Transcriptomics is the study of RNA in cells and tissues. The transcriptome refers to the complete set of transcripts in a cell under a specific condition. Understanding the transcriptome reveals the functional elements of the genome and molecular constituents of cells. Techniques for studying the transcriptome include microarray analysis and RNA sequencing. Microarrays measure gene expression levels using fluorescently-labeled cDNA hybridized to probes on an array. RNA sequencing determines expression levels by sequencing individual cDNAs produced from target RNA. Transcriptomics provides insights into development, disease, and varying gene expression under different environmental conditions.
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.
A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface.
The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs.
Transcriptome analysis is the study of the set of all RNA molecules, including mRNA, rRNA, tRNA, and non-coding RNAs produced in a population of cells. The transcriptome can vary between different cell types, body parts, and environmental conditions. Transcriptomics aims to catalogue all transcript species and quantify changing expression levels during development and in different conditions. The two main techniques are DNA microarrays and RNA sequencing. Microarrays involve fluorescent labeling and hybridization of samples to probe arrays, while RNA sequencing replaces hybridization with sequencing of individual cDNAs produced from target RNA.
This document provides an overview of RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq). It describes that RNA-Seq is used to profile transcriptomes and determine gene expression levels, while ChIP-Seq identifies the binding sites of DNA-associated proteins. The key steps of RNA-Seq are RNA preparation, library preparation, sequencing, and analysis to map reads, detect isoforms and expression levels. ChIP-Seq combines chromatin immunoprecipitation with sequencing to precisely map global binding sites of proteins of interest to understand gene regulation. Both techniques provide high-quality, genome-wide data with low input requirements compared to previous methods.
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.
Applications of transcriptomice s in modern biotechnology 2Pakeeza Rubab
Transcriptomics is the study of transcriptomes, which are the complete set of RNA transcripts produced in a cell or tissue under a specific set of conditions. Next-generation sequencing techniques like Illumina sequencing have enabled comprehensive analysis of transcriptomes. Transcriptomics has many applications in biotechnology including agriculture, stem cell research, disease studies, and assessing chemical safety. It can be used to discover gene functions, biomarkers, and responses to environmental changes. Common transcriptomics techniques are real-time PCR, microarrays, and next-generation sequencing which provide information on RNA expression levels.
The analysis of global gene expression and transcription factor regulation, global approaches to alternative splicing and its regulation, long noncoding RNAs, gene expression models of signalling pathways, from gene expression to disease phenotypes, introduction to isoform sequencing, systematic and integrative analysis of gene expression to identify feature genes underlying human diseases.
Transcriptomics is the study of RNA in cells and tissues. The transcriptome refers to the complete set of transcripts in a cell under a specific condition. Understanding the transcriptome reveals the functional elements of the genome and molecular constituents of cells. Techniques for studying the transcriptome include microarray analysis and RNA sequencing. Microarrays measure gene expression levels using fluorescently-labeled cDNA hybridized to probes on an array. RNA sequencing determines expression levels by sequencing individual cDNAs produced from target RNA. Transcriptomics provides insights into development, disease, and varying gene expression under different environmental conditions.
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.
A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid surface.
The core principle behind microarrays is hybridization between two DNA strands, the property of complementary nucleic acid sequences to specifically pair with each other by forming hydrogen bonds between complementary nucleotide base pairs.
Transcriptome analysis is the study of the set of all RNA molecules, including mRNA, rRNA, tRNA, and non-coding RNAs produced in a population of cells. The transcriptome can vary between different cell types, body parts, and environmental conditions. Transcriptomics aims to catalogue all transcript species and quantify changing expression levels during development and in different conditions. The two main techniques are DNA microarrays and RNA sequencing. Microarrays involve fluorescent labeling and hybridization of samples to probe arrays, while RNA sequencing replaces hybridization with sequencing of individual cDNAs produced from target RNA.
This document provides an overview of RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq). It describes that RNA-Seq is used to profile transcriptomes and determine gene expression levels, while ChIP-Seq identifies the binding sites of DNA-associated proteins. The key steps of RNA-Seq are RNA preparation, library preparation, sequencing, and analysis to map reads, detect isoforms and expression levels. ChIP-Seq combines chromatin immunoprecipitation with sequencing to precisely map global binding sites of proteins of interest to understand gene regulation. Both techniques provide high-quality, genome-wide data with low input requirements compared to previous methods.
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.
Applications of transcriptomice s in modern biotechnology 2Pakeeza Rubab
Transcriptomics is the study of transcriptomes, which are the complete set of RNA transcripts produced in a cell or tissue under a specific set of conditions. Next-generation sequencing techniques like Illumina sequencing have enabled comprehensive analysis of transcriptomes. Transcriptomics has many applications in biotechnology including agriculture, stem cell research, disease studies, and assessing chemical safety. It can be used to discover gene functions, biomarkers, and responses to environmental changes. Common transcriptomics techniques are real-time PCR, microarrays, and next-generation sequencing which provide information on RNA expression levels.
The analysis of global gene expression and transcription factor regulation, global approaches to alternative splicing and its regulation, long noncoding RNAs, gene expression models of signalling pathways, from gene expression to disease phenotypes, introduction to isoform sequencing, systematic and integrative analysis of gene expression to identify feature genes underlying human diseases.
The document provides a history of transcriptomics and an overview of RNA analysis techniques. It begins by explaining Francis Crick's central dogma and the discovery of messenger RNA, transfer RNA, and ribosomal RNA in the 1960s. Later developments include the discoveries of RNA splicing, ribozymes, RNA interference, and small interfering RNA. The document then defines the transcriptome and describes methods for analyzing RNA expression like transcript formation, RNA structure, positional integration on the transcriptome, Northern blotting, serial analysis of gene expression (SAGE), and the basic steps of SAGE.
description of functional genomics and structural genomics and the techniques involved in it and also decribing the models of forward genetics and techniques involved in it and reverse genetics and techniques involved in it
The document provides an overview of the history and techniques of transcriptome analysis. It discusses how RNA was separated from DNA with the formulation of the central dogma in 1958. Key developments include the discoveries of messenger RNA, transfer RNA, and ribosomal RNA in the 1960s. The document outlines techniques such as serial analysis of gene expression (SAGE) and RNA sequencing (RNA-seq) that allow comprehensive analysis of gene expression patterns. It provides details on the basic steps and advantages of SAGE and describes how next generation sequencing revolutionized transcriptome analysis through massive parallel sequencing.
Single cell RNA-seq was performed on 18 mouse bone marrow dendritic cells. 982 genes were found to be differentially expressed between two cells, while the majority of genes showed similar expression levels. Future work will analyze the functions of differentially expressed genes to better understand heterogeneity between cells and potential roles in disease.
This document discusses the use of 16S ribosomal RNA (rRNA) gene sequencing for bacterial identification and phylogenetic analysis. It explains that the 16S rRNA gene is highly conserved, making it useful for comparing distantly related organisms. The document outlines the process of 16S rRNA gene sequencing, including PCR amplification using conserved primer regions and sequencing of variable regions. It also discusses various methods that have been developed using 16S rRNA, such as TRFLP profiling and ribotyping, to study microbial communities.
Method of detection of food borne pathogen(methods).docxOsama Alam
PCR and RT-PCR are commonly used molecular techniques for detecting foodborne pathogens through amplification of pathogen DNA or RNA. Multiplex PCR (mPCR) allows simultaneous detection of multiple pathogens. Real-time PCR monitors amplification in real-time without gel electrophoresis. Other methods like LAMP, NASBA, and microarrays provide isothermal amplification or detect multiple targets but require different primers or probes. Optical and electrochemical biosensors detect binding through surface plasmon resonance or changes in electrical signals. Mass-based sensors measure added mass through resonant frequency changes of piezoelectric crystals. ELISA is a common immunological technique that sandwiches the target antigen between immobilized and enzyme-conjugated antibodies for colorimetric detection.
This document discusses functional genomics and different methods for analyzing gene expression at the whole genome level. Functional genomics focuses on determining gene functions through high-throughput experimental approaches. Two main methods described are sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. Microarrays allow analysis of thousands of genes simultaneously through hybridization of fluorescently-labeled cDNA to probes on a chip, while ESTs and SAGE involve sequencing of cDNA fragments to determine expression levels. Both methods aim to provide information on overall gene expression patterns in a genome under different conditions.
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.
SAGE- Serial Analysis of Gene ExpressionAashish Patel
Serial Analysis of Gene Expression (SAGE) is a method to quantify gene expression in cells. It involves extracting short sequence tags from mRNA transcripts and concatenating them for efficient sequencing. This allows simultaneous analysis of thousands of transcripts. SAGE provides quantitative gene expression data without prior knowledge of genes and can identify differentially expressed genes between cell types or conditions. While powerful, it requires substantial sequencing and computational analysis of large datasets.
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.
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.
Gene expression and transcript profiling involves determining the pattern of genes expressed at the transcriptional level under specific circumstances by measuring the expression of thousands of genes simultaneously. This allows one to understand cellular function. Common techniques for profiling include DNA microarrays, RNA sequencing, and EST tags. DNA microarrays involve hybridizing cDNA or cRNA samples to probes on a chip to determine relative abundance of sequences. RNA sequencing uses next-generation sequencing to reveal presence and quantity of RNA in a sample.
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.
1) The document discusses a study analyzing the impact of gene length on detecting differentially expressed genes using RNA-seq technology.
2) The study will first test the reproducibility of RNA-seq and the effect of normalization. It will then compare different statistical tests for identifying differentially expressed genes.
3) Finally, the study will specifically test how gene length impacts the likelihood of a gene being identified as differentially expressed, as longer genes are easier to map with short reads.
This document provides an overview of basic molecular genetic methodologies and their applications in studying atherosclerosis. It describes several key techniques used in molecular genetics research, such as polymerase chain reaction (PCR), gel electrophoresis, Southern blotting, and DNA sequencing. It also discusses methods for detecting genetic variations like single nucleotide polymorphisms. The document then covers various applications of these techniques in genomic analysis and molecular studies of cardiovascular diseases like atherosclerosis.
This document provides an overview of basic molecular genetic methodologies and their applications in studying atherosclerosis. It describes several key techniques used in molecular genetics research, such as polymerase chain reaction (PCR), gel electrophoresis, Southern blotting, and DNA sequencing. It also discusses methods for detecting genetic variations like single nucleotide polymorphisms. The document then covers applications of these techniques for analyzing specific nucleic acids and genomic studies of atherosclerosis.
212 basic molecular genetic studies in atherosclerosisSHAPE Society
Basic molecular genetic studies of atherosclerosis involve analyzing genes and genetic variations using various techniques. Key techniques discussed are PCR to amplify DNA, gel electrophoresis to separate DNA fragments by size, DNA sequencing to determine nucleotide order, and DNA microarrays where many genes are attached to a chip to analyze expression levels. These techniques are furthering our understanding of genetic factors contributing to atherosclerosis development and progression.
This document provides an overview of basic molecular genetic methodologies and their applications in studying atherosclerosis. It describes several key techniques used in molecular genetics research, such as polymerase chain reaction (PCR), gel electrophoresis, Southern blotting, and DNA sequencing. It also discusses methods for detecting genetic variations like single nucleotide polymorphisms. The document then covers various applications of these techniques in genomic analysis and molecular studies of cardiovascular diseases like atherosclerosis.
Transcriptomics is the study of the transcriptome, which is the complete set of RNA transcripts produced by the genome under certain conditions, using high-throughput methods like microarray analysis. The transcriptome includes mRNA, rRNA, tRNA and other non-coding RNA transcribed in a cell or population of cells. Oncogenomics applies transcriptomics to characterize genes associated with cancer. Gene expression analysis focuses on relevant target genes and their location and distances on chromosomes can be determined through sequence mapping. Non-coding RNAs regulate gene expression at the transcriptional and post-transcriptional levels.
The document provides a history of transcriptomics and an overview of RNA analysis techniques. It begins by explaining Francis Crick's central dogma and the discovery of messenger RNA, transfer RNA, and ribosomal RNA in the 1960s. Later developments include the discoveries of RNA splicing, ribozymes, RNA interference, and small interfering RNA. The document then defines the transcriptome and describes methods for analyzing RNA expression like transcript formation, RNA structure, positional integration on the transcriptome, Northern blotting, serial analysis of gene expression (SAGE), and the basic steps of SAGE.
description of functional genomics and structural genomics and the techniques involved in it and also decribing the models of forward genetics and techniques involved in it and reverse genetics and techniques involved in it
The document provides an overview of the history and techniques of transcriptome analysis. It discusses how RNA was separated from DNA with the formulation of the central dogma in 1958. Key developments include the discoveries of messenger RNA, transfer RNA, and ribosomal RNA in the 1960s. The document outlines techniques such as serial analysis of gene expression (SAGE) and RNA sequencing (RNA-seq) that allow comprehensive analysis of gene expression patterns. It provides details on the basic steps and advantages of SAGE and describes how next generation sequencing revolutionized transcriptome analysis through massive parallel sequencing.
Single cell RNA-seq was performed on 18 mouse bone marrow dendritic cells. 982 genes were found to be differentially expressed between two cells, while the majority of genes showed similar expression levels. Future work will analyze the functions of differentially expressed genes to better understand heterogeneity between cells and potential roles in disease.
This document discusses the use of 16S ribosomal RNA (rRNA) gene sequencing for bacterial identification and phylogenetic analysis. It explains that the 16S rRNA gene is highly conserved, making it useful for comparing distantly related organisms. The document outlines the process of 16S rRNA gene sequencing, including PCR amplification using conserved primer regions and sequencing of variable regions. It also discusses various methods that have been developed using 16S rRNA, such as TRFLP profiling and ribotyping, to study microbial communities.
Method of detection of food borne pathogen(methods).docxOsama Alam
PCR and RT-PCR are commonly used molecular techniques for detecting foodborne pathogens through amplification of pathogen DNA or RNA. Multiplex PCR (mPCR) allows simultaneous detection of multiple pathogens. Real-time PCR monitors amplification in real-time without gel electrophoresis. Other methods like LAMP, NASBA, and microarrays provide isothermal amplification or detect multiple targets but require different primers or probes. Optical and electrochemical biosensors detect binding through surface plasmon resonance or changes in electrical signals. Mass-based sensors measure added mass through resonant frequency changes of piezoelectric crystals. ELISA is a common immunological technique that sandwiches the target antigen between immobilized and enzyme-conjugated antibodies for colorimetric detection.
This document discusses functional genomics and different methods for analyzing gene expression at the whole genome level. Functional genomics focuses on determining gene functions through high-throughput experimental approaches. Two main methods described are sequence-based approaches like expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), and microarray-based approaches. Microarrays allow analysis of thousands of genes simultaneously through hybridization of fluorescently-labeled cDNA to probes on a chip, while ESTs and SAGE involve sequencing of cDNA fragments to determine expression levels. Both methods aim to provide information on overall gene expression patterns in a genome under different conditions.
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.
SAGE- Serial Analysis of Gene ExpressionAashish Patel
Serial Analysis of Gene Expression (SAGE) is a method to quantify gene expression in cells. It involves extracting short sequence tags from mRNA transcripts and concatenating them for efficient sequencing. This allows simultaneous analysis of thousands of transcripts. SAGE provides quantitative gene expression data without prior knowledge of genes and can identify differentially expressed genes between cell types or conditions. While powerful, it requires substantial sequencing and computational analysis of large datasets.
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.
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.
Gene expression and transcript profiling involves determining the pattern of genes expressed at the transcriptional level under specific circumstances by measuring the expression of thousands of genes simultaneously. This allows one to understand cellular function. Common techniques for profiling include DNA microarrays, RNA sequencing, and EST tags. DNA microarrays involve hybridizing cDNA or cRNA samples to probes on a chip to determine relative abundance of sequences. RNA sequencing uses next-generation sequencing to reveal presence and quantity of RNA in a sample.
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.
1) The document discusses a study analyzing the impact of gene length on detecting differentially expressed genes using RNA-seq technology.
2) The study will first test the reproducibility of RNA-seq and the effect of normalization. It will then compare different statistical tests for identifying differentially expressed genes.
3) Finally, the study will specifically test how gene length impacts the likelihood of a gene being identified as differentially expressed, as longer genes are easier to map with short reads.
This document provides an overview of basic molecular genetic methodologies and their applications in studying atherosclerosis. It describes several key techniques used in molecular genetics research, such as polymerase chain reaction (PCR), gel electrophoresis, Southern blotting, and DNA sequencing. It also discusses methods for detecting genetic variations like single nucleotide polymorphisms. The document then covers various applications of these techniques in genomic analysis and molecular studies of cardiovascular diseases like atherosclerosis.
This document provides an overview of basic molecular genetic methodologies and their applications in studying atherosclerosis. It describes several key techniques used in molecular genetics research, such as polymerase chain reaction (PCR), gel electrophoresis, Southern blotting, and DNA sequencing. It also discusses methods for detecting genetic variations like single nucleotide polymorphisms. The document then covers applications of these techniques for analyzing specific nucleic acids and genomic studies of atherosclerosis.
212 basic molecular genetic studies in atherosclerosisSHAPE Society
Basic molecular genetic studies of atherosclerosis involve analyzing genes and genetic variations using various techniques. Key techniques discussed are PCR to amplify DNA, gel electrophoresis to separate DNA fragments by size, DNA sequencing to determine nucleotide order, and DNA microarrays where many genes are attached to a chip to analyze expression levels. These techniques are furthering our understanding of genetic factors contributing to atherosclerosis development and progression.
This document provides an overview of basic molecular genetic methodologies and their applications in studying atherosclerosis. It describes several key techniques used in molecular genetics research, such as polymerase chain reaction (PCR), gel electrophoresis, Southern blotting, and DNA sequencing. It also discusses methods for detecting genetic variations like single nucleotide polymorphisms. The document then covers various applications of these techniques in genomic analysis and molecular studies of cardiovascular diseases like atherosclerosis.
Transcriptomics is the study of the transcriptome, which is the complete set of RNA transcripts produced by the genome under certain conditions, using high-throughput methods like microarray analysis. The transcriptome includes mRNA, rRNA, tRNA and other non-coding RNA transcribed in a cell or population of cells. Oncogenomics applies transcriptomics to characterize genes associated with cancer. Gene expression analysis focuses on relevant target genes and their location and distances on chromosomes can be determined through sequence mapping. Non-coding RNAs regulate gene expression at the transcriptional and post-transcriptional levels.
Similar to METHODS OF TRANSCRIPTOME ANALYSIS....pptx (20)
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
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This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
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The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
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2. ● The transcriptome, is the complete set of all RNA molecules (mRNA, rRNA, RNA
intron etc) in a cell, a population of cells or in an organism at a given time.
● Changes with time.
● After the genome has been sequenced, transcriptome analysis allows us to
understand the expression of genome at the transcription level, which provides
information on gene structure, regulation of gene expression, gene product
function, and genome dynamics.
● Study of transcriptome is known as transcriptomics.
TRANSCRIPTOME 2
3. ● The genome content and genes remain almost same in all the cells of an
organism but for responding to specific environmental change or
developmental stage different tissues or the cells needs to decode this genomic
information suitably .
● So this specific set of genes are transcribed to rnawhich are short
livedmessenger molecules.
● As transcriptome referred to expressed part of genome, it also known as
functional genome.
● While transcriptomics is most commonly applied to the mRNAs, the coding
transcripts, transcriptomics also provides important data regarding content of
the cell noncoding RNAs, including rRNA, tRNA, lncRNA, siRNA, and others.
3
3
4. • Transcriptomics is the study of RNA in any of its forms.
• A study of transcriptome elucidates the complex interactions which generally take
place among the transcripts before these are translated.
• Also an independent analysis of transcriptome in thousand of cell types organs and
tissues elucidates regulation of gene expression in time and space through the study
of relative abundance of different individual transcripts.
TRANSCRIPTOMICS 4
5. TRANSCRIPTOMIC AIMS
To catalogue all species of transcripts, including mRNAs, non coding
RNAs and small RNAs
To determine the transcriptional structure of genes in terms of their start
sites, 5'and 3' ends, and other post transcriptional modifications
To quantify the changing expression levels of each transcript during
development and under different conditions
5
6. TRANSCRIPTOME ANALYSIS
There are four methods to analyse a transcriptome
❏ Hybridization based Microarray
❏ Northern Blotting
❏ SAGE
❏ Sequencing based RNA-seq
4
6
7. Hybridization based Microarray
● Microarray is a tool which can detect the expression of thousands of genes
simultaneously
● They also referred as gene chips
● Slide has many spots containing specific known DNA oligomers which
represents entire genome of an organism and this act as a probe to detect
gene expression
7
8. HYBRIDIZATION BASED
In microarray a predetermined set of probes representing sequence of
gene fragments are fixed on a solid chip .
The major limitation of this technique is that gene sequence needs to be
known for designing the chip .
thus for non model species that like genomic sequence information
microarray is not viable option as the chips are not readily available
and needs to be designed and designing a chip is very costly.
8
9. The technology has been developed in several variants but in the following we only discuss
the two most popular:
"two colour" (or cDNA or two-channel) microarrays
"one colour" (or oligonucleotides or one-channel) microarrays.
Two colour microarrays are based on the competitive hybridization of two samples each of
which has been labeled with a different fluorescent dye (e.g. red or green).
After hybridization, the array is exposed to red and green laser light the array emits
fluorescence proportional to the quantity of RNA the image produced is scanned yielding after
some corrections a value which represents the expression of one sample relative to the other.
9
10. One channel microarrays are based on RNA of one sample which has been labeled
with a fluorescent dye and hybridized to a single array where millions of copies of
short (around 24 base pairs) oligonucleotide probes representing all known genes
(several probes for gene form a “probeset” ) have been synthesized.
After exposition to laser light and scanner the intensity of each location is measured
yielding a value which represents an absolute measure of expression.
10
11. Slide scanning:
• The image of hybridized array is captured using a laser scanner. Two wavelengths
of laser beams are used to excite the red and green flourescent dyes.
• A photomultiplier tube detects the flourecence. The two florescent images from
the scanner are then overlaid to create a composite image which indicate the
relative expression of each gene. The colour intensity measure of the gene
expression levels.
11
12. DATA ANALYSIS :-
• The array signals are then converted to numbers and are reported as ratios
between the two colors.
• This ratio is a measure of the gene expression changes between two samples.
• Micro scanners are normally provided with software programs to carry out
microarray image analysis.
• There are also a number of free processing software programs available on the
internet.
• Eg: ScanAlyze , ArrayDB etc.
12
13. SAGE- serial analysis of gene expression
Serial analysis of gene expression is a highly efficient technology that can give a global
expression of profile of a particular type of cell or tissue.
PRINCIPLES:
● 1) A short sequence tags in a defined position in cDNA that contain sufficient
information to uniquely identify a transcript
2) the concatenation of tag which allows for efficient sequence based analysis of
transcription
3) The tags are then concatenated by ligation with other tags, amplified in a bacterial
host and then sequenced
4) The number of times that a specific sequence tag is found determines the relative
abundance of the transcript in that sample.
13
15. ● 2) mix cDNA with streptavidin bead(these are superparamagnetic particles
covalently coupled to ahighly pure form of streptavidin)
● this beads will bind to the biotin cDNA complex
15
16. ● cDNA is cleaved using restriction endonuclease called an anchoring enzyme.
● Result of this cleavage is that the beads are bound to cDNA fragments of the
various lengths with the same sequence at their exposed end.
16
17. ● Cleaved cDNA that is no longer bound to the bead is now removed by rinsing
● Remaining bound cDNA is divided into two solutions
● Add A & B adaptors (either A or B to each)
Sticky ends containg anchoring enzyme cut site
Restriction enzyme site- tagging enzyme ( downstream)
● RE used to cleave the cDNA nearly 15bp downstream
● These remove cDNA from the beads to create a short gun of around 11
nucleotides
17
21. ● Adaptors are removed using Anchoring enzymes
● Amplification in bacteria 21
22. ● The amplified sequences are isolated and sequenced using modern
high throughput DNA sequencers
● Sequences can be analysed with computer programs which quantify
the recurrence of individual tags
22
23. Northern blotting
● Northern blot is a technique based on the principle of blotting for the
analysis of specific RNA in a complex mixture.
● The quantity of mRNA transcript for a single gene directly reflects how
much transcription of that gene has occurred
● Tracking of that quantity will therefore indicate how vigorously a gene
transcribed or expressed
23
24. ● Used to visualise differences in quantity of mRNA produced by different groups of
cells at different time
● Different fragments of mRNA are separated from one another via gel
electrophoresis and transferred to a filter or other solid support using a technique
known as blotting
● To identify the desired mRNA single standard complimentary RNA that is labelled
with the radioactive molecule is allowed to hybridize.
● Later when the filter paper is placed against X ray film radioactivity in the probe
will expose the film thereby making marks on it
● The intensity of the resulting marks, called bands will tell how much mRNA was
in the sample which is direct indicator of how strongly the gene of interest is
expressed
24
26. RNA Sequencing (RNA-Seq)
RNA Sequencing (RNA-Seq) is a revolutionary technique for transcriptome-wide
analysis of gene expression. Given its high accuracy and sensitivity for measuring
expression, it has become the standard for studying transcriptomic dynamics and
identifying differences in expression between tissues, physiological stages, diseases
and a wide range of other experimental designs.
26
27. This approach offers a number of advantages
● Provides sensitive, accurate measurement of gene expression
● does not require predesigned probes
● Generates both qualitative and quantitative data
● Reveals the full transcriptome, not just a few selected transcripts
● Can be applied to any species, even if a reference sequence is not
available
27
28. Double stranded DNA is more stable than RNA and can be
easily amplified and modified.
28
30. Sequence the library
The machine has flourescent probe that are colour coded according to the type of
nucleotide they can bind
30
31. The probes are attached to the first base in each sequence.
31
32. ● First line is the unique id for the sequence
● Bases called for the sequenced fragment
● ‘+’ Sign
● Quality score for each base in the fragment
47
32
33. REFERENCES
• Sanchez-Pla, A., Reverter, F., Ruíz de Villa, M. C., & Comabella , M. (2012).
Transcriptomics: mRNA and alternative splicing. Journal of
Neuroimmunology .
• Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool
for transcriptomics. Nature Reviews Genetics, 10(1), 57-63
• https://www.frontiersin.org/articles/10.3389/fgene.2018.00636/full
• https://youtu.be/Ou3ga39SVfQ
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