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Genetics

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Genomics

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Full-Text Articles in Life Sciences

Saccharomyces Genome Database & Uniprot Bioinformatics Analysis, Ray A. Enke Dec 2018

Saccharomyces Genome Database & Uniprot Bioinformatics Analysis, Ray A. Enke

Ray Enke Ph.D.

This in class activity introduces basic bioinformatics analysis using the Saccharomyces Genome Database (SGD) and the UniProt Database. The yeast URA3 gene is studied in this activity, however, any other yeast gene can be substituted. This activity is designed for novice instructors and students for implementation into core biology lecture or lab courses.

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Making Sense Of Genomic Variation: Part 1 Snp Annotation, Rolando Garcia-Milian Mar 2016

Making Sense Of Genomic Variation: Part 1 Snp Annotation, Rolando Garcia-Milian

Rolando Garcia-Milian

The  specific combination of genetic variation in an individual defines not  only the external appearance but also susceptibility to diseases,  cancer, genetic disorders, drug response, etc. This explains the great  interest in discovering and cataloging these variations and using them  for disease association and functional studies, among others. In this  session we will review the most popular databases and tools to annotate,  analyze and visualize genetic variations. Some of the databases and  tools that will be discussed are:
-dbSNP
- Online Mendelian Inheritance in Man a comprehensive, authoritative compendium of human genes and genetic phenotypes.
- GWAS Catalog
-  EBI's …

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Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull Jan 2015

Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull

Jeffrey S. Morris

Current methods for conducting expression Quantitative Trait Loci (eQTL) analysis are limited in scope to a pairwise association testing between a single nucleotide polymorphism (SNPs) and expression probe set in a region around a gene of interest, thus ignoring the inherent between-SNP correlation. To determine association, p-values are then typically adjusted using Plug-in False Discovery Rate. As many SNPs are interrogated in the region and multiple probe-sets taken, the current approach requires the fitting of a large number of models. We propose to remedy this by introducing a flexible function-on-scalar regression that models the genome as a functional outcome. The …

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