Functional Bioinformatics Lecture 9 PDF

Bioinformatics BIO417 Lecture 9 Functional Bioinformatics Prepared by Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics  Functional bioinformatic...

Bioinformatics BIO417 Lecture 9 Functional Bioinformatics Prepared by Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics  Functional bioinformatic Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics  The generation of the large scale of biomedical data in the past decade in genomics, proteomics, and metabolomics have transformed our basic understanding of biology and medicine.  Functional bioinformatics is a subarea of computational biology that utilizes the massive amount of data derived from genomics, transcriptomics, proteomics, metabolomics and other large-scale “omics” experiments in interrelated areas.  Functional bioinformatic  The whole set of DNA found in each cell is defined as the genome.  Each cell contains a complete copy of the genome, distributed along chromosomes.  In functional genomics, the roles of genes are determined using high-throughput technologies such as the microarrays, and next-generation sequencing approaches.  It decodes how genomes, proteomes and metabolomes result in different cellular phenotypes.  It analyses how changes in genomes alter both cellular and molecular functions that regulate the expression of proteins and metabolites in the cells.  An array of computational tools are used in functional bioinformatics approaches. Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics  Techniques in Functional Genomics Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics  Techniques used in functional genomics range from:  Low-throughput techniques  Real-time quantitative PCR (SYBR Green and TaqMan methods).  Serial analysis of gene expression (SAGE).  High-throughput technologies  Microarrays and next generation sequencing technologies (mainly RNASeq).  Functional genomics experiments measure changes in the genome, transcriptome, proteome, and metabolites.  It measures interactions between DNA/RNA/proteins and metabolites that significantly modulate the phenotype of an individual or a biological sample.  The functional genomics techniques are mainly used for transcription profiling, epigenetic profiling, nucleic acid- protein interactions and genotyping for single-nucleotide polymorphisms (SNP) in biological samples.  Microarray Technology Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics  Microarrays are made up of short oligonucleotide probes (DNA), which are evenly bound in defined positions onto a solid surface, such as a glass slide, onto which DNA fragments derived from the biological samples will be hybridized.  Importantly, the microarrays can be classified into two types; one-color arrays (Affymetrix) and two-color arrays (Agilent).  In one-color arrays (Affymetrix arrays), the probes are synthesized in situ on a solid surface by photolithography.  In two-color arrays (Agilent arrays), the oligonucleotides (probes) are coated onto the glass slides using inkjet printing technology.  The single-stranded cDNA or antisense RNA molecules derived from biological samples are then hybridized to these DNA microarrays using stringent methods.  The quantity of hybridization measured for each specific probe is directly proportional to the number of specific mRNA transcripts present in the biological samples.  Dr. Rami Elshazli Biological Interpretation of Gene Expression Data Associate Professor of Biochemistry and Molecular Genetics  The biological interpretation of the massive gene expression data is obtained using high-throughput techniques such as microarrays and RNAseq using heat maps and gene enrichment analysis. Heat Maps and Clustering Algorithms  The generation of dendrograms = heat maps is the most common way of representing gene expression data from high-throughput techniques such as microarray.  The heat map may be combined with clustering methods which group genes and samples together based on the similarity of their gene expression pattern.  This can be useful for identifying genes that are commonly regulated or biological signatures associated with a particular condition (e.g., a disease, drug treatment, an environmental condition).  Dr. Rami Elshazli Heat Maps and Clustering Algorithms Associate Professor of Biochemistry and Molecular Genetics  There are many open-source software freely available for academic purposes such as Genesis, which can be used for the generation of heat maps as well as hierarchical clusters and complete linkage to deduce gene signatures.  Commercial software used for microarray data analysis such as GeneSpring, Partek Genomic Suite can also generate the heat maps and clusters from the gene expression data.  In heat maps, each row denotes a gene, and each column denotes a sample.  The color and the intensity of each row (gene) varies based on the changes in the expression of the individual gene. https://genome.tugraz.at/genesisclient/genesisclient_download.shtml  Dr. Rami Elshazli Gene Set Enrichment Analysis and Pathway Analysis Associate Professor of Biochemistry and Molecular Genetics  The Gene Ontology (GO) is a complete source of computable knowledge about the functions of genes and gene products.  It is used in biomedical research specifically for the analysis of omics and related data.  The gene set enrichment analysis (GSEA) based on the GO functional annotation of the differentially expressed genes interpret the differentially expressed gene sets to decipher its association with a molecular function or chromosomal location.  The GSEA can be performed using the desktop application GSEA-P.  The most common tools used for gene set enrichment and pathway enrichment analyses are:  Database for Annotation, Visualization and Integrated Discovery (DAVID),  Ingenuity Pathway Analysis (IPA).  Reactome, KEGG, and STRING. https://www.gsea-msigdb.org/gsea/index.jsp  Dr. Rami Elshazli Next-Generation Sequencing Technology Associate Professor of Biochemistry and Molecular Genetics  Next-generation sequencing (NGS) is used to analyze DNA and RNA samples with a single nucleotide resolution.  It is very easy to study spliced transcripts, allelic gene variants and single nucleotide polymorphisms (SNPs).  NGS has higher reproducibility and needs less DNA/RNA concentration (nanograms) compared to the microarrays.  RNA sequencing (RNAseq) is obtained by reverse transcription from RNA to get information about the RNA content of a sample.  RNAseq is used to study differential gene expression, alternative splicing events, and allele-specific expression.  Single cells RNAseq (single-cell transcriptomics) is used to study differential gene expression, unique cellular processes, cellular diversity in regenerative medicine, immunology, neurobiology and cardiovascular diseases.  Dr. Rami Elshazli Metanalysis Using Functional Bioinformatics Tools Associate Professor of Biochemistry and Molecular Genetics  Meta-analysis is a subarea of functional genomics where data derived from previous experiments may either be analyzed alone or combined with new data to create statistically robust models.  The processed data deposited in the functional genomics databases like Gene Expression Omnibus (GEO) can be used for meta-analyses of the high throughput microarray and NGS data.  Dr. Rami Elshazli Metanalysis Using Functional Bioinformatics Tools Associate Professor of Biochemistry and Molecular Genetics  Dr. Rami Elshazli Associate Professor of Biochemistry and Molecular Genetics