DNA microarrays allow researchers to analyze the expression levels of many genes simultaneously. They work by attaching DNA fragments from thousands of genes to a microchip, then measuring how much cDNA from a cell binds to each fragment. This reveals which genes are more or less active. The document describes how microarrays are prepared and used, and how the resulting gene expression data can help classify diseases and guide treatment. It proposes an interactive class exercise where students mimic genes on a microarray to recognize patterns in cancer patients' gene expression that could predict drug responses.
Functional genomics uses genome-wide experimental approaches to assess gene function on a large scale. It analyzes gene expression through techniques like transcriptomics and proteomics. Transcriptomics analyzes gene expression profiles through RNA sequencing or microarray analysis. Microarray analysis involves hybridizing fluorescently-labeled cDNA or cRNA to microarrays containing DNA probes to measure gene expression levels across thousands of genes simultaneously. Functional genomics provides a global understanding of gene function and molecular interactions through integrated omics approaches.
This document discusses various techniques for normalizing gene expression data from microarray experiments, including total intensity normalization, normalization using regression techniques, and normalization using ratio statistics. It also covers calculating distances between gene expression profiles using measures like Euclidean distance and Pearson correlation coefficient. Finally, it examines clustering analysis methods like hierarchical and non-hierarchical clustering that can group genes or samples based on similar expression patterns.
DNA chips, also known as microarrays, contain thousands of genes arranged on a small chip. Each gene occupies its own spot on the chip. By introducing genetic samples onto the chip, researchers can determine which genes are most active and gain insights into how genetics affect traits and diseases. Emerging nanotechnologies allow the creation of even smaller DNA chips with higher resolution using techniques like dip pen nanolithography and electrochemical sensing. These nano-scale DNA chips have the potential to revolutionize the study of genomics and functional genomics.
A micro-array is a tool for analyzing gene expression that consists of a small membrane or glass slide containing samples of many genes arranged in a regular pattern.
This was made by me while I was in Masters. I have made few animations. I hope it makes understanding better.
The content is made by searching through internet and referencing books. I do not claim any content in whole presentation except the animations made on the subject.
This document discusses high-resolution views of the cancer genome using various technologies including DNA microarrays, comparative genomic hybridization, tiling arrays, next-generation sequencing, and DNAse-Seq. It describes how these technologies can be used to analyze gene expression, copy number variation, chromatin structure, and more to better understand cancer at the genomic level. Integrating data from all these sources presents challenges but may help improve individual health outcomes.
This document presents a flexible architecture for studying plant functional genomics in space environments. The goals are to remotely measure plant responses to space conditions, determine gene function, and optimize plant performance. The basic architecture involves subjecting a test plant to a space environment, analyzing gene expression with microarrays, isolating genes, disrupting genes, and performing functional analysis. Arabidopsis and moss are proposed as model systems. Future work includes generating gene knockouts in moss, introducing additional environmental conditions, and adapting the architecture for space flight.
The study of the complete set of RNAs (transcriptome) encoded by the genome of a specific cell or organism at a specific time or under a specific set of conditions is called Transcriptomics.
Transcriptomics aims:
I. To catalogue all species of transcripts, including mRNAs, noncoding RNAs and small RNAs.
II. To determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications.
III. To quantify the changing expression levels of each transcript during development and under different conditions.
This document provides an overview of functional genomics and methods for transcriptome analysis. It discusses two main approaches - sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. For sequence-based approaches, it describes how ESTs can provide gene discovery and expression information but have limitations. It outlines the SAGE methodology and gene index construction to organize EST data. For microarrays, it summarizes the basic workflow including sample preparation, hybridization, image analysis and data normalization to identify differentially expressed genes through statistical tests.
Functional genomics uses genome-wide experimental approaches to assess gene function on a large scale. It analyzes gene expression through techniques like transcriptomics and proteomics. Transcriptomics analyzes gene expression profiles through RNA sequencing or microarray analysis. Microarray analysis involves hybridizing fluorescently-labeled cDNA or cRNA to microarrays containing DNA probes to measure gene expression levels across thousands of genes simultaneously. Functional genomics provides a global understanding of gene function and molecular interactions through integrated omics approaches.
This document discusses various techniques for normalizing gene expression data from microarray experiments, including total intensity normalization, normalization using regression techniques, and normalization using ratio statistics. It also covers calculating distances between gene expression profiles using measures like Euclidean distance and Pearson correlation coefficient. Finally, it examines clustering analysis methods like hierarchical and non-hierarchical clustering that can group genes or samples based on similar expression patterns.
DNA chips, also known as microarrays, contain thousands of genes arranged on a small chip. Each gene occupies its own spot on the chip. By introducing genetic samples onto the chip, researchers can determine which genes are most active and gain insights into how genetics affect traits and diseases. Emerging nanotechnologies allow the creation of even smaller DNA chips with higher resolution using techniques like dip pen nanolithography and electrochemical sensing. These nano-scale DNA chips have the potential to revolutionize the study of genomics and functional genomics.
A micro-array is a tool for analyzing gene expression that consists of a small membrane or glass slide containing samples of many genes arranged in a regular pattern.
This was made by me while I was in Masters. I have made few animations. I hope it makes understanding better.
The content is made by searching through internet and referencing books. I do not claim any content in whole presentation except the animations made on the subject.
This document discusses high-resolution views of the cancer genome using various technologies including DNA microarrays, comparative genomic hybridization, tiling arrays, next-generation sequencing, and DNAse-Seq. It describes how these technologies can be used to analyze gene expression, copy number variation, chromatin structure, and more to better understand cancer at the genomic level. Integrating data from all these sources presents challenges but may help improve individual health outcomes.
This document presents a flexible architecture for studying plant functional genomics in space environments. The goals are to remotely measure plant responses to space conditions, determine gene function, and optimize plant performance. The basic architecture involves subjecting a test plant to a space environment, analyzing gene expression with microarrays, isolating genes, disrupting genes, and performing functional analysis. Arabidopsis and moss are proposed as model systems. Future work includes generating gene knockouts in moss, introducing additional environmental conditions, and adapting the architecture for space flight.
The study of the complete set of RNAs (transcriptome) encoded by the genome of a specific cell or organism at a specific time or under a specific set of conditions is called Transcriptomics.
Transcriptomics aims:
I. To catalogue all species of transcripts, including mRNAs, noncoding RNAs and small RNAs.
II. To determine the transcriptional structure of genes, in terms of their start sites, 5′ and 3′ ends, splicing patterns and other post-transcriptional modifications.
III. To quantify the changing expression levels of each transcript during development and under different conditions.
This document provides an overview of functional genomics and methods for transcriptome analysis. It discusses two main approaches - sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. For sequence-based approaches, it describes how ESTs can provide gene discovery and expression information but have limitations. It outlines the SAGE methodology and gene index construction to organize EST data. For microarrays, it summarizes the basic workflow including sample preparation, hybridization, image analysis and data normalization to identify differentially expressed genes through statistical tests.
Human genome, genetic mapping, cloning, and cryonicsEemlliuq Agalalan
This document discusses the science of cryonics, which is the process of preserving humans in extremely cold temperatures after death with the goal of future revival. It explains that cryonics was first proposed by Robert Ettinger in the 1960s based on the idea that future medical advances may allow revival after freezing. The document outlines the cryonics process which involves replacing water in the body with antifreeze chemicals to prevent ice damage before freezing the body. It notes that the goal of cryonics is to use future nanotechnology and repair techniques to potentially revive frozen patients. The oldest cryonics patient currently preserved is from 1967.
DNA Microarray introdution and applicationNeeraj Sharma
DNA microarrays allow researchers to analyze gene expression levels across thousands of genes simultaneously. A DNA microarray contains many DNA probes attached to a solid surface in a regular pattern. Researchers isolate mRNA from samples, convert it to cDNA, and label the cDNA with fluorescent dyes. They then hybridize the labeled cDNA to the probes on the microarray. A scanner detects the fluorescence at each probe location, allowing researchers to compare gene expression levels between samples by the intensity and color of fluorescence. Microarrays have applications in medicine, agriculture, forensics and toxicology by enabling the comparison of gene expression in different tissues or in response to different conditions.
The document discusses transcriptomics and the relationship between transcriptome size and organism complexity. It questions how gene expression contributes to transcriptome size and what new studies reveal about size and complexity. Specifically, it notes that alternative splicing and RNA editing increase transcriptome size and complexity. It also discusses that the human genome is pervasively transcribed, with one stretch of DNA encoding many RNAs, including microRNAs, which control mRNA expression and are involved in development, gene regulation, and diseases like cancer.
High throughput next generation sequencing and robust transcriptome analysis help with gene expression profiling, gene annotation or discovery of non-coding RNA.
The document discusses several types of genomics: structural genomics aims to determine the 3D structure of every protein encoded in a genome. Functional genomics determines the biological functions of genes and their products. Mutational genomics characterizes mutation-associated genes and links genotypes to transcriptional states. Comparative genomics compares genomic features between species to study evolution and identify conserved and unique genes.
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
Genomics is the study of genomes through sequencing and analysis. Key points:
- Genomics involves mapping and sequencing genomes to understand genes and how they function. It uses techniques from genetics and molecular biology.
- The human genome contains 23 chromosome pairs and around 24,000 genes. Genomics aims to sequence whole genomes and analyze gene function.
- Early developments included identifying DNA's structure in 1953 and sequencing the first genome in the 1970s. The Human Genome Project aimed to map the entire human genome between 1990-2003.
- Genomics has applications in medicine like gene therapy for genetic diseases and in understanding health, disease, and drug responses through analysis of genetic variations.
Genomic sequencing a sub-disciplinary branch of genetics and difference between the two sequencers used to sequence the genome basically automated sequencer and fluorescence sequencers and its applications.
DNA microarrays allow researchers to analyze gene expression across an entire genome. Microarrays contain many copies of single genes arranged on a small chip. By detecting differences in gene expression levels between cell populations, microarrays can identify genes involved in processes like development, cancer, and cellular responses. Microarrays have been used to identify two subtypes of diffuse large B cell lymphoma with distinct gene expression profiles, which helps physicians predict patient outcomes and guide development of targeted cancer treatments.
The document discusses microarray classification using computational intelligent techniques. It describes how microarrays can be used to analyze gene expression levels and classify samples, such as diagnosing tumors. Intelligent techniques like neural networks, genetic algorithms, and fuzzy logic are well-suited for microarray classification due to their ability to handle large and noisy biological data. The objective is to implement these techniques to maximize the accuracy of microarray classifiers for important medical applications.
Structural genomics is a field that aims to determine the 3D structures of all proteins encoded by a genome. It involves determining structures on a large scale using techniques like X-ray crystallography and NMR. This allows identification of novel protein folds and potential drug targets. Comparative genomics compares genomic features between organisms and provides insights into evolution and conserved sequences and functions. It is a key tool in fields like medicine and agriculture.
DNA microarrays allow for the high-throughput analysis of differential gene expression. They work by hybridizing fluorescently-labeled cDNA from experimental and control RNA samples to a large number of gene sequences spotted on a glass slide. After hybridization, scanned images are analyzed to determine differences in gene expression levels between the two samples. While a powerful tool, microarray results often require confirmation through low-throughput methods like quantitative RT-PCR due to the risk of false positives. Studies have used microarrays to identify genes involved in atherosclerosis, response to oxidized LDL, and effects of shear stress on endothelial cells.
r-DNA technology allows the manipulation of DNA fragments through restriction endonucleases, cloning techniques, and specific probes. Restriction endonucleases cut DNA into fragments, cloning techniques amplify specific sequences, and probes identify sequences of interest. Real-time PCR and restriction fragment length polymorphism (RFLP) are techniques used to analyze DNA fragments.
The document discusses DNA microarrays or DNA chips. It explains that a DNA chip contains many copies of unique DNA sequences attached to a solid surface that are used to measure gene expression levels in cells. The presentation describes how DNA chips can compare gene expression levels between healthy and cancer cells to identify genes that are turned on or off in cancer. It provides examples of extracting RNA from tissue samples, converting it to cDNA, hybridizing it to a DNA chip, and analyzing the results to see differences in gene expression patterns between healthy and cancer cells. In conclusion, it states that DNA chips are a powerful comparative genomics tool for disease diagnosis, drug discovery, and toxicology research.
DNA microarrays contain thousands of DNA sequences attached to a solid surface in defined positions. Each DNA spot represents a single gene. The document describes the basic protocol for a DNA microarray experiment which involves isolating mRNA from samples, labeling the mRNA, hybridizing it to the microarray, and scanning the microarray to quantify gene expression levels. It also discusses various types of microarrays classified by their probes, such as cDNA, oligonucleotide, and SNP microarrays, as well as parameters that affect microarray fabrication.
DNA microarrays contain thousands of DNA probes attached to a solid surface that allow for the simultaneous analysis of gene expression across many genes. The core principle is based on DNA hybridization, where fluorescently labeled cDNA or RNA samples are hybridized to complementary probes on the array. By detecting which probes light up after hybridization and washing, researchers can determine which genes are expressed or detect genetic variations in the sample. Microarrays have numerous applications, including gene expression analysis, disease diagnosis, drug discovery, and toxicology research. They provide a fast way to study thousands of genes but results require further validation and correlations do not necessarily indicate causation.
DNA microarrays contain multiple DNA sequences spotted on a small surface, allowing simultaneous monitoring of thousands of gene expressions. They are valuable tools in research requiring identification or quantitation of specific DNA sequences. In medicine, microarrays can determine gene transcriptional programs for cell functions, compare programs to aid disease diagnosis and classification, and identify new therapeutic targets. Cancer analysis through microarrays involves isolating mRNA from normal and cancerous cells, synthesizing cDNA, labeling with dyes, hybridizing to a microarray, and scanning to identify differently expressed genes involved in cancer.
MPSS is a technique for analyzing gene expression that involves sequencing cDNA fragments cloned onto microbeads. It allows for the simultaneous sequencing of over 1 million cDNA clones. MPSS generates 17-base signature sequences that uniquely identify mRNA transcripts. Gene expression levels are quantified by counting the number of signatures for each gene. MPSS provides a more in-depth analysis of gene expression compared to other methods as it can detect genes expressed at very low levels and does not require prior knowledge of gene sequences.
Human genome, genetic mapping, cloning, and cryonicsEemlliuq Agalalan
This document discusses the science of cryonics, which is the process of preserving humans in extremely cold temperatures after death with the goal of future revival. It explains that cryonics was first proposed by Robert Ettinger in the 1960s based on the idea that future medical advances may allow revival after freezing. The document outlines the cryonics process which involves replacing water in the body with antifreeze chemicals to prevent ice damage before freezing the body. It notes that the goal of cryonics is to use future nanotechnology and repair techniques to potentially revive frozen patients. The oldest cryonics patient currently preserved is from 1967.
DNA Microarray introdution and applicationNeeraj Sharma
DNA microarrays allow researchers to analyze gene expression levels across thousands of genes simultaneously. A DNA microarray contains many DNA probes attached to a solid surface in a regular pattern. Researchers isolate mRNA from samples, convert it to cDNA, and label the cDNA with fluorescent dyes. They then hybridize the labeled cDNA to the probes on the microarray. A scanner detects the fluorescence at each probe location, allowing researchers to compare gene expression levels between samples by the intensity and color of fluorescence. Microarrays have applications in medicine, agriculture, forensics and toxicology by enabling the comparison of gene expression in different tissues or in response to different conditions.
The document discusses transcriptomics and the relationship between transcriptome size and organism complexity. It questions how gene expression contributes to transcriptome size and what new studies reveal about size and complexity. Specifically, it notes that alternative splicing and RNA editing increase transcriptome size and complexity. It also discusses that the human genome is pervasively transcribed, with one stretch of DNA encoding many RNAs, including microRNAs, which control mRNA expression and are involved in development, gene regulation, and diseases like cancer.
High throughput next generation sequencing and robust transcriptome analysis help with gene expression profiling, gene annotation or discovery of non-coding RNA.
The document discusses several types of genomics: structural genomics aims to determine the 3D structure of every protein encoded in a genome. Functional genomics determines the biological functions of genes and their products. Mutational genomics characterizes mutation-associated genes and links genotypes to transcriptional states. Comparative genomics compares genomic features between species to study evolution and identify conserved and unique genes.
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
Genomics is the study of genomes through sequencing and analysis. Key points:
- Genomics involves mapping and sequencing genomes to understand genes and how they function. It uses techniques from genetics and molecular biology.
- The human genome contains 23 chromosome pairs and around 24,000 genes. Genomics aims to sequence whole genomes and analyze gene function.
- Early developments included identifying DNA's structure in 1953 and sequencing the first genome in the 1970s. The Human Genome Project aimed to map the entire human genome between 1990-2003.
- Genomics has applications in medicine like gene therapy for genetic diseases and in understanding health, disease, and drug responses through analysis of genetic variations.
Genomic sequencing a sub-disciplinary branch of genetics and difference between the two sequencers used to sequence the genome basically automated sequencer and fluorescence sequencers and its applications.
DNA microarrays allow researchers to analyze gene expression across an entire genome. Microarrays contain many copies of single genes arranged on a small chip. By detecting differences in gene expression levels between cell populations, microarrays can identify genes involved in processes like development, cancer, and cellular responses. Microarrays have been used to identify two subtypes of diffuse large B cell lymphoma with distinct gene expression profiles, which helps physicians predict patient outcomes and guide development of targeted cancer treatments.
The document discusses microarray classification using computational intelligent techniques. It describes how microarrays can be used to analyze gene expression levels and classify samples, such as diagnosing tumors. Intelligent techniques like neural networks, genetic algorithms, and fuzzy logic are well-suited for microarray classification due to their ability to handle large and noisy biological data. The objective is to implement these techniques to maximize the accuracy of microarray classifiers for important medical applications.
Structural genomics is a field that aims to determine the 3D structures of all proteins encoded by a genome. It involves determining structures on a large scale using techniques like X-ray crystallography and NMR. This allows identification of novel protein folds and potential drug targets. Comparative genomics compares genomic features between organisms and provides insights into evolution and conserved sequences and functions. It is a key tool in fields like medicine and agriculture.
DNA microarrays allow for the high-throughput analysis of differential gene expression. They work by hybridizing fluorescently-labeled cDNA from experimental and control RNA samples to a large number of gene sequences spotted on a glass slide. After hybridization, scanned images are analyzed to determine differences in gene expression levels between the two samples. While a powerful tool, microarray results often require confirmation through low-throughput methods like quantitative RT-PCR due to the risk of false positives. Studies have used microarrays to identify genes involved in atherosclerosis, response to oxidized LDL, and effects of shear stress on endothelial cells.
r-DNA technology allows the manipulation of DNA fragments through restriction endonucleases, cloning techniques, and specific probes. Restriction endonucleases cut DNA into fragments, cloning techniques amplify specific sequences, and probes identify sequences of interest. Real-time PCR and restriction fragment length polymorphism (RFLP) are techniques used to analyze DNA fragments.
The document discusses DNA microarrays or DNA chips. It explains that a DNA chip contains many copies of unique DNA sequences attached to a solid surface that are used to measure gene expression levels in cells. The presentation describes how DNA chips can compare gene expression levels between healthy and cancer cells to identify genes that are turned on or off in cancer. It provides examples of extracting RNA from tissue samples, converting it to cDNA, hybridizing it to a DNA chip, and analyzing the results to see differences in gene expression patterns between healthy and cancer cells. In conclusion, it states that DNA chips are a powerful comparative genomics tool for disease diagnosis, drug discovery, and toxicology research.
DNA microarrays contain thousands of DNA sequences attached to a solid surface in defined positions. Each DNA spot represents a single gene. The document describes the basic protocol for a DNA microarray experiment which involves isolating mRNA from samples, labeling the mRNA, hybridizing it to the microarray, and scanning the microarray to quantify gene expression levels. It also discusses various types of microarrays classified by their probes, such as cDNA, oligonucleotide, and SNP microarrays, as well as parameters that affect microarray fabrication.
DNA microarrays contain thousands of DNA probes attached to a solid surface that allow for the simultaneous analysis of gene expression across many genes. The core principle is based on DNA hybridization, where fluorescently labeled cDNA or RNA samples are hybridized to complementary probes on the array. By detecting which probes light up after hybridization and washing, researchers can determine which genes are expressed or detect genetic variations in the sample. Microarrays have numerous applications, including gene expression analysis, disease diagnosis, drug discovery, and toxicology research. They provide a fast way to study thousands of genes but results require further validation and correlations do not necessarily indicate causation.
DNA microarrays contain multiple DNA sequences spotted on a small surface, allowing simultaneous monitoring of thousands of gene expressions. They are valuable tools in research requiring identification or quantitation of specific DNA sequences. In medicine, microarrays can determine gene transcriptional programs for cell functions, compare programs to aid disease diagnosis and classification, and identify new therapeutic targets. Cancer analysis through microarrays involves isolating mRNA from normal and cancerous cells, synthesizing cDNA, labeling with dyes, hybridizing to a microarray, and scanning to identify differently expressed genes involved in cancer.
MPSS is a technique for analyzing gene expression that involves sequencing cDNA fragments cloned onto microbeads. It allows for the simultaneous sequencing of over 1 million cDNA clones. MPSS generates 17-base signature sequences that uniquely identify mRNA transcripts. Gene expression levels are quantified by counting the number of signatures for each gene. MPSS provides a more in-depth analysis of gene expression compared to other methods as it can detect genes expressed at very low levels and does not require prior knowledge of gene sequences.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
1. Teori gelombang-partikel menyatakan bahwa partikel dapat berperilaku seperti gelombang dan sebaliknya.
2. Pada tahun 1923, Louis de Broglie mengusulkan bahwa setiap partikel yang bergerak memiliki sifat gelombang dengan panjang gelombang terkait dengan momentum partikel.
3. Sifat gelombang dari partikel sulit diamati secara mikroskopis kecuali dengan peralatan khusus karena konstant
Prinsip prinsip etika lingkungan hidup_theopilia(LILI)Theopilia Sagala
Dokumen tersebut membahas sembilan prinsip etika baru lingkungan yang mencakup sikap hormat terhadap alam, tanggung jawab moral untuk menjaga lingkungan, solidaritas dengan seluruh makhluk hidup, kasih sayang dan kepedulian terhadap alam, tidak melakukan kerusakan, hidup sederhana selaras dengan alam, keadilan, demokrasi, serta integritas moral. Prinsip-prinsip ini bertujuan membentuk kesadaran
Thank you for the summary. This gives a good overview of the key steps and findings from the microarray experiment comparing gene expression in normal vs cancerous cells.
DNA microarrays allow scientists to measure gene expression levels across large numbers of genes simultaneously. A DNA microarray consists of microscopic DNA spots attached to a solid surface. There are five main steps to performing a microarray: sample preparation and labeling, hybridization, washing, image acquisition, and data analysis. Microarrays use the principle of hybridization between complementary DNA strands, where fluorescent labeled target sequences bind to probe sequences on the array, generating signals to measure expression levels. Microarrays have applications in gene expression profiling, comparative genomics, disease diagnosis, drug discovery, and toxicological research.
Microarrays allow researchers to analyze gene expression levels across thousands of genes simultaneously. DNA microarrays work by hybridizing fluorescently-labeled cDNA or cRNA to complementary DNA probes attached to a solid surface. This technology has applications in gene expression profiling, disease diagnosis, drug discovery, and toxicology research. While microarrays provide high-throughput analysis, their limitations include not reflecting true protein levels, complex data analysis, expense, and short shelf life of DNA chips.
The document describes an analysis of gene expression data from a glioblastoma cell line before and after treatment with the chemotherapeutic drug temozolomide (TMZ). RNA sequencing data was analyzed using the Deseq2 model to identify differentially expressed genes between untreated and treated conditions. Weighted gene co-expression network analysis (WGCNA) was also used to cluster genes into modules and identify co-expression patterns. Additionally, a software application was developed to analyze single-cell RT-PCR data on 96 genes of interest identified from the RNA-seq analysis, in order to investigate tumor heterogeneity. The results highlighted genes and gene modules related to drug resistance in glioblastoma.
Microarray analysis allows scientists to determine which genes are active or inactive in a cell. It involves isolating genetic material from a cell and identifying which genes have messenger RNA present, indicating they are turned on. DNA microarrays contain thousands of unique DNA sequences spotted onto a solid surface that fluorescently-labeled genetic material from a sample can bind to. This allows scientists to measure gene expression levels across the entire genome simultaneously and understand molecular mechanisms of toxicity. Protein microarrays similarly contain arrays of capture proteins that can be used to analyze protein-protein interactions and activities on a large scale.
This document provides an overview of the field of bioinformatics, including its history and applications. It discusses how bioinformatics merges biology, computer science, and information technology. It also summarizes key applications like using bioinformatics for human and animal genomics, molecular medicine, microbiology, and more. Microarray technology is introduced, explaining how DNA microarrays work to analyze gene expression levels. Different types of microarrays and platforms are also outlined.
This document provides an overview of the field of bioinformatics, including its history and applications. It discusses how bioinformatics merges biology, computer science, and information technology. It also summarizes key applications like using bioinformatics for human and animal genomics, molecular medicine, microbiology, and more. Microarray technology is introduced, explaining how DNA microarrays work to analyze gene expression levels. Different types of microarrays and platforms are also outlined.
Microarray technology allows researchers to study the expression of thousands of genes simultaneously. It involves placing short DNA sequences from known genes onto a glass slide in a controlled array. Researchers apply fluorescent-tagged DNA or RNA samples to the array to see which sequences bind, indicating gene expression. Microarrays can be used for gene expression profiling, comparative genomics, disease diagnosis, drug discovery, and toxicology research. They allow analysis of entire genomes quickly and efficiently. However, microarrays are costly to produce and analyze, and the chips have a limited shelf life.
Molecular Biology research evolves through the development of the technologies used for carrying them out. It is not possible to research on a large number of genes using traditional methods
This document summarizes key concepts from Chapter 20 of an AP Biology textbook. It discusses several topics:
1) Genomics is the study of genomes and how they are organized and regulated. Genome sequences provide insights into fundamental biological questions.
2) Computer analysis can identify protein-coding genes in DNA sequences by looking for start/stop signals and other features. With 25,000 genes in humans, this analysis is a huge undertaking without technology.
3) Genome sizes vary greatly between organisms, but size does not always correlate with complexity. Some plants have genomes much larger than humans despite fewer genes.
Genomics is the study of genomes through DNA sequencing and analysis. There are several types of genomics including structural, functional, comparative, epigenomics, and metagenomics. DNA sequencing methods have advanced from Sanger sequencing to next-generation sequencing using high-throughput technologies. Genome sequencing has provided insights into disease, evolution, and applications in medicine, agriculture, and environmental science.
The DNA microarray is a tool used to determine whether the DNA from a particular individual contains a mutation in genes like BRCA1 and BRCA2. The chip consists of a small glass plate encased in plastic. Some companies manufacture microarrays using methods similar to those used to make computer microchips.
A DNA microarray 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 of a specific DNA sequence, known as probes.
This chapter provides an overview of DNA microarrays. Microarrays are a technology in which 1000’s of nucleic acids are bound to a surface and are used to measure the relative concentration of nucleic acid sequences in a mixture via hybridization and subsequent detection of the hybridization events. We first cover the history of microarrays and the antecedent technologies that led to their development. We then discuss the methods of manufacture of microarrays and the most common biological applications. The chapter ends with a brief discussion of the limitations of microarrays and discusses how microarrays are being rapidly replaced by DNA sequencing technologies.
The DNA microarray is a tool used to determine whether the DNA from a particular individual contains a mutation in genes like BRCA1 and BRCA2. The chip consists of a small glass plate encased in plastic. Some companies manufacture microarrays using methods similar to those used to make computer microchips.
This document discusses DNA microarray technology. It begins with an introduction, explaining that DNA microarrays allow analysis of thousands of genes simultaneously through hybridization of fluorescently labeled DNA probes to a microarray slide. It then covers the principles of DNA microarrays, including the types (cDNA and oligonucleotide), how they are constructed with probes immobilized on a solid surface, and how hybridization allows analysis of gene expression profiles. Applications discussed include drug discovery, disease diagnostics, and functional genomics. Advantages are high-throughput analysis and ability to study many genes, while disadvantages include potential for false results from single experiments.
The document discusses the Human Genome Project (HGP). It provides 3 key reasons for choosing this topic: 1) HGP was an amazing adventure into understanding human DNA, 2) It was one of the most ambitious scientific undertakings involving hundreds of scientists from 6 countries over 13 years, 3) The project sequenced the 3 billion DNA letters that make up human DNA. The HGP involved mapping genes, developing genome maps, sequencing DNA, and annotating genes. It cost $3 billion over 15 years but provided important insights into human genetics and has applications in disease treatment and drug development.
Microarray and dna chips for transcriptome studyBia Khan
Microarrays and DNA chips can be used to study transcriptomes by comparing gene expression profiles. They work by immobilizing reference cDNA or oligonucleotides on a glass slide, then hybridizing labeled cDNA from the cells of interest. This allows determining which genes are expressed and their relative expression levels based on fluorescence intensities. While powerful, the method has complications like cross-hybridization of similar mRNAs and experimental errors. Normalization procedures help account for these. Yeast is commonly used as a model organism in transcriptome studies due to its stable yet responsive gene expression. Applications include stem cell research, cancer studies, and embryonic development.
DNA microarray technology allows researchers to study which genes are active or inactive in different cell types. Microarrays contain thousands of gene sequences arranged on a microscope slide. Researchers extract messenger RNA from cells, convert it to cDNA labeled with fluorescent dyes, and hybridize the cDNA to the gene sequences on the microarray. The fluorescent intensity of each spot indicates the activity level of the corresponding gene, showing which genes are highly active, somewhat active, or inactive. This technique helps researchers classify and develop targeted treatments for cancers based on differences in gene expression between tumor and normal cells.
Microarrays allow researchers to study gene expression across thousands of genes at once. They work by immobilizing DNA probes on a solid surface, then exposing the surface to fluorescently labeled cDNA or cRNA from samples. The microarray is then scanned to see which probes fluoresce, indicating gene expression. Microarrays have many applications including disease diagnosis, drug discovery, and toxicology. While powerful, they also have limitations like expense and complexity of data analysis. Standards are being developed to allow use of microarray data in regulatory decision making.
whole genome analysis
history
needs
steps involved
human genome data
NGS
pyrosequencing
illumina
SOLiD
Ion torrent
PacBio
applications
problems
benefits
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
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DNA Microarray Technology.
What is it and how is it useful?
Mandana Sassanfar and Graham Walker
Department of Biology
Massachusetts Institute of Technology
Cambridge, MA
NOTE: The following material was developed for high school audiences familiar with the structure
of DNA and the central dogma of molecular biology.
THE AUTHORS:
Mandana Sassanfar received her Ph.D from Cornell University and is currently an Instructor in
biology and the science outreach coordinator for the Department of Biology at MIT.
GrahamWalker received his PhD from the University of Illinois. He is a Professor of Biology, an
American Cancer Society Research Professor, a Howard Hughes Medical Institute Professor and the
Howard Hughes Medical Institute Undergraduate Education Program Director at MIT.
The material presented here can be used alone or as a complement to a lecture given by Eric Lander,
Professor of Biology at MIT and director of the MIT/Whitehead Genome Center, at the Howard
Hughes Medical Institute in 2002.
The lecture by Professor Eric Lander “Human Genomics: A New Guide For Medicine” is
available on a DVD produced by the Howard Hughes Medical Institute as part of the 2002 HHMI
Holiday lecture series “Scanning Life’s Matrix ; genes, proteins, and small molecules” .
The concepts covered in chapter 20-29 of lecture 3 “Human Genomics: A New Guide For
Medicine” are particularly relevant to the interactive class activity described in part D.
A free copy of the DVD lecture can be obtained through HHMI's online catalog
(http://www.hhmi.org/biointeractive/order.html)
Copyright 2003 MIT Dept of Biology
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TABLE of CONTENT
A. What students must already know:
This part provides a list of concepts that students should be familiar with in order to
comprehend DNA microarray technology and its application.
B. DNA Microarray Technology:
This part provide a description and discussion of the DNA microarray technology, how to
prepare a DNA chip, and how to use it to follow the activity of many genes simultaneously in
a cell.
C. DNA microarray technology flow chart
This color flow chart shows the various steps required to compare gene expression between 2
populations of cells using DNA microarrays
D. Interactive Class Exercise on DNA Microarray and Medicine:
This is an interactive classroom activity in which students mimic genes on a DNA chip.
Students do not need to understand the details of the DNA microarray technology to do this
activity. Rather students must be able to observe a common pattern of gene expression for
various cancer patients and decide if a specific drug would be effective for the treatment of
various patients with different subtype of cancers.
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A. What students must already know.
• The structure of DNA and the definition of a gene.
• The central dogma of molecular biology: DNA is transcribed into mRNA which in turn is
translated into proteins which have enzymatic activity and make things happen in the cell.
• What information one can obtain from sequencing a gene:
The sequence of the protein it encodes
Can guess the function of the gene
Can look for the presence of mutations
Can compare the gene sequence and the protein it encodes in different animal species.
Can study the evolution of genes
• Gene expression: The sequence of a gene does not give information about it’s activity. A
gene must be transcribed into mRNA to be expressed (or turned ON). The level of
expression (or activity) of a gene can therefore be studied by measuring how much
corresponding mRNA is made (transcribed) from a particular gene.
• The sequence of a gene is complementary to the sequence of its mRNA. Therefore the
mRNA can base pair (or hybridize) to the DNA strand it was copied from (template strand).
Scientists study gene expression by measuring the amount of mRNA for different genes.
• Humans are diploid and have two sets of each gene, one from Mom and one from Dad.
The two copies can be identical or different . Mutations can be inherited or accumulate
during our lifetime.
• ALL of our cells contain the exact same DNA sequence. All cells arose from a single cell
(the egg). However different type of cells in the body express different sets of genes. For
example, a liver cell expresses some of the same genes expressed in lung cells or skin cells
but also expresses genes that no other cell type expresses (liver-specific genes). As a result
liver cells look different from other cells in the body and do things that no other cell can do.
• Reversetranscriptase is an enzyme found in retroviruses. It can make DNA from RNA.
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B. DNA MICROARRAY TECHNOLOGY
Usefulness of the DNA microarray technology;
• Can follow the activity of MANY genes at the same time.
• Can get a lot of results fast
• Can COMPARE the activity of many genes in diseased and healthy cells
• Can categorize diseases into subgroups
Drawback:
• Too much data all at once. Can take quite a while to analyze all the results.
• The results may be too complex to interpret
• The results are not always reproducible
• The results are not always quantitative enough
• The technology is still too expensive
How to set up for a Microarray experiment. (See low diagram on page 6)
There are three parts to the experiments:
I. Preparing a DNA chip (or buying one)
II. Carrying out the reaction (isolate mRNA, process the mRNA and hybridize to DNA chip)
III. Collecting and analyzing the results
I. Preparing a DNA chip
• You must know the sequence (or part of the sequence) of all the genes you want to
study.
• You must synthesize a piece of each gene as a short single strand DNA (oligonucleotide)
about 25 bases long. This 25-base sequence will be sufficient to hybridize specifically to
a complementary sequence of RNA or DNA.
• You must fix the short DNA sequences for each gene on a tiny spot on a glass slide
(usually the DNA fragments are synthesized directly on the slide or can be linked to the
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slide after synthesis). You can fit 10,000 different genes (10,000 tiny spots) on a single
slide (DNA chip). Each spot represent one single gene and has billions of copies of the
same 25-base sequence.
II. Carrying out the reaction
• You first need to choose what cells or tissue you want to study and grow them under specific
conditions.
For example you may wonder:
1. How gene expression changes when cells are irradiated with UV light or exposed to a
toxic chemical?
2. What happens to the activity of various genes in yeast cells when the cells are shifted
from 25 ºC to 37 ºC?
3. What genes are only expressed in fish embryos and not in adult fish?
4. What genes have increased expression in cancer cells compared to normal cells?
• You isolate total mRNA from the cells you are studying (both normal and treated or
cancerous cells)
• You want to be able to count how many copies of each mRNA there is in the cell in order to
determine the activity of each gene. So you need to label the mRNA (so that you can detect
it and count how much of each mRNA sequence there is). The easiest way to do that is to
reverse transcribe the mRNA into cDNA and introduce modified fluorescent bases into the
DNA. In order to distinguish later between the mRNAs coming from the normal cells and
those coming from the treated or diseased cells you label the two cDNAs populations with
different fluorescent colors (such as green and red)
• You mix the two populations of fluorescently labeled cDNAs and hybridize them to the
same DNA chip in a special chamber. You then wash away all the unbound cDNA.
III. Collecting and analyzing the results
• Using a scanner hooked to a computer you measure how much of each labeled cDNA (green
and red) is bound to each spot on the slide. The more label on a spot the more active the
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gene is. BUT remember! You are comparing two populations of cells. So what counts is
not how much total cDNA binds to each spot but how much of each color bind to the same
spot. The color intensities are subtracted from each other.
• By measuring the difference of intensity between the two colors for each spot you can
determine for example if a gene is more active or less active in a cancer cell compared to a
normal cell.
• You generate a grid that uses a color code to show the change in activity for each gene
• You try to figure out if there is a pattern in gene expression that is clearly visible, and
reproducible.
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C. DNA microarray technology flow chart
(downloaded from http://www.biologie.ens.fr/microarrays.html
DNA Microarray Methodology - Flash Animation by A Malcolm Campbell
http://www.bio.davidson.edu/courses/genomics/chip/chip.html
Anatomy of a Comparative Gene Expression Study by Jeremy Buhler
http://www.cs.wustl.edu/~jbuhler//research/array/
Additional reference:
“Chip Critics Countered” in The Scientist, August 25, 2003 pp 30-31.
RNA Extraction
PCR
Hybridization Read Out
Data analysis
ORF spotting
Reverse
transcription
of mRNAs
Yeast cultures
25 ºC 37 ºC
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D. Interactive Class Exercise on DNA Microarray and Medicine
Note to teachers.
The exercise does not require students to know the details of the DNA microarray technology but
rather to understand what type of data one gets from DNA microarray experiments and how this
data can be useful in the diagnosis and treatment of human diseases.
Goal of this exercise:
To make students realize that the power of DNA microarray technology in medical applications
relies principally on observing a common change in gene expression pattern in patients with the
same type of diseases. Students will have to recognize common patterns of gene expression and
realize the usefulness of such observation. To simplify the exercise you can decrease the number of
genes studied.
You can also provide additional guidance to the students and ask them to reorder the genes in a
specific pattern to facilitate the observation.
More advanced students may think about the function of some of the genes whose activity are
required to prevent cancer or whose activity are required to develop cancer.
Brief overview:
Students will mimic individual genes on DNA chips. The number of genes on a chip will depend on
the class size. Each DNA chip will be used to test the gene expression profile for one cancer
patient, and should contain at least 10 genes to make it significant.
• If the gene is ON (or increased gene activity) the student will stand up (or raise their hand).
• If the gene is OFF (or decreased gene activity) the student will sit down.
In order for students to observe a specific pattern of gene expression associated with a disease, there
need to be at least 4 DNA chips, one for each cancer patient. Students representing genes on a
DNA chip will each receive a card that indicates the state of their gene (ON [black circle] or OFF
[white circle]) for individual patients. Students representing genes will line up (with a chair behind
them). The students will act out gene expression for the first patient, then the next and so on.
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The pattern of gene expression must be recorded. For this exercise students can be divided into
those representing genes and those recording and analyzing the data, or all the students can both
represent genes and do the analysis.
Example:
Student A has a card labeled GENE 1 indicating the state of GENE 1 in 5 different patients.
GENE 1
Patient # 1: OFF (SIT)
Patient # 2: OFF (SIT)
Patient # 3: ON (STAND)
Patient # 4: OFF (SIT)
Patient # 5: ON (STAND)
Students participating in the exercise and those observing should realize that
• Some genes are always ON, some are always OFF
• Some genes are either ON or OFF depending on the patient
• Some patients have the same or a very similar gene expression pattern
The examples shown below can be modified at will and you can come up with your own number of
genes and patterns of gene expression.
Copyright 2003 MIT Dept of Biology
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EXAMPLE OF A RESULT WITH 10 STUDENTS (10 GENES) and 5 CANCER PATIENTS (5
DNA CHIPS)
GENE 1 2 3 4 5 6 7 8 9 10
Patient #1 Chip 1
Patient # 2 Chip 2
Patient # 3 Chip 3
Patient # 4 Chip 4
Patient # 5 Chip 5
QUESTIONS:
• Which genes are always expressed? What might be their function? How would you test
that?
• Which genes are never expressed? What might be their function?
• Of the 10 genes studied, is there any which might potentially play a direct role in cancer?
How would you test that?
• CAN YOU SEE SIMILARITIES BETWEEN PATIENTS?
NOTE: Some students may be very fast at identifying patterns. Others may need a more systematic
approach: In order to observe a clear gene expression pattern that links various cancer patients,
students need to group genes that are always ON or OFF together. Student should create a table that
lists, for each patient, which genes are ON and which genes are off in numerical order (see table
below). This process mimics, at a very simple level, what a computer program does when
processing simultaneously the results for thousand of genes and several dozen patients.
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Patient # 1 2 3 4 5
Genes ON 2 2 1 2 1
4 3 3 3 4
5 4 4 4 6
8 8 5 8 7
10 6 10 10
7
10
Genes OFF 1 1 2 1 2
3 5 8 5 3
6 6 9 6 5
7 7 7 8
9 9 9 9
10
• Based on the results presented in the table you can GROUP BOTH PATIENTS AND
GENES to show the two main patterns of gene expression.
GENE 1 6 7 9 2 8 4 10 3 5
Patient # 1
Patient # 2
Patient # 4
Patient # 3
Patient # 5
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1. Is the function of gene 4 potentially required for cancer? What additional experiment do you
need to do to test this?
2. What about gene 9?
3. Are the patterns of expression for genes 3, 5 and 10 relevant?
EXERCISE:
A patient with cancer A has the following gene expression pattern. (You can increase or decrease
the number of genes to fit your class size)
Gene # 1 2 3 4 5 6 7 8 9 10
This patient’s cancer responds very well to Gleevec, a drug recently approved by the FDA for the
treatment of chronic myeloid leukemia, a rare type of leukemia. The drug has lower side effects
and toxicity compared to standard cancer chemotherapy. Doctors are therefore wondering which
other type of cancers would respond well to treatment with Gleevec. Rather than randomly testing
the drug on other types of cancers, the doctors have decided to use DNA microarray experiments to
predict which cancers are more likely to be susceptible to the drug.
The doctors test the tumors of five cancer patients and find the following results (see next page).
1. Can you tell from these preliminary results which type of cancer would be most likely to respond
to Gleevec?
2. Are the results clear enough?
3. How would you improve the accuracy of your predictions?
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RESULTS OF DNA MICROARRAY EXPERIMENTS
Gene # 1 2 3 4 5 6 7 8 9 10
Patient #1
Patient #2
Patient #3
Patient #4
Patient # 5
Gene # 1 2 3 4 5 6 7 8 9 10
Cancer A
• As a physician, would you conclude with confidence which patient may be responsive to
Gleevec?
• Which patients, if any, would you treat with Gleevec?
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USE THESE BLANK ARRAYS TO DESIGN YOUR OWN EXERCISE
Gene # 1 2 3 4 5 6 7 8 9 10
Patient #1
Patient #2
Patient #3
Patient #4
Patient # 5
Patient # 6
Copyright 2003 MIT Dept of Biology