseases of unknown etiology.” 2014. Web. 30 Nov 2020.

Vancouver:

Wang, Nan 1. Exome sequencing to identify the genetic bases for lysosomal storage diseases of unknown etiology. [Internet] [Masters thesis]. Rutgers University; 2014. [cited 2020 Nov 30]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45559/.

Council of Science Editors:

Wang, Nan 1. Exome sequencing to identify the genetic bases for lysosomal storage diseases of unknown etiology. [Masters Thesis]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45559/


Rutgers University

2. Diao, Liyang, 1986-. Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery.

Degree: PhD, Computational Biology and Molecular Biophysics, 2014, Rutgers University

If sheer number of papers published is indicative of anything, it suggests that the age of genome-wide association studies, or GWAS, is here to stay.… (more)

Subjects/Keywords: Linear models (Statistics); Genomes – Analysis

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APA (6th Edition):

Diao, Liyang, 1. (2014). Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/45229/

Chicago Manual of Style (16th Edition):

Diao, Liyang, 1986-. “Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery.” 2014. Doctoral Dissertation, Rutgers University. Accessed November 30, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/45229/.

MLA Handbook (7th Edition):

Diao, Liyang, 1986-. “Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery.” 2014. Web. 30 Nov 2020.

Vancouver:

Diao, Liyang 1. Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery. [Internet] [Doctoral dissertation]. Rutgers University; 2014. [cited 2020 Nov 30]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45229/.

Council of Science Editors:

Diao, Liyang 1. Applications of the mixed linear model in genome-wide association studies and small RNA motif discovery. [Doctoral Dissertation]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45229/


Rutgers University

3. Zhou, Lisheng, 1989-. A statistical method for genotypic association that is robust to sequencing misclassification.

Degree: PhD, Microbiology and Molecular Genetics, 2017, Rutgers University

In analyzing human genetic disorders, association analysis is one of the most commonly used approaches. However, there are challenges with association analysis, including differential misclassification… (more)

Subjects/Keywords: Genomics – Data processing; Computational biology

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APA (6th Edition):

Zhou, Lisheng, 1. (2017). A statistical method for genotypic association that is robust to sequencing misclassification. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/54057/

Chicago Manual of Style (16th Edition):

Zhou, Lisheng, 1989-. “A statistical method for genotypic association that is robust to sequencing misclassification.” 2017. Doctoral Dissertation, Rutgers University. Accessed November 30, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/54057/.

MLA Handbook (7th Edition):

Zhou, Lisheng, 1989-. “A statistical method for genotypic association that is robust to sequencing misclassification.” 2017. Web. 30 Nov 2020.

Vancouver:

Zhou, Lisheng 1. A statistical method for genotypic association that is robust to sequencing misclassification. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2020 Nov 30]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54057/.

Council of Science Editors:

Zhou, Lisheng 1. A statistical method for genotypic association that is robust to sequencing misclassification. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54057/


Rutgers University

4. Shanku, Alexander G., 1979-. Insights Into evolution and adaptation using computational methods and next generation sequencing.

Degree: PhD, Computational Biology and Molecular Biophysics, 2016, Rutgers University

 Historically, much of the research in evolutionary biology and population genetics has involved analysis at the level of either a single locus or a few… (more)

Subjects/Keywords: Evolution (Biology) – Mathematical models; Genomes – Analysis

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APA (6th Edition):

Shanku, Alexander G., 1. (2016). Insights Into evolution and adaptation using computational methods and next generation sequencing. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/50176/

Chicago Manual of Style (16th Edition):

Shanku, Alexander G., 1979-. “Insights Into evolution and adaptation using computational methods and next generation sequencing.” 2016. Doctoral Dissertation, Rutgers University. Accessed November 30, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/50176/.

MLA Handbook (7th Edition):

Shanku, Alexander G., 1979-. “Insights Into evolution and adaptation using computational methods and next generation sequencing.” 2016. Web. 30 Nov 2020.

Vancouver:

Shanku, Alexander G. 1. Insights Into evolution and adaptation using computational methods and next generation sequencing. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2020 Nov 30]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/50176/.

Council of Science Editors:

Shanku, Alexander G. 1. Insights Into evolution and adaptation using computational methods and next generation sequencing. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/50176/


Rutgers University

5. Lin, Timothy, 1986-. Developing a nanopore sequencing data processing pipeline for structure variation identification.

Degree: MS, Microbiology and Molecular Genetics, 2019, Rutgers University

 Many genomic sequencing technologies have been developed since the Human Genome Project. These next-generation sequencing (NGS) technologies from various companies reshaped the genomics field and… (more)

Subjects/Keywords: Nanopores; Nucleotide sequence

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APA (6th Edition):

Lin, Timothy, 1. (2019). Developing a nanopore sequencing data processing pipeline for structure variation identification. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61799/

Chicago Manual of Style (16th Edition):

Lin, Timothy, 1986-. “Developing a nanopore sequencing data processing pipeline for structure variation identification.” 2019. Masters Thesis, Rutgers University. Accessed November 30, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/61799/.

MLA Handbook (7th Edition):

Lin, Timothy, 1986-. “Developing a nanopore sequencing data processing pipeline for structure variation identification.” 2019. Web. 30 Nov 2020.

Vancouver:

Lin, Timothy 1. Developing a nanopore sequencing data processing pipeline for structure variation identification. [Internet] [Masters thesis]. Rutgers University; 2019. [cited 2020 Nov 30]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61799/.

Council of Science Editors:

Lin, Timothy 1. Developing a nanopore sequencing data processing pipeline for structure variation identification. [Masters Thesis]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61799/


Rutgers University

6. Wang, Yanran. A computational pipeline to identify complex disease signals in whole exome sequencing data.

Degree: PhD, Exome sequencing, 2020, Rutgers University

The recent improvement in high throughput sequencing technologies has led to the sharp decrease in the cost of sequencing and, thus, to accumulation of large… (more)

Subjects/Keywords: Microbiology and Molecular Genetics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, Y. (2020). A computational pipeline to identify complex disease signals in whole exome sequencing data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/64263/

Chicago Manual of Style (16th Edition):

Wang, Yanran. “A computational pipeline to identify complex disease signals in whole exome sequencing data.” 2020. Doctoral Dissertation, Rutgers University. Accessed November 30, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/64263/.

MLA Handbook (7th Edition):

Wang, Yanran. “A computational pipeline to identify complex disease signals in whole exome sequencing data.” 2020. Web. 30 Nov 2020.

Vancouver:

Wang Y. A computational pipeline to identify complex disease signals in whole exome sequencing data. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Nov 30]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64263/.

Council of Science Editors:

Wang Y. A computational pipeline to identify complex disease signals in whole exome sequencing data. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64263/

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