1. A number of slides taken/modified from:
CIS 595 Bioinformatics
Lecture 2
Introduction to Bioinformatics
A number of slides taken/modified from:
Russ B. Altman (
Patrik Medstrand ( old/download/intro2003/databases_handouts.pdf)
Mark Gerstein (

2. What is Bioinformatics?
Every application of computer science to biology
Sequence analysis, images analysis, sample management, population modeling, …
Analysis of data coming from large-scale biological projects
Genomes, transcriptomes, proteomes, metabolomes, etc…

3. The New Biology Traditional biology New “high-throughput” biology
Small team working on a specialized topic
Well defined experiment to answer precise questions
New “high-throughput” biology
Large international teams using cutting edge technology defining the project
Results are given raw to the scientific community without any underlying hypothesis

4. Examples of “High-Throughput”
Complete genome sequencing
Simultaneous expression analysis of thousands of genes (DNA microarrays, SAGE)
Large-scale sampling of the proteome
Protein-protein analysis large-scale 2-hybrid (yeast, worm)
Large-scale 3D structure production (yeast)
Metabolism modeling
Biodiversity

5. Role of Bioinformatics
Control and management of the data
Sequence, Structure and Function analysis
Analysis of primary data e.g.
Mass spectra analysis
DNA microarrays image analysis
Statistics
Database storage and access
Interpreting results in a biological context

6. Sequence, Structure and Function Analysis
In order to gather insight into the ways in which genes and gene products (proteins) function perform:
SEQUENCE ANALYSIS: Analyze DNA and protein sequences, searching for clues about structure, function, and control.
STRUCTURE ANALYSIS: Analyze biological structures, searching for clues about sequence, function and control.
FUNCTION ANALYSIS: Understand how the sequences and structures leads to the functions.

7. Evolution and Bioinformatics
Common descent of organisms implies that they will share many “basic technologies.”
Development of new phenotypes in response to environmental pressure can lead to “specialized technologies.”
More recent divergence implies more shared technologies between species.
All of biology is about two things: understanding shared or unshared features.

8. Biology is Fundamentally Information Science
Where is information:
DNA Sequences
GENBANK release 128 (2/02) contains 17,089,143,893 bases in 1,546,532 sequences
Protein Sequences
PIR or Swiss-prot (as of 3/02); 106,736 sequences, 39,242,287 total amino acids
Protein 3D Structures
Protein Data Bank (PDB), as of March 2002: 17,679 Coordinate Entries; 15,855 proteins, 1060 nucleic acids, 746 protein/nucleic acid complex 18 carbohydrates

9. Biology is Fundamentally Information Science
Where is information:
Online access to DNA microarray data
10,000 to 40,000 genes per chip; Each set of experiments involves 3 to 100 “conditions”
Medical Literature on line.
Online database of published literature since 1966 = Medline = PubMED resource 4,600 journals 11,000,000+ articles (most with abstracts)
ETC…

10. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

11. Sequence Alignment What is sequence alignment? Why align sequences?
Given two sequences and a scoring scheme find the optimal pairing of letters.
RKVA--GMAKPNM
RKIAVAAASKPAV
Why align sequences?
A few sequences with known structure and function; much more with unknown properties.
If one of them has known structure/function, then alignment to the other yields insight about another
Similarity may be used as evidence of homology, but does not necessarily imply homology

12. Sequence Alignment Types of alignment: Local vs. global;
Pairwise vs. multiple
d1dhfa_ LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQ-NLVIMGKKTWFSI
d8dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTSTSHVEGKQ-NAVIMGKKTWFSI
d4dfra_ ISLIAALAVDRVIGMENAMPWN-LPADLAWFKRNTL NKPVIMGRHTWESI
d3dfr__ TAFLWAQDRDGLIGKDGHLPWH-LPDDLHYFRAQTV GKIMVVGRRTYESF

13. Sequence Alignment How to measure the alignment quality?
Define scoring matrix (PAM250)

14. Sequence Alignment Alignment algorithms: Similarity strength:
dot matrix
dynamic programming
Fasta,
Blast,
Psi-Blast;
Clustal
Similarity strength:
Percent identity
E-value (statistical measure)

15. Sequence Alignment

16. Sequence Motifs
A subsequence that occurs in multiple sequences with a biological importance.
Protein motifs often result from structural features
DNA sequences that provide signals for protein binding or nucleic acid folding

17. Sequence Motifs
PROSITE Database a collection of motifs (1135 different motifs):
A manually created collection of regular expressions associated with different protein families/functions.
Globin sequence signature (PDOC00933):
F-[LF]-x(5)-G-[PA]-x(4)-G-[KRA]-x-[LIVM]-x(3)-H

18. Gene Finding
Problem : Identify the genes within raw genomic DNA sequence
Input: Raw DNA sequence
Output: Location of gene elements in the raw sequence (including exons, introns, other sequence annotations)

19. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

20. Computing with Biological Structures
General Issues
How do we represent structure for computation?
How do we compare structures?
How can we summarize structural families?

21. Computing with Biological Structures
Applications:
Structure alignment
Build fold library Hb
Alignment of Individual Structures
Fusing into a Single Fold “Template” Mb

22. Computing with Biological Structures
Why align structures:
Provides the “gold standard” for sequence alignment
For nonhomologous proteins, identify common substructures of interest
Classify proteins into clusters, based on structural similarity (SCOP)

23. Computing with Biological Structures
Applications:
Predicting RNA Secondary Structure (the MFOLD Program

24. Computing with Biological Structures
Protein secondary structure prediction
Sequence RPDFCLEPPYTGPCKARIIRYFYNAKAGLVQTFVYGGCRAKRNNFKSAEDAMRTCGGA
Structure CCGGGGCCCCCCCCCCCEEEEEEETTTTEEEEEEECCCCCTTTTBTTHHHHHHHHHCC

25. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

26. Phylogenetic Algorithms
Why build evolutionary tree?
Understand the lineage of different species.
Have an organizing principle for sorting species into a taxonomy
Understand how various functions evolved.
Understand forces and constraints on evolution.
To do multiple alignment.

27. Phylogenetic Algorithms
Multiple Alignment and Trees
Progressive alignment methods do multiple alignment and evolutionary tree construction at the same time.
Sequence alignment provides scores which can be interpreted as inversely related to distances in evolution.
Distances can be used to build trees.
Trees can be used to give multiple alignments via common parents.

28. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

29. Microarray Data Analysis
Experimental Protocol

30. Microarray Data Analysis

31. Microarray Data Analysis
What are expression arrays good for?
Follow population of (synchronized) cells over time, to see how expression changes (vs. baseline).
Expose cells to different external stimuli and measure their response (vs. baseline).
Take cancer cells (or other pathology) and compare to normal cells.
(Also some non-expression uses, such as assessing presence/absence of sequences in the genome)

32. Microarray Data Analysis
Preprocessing
Merging
replicate
experiments
Score
differential
hybridization
Background
correction
Cy5/Cy3
normalization
Data input
Duplicate spot variability
Replicate experiment variability
Spot
quality
Artifactual regions

33. Microarray Data Analysis
Convert microarray images to data

34. Microarray Data Analysis
Clustering:
If two genes are expressed in the same way, they may be functionally related.
If a gene has unknown function, but clusters with genes of known function, this is a way to assign its general function.
We may be able to look at high resolution measurements of expression and figure out which genes control which other genes.
E.g. peak in cluster 1 always precedes peak in cluster 2 => cluster 1 turns cluster 2 on?

35. Microarray Data Analysis
Classification:
Uses known groups of interest (from other sources) to
learn the features associated with these groups in the primary data,
create rules for associating the data with the groups of interest.
Often called “supervised machine learning.”

36. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

37. Genetic Networks What is a genetic network?
Individual genes have a function (e.g. transforming a substance or binding to a substance)
Sets of functions when sequenced can produce pathways (e.g. output of one transformation is the input to another)
Sets of pathways, as they interact with other pathways, create a genetic network of interactions.

38. Genetic Networks Reconstructing Genetic Regulatory Networks:
Hard problem.
Given N genes, there are an exponential number of connections between the genes.
Relationships are not generally +/- but are but are continuous valued.
Must use knowledge about expected function and membership in pathways to prune the list of possible network interactions.

39. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

40. Comparative Genomics Large scale comparison of genomes to Assumption:
understand the biology of individual genomes
extract general principles applying to groups of genomes.
Assumption:
many biological sequences, structures, and functions are shared across organisms,
the signal from these organisms can be increased by combining them in analyses.

41. Comparative Genomics Important issues for Comparative Genomics
Aligning very large sequences
Comparative approaches to gene finding
Comparative approaches to assigning function
Comparative approaches to identifying key regulatory regions

42. Example: Assigning protein functions
Comparative Genomics
Example: Assigning protein functions

43. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

44. Proteomics What is PROTEOMICS?
-OMICS has become the suffix to denote the study of the entire set of something
Genomics: study of all genes
Proteomics: study of all proteins
Transcriptomics: study of all mRNA transcripts
Metabolomics: study of metabolites in cell

45. Proteomics Proteomics questions
Which proteins are made from the genome?
What is their 3D structure?
Where they are?
What they do?
Which other proteins they interact with?
Are they modified in the cell post-translationally?

46. Proteomics Key proteomic technologies
3D structure determination (X-ray/NMR)
2D Gels to assess all the proteins in a cell.
Mass spectrometry to identify proteins, protein modifications.
Yeast-Two-Hybrid systems to assess protein-protein interactions
Protein Arrays to assess all proteins in a cell using antibodies or other recognition technology.

47. Topics Sequence Alignment; Sequence Motifs; Gene Finding
Computing with Biological Structures
Phylogenetic Algorithms
Microarray Data Analysis
Genetic Networks
Comparative Genomics
Proteomics
Biological Ontologies; Biological Text Mining

48. Biomedical Ontologies
In order to communicate effectively we need:
common language
basic knowledge
Example:
Metabolic Pathways:
language: names of products, enzymes, substrates and pathways
knowledge: what is a reaction, how do enzymes and substrates participate, what are the legal components of a pathway

49. Biomedical Ontologies
Gene Ontology (
Used to classify gene function.
A controlled listing of three types of function:
Molecular Function
Biological Process
Cellular Component

50. Biological Text Mining
Literature in Biomedicine
Much literature generated quickly.
11 million citations in MEDLINE.
400,000 added yearly.
Need methods to deal with data.
Query
Summarize
Organize
Understand

51. Long term challenges Computational model of physiology.
Can we give a medication to a computer before we give it to a human?
Design of new compounds for medical and industrial use.
Can we design a protein or nucleic acid to have a specified function?
Engineering new biological pathways.
Can we devise methods for designing and implementing new metabolic capabilities for treating disease?
Data mining for new knowledge.
Can we ask computer programs to examine data (in the context of our models) and create new knowledge?