1. Introduction to Bioinformatics
IBGP/BMI 730
Introduction to Bioinformatics
Director: Prof. Victor Jin

2. Basic Molecular Biology
All living things are made of Cells
Prokaryote, Eukaryote
Cell Signaling
What is Inside the cell: From DNA, to RNA, to Proteins

3. Cells Fundamental working units of every living system.
Every organism is composed of one of two
radically different types of cells:
prokaryotic cells or eukaryotic cells.
Prokaryotes and Eukaryotes are descended from the same primitive cell.
All extant prokaryotic and eukaryotic cells are the result of a total of 3.5 billion years of evolution.
Crowded slide
Organism of life….
Cells

4. Cell Structure
A cell is a smallest structural unit of an organism that is capable of independent functioning
All cells have some common features

5. Cell Cycle
Born, eat, replicate, and die

6. The Tree of Life
According to the most recent evidence, there are three main branches to the tree of life.
Prokaryotes include Archaea (“ancient ones”) and bacteria.
Eukaryotes are kingdom Eukarya and includes plants, animals, fungi and certain algae.

7. Prokaryotes and Eukaryotes
Single cell
Single or multi cell
No nucleus
Nucleus
No organelles
Organelles
One piece of circular DNA
Chromosomes
No mRNA post transcriptional modification
Exons/Introns splicing

8. Signaling Pathways: Control Gene Activity
Instead of having brains, cells make decision through complex networks of chemical reactions, called pathways
Synthesize new materials
Break other materials down for spare parts
Signal to eat or die

9. An Example -- Cell Cycle Signaling

10. Cells Information and Machinery
Cells store all information to replicate itself
Human genome is around 3 billions base pair long
Almost every cell in human body contains same set of genes
But not all genes are used or expressed by those cells
Machinery:
Collect and manufacture components
Carry out replication
Kick-start its new offspring

11. Terminology Genome: an organism’s genetic material
Gene: a discrete units of hereditary information located on the chromosomes and consisting of DNA
Genotype: The genetic makeup of an organism
Phenotype: the physical expressed traits of an organism
Nucleic acid: Biological molecules(RNA and DNA) that allow organisms to reproduce
Amino acid: Organic molecules that build blocks of proteins.
Protein: a large, complex molecule that is essential part of organisms and participates in every process within cells and achieve a particular function.

12. Three critical molecules
DNAs
Hold information on how cell works
RNAs
Act to transfer short pieces of information to different parts of cell
Provide templates to synthesize into protein
Proteins
Form enzymes that send signals to other cells and regulate gene activity
Form body’s major components (e.g. hair, skin, etc.)

13. Overview of DNA to RNA to Protein
A gene is expressed in two steps
Transcription: RNA synthesis
Translation: Protein synthesis

14. DNA the Genetics Makeup
Genes are inherited and are expressed
genotype (genetic makeup)
phenotype (physical expression)
On the left, is the eye’s phenotypes of green and black eye genes.

15. Central Dogmas of Molecular Biology
1) The concept of genes is historically defined on the basic of genetic inheritance of a phenotype. (Mendellian Inheritance)
2) The DNA an organism encodes the genetic information. It is made up of a double stranded helix composed of ribose sugars.
Adenine(A), Citosine (C), Guanine (G) and Thymine (T).
[note that only 4 values nees be encode ACGT.. Which can be done using 2 bits.. But to allow redundant letter combinations (like N means any 4 nucleotides), one usually resorts to a 4 bit alphabet.]

16. Central Dogmas of Molecular Biology
3) Each side of the double helix faces it´s complementary base.
A T, and G  C.
4) Biochemical process that read off the DNA always read it from the 5´´side towards the 3´ side. (replication and transcription).
5) A gene can be located on either the ´plus strand´ or the minus strand. But rule 4) imposes the orientation of reading .. And rule 3 (complementarity) tells us to complement each base E.g.
If the sequence on the + strand is ACGTGATCGATGCTA, the – strand must be read off by reading the complement of this sequence going ´backwards´
e.g. TAGCATCGATCACGT

17. Central Dogmas of Molecular Biology
6) DNA information is copied over to mRNA that acts as a template to produce proteins.
We often concentrate on protein coding genes, because proteins are the building blocks of cells and the majority of bio-active molecules. (but let´s not forget the various RNA genes)

18. Bioinformatics
Bioinformatics (computational biology) solves biological problems on the molecular level with the use of techniques including:
applied mathematics
statistics
computer science
artificial intelligence
Crowded slide
Organism of life….
Cells

19. Bioinformatics
Biological
Data
Computer
Calculations +

20. Molecular Biology as an Information Science
Central Paradigm for Bioinformatics -> Genomic Sequence -> Transcript > Protein Structure > Protein Function
Large Amounts of Information
Data Management
Computer Algorithms
Statistical Methods
Central Dogma of Molecular Biology DNA -> RNA -> Protein -> Phenotype
Molecules
Sequence, Structure, Function
Processes
Mechanism, Specificity, Regulation

21. Major research efforts
Sequence alignment
Gene finding
Genome assembly
RNA structure prediction
Protein structure prediction
Analysis of gene regulation
Prediction of protein-protein interactions
Modeling of evolution

22. Major research areas Sequence analysis Genome annotation
Computational evolutionary biology
Measuring biodiversity
Analysis of gene expression
Analysis of regulation
Analysis of protein expression
Analysis of mutations in cancer
Analysis of epigenetics in cancer
High-throughput in vivo binding analysis
Prediction of protein structure
Comparative genomics
Modeling biological systems
High-throughput image analysis
Protein-protein docking
Software and tools
Databases
Web services in bioinformatics

23. Data types DNA sequences RNA sequences Protein sequences
Gene Expression
cDNA, mRNA microarray data
Now tiling array technology
50 M data points to tile the human genome at ~50 bp res.
Can only sequence genome once but can do an infinite variety of array experiments
Protein-DNA interactions
ChIP-chip, ChIP-seq, ChIP-PET and so on
Phenotype Experiments
KOs
Protein Interactions
Yeast hybrid
Proteomics

24. Other Integrative Data
Information to understand genomes
Metabolic Pathways
Regulatory Networks
Signaling Networks
Whole Organisms Phylogeny
The Literature (MEDLINE)

25. GenBank Growth

26. Exponential Growth of Data Matched by Development of Computer Technology
CPU vs Disk & Internet
Driving Force in Bioinformatics
Internet Hosts
No. Protein Domain Structures

27. Types of Relational databases
The Internet can be thought of as one enormous relational database.
The “links”/URL are the primary keys.
SQL (Standard Query Language)
Sybase; Oracle ; Access; (Databases systems)
Sybase used at NCBI.
SRS(One type of database querying system of use in Biology)

28. XML Database and vocabularies for life science
HTML: Hypertext Markup Language
XML: a general-purpose specification for creating custom markup languages. It is classified as an extensible language, because it allows the user to define the mark-up elements
BSML: an extensible language specification and container for bioinformatic data. BSML was developed under a 1997 grant from the National Human Genome Research Institute (NHGRI) as an evolving public domain standard for the bioinformatics community

29. Examples of XML <?xml version="1.0" encoding="UTF-8"?>
<element_name attribute_name="attribute_value">Element Content</element_name>
<book>This is a book... </book>

30. Primary Databases
A primary Database is a repository of data derived from experiments or from research knowledge.
Genbank (Nucleotide repository)
Protein DB, Swissprot
PDB (MMDB) are primary databases.
Pubmed (literature)
Genome Mapping databases.
Kegg Database.(pathways)

31. Secondary Databases
A secondary database contains information derived from other sources.
Refseq (Currated collection of Genbank at NCBI)
UniGene (Clustering of ESTs at NCBI)
GeneID (Unique ID for each Gene at NCBI)
Organism-specific databases are often a mix between primary and secondary.

32. Biological Databases Nucleotide databases:
Genbank: International Collaboration
NCBI (USA), EMBL (Europe), DDBJ (Japan and Asia)
A “bank” No curation.. Submission to these database is required for publication in a journal.
Organism specific databases
(Quick quiz: Find URLs using search engines)
FlyBase
ChickGBASE
pigbase
wormpep
YPD (Yeast Protein Database)
SGD(Saccharomyces Genome Database)

33. Protein Databases: NCBI: More on next week
Swiss Prot:(Free for academic use, otherwise commercial. Licensing restrictions on discoveries made using the DB version free of any licensing)
pay version)
NCBI has the latest free version.
Translated Proteins from Genbank Submissions
EMBL
TrEMBL is a computer-annotated supplement of SWISS-PROT that contains all the translations of EMBL nucleotide sequence entries not yet integrated in SWISS-PROT
PIR

34. Genome mapping information:
Structure databases:
PDB: Protein structure database.
MMDB: NCBI’s version of PDB with entrez links.
Genome mapping information:
NCBI (Human)
Genome Centers:
Stanford, Washington University, UC Berkeley
Research Centers and Universities

35. Literature databases:
NCBI: Pubmed: All biomedical literature.
Abstracts and links to publisher sites for
full text retrieval/ordering
journal browsing.
Publisher web sites.
Biomednet: Commercial site for litterature search.
Pathways database:
KEGG: Kyoto Encyclopedia of Genes and Genomes:
Genome Search and Visualization database:
UCSC Genome Browser (genome.uscs.edu/)

36. Information techniques
Geometry
Robotics
Graphics (Surfaces, Volumes)
Comparison and 3D Matching (Vision, recognition)
Physical Simulation
Newtonian Mechanics
Electrostatics
Numerical Algorithms
Simulation
Databases
Building, Querying
Complex data
Text String Comparison
Text Search
1D Alignment
Significance Statistics
Alta Vista, grep
Finding Patterns
Machine Learning
Clustering
Data mining

37. Bioinformatics as New Paradigm for Scientific Computing
Physics
Prediction based on physical principles
EX: Exact Determination of Rocket Trajectory
Emphasizes: Supercomputer, CPU
Biology
Classifying information and discovering unexpected relationships
EX: Gene Expression Network
Emphasizes: networks, “federated” database

38. Topics -- Genome Sequence
Finding Genes in Genomic DNA
introns
exons
promotors
Characterizing Repeats in Genomic DNA
Statistics
Patterns
Duplications in the Genome
Large scale genomic alignment
Whole-Genome Comparisons
Finding Structural RNAs

39. Topics -- Protein Sequence
Sequence Alignment
How to align two strings optimally via Dynamic Programming
Local vs Global Alignment
Suboptimal Alignment
Hashing to increase speed (BLAST, FASTA)
Amino acid substitution scoring matrices
Multiple Alignment and Consensus Patterns
How to align more than one sequence and then fuse the result in a consensus representation
HMMs, Profiles
Motifs
Scoring schemes and Matching statistics
How to tell if a given alignment or match is statistically significant
A P-value or An E-value)?
Score Distributions
Low Complexity Sequences
Evolutionary Issues
Rates of mutation and change

40. Topics – Structures Secondary Structure “Prediction”
via Propensities
Neural Networks, Genetic Alg.
Simple Statistics
TM-helix finding
Assessing Secondary Structure Prediction
Structure Prediction: Protein vs RNA
Tertiary Structure Prediction
Fold Recognition
Threading
Ab initio
Direct Function Prediction
Active site identification
Relation of Sequence Similarity to Structural Similarity

41. Topics -- Structures Structural Alignment
Structure Comparison
Basic Protein Geometry and Least-Squares Fitting
Distances, Angles, Axes, Rotations
Calculating a helix axis in 3D via fitting a line
LSQ fit of 2 structures
Molecular Graphics
Calculation of Volume and Surface
How to represent a plane
How to represent a solid
How to calculate an area
Hinge prediction
Packing Measurement
Structural Alignment
Aligning sequences on the basis of 3D structure.
DP does not converge, unlike sequences, what to do?
Other Approaches: Distance Matrices, Hashing
Fold Library
Docking and Drug Design as Surface Matching

42. Topics – Function Genomics
Genome Comparisons
Ortholog Families, pathways
Large-scale censuses
Frequent Words Analysis
Genome Annotation
Identification of interacting proteins
Networks
Global structure and local motifs
Structural Genomics
Folds in Genomes, shared & common folds
Bulk Structure Prediction
Genome Trees
Expression Analysis
Time Courses clustering
Measuring differences
Identifying Regulatory Regions
Large scale cross referencing of information
Function Classification and Orthologs
The Genomic vs. Single-molecule Perspective

43. Bioinformatics tools
Sequence comparison (pairwise and multiple alignments, e.g. ClustalW, Blastz, )
Phylogenetic reconstruction (e.g. Phylip, IQPNNI, SplitsTree)
Database search (e.g. BLAST, HMMer)
Comparative sequence assembly (e.g. OSLay)
Gene finding (e.g. genscan, FirstEF)
Motif discovery (e.g. MEME, Weeder)
Protein structure (e.g. CE)

44. Bioinformatics algorithms
Dynamic Programming
EM algorithms
Neural Networks
Hidden Markov Models
Support Vector Machine
Phylogenetic Trees
Clustering

45. Bioinformatics Topics?
(YES?) Digital Libraries
Automated Bibliographic Search and Textual Comparison
Knowledge bases for biological literature
(YES) Motif Discovery Using Gibb's Sampling
(YES) Metabolic Pathway Simulation
(YES) Gene identification by sequence inspection
Prediction of splice sites
(YES) Linkage Analysis
Linking specific genes to various traits
YES) RNA structure prediction Identification in sequences
(YES) Homology modeling