Functional Bioinformatics Overview
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Questions and Answers

What is a main advantage of NGS compared to microarrays?

  • Higher sensitivity to low concentrations of RNA
  • Capability of sequencing without reverse transcription
  • Ability to analyze protein expression levels directly
  • Higher reproducibility and less required DNA/RNA concentration (correct)

Which application is NOT typically associated with RNA sequencing (RNAseq)?

  • Investigating alternative splicing events
  • Analyzing protein folding patterns (correct)
  • Studying differential gene expression
  • Examining allele-specific expression

What does single-cell RNA sequencing (single-cell transcriptomics) primarily study?

  • Holistic patterns of gene expression in tissues
  • Differential gene expression and cellular diversity (correct)
  • Metabolic pathways in singular cell types
  • Genomic mutations across populations of cells

What is meta-analysis used for in the context of functional genomics?

<p>To integrate data from previous experiments for reliable insights (D)</p> Signup and view all the answers

Which database is mentioned as a source for processed data used in meta-analyses?

<p>Gene Expression Omnibus (GEO) (B)</p> Signup and view all the answers

What is the main purpose of functional genomics?

<p>To determine the roles of genes and their impact on cellular phenotypes (C)</p> Signup and view all the answers

Which of the following is classified as a high-throughput technology in functional genomics?

<p>Microarrays (D)</p> Signup and view all the answers

Which of the following techniques is specifically used in low-throughput functional genomics?

<p>TaqMan methods (B)</p> Signup and view all the answers

Which of the following statements regarding the genome is correct?

<p>The complete genome is contained in each cell along chromosomes (C)</p> Signup and view all the answers

What does functional bioinformatics primarily analyze?

<p>Interactions between DNA, RNA, proteins, and metabolites affecting phenotypes (A)</p> Signup and view all the answers

Which of the following is NOT a focus area of 'omics' experiments discussed in functional bioinformatics?

<p>How changes in genomes affect protein expression and metabolite regulation (C)</p> Signup and view all the answers

What is a key outcome of utilizing high-throughput technologies in functional genomics?

<p>The ability to analyze multiple biological samples simultaneously (A)</p> Signup and view all the answers

Which software is primarily used for generating heat maps and clustering for academic purposes?

<p>Genesis (C)</p> Signup and view all the answers

What does each row in a heat map typically represent?

<p>A gene (C)</p> Signup and view all the answers

Which of the following tools is NOT mentioned as commonly used for gene set enrichment analysis?

<p>BLAST (A)</p> Signup and view all the answers

What is the primary function of functional genomics techniques?

<p>To profile transcription and epigenetic changes (B)</p> Signup and view all the answers

What type of analysis can be performed with next-generation sequencing technology?

<p>Single nucleotide resolution analysis (D)</p> Signup and view all the answers

Which type of microarray is synthesized directly on a solid surface by photolithography?

<p>Affymetrix arrays (C)</p> Signup and view all the answers

Gene set enrichment analysis (GSEA) is based on what kind of annotation?

<p>Gene Ontology functional annotation (B)</p> Signup and view all the answers

Which tool is mentioned for interpreting gene sets related to molecular function in GSEA?

<p>GSEA-P (B)</p> Signup and view all the answers

How are the oligonucleotides in two-color arrays primarily applied to slides?

<p>Inkjet printing (B)</p> Signup and view all the answers

What aspect of DNA microarrays reflects the number of specific mRNA transcripts present?

<p>The quantity of hybridization (C)</p> Signup and view all the answers

What is the purpose of heat maps in gene expression analysis?

<p>To display changes in gene expression (A)</p> Signup and view all the answers

Which of the following is NOT a common tool for pathway enrichment analyses?

<p>BLAST (B)</p> Signup and view all the answers

What high-throughput technique is commonly used along with microarrays for gene expression data interpretation?

<p>RNA sequencing (RNAseq) (C)</p> Signup and view all the answers

What visual representation is most commonly used to display gene expression data from high-throughput techniques?

<p>Heat maps (B)</p> Signup and view all the answers

What aspect of gene expression can be easily studied using next-generation sequencing technology?

<p>Allelic gene variants (A)</p> Signup and view all the answers

Which method groups genes and samples based on the similarity of their gene expression patterns?

<p>Clustering algorithms (B)</p> Signup and view all the answers

According to the Gene Ontology, which of the following is a primary focus of its analysis?

<p>Functions of genes and gene products (A)</p> Signup and view all the answers

What can be identified using heat maps combined with clustering methods in gene expression analysis?

<p>Commonly regulated genes (A)</p> Signup and view all the answers

What type of information can gene enrichment analysis provide in the context of gene expression data?

<p>Classification of pathways involved (D)</p> Signup and view all the answers

What role do single-stranded cDNA or antisense RNA molecules play in the microarray process?

<p>They are hybridized to the microarrays. (B)</p> Signup and view all the answers

Flashcards

Functional Bioinformatics

The study of how genes and their products (proteins and metabolites) work together to influence biological processes and phenotypes.

Genome

The complete set of DNA found in each cell of an organism.

Proteomics

The study of the complete set of proteins produced by an organism.

Metabolomics

The study of the complete set of metabolites (small molecules) in an organism.

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RNA-Seq

A technique that measures the amount of specific RNA molecules present in a sample.

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Microarrays

A high-throughput technique that measures the expression levels of thousands of genes simultaneously.

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Real-time quantitative PCR (qPCR)

A technique that measures gene expression by amplifying and quantifying specific DNA sequences.

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Serial analysis of gene expression (SAGE)

A technique that measures gene expression by counting the number of mRNA fragments.

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Heat map

A visual representation of gene expression data, where rows represent genes, columns represent samples, and colors represent expression levels.

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Hierarchical clustering

A method used for clustering genes based on their expression patterns across different samples.

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Genesis

A software tool used for analyzing microarray data, including generating heat maps and hierarchical clusters.

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Gene Ontology (GO)

A database that provides information about gene function and products.

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Gene Set Enrichment Analysis (GSEA)

A method used for analyzing lists of differentially expressed genes to determine if they are enriched in any specific biological pathways or functions.

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GSEA-P

Software used for performing Gene Set Enrichment Analysis (GSEA).

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Ingenuity Pathway Analysis (IPA)

A widely used software for pathway enrichment analysis. It allows you to identify relevant pathways affected by changes in gene expression.

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Next-generation sequencing (NGS)

A technology that allows for the analysis of DNA or RNA samples with high resolution, enabling the identification of individual nucleotide changes.

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Single nucleotide polymorphisms (SNPs)

Variations in the DNA sequence that occur at single nucleotide positions.

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RNA sequencing

A method used for analyzing gene expression data by sequencing RNA molecules.

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What are the advantages of NGS over microarrays?

NGS (Next-Generation Sequencing) is a powerful technology that offers high reproducibility and requires less DNA or RNA sample concentration (nanograms) compared to microarrays.

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What is RNA sequencing (RNAseq) and what does it tell us?

RNA sequencing (RNAseq) is a technique that converts RNA into DNA (reverse transcription) to analyze the RNA content of a sample. This process helps us understand which genes are active and how much RNA is present in a cell or tissue.

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What makes single-cell RNA sequencing (scRNAseq) special?

Single-cell RNA sequencing (scRNAseq) is a method used to study gene expression in individual cells. This technique allows researchers to analyze the unique cellular processes, diversity, and differences between cells, particularly in areas like regenerative medicine, immunology, neurobiology, and cardiovascular disease.

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What is meta-analysis in functional genomics?

Meta-analysis is a type of functional genomics analysis where data from previous studies can be analyzed independently or combined with new data to create statistically robust models.

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How are databases like GEO used in functional genomics?

Functional genomics databases, like Gene Expression Omnibus (GEO), store processed data from high-throughput experiments, such as microarrays and NGS. This data can be utilized for meta-analysis, allowing researchers to explore patterns and connections across different studies.

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Oligonucleotide probes

These are short DNA sequences attached to a solid surface like a glass slide, used to capture and quantify complementary DNA fragments from a sample.

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One-color microarray

A type of microarray where each probe on the slide represents a specific gene and is labeled with a single fluorescent dye. The intensity of the signal reflects the amount of mRNA present in the sample.

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Two-color microarray

A type of microarray where two samples with different fluorescent dyes are hybridized on the same slide. The intensity of the two colors allows comparison of gene expression between the samples.

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cDNA

A single-stranded DNA copy of an mRNA molecule, used in microarrays to hybridize to the probes.

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Clustering

The process of grouping genes or samples together based on their similarities in gene expression patterns. This helps identify genes that are co-regulated or associated with a specific condition.

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Gene Enrichment Analysis

A way to analyze large gene expression datasets using statistical methods to identify groups of genes that are enriched for a particular function or pathway.

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Functional Genomics

Techniques that study the functions of genes and their interactions in biological systems.

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Study Notes

Functional Bioinformatics

  • Functional bioinformatics is a subarea of computational biology
  • It uses vast biomedical data from genomics, proteomics, metabolomics and other omics experiments
  • This data has transformed the fundamental understanding of biology and medicine
  • It effectively uses computational tools to analyze large-scale omics data

Multi-Omics

  • Multi-omics involves multiple "omic" experiments (e.g., genomics, transcriptomics, proteomics)
  • It looks at interrelationships within these areas
  • It utilizes data from these large-scale experiments in interconnected areas

Functional Bioinformatics

  • This analyzes how genomes, proteomes, and metabolomes influence cellular phenotypes
  • This specifically looks at changes in genomes affecting cell and molecular functions, and protein/metabolite expression
  • Utilizes diverse computational tools

Microarray Technology

  • Microarrays use oligonucleotide probes fixed to a solid surface
  • These probes are specific to DNA sequences in samples
  • Two types: one-color arrays (Affymetrix) and two-color arrays (Agilent)
  • Probe hybridization directly related to specific mRNA levels in the samples

Gene Expression Data Interpretation

  • Heat maps utilize dendrograms to represent gene expression data from high-throughput techniques (like microarrays)
  • Clustering methods group genes based on their expression patterns which can identify genes/biological signatures and relate these to particular conditions (e.g., illness or environmental factors)
  • These techniques help interpret the large dataset

Heat Maps and Clustering Algorithms

  • Open-source software (like Genesis) can generate heat maps and hierarchical clusters for gene expression data
  • Commercial software (like GeneSpring, Partek Genomic Suite) is also used for this analysis
  • Heat maps display gene expression data; rows are genes; columns are samples
  • Color/intensity of entries represent how much each gene is expressed in each sample

Gene Set Enrichment Analysis

  • Gene Ontology (GO) provides a comprehensive knowledge base of gene functions. It helps analyze data.
  • Gene Set Enrichment Analysis (GSEA) analyzes groups of genes whose expression differs significantly (using GO functional annotations)
  • GSEA-P is a desktop program for conducting these analysis
  • Tools include DAVID for pathway/enrichment analysis; Ingenuity Pathway Analysis, Reactome, KEGG, and STRING

Next-Generation Sequencing (NGS)

  • NGS analyzes DNA/RNA with single-nucleotide resolution. It’s effective in studying a range of biological components like spliced transcripts, allelic variants, SNPs, and more
  • RNA sequencing (RNAseq), which is a method involving NGS, provides RNA content measurement from samples which are helpful in exploring differential gene expression, and alternative splicing events
  • Single-cell RNAseq studies specific cellular processes, cellular diversity, etc, in various fields including medicine, immunology, and neurobiology

Meta-Analysis

  • Meta-analysis integrates data from multiple studies using a particular bioinformatics approach to create a large sample for statistically strong outcomes.
  • This is used with experimental genomics data and combines studies to build strong models.
  • GEO (Gene Expression Omnibus) is a resource for processed datasets that is helpful for meta-analyses of high-throughput microarray data and NGS data.

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Functional Bioinformatics Lecture 9 PDF

Description

This quiz explores the fundamentals of functional bioinformatics, focusing on the analysis of genomic, proteomic, and metabolomic data. It highlights the importance of multi-omics and computational tools in understanding biological systems and cellular functions. Dive into how these advanced techniques are revolutionizing medicine and research.

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