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Lecture

Quantile Normalization in Genomics

In course

BIO-373: Genetics and genomics

The theoretical part of this course covers classical genetics and contemporary genomics. Because bioinformatics has become important for genomic research, the course also includes practical applications to genomic analyses using Python, including group pro ...

Description

This lecture covers the concept of quantile normalization in genomics, focusing on the process of preparing the R environment, loading and filtering data, and performing quantile normalization. The instructor explains the importance of normalizing data to ensure accurate analysis and interpretation of gene expression levels in single-cell RNA sequencing experiments.

Instructors (2)

Bart Deplancke

Jacques Fellay

Jacques Fellay is a medical scientist with expertise in infectious diseases and human genomics. He obtained his MD from the University of Lausanne in 2002 and his PhD from University of Utrecht. After a clinical training in infectious diseases in Switzerland and a 4-years postdoctoral fellowship at Duke University, he joined the EPFL in April 2011 with an SNF Professorship. On top of his EPFL affiliation, Jacques is also Head of Precision Medicine at the University Hospital (CHUV) in Lausanne, Group Leader at the Swiss Institute of Bioinformatics, and Co-director of the Health2030 Genome Center at Campus Biotech in Geneva.

Official source

Ontological neighbourhood

Biology

Genetics: Genomics

Related concepts (27)

Genomics

Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism.

Microarray analysis techniques

Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment. Such experiments can generate very large amounts of data, allowing researchers to assess the overall state of a cell or organism. Data in such large quantities is difficult - if not impossible - to analyze without the help of computer programs.

DNA microarray

A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Each DNA spot contains picomoles (10−12 moles) of a specific DNA sequence, known as probes (or reporters or oligos). These can be a short section of a gene or other DNA element that are used to hybridize a cDNA or cRNA (also called anti-sense RNA) sample (called target) under high-stringency conditions.

Gene expression profiling

In the field of molecular biology, gene expression profiling is the measurement of the activity (the expression) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment. Many experiments of this sort measure an entire genome simultaneously, that is, every gene present in a particular cell. Several transcriptomics technologies can be used to generate the necessary data to analyse.

Gene expression

Gene expression is the process by which information from a gene is used in the synthesis of a functional gene product that enables it to produce end products, proteins or non-coding RNA, and ultimately affect a phenotype. These products are often proteins, but in non-protein-coding genes such as transfer RNA (tRNA) and small nuclear RNA (snRNA), the product is a functional non-coding RNA.

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