EB3233Bioinformatics
Introduction to Bioinformatics
What is Bioinformatics?
Bioinformatics is an interdisciplinary research area at the interface between computer science and biological science.
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data.
Bioinformatics involves the technology that uses computers for storage, retrieval, manipulation, and distribution of information related to biological macromolecules such as DNA, RNA, and proteins.
Interface of biology and computers which analysis proteins, genes and genomes using computer algorithms and computer databases
Computational approaches to biological questions
• Understanding one genome
• Understanding many genomes
• Identifying causal genes for a disease
• Predicting outcome under perturbations
• String and graph based algorithms for sequence assembly
• Comparing multiple genomes using trees and hidden markov models
• Clustering/Network inference
• Classification/Regression
Biological question Computational approach
What is biological data?
• Information about • the elements that make up a living system
• DNA, RNA, proteins, metabolites
• interactions among elements
• Biological data comes in many forms• sequence• secondary and tertiary structures• Knowledge bases: functions• activity levels: mRNA, protein, metabolite levels• networks of interactions among biomolecules
Biological data: Collection of “omes”
• Genome: Full DNA sequence complement of an organism
• Transcriptome: The full RNA complement of an organism (condition-specific)
• Proteome: The set of all proteins
• Metabolome: The set of all metabolites
• Interactome: The set of interactions (protein-protein, protein-DNA, genetic ..)
• …
Biological data comes in many forms
• Sequence• DNA and protein sequence
• Structure• RNA Secondary structure, protein secondary and tertiary
structure
• Real-value measurements• Gene expression, protein level
• Graphs• Biological networks
Three perspectives on bioinformatics
• The cell
• The organism
• The tree of life
First perspective: the cell
DNA RNA protein
Central dogma of molecular biology
genome transcriptome proteome
Central dogma of bioinformatics and genomics
DNA RNA
cDNAESTsUniGene
phenotype
genomicDNAdatabases
protein sequence databases
protein
Fig. 2.2Page 18
Growth of GenBank
Year
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GenBankEMBL DDBJ
Housedat EBI
EuropeanBioinformatics
Institute
There are three major public DNA databases
Housed at NCBINational
Center forBiotechnology
Information
Housed in Japan
Time ofdevelopment
Body region, physiology, pharmacology, pathology
Second perspective: the organism
After Pace NR (1997) Science 276:734
Third perspective: the tree of life
Overview of lecture topics
• Assembling genomes
• Comparing genomes
• Annotating genomes
• Analyzing functional genomics datasets (mRNA levels, protein levels)
• Inferring and analyzing biological networks
Sequencing and assembly: What is the DNA sequence of a organism?
Topics in sequence assembly
• DNA sequencing
• Graph theory• Shortest substring problem
• Hamiltonian Paths
• Survey of popular algorithms in assembly
Sequence comparison: How similar are the sequences?
Topics in sequence alignment
• Pairwise-alignment• Dynamic programming
• Local and global alignment
• Algorithms for sequence alignment
How are these organisms related?
Tohet al, Nature, 2011
Topics in comparing many genomes
• Multiple sequence alignment
• Phylogenetic trees• distance-based approaches
• parsimony-based approaches
• probabilistic methods
• examining genetic variation
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Where are the genes in this genome?Where are the genes in this genome?
CCACACCACACCCACACACCCACACACCACACCACACACCACACCACACCCACACACACACATCCTAACACTACCCTAACACAGCCCTAATCTAACCCTGGCCAACCTGTCTCTCAACTTACCCTCCATTACCCTGCCTCCACTCGTTACCCTGTCCCATTCAACCATACCACTCCGAACCACCATCCATCCCTCTACTTACTACCACTCACCCACCGTTACCCTCCAATTACCCATATCCAACCCACTGCCACTTACCCTACCATTACCCTACCATCCACCATGACCTACTCACCATACTGTTCTTCTACCCACCATATTGAAACGCTAACAAATGATCGTAAATAACACACACGTGCTTACCCTACCACTTTATACCACCACCACATGCCATACTCACCCTCACTTGTATACTGATTTTACGTACGCACACGGATGCTACAGTATATACCATCTCAAACTTACCCTACTCTCAGATTCCACTTCACTCCATGGCCCATCTCTCACTGAATCAGTACCAAATGCACTCACATCATTATGCACGGCACTTGCCTCAGCGGTCTATACCCTGTGCCATTTACCCATAACGCCCATCATTATCCACATTTTGATATCTATATCTCATTCGGCGGTCCCAAATATTGTATAACTGCCCTTAATACATACGTTATACCACTTTTGCACCATATACTTACCACTCCATTTATATACACTTATGTCAATATTACAGAAAAATCCCCACAAAAATCACCTAAACATAAAAATATTCTACTTTTCAACAATAATACATAAACATATTGGCTTGTGGTAGCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCAATATTGCAATTTGCTTGAACGGATGCTATTTCAGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCTTTATTCACCGAGCAATAATACGGTAGTGGCTCAAACTCATGCGGGTGCTATGATACAATTATATCTTATTTCCATTCCCATATGCTAACCGCAATATCCTAAAAGCATAACTGATGCATCTTTAATCTTGTATGTGACACTACTCATACGAAGGGACTATATCTAGTCAAGACGATACTGTGATAGGTACGTTATTTAATAGGATCTATAACGAAATGTCAAATAATTTTACGGTAATATAACTTATCAGCGGCGTATACTAAAACGGACGTTACGATATTGTCTCACTTCATCTTACCACCCTCTATCTTATTGCTGATAGAACACTAACCCCTCAGCTTTATTTCTAGTTACAGTTACACAAAAAACTATGCCAACCCAGAAATCTTGATATTTTACGTGTCAAAAAATGAGGGTCTCTAAATGAGAGTTTGGTACCATGACTTGTAACTCGCACTGCCCTGATCTGCAATCTTGTTCTTAGAAGTGACGCATATTCTATACGGCCCGACGCGACGCGCCAAAAAATGAAAAACGAAGCAGCGACTCATTTTTATTTAAGGACAAAGGTTGCGAAGCCGCACATTTCCAATTTCATTGTTGTTTATTGGACATACACTGTTAGCTTTATTACCGTCCACGTTTTTTCTACAATAGTGTAGAAGTTTCTTTCTTATGTTCATCGTATTCATAAAATGCTTCACGAACACCGTCATTGATCAAATAGGTCTATAATATTAATATACATTTATATAATCTACGGTATTTATATCATCAAAAAAAAGTAGTTTTTTTATTTTATTTTGTTCGTTAATTTTCAATTTCTATGGAAACCCGTTCGTAAAATTGGCGTTTGTCTCTAGTTTGCGATAGTGTAGATACCGTCCTTGGATAGAGCACTGGAGATGGCTGGCTTTAATCTGCTGGAGTACCATGGAACACCGGTGATCATTCTGGTCACTTGGTCTGGAGCAATACCGGTCAACATGGTGGTGAAGTCACCGTAGTTGAAAACGGCTTCAGCAACTTCGACTGGGTAGGTTTCAGTTGGGTGGGCGGCTTGGAACATGTAGTATTGGGCTAAGTGAGCTCTGATATCAGAGACGTAGACACCCAATTCCACCAAGTTGACTCTTTCGTCAGATTGAGCTAGAGTGGTGGTTGCAGAAGCAGTAGCAGCGATGGCAGCGACACCAGCGGCGATTGAAGTTAATTTGACCATTGTATTTGTTTTGTTTGTTAGTGCTGATATAAGCTTAACAGGAAAGGAAAGAATAAAGACATATTCTCAAAGGCATATAGTTGAAGCAGCTCTATTTATACCCATTCCCTCATGGGTTGTTGCTATTTAAACGATCGCTGACTGGCACCAGTTCCTCATCAAATATTCTCTATATCTCATCTTTCACACAATCTCATTATCTCTATGGAGATGCTCTTGTTTCTGAACGAATCATAAATCTTTCATAGGTTTCGTATGTGGAGTACTGTTTTATGGCGCTTATGTGTATTCGTATGCGCAGAATGTGGGAATGCCAATTATAGGGGTGCCGAGGTGCCTTATAAAACCCTTTTCTGTGCCTGTGACATTTCCTTTTTCGGTCAAAAAGAATATCCGAATTTTAGATTTGGACCCTCGTACAGAAGCTTATTGTCTAAGCCTGAATTCAGTCTGCTTTAAACGGCTTCCGCGGAGGAAATATTTCCATCTCTTGAATTCGTACAACATTAAACGTGTGTTGGGAGTCGTATACTGTTAGGGTCTGTAAACTTGTGAACTCTCGGCAAATGCCTTGGTGCAATTACGTAATTTTAGCCGCTGAGAAGCGGATGGTAATGAGACAAGTTGATATCAAACAGATACATATTTAAAAGAGGGTACCGCTAATTTAGCAGGGCAGTATTATTGTAGTTTGATATGTACGGCTAACTGAACCTAAGTAGGGATATGAGAGTAAGAACGTTCGGCTACTCTTCTTTCTAAGTGGGATTTTTCTTAATCCTTGGATTCTTAAAAGGTTATTAAAGTTCCGCACAAAGAACGCTTGGAAATCGCATTCATCAAAGAACAACTCTTCGTTTTCCAAACAATCTTCCCGAAAAAGTAGCCGTTCATTTCCCTTCCGATTTCATTCCTAGACTGCCAAATTTTTCTTGCTCATTTATAATGATTGATAAGAATTGTATTTGTGTCCCATTCTCGTAGATAAAATTCTTGGATGTTAAAAAATTAAAGGGACTATATCTAGTCAAGACGATACTGTCAGTAGCAGCGATGGCAGCGTGGCTTGTGGTAGCAACACTATCATGGTATCACTAACGTAAAAGTTCCTCAATATTGCAATTTGCTTGAACGGATGCTATTTCAGAATATTTCGTACTTACACAGGCCATACATTAGAATAATATGTCACATCACTGTCGTAACACTCTTTATTCACCGAGCAATAATACGGTAGTGGCTCAAACTCATGCGGGTGCTATGATACAATTATATCTTATTTCCATTCCCATATGCTAACCGCAATATCCTAAAAGCATAACTGATGCATCTTTAATCTTGTATGTGACACTACTCATACGAAGGGACTATATCTAGTCAAGACGATACTGTGATAGGTACGTTATTTAATAGGATCTATAACGAAATGTCAAATAATTTTACGGTAATATAACTTATCAGCGGCGTATACTAAAACGGACGTTACGATATTGTCTCACTTCATCTTACCACCCTCTATCTTATTGCTGATAGAACACTAACCCCTCAGCTTTATTTCTAGTTACAGTTACACAAAAAACTATGCCAACCCAGAAATCTTGATATTTTACGTGTCAAAAAATGAGGGTCTCTAAATGAGAGTTTGGTACCATGACTTGTAACTCGCACTGCCCTGATCTGCAATCTTGTTCTTAGAAGTGACGCATATTCTATACGGCCCGACGCGACGCGCCAAAAAATGAAAAACGAAGCAGCGACTCATTTTTATTTAAGGACAAAGGTTGCGAAGCCGCACATTTCCAATTTCATTGTTGTTTATTGGACATACACTGTTAGCTTTATTACCGTCCACGTTTTTTCTAGCACCATATACTTACCACTCCATTTATGAATCAGTACC
Protein coding sequence
Protein coding sequence
Regulatory sites
Sequence annotation: What are the genes, and regulatory regions?
Genes
Chromosome IV
Topics in sequence annotation
• Markov chains
• hidden Markov models
• Forward/Backward/Viterbi algorithms
• applications to gene finding and motif modeling
What genes are associated with what functions?
• Measure mRNA/proteins levels under different environmental conditions
• Compare levels of genes under different conditions
Gen
es
Environmental Conditions
Gaschet al., 2000
Topics in Data Analysis from High-Throughput Experiments
• clustering algorithms
• hierarchical clustering
• k-means clustering
• EM-based clustering
• classification algorithms (simple methods for supervised learning)
• multiple hypothesis testing and the false discovery rate
What’s next?
•Introduction to Biological Databases