Spring 2002	Seminar in Biotechnology	Douglas W. Smith
2130 Bonner Hall	BILD94	5254 Muir Biology Building
	Suresh Subramani, Instructor	x42620; dsmith@ucsd.edu

Introduction to Bioinformatics

Web page: http://elcapitan.ucsd.edu/bild94/

Search Google et al for "bild94 bioinformatics"

| DNASYSTEM | Lecture |

Topics:

I. What is Bioinformatics?

 

II. Sequence Databases and Their Use

A. Primary Sequence Databases

B. Uses of Sequence Databases

C. Retrieve Information from Sequence Databases

D. Analysis using Sequences: finding Homologues

1. BLAST: Basics, Filters, Variations

2. FASTA

E. Analysis using Sequences: finding Genes in DNA

1. Grail

2. BCM Gene Finder - BCM Search Launcher

3. GeneMark

F. Analysis using Sequences: finding Motifs

1. Motifs

2. Protein Family Classifications

G. Multiple Sequence Analysis

1. Clustal W

2. Web Databases of Multiple Sequence Alignments

H. Phylogenetics

1. Basics and Methods

2. Confidence Levels

 

III. Whole Genomes

A. Implications

B. TIGR

 

IV. Organism and Other Databases

A. Need for Organism Databases

B. Paradigm: ACeDB

C. Advantages of ACeDB for Organism DBs

D. ACeDB on Web: WebAce and AceBrowser

1. ACeDB: Disadvantages

2. WebAce and AceBrowser

E. Example of ACeDB: DictyDB

1. Graphics, Text Displays

2. WebAce and AceBrowser

F. Other Web Organism Databases

 

V. Problems ... Directions to Go

A. Problems

B. Need: "smart" Analysis Packages

C. "Training" of "smart" Analysis Packages

 

VI. Additional Materials

A. Books

B. Recent Short Articles

I. What is Bioinformatics?

Bioinformatics:

• computerized annotation of genomic and biological information and data (databases)
• transformation and manipulation of these data (software tools)
  • computational analysis of biological data

Overall Aim of Bioinformatics:

• provide biologically important predictions from annotated data and transformation / manipulation of these data

Databases:

• Primary and added-value databases
• Sequence vs organismic databases
• 'Federated' databases: global computer networks ... WWW
  • Software tools access multiple databases, often at different sites
    

Software tools (computer programs):

• Software tools: sequence analysis, database construction and management, evolutionary relations, structural analyses, pathways, microarray analysis, proteomic analysis
• Software tools integrated into databases

Key words from some of the review articles listed at end:

• Finding genes, locating coding regions, predicting function: automate
• Function, Evolution, Sequence, Structure (FESS relationships)
• Metabolic genotype, phenotype, redundancy
• Genes to Pathways; Genes to biological knowledge
• Assigning gene sets to different species: homologs vs paralogs
• Finding conserved proteins common to all life
• Expression profiles, relation to metabolic pathways / genetic networks
• Gene synteny between species: gene adjacency in genomes

The Need for Bioinformatics:

• Whole Genome Analyses and Sequences
• Experimental Analyses involving Thousands of Genes simultaneously
• DNA Chips and Array Analyses
  • Expression Arrays
  • Comparative Analyses between Species and Strains
• Proteomics: 'Proteome' of an Organism ... 2D gels, Mass Spec
• Medical applications: Genetic Disease ... SNPs
  • Pharmaceutical and Biotech Industry
• Forensic applications
• Agricultural applications

Bioinformatics is New, Hot, and Growing:

Chronology of Review Articles:

446 at PubMed for 'bioinformatics AND review',

(101 for 'bioinformatics AND review AND 2002')

257 for 'bioinformatics AND review AND 2001'
144 for 'bioinformatics AND review AND 2000'
67 for 'bioinformatics AND review AND 1999'
52 for 'bioinformatics AND review AND 1998'
18 for 'bioinformatics AND review AND 1997'
13 for 'bioinformatics AND review AND 1996'
6 for 'bioinformatics AND review AND 1995'
7 for 'bioinformatics AND review AND 1994'
1 for 'bioinformatics AND review AND 1993'
2 for 'bioinformatics AND review AND 1992'
0 for 'bioinformatics AND review AND 1991'

II. Sequence Databases and Their Use:

A. Primary Sequence Databases:

• Nucleic Acid Databases
  • NCBI (Natl Center Biotech Information) - GenBank
    • http://www.ncbi.nlm.nih.gov/
  • EBI (European Bioinformatics Institute) - EMBL
    • http://www.ebi.ac.uk/
  • DISC - DNA Information and Stock Center, Japan
    • http://www.dna.affrc.go.jp/
      
• Protein Databases
  • NCBI - GenPept
    • http://www.ncbi.nlm.nih.gov/
  • ExPASy - SwissProt and TrEMBL
    • http://www.expasy.ch/
  • EBI (European Bioinformatics Institute)
    • SwissProt, TrEMBL, PIR
    • http://www.ebi.ac.uk/
  • DISC - DNA Information and Stock Center, Japan
    • http://www.dna.affrc.go.jp/

B. Uses of Sequence Databases:

• Information Retrieval
• Analysis: "given a new DNA sequence, what's in it?"
  • Finding Homologues
  • Finding Genes
  • Finding Motifs - DNA Binding Sites

C. Retrieve Info from Sequence Databases:

• NCBI - Entrez
  • http://www.ncbi.nlm.nih.gov/Entrez/
  • Types of Databases Available
  • Entrez Help
  • Retrieve Large Data Sets
    
    
• ExPASy - SwissProt and TrEMBL
  • http://www.expasy.ch/
  • SwissProt - Bairoch well-annotated non-redundant protein DB
  • TrEMBL - Translation of EMBL DNA coding sequences
    
    
• EBI - SwissProt, TrEMBL, PIR
  • http://www.ebi.ac.uk/
  • SRS - Sequence Retrieval System
  • Software Tools - FASTA, WU-Blast2, ClustalW
  • EBI2 - second server at EBI
    
    
• DISC - DNA Information and Stock Center, Japan - DDBJ
  • http://www.dna.affrc.go.jp/
  • SRS - Sequence Retrieval System
  • Software Tools - FASTA, BLAST, MpSrch
    
    
• PDB - Protein DataBank
  • http://www.rcsb.org/pdb/
  • Protein 3D Structure database

D. Sequence Analysis: finding Homologues

• Homologues - sequences descending from common ancestor
  
  
• Comparison of Sequences using Distance Matrix approach
  • PAM (Point Adjusted Mutation) matrices
  • BLOSUM matrices
  • Unitary matrix for nucleic acid comparisons
    
    
• DOT PLOTS - 2D graph of alignment of two sequences
  • Use same sequence to find Direct Repeats, e.g. DNA tandem repeats
  • Use DNA strand against complementary strand to find Inverted Repeats - e.g. RNA stem-loop secondary structure
  • 'BLAST 2 Sequences' at NCBI one of the few Web sites to do this ... but just gives the best alignment
  • BCM Search Launcher does similarly
    
    
• BLAST - Basic Local Alignment Sequence Tool
  • http://www.ncbi.nlm.nih.gov/BLAST/
    
    
• FASTA - fast, global database search tool of Pearson and Lipman
  
  
• Others:
  • WU-Blast - Washington Univ gapped BLAST version
  • SSEARCH - Smith-Waterman algorithm used
  • BLITZ - Smith-Waterman on Parallel Processing computer
  • MpSrch - similar to BLITZ

1. BLAST:

Basic BLAST Algorithm:

• Objective: find all Local regions of similarity distinguishable from random
  
  
• Only Local alignments permitted - no Gaps
  • Mathematically sound - Karlin/Altschul statistics
    
    
• 3 step algorithm:
  • Compile list of high scoring words of length w
    • w = 4 for proteins, w = 12 for nucleic acids
  • Scan for 'word hits' of score greater than Threshold T
  • Extend word hits in both directions to find High Scoring Pairs (HSPs) with scores greater than S
    
    
• Word list compiled from words in Query sequence: Hash Table
  
  
• S determined from EXPECT parameter: number of hits expected to be found at random (default: EXPECT = 10)
  
  
• Distribution of hits found with given Score is an Extreme Value Distribution (distribution of Maximum, rather than Sum, of many indep random variables)

BLAST Programs:

• BLASTN - NA query against NA database
• BLASTP - Protein query against Protein database
• BLASTX - NA query (translated) against Protein database
• TBLASTN - Protein query against NA (translated) database
• TBLASTX - NA query (translated) against NA (translated) database

Filters:

• SEG filter - ignor Low Information Content / Low Complexity seqs
• DUST filter - like SEG but used for DNA
• XNU filter - used to ignor repeated sequences (not used much)

Variations on basic BLAST algorithm:

• Gapped-BLAST or BLAST 2.0
  • Extend Word Pairs via 'Two-hit Method': require two on same 'diagonal' (no gaps) - then extend
  • Generate Gapped Alignments: use best Word Pair Extension, do NWS, accepting other Extensions that drop Score no more than an amount X_G
    
    
• PSI-BLAST (Position-Specific Iterated BLAST)
  • Gapped-BLAST used
  • Iterate BLAST searches using 'Position Specific Score Matrix' generated in one iteration for the next iteration.
  • This 'Position Specific Score Matrix' or 'Profile' is used instead of the Input Sequence and Distance Matrix.
    
    
• PHI-BLAST (Pattern-Hit Initiated BLAST)
  • Combines matching of 'Regular Expressions' with BLAST Extensions to give local alignments about the match.
  • Used for Protein Motif tasks
  • Can be combined with PSI-BLAST

Output: example of BLASTP with filter, defaults here

• Java Applet Graphic - color coded hits, MouseOver links
• One-liners - Genbank entry info, description, Score (bits), E value
• Alignments - statistics, Xs for filtered residues, links to Entrez
• Statistics - BLAST run statistics

2. FASTA:

Rapid, heuristic (not mathematically sound) global alignment

FASTA 4-Step Algorithm:

• Find best alignments on diagonals ... ktup = n: number of perfect matches required for Word ... join Words on given diagonal
  • ktup = 1-2 for proteins; ktup = 4-6 for nucleic acids
• Rescue 10 best Regions, using Distance Matrix scoring
  • Best single Diagonal Score - init1 score
• Join Initial Regions ... use only hits where init1 > Cutoff
  • Gaps and Gap Penalty - Distance between Diagonals < GapPen
  • Best joined Score - initn score
• Optimize alignment via NWS Dynamic Programming
  • Final Score - opt score
    
    

Output: example here

• Histogram - Extreme Value Distribution
• One-liners - initn, init1, opt, Z-score, E value
• Alignments - with gaps
  
  

E. Sequence Analysis: finding Genes in DNA

Methods:

• Gene Search by Signal
  • Algorithm: Postion Specific Weight Matrix - Sliding Window
  • Look for Signals - Promoter Sites, Splice Sites, ...
    
    
• Gene Search by Content
  • Open Reading Frames
  • Use of Statistical Properties of Protein Coding Regions
    • Unequal use of amino acids
    • Unequal numbers of codons per amino acid
    • Codons available not equally used - Codon Usage
  • Methods:
    • Positional Base Frequencies - nucleotide composition is highly dependent on Reading Frame
    • Codon Usage / Codon Preference
    • Base Composition bias
      
• The major problem - small Exons ( < 50 nucs)

Grail:

• Basic Algorithm: 7 'Sensor Algorithms' - into Neural Net
• Current version of Grail per se: Grail 1.3
• Recent Developments:
  • GrailEXP - Clusters of overlapping exon candidates
    • 13 'Sensor Algorithms'
    • Coding Region Candidates -> Gene Modeling
    • InDel Error Detection and 'Correction'
    • Splice Junction recognition
    • Repetitive DNA recognition
    • CpG Island recognition
    • RNA polymerase II promoters (TATA, CAAT, ...)
    • Developed from Grail 2.0 and Grail 3.0
  • GAP III (Gene Assembly Program)
    • Optimal and consistent gene model built from exon models
• Other ORNL directions:
  • Automatic - automated annotation of senomic sequences
  • Batch GRAIL - automated analysis during sequencing project
  • Interactive XGRAIL and genQuest
  • Genome Channel

BCM Gene Finder:

• Series of Gene Finding programs at Baylor College of Medicine
• Associated with the BCM Search Launcher set of programs
• Excellent variety of useful programs, with Help facility

GeneMark: more info here.

• Algorithm - inhomogeneous Markov chain models
• Now combined with Maximum Likelihood approach to coding region
• General resource on Markov models: Durbin et al book
• Used extensively in analyses of complete bacterial genomes - data

Other Programs:

• Many, many available on the Web
• Links here and here and here

F. Sequence Analysis: finding Motifs

Motifs:

• Motif - a recurrent thematic element
• Structural motifs - pieces of folded 3D structure
• Sequence motifs - conserved "blocks" of sequences

DNA Motifs:

• Protein binding sites ... regulatory elements
• Relatively short
• Statistically difficult
• Cooperative binding often important
• Structural elements may be important - bends, kinks

Protein Motifs:

• Secondary structure - alpha helices, beta sheets
• Super secondary structure - 4 helix bundle, TIM barrel, etc

Basic Methods:

• Consensus sequence - single, best sequence
• Regular Expression - multiple characters per site
• Weight-Matrix - any character per site, with score - Profile
• Hidden Markov Model

Protein Family Classifications

Prosite:

• Database of protein families and domains - at ExPASy and elsewhere
• Regular expressions (Patterns) and Profiles
• Programs
  • Search Prosite for Pattern or Profile
  • ScanProsite - scan a sequence against ProSite, or pattern against SwissProt
  • ProfileScan - scan a sequence against Profile Database

BLOCKS:

• Multiply aligned ungapped segments of most highly conserved protein regions
• BLOCKS Database
• Programs or Tools available - others also available
  • BlockSearcher - compare a protein or DNA sequence against BLOCKS database
  • GetBlocks - retrieve Blocks
  • BlockMaker - create new Blocks

DOMO:

• > 9000 multiple sequence alignments of > 100,000 protein domains
• Example: APPLE domain - a C-rich domain

Pfam:

• Database of protein multiple sequence alignments and Profile -HMMs (Hidden Markov Models) for common protein domains
• Programs
  • Search Pfam
  • Download Pfam databases via ftp
• Example: C2H2 family of Zinc Fingers

Protein Structural Classifications

• Many efforts underway ...
• Links to these Motif Databases are present in SwissProt entries

SCOP:

• 'Structural Classification Of Proteins'
• Heuristic classification based on crystallography
• Family: > 30 % identity, clear homologues
• Superfamily: low identity, probably homologues
• Fold - major secondary structures in same arrangement
• Example: Cytochrome C

CATH:

• Systematic semi-automatic process, clearly defined
• CATH: Class, Architecture, Topology, Homologous superfamily
  • Class - alpha, alpha/beta, etc
  • Architecture - overall shape of the domain
  • Topology - fold family
  • Homologue - share a common ancestor
• Example: Elongation Factor Ts, domain 2

G. Multiple Sequence Analysis

Basics

• Progressive Sequence Alignment
  • Pairwise alignment of most similar, then next most similar, etc
• Steps
  • Do pairwise alignment for all sequences
  • Get Matrix of approximate Distances between each pair
  • Create an approximate phylogenetic tree - Guide Tree
  • Use this to determine order of addition of sequences to alignment
  • Align: two sequences; seq to subalignment; two subalignments
  • Keep GAPS that appear early - 'Once a gap, always a gap'
• Web sites for Multiple Sequence Alignment

Clustal W:

• Weighting - different weights given to unequally sampled sequences
• Position Dependent Weights
  • Position-Specific Gap Penalties (Opening vs Extension)
  • Sequence Weighting
  • Weights for Adding New Sequences to existing Alignment - extra weight to sequences most similar to alignment
• Clustal W Servers

Other Web Programs

• MAP, PIMA, MSA
• Many others available

Web Databases of Multiple Sequence Alignments

• Fold Classification via Structure-Structure Protein alignments (FSSP)
• Homology derived Secondary Structure Assignments (HSSP)
• Database of Secondary Structure Assignments (DSSP)

H. Phylogenetics

Basics:

• Trees - Rooted vs Unrooted
  
  • Rooted Tree - position of Ancestor is known
  • Unrooted Tree - no Ancestral Node
  • Topology - Branching Pattern of the Tree
    
    
• Terminology
  

1, 2, 3, 4, 5:   Taxa or External Nodes (or OTUs)
X, Y, Z:         Internal Nodes
R1:              Root
a, b, c, d, e:   External Branches
f, g:            Internal Branches
h:               Internal Branch ONLY IF tree is Rooted;
                       else h is part of e
Outgroup:        Taxan 5 ... used to "root trees"

Methods:

• Distance Matrix methods
  • UPGMA - Unweighted Pair Group Method of Averages
    • Fixed 'clock', averages used to get distances
  • Fitch & Margoliash - 3 branches calculated at a time
  • Neighbor Joining - Pairs of taxa, finding closest pair
    • tree with smallest sum of Branch Lengths
  • Other methods also available

• Parsimony methods
  • Find tree with fewest inferred mutations
  • Programs: PHYLIP package; PAUP

• Maximum Likelihood methods
  • Use a mathematical model of process of evolution
  • Model contains a parameter which is used to Maximize the Likelihood that observed changes took place

• Example - Unrooted Tree of A. thaliana ST Phosphatases w Homologues

Confidence - "How good is the Tree?"

• Bootstrap - permutation resampling of the sequences
  • How robust is the tree to such resampling? always same tree?
    
    
• How much better is this "best" tree than other trees?
  • Use set of "User defined" Trees ... how good is each?
  • PHYLIP programs

III. Whole Genomes

A. Implications

• TOTAL information on Heritable Properties of an Organism
  
  
• What an Organism CAN do ... and CAN NOT do ...
  
  
• Major step toward Understanding an Organism
  and toward making Biology a PREDICTIVE SCIENCE
  
  
• Current: identify Genes, predict Function
  
  
• Next:
  • Deduce Life Style of the Organism
  • Predict Metabolic and Genetic Pathways
  • Predict Adaptive Responses, Developmental Pathways
    
    
• These Implications implicitly lead to ORGANISM DATABASES

B. TIGR - The Institute for Genomic Research

• First to Sequence whole Genome of Freeliving Organism
  
  
• Sequenced the first Three Eubacteria and First Two Archae
  
  
• TIGR Database (TDB) - links to specific organisms
  
  
• TIGR Microbial Database:
  • Listing of published and in progress microbial genomes
  • Mar, 2002: 68 finished, 179 in progress
    
    
• Example: Methanococcus jannaschii - first archae
  • Search facilities - Locus, Text, Sequence
  • Download facilities
  • Genome Browser for M. jannaschii
    
    
• TIGR Gene Indices (TGI)
  • Analyses of ESTs from sequencing projects
  • Human, Mouse, Rat, Fly, Rice, Arabidopsis, Zebrafish, ...
    
    
• Comprehensive Microbial Resource (CMR)
  • Access all bacterial genome sequences completed to date
  • Database approach to Genome Annotation (information)
    
    
• Other facilities: software, links, ftp site
  

IV. Organism and Other Databases

A. Need for Organism Databases

• Direct result of Genome Physical Mapping efforts
  
  
• Need for Maps, Genes, Sequences, References
  
  
• Incomplete Genome Information plus other Information
  
  
• NOW: Complete Genome Information

B. Paradigm: ACeDB

• ACeDB - A C. elegans Data Base
  
  
• Created by Durbin and Thierry-Mieg for Sulston R.Mapping Program
  
  
• Over 40 organisms represented in ACeDB databases
  
  
• Highly variable Types of Information in each
  
  
• Examples: C. elegans, yeast, fly, grains, Arabidopsis, human chroms
  
  

C. Advantages of ACeDB for Organism DBs

• Schema: highly adaptable, easily changed and modifiable AFTER the database has been built.
  
  
• Data Transfer: Text .ace files, easy to completely rebuild a database, easy to transfer updates over the Web, maintain at ftp sites.
  
  
• Good Graphics: maps, sequences, tables, grids, pathways.
  
  
• Easy to use via Browsing Approach: Hypertext
  
  
• And also has: Sophisticated SQL Search capabilities
  
  
• Contains some Analysis Tools: GeneFinder, Dotter, Blixim, Metabolic Pathways, Sequence Assembly.
  
  
• Can link to other Tools: RasMol, Pictures, Video
  
  
  
  

D. ACeDB on Web: WebAce and AceBrowser

1. ACeDB - Disadvantages

• Not truly relational - XREF only for crosslinking
  
  
• Data input requires .ace file construction, with correct XREFs
  
  
• Largely a Monolithic system: implemented on local computers
  
  
• Issues of "scalability", although works well with the worm C. elegans
  
  
• Designed for Unix ... MacAce and WinAce less fully implemented

2. However: WebAce and AceBrowser

• Two current Web implimentations:
  • WebAce - from NAL and Durbin at Cambridge
  • AceBrowser - from L. Stein at CSH
    
    
• Links to Genbank, SwissProt, PubMed ... federated DBs
  
  
• Automated Link and Submission to other Tools, eg BLAST
  
  
• JavaScript version is rapid

E. Example of ACeDB: DictyDB

1. MacAce: Graphics, Text Displays

• Data Items in Released Versions of DictyDB here
• MacAce Main Window and Dicty_cDB cDNA Clones here
• Dicty_cDB cDNA Clone Groups here
• Dicty_cDB cDNA Seqs as SubSeqs; Homologue info here
• Sequence and Genetic Map Displays - mitoDNA here
• cox1/2 mito gene -Sequence and Text Displays here

2. Web ACeDB: WebAce and AceBrowser at Cornell

• WebAce2: Main Window here
• WebAce2: Simple Searches here
• WebAce2: Text Displays and Graphics Configuration here
• WebAce2: Genetic Map and Locus Text Displays here
• WebAce2: Sequence Graphics and Text Displays here
• AceBrowser: Main Window here
• AceBrowser: Simple and "In Depth" Searches here
• AceBrowser: Reference Text and Enhanced Annotation here
• AceBrowser: Genetic Map and Locus Text Displays here
• AceBrowser: Sequence Graphics and Text Displays here
• Large DNA Sequence - 700 Mb C.elegans Sequence here
• AceBrowser Data Enhancements - Metabolic Pathways here

F. Other Web Organism Databases

• Many are available
  
  
• Saccharomyces Genome Database (SGD)
  • Basic database is Web enhancement over ACeDB SacchDB
  • Excellent interface to yeast genome maps: Genomic View
  • Many resources including analysis tools
    • BLAST and FASTA facilities
  • SacchDB extended to include
    • Genome Deletion Project
    • Yeast Evolution Project
    • Sacch3D - protein 3D structure information
    • Worm and Mammalian Homology to Yeast
    • Yeast SAGE data
      
      
• The Arabidopsis Information Resource (TAIR): Arabidopsis thaliana
  • Database based on Oracle relational database system
  • Much underlying information from ACeDB AatDB
  • Analysis tools and Viewers, including BLAST and FASTA
  • Arabidopsis Genome Initiative (AGI)
    
    
• PlantsP: Plant Phosphorylation Proteins (kinases, phosphatases)
  • underlying MySQL database
  • display and usage is Web based
  • many other resources, links, download, etc
    
    
• Berkeley Drosophila Genome Project (BDGP)
  • Outgrowth of Encyclopedia of Drosophila (EofD)
  • Excellent Map Viewers - largely Java applets
    • Example: CytoView
  • Includes FlyBase, ACeDB database of Drosophila
    
    
• Mouse Genome Informatics (MGI)
  • Integrated access to mouse genetics and biology
  • Mouse Genome Database (MGD)
  • Mouse Gene Expression Database (GXD)
  • Encyclopedia of the Mouse Genome
  • links to
    • Mouse Tumor Biology database
    • Rat Data resource
      
      
      
• Human Genome Resources at NCBI
  • Information and links to Human Genome Project
  • Human Genes
    • OMIM - Online Mendelian Inheritance in Man
      • McKusick catalog of human genes and disorders
      • Over 10,000 entries
    • LocusLink - single interface to all human locus info
  • Human/Mouse Homology Relationships
  • Examples of Info on Candidate Human Genes for Hypertension
    
    
    
    
    

VI. Problems ... Directions to Go

A. Problems:

• Sequence DBs and Others are Flat File Database
  • one piece of information at a time
    
    
• Analysis Tools are largely Single Task oriented
  • from Task to Task, User must make Decisions
    
    
• Automate Basic Analytical Tasks for new DNA Sequences
  • This is now done currently in some facilities
    and in some expensive commercial packages
  • Examples: Pangea, Incyte

B. Need: "smart" Analysis Packages

• Need "smart" Analysis Packages that can "learn" from DB info
  
  
• Such "smart" Analysis Packages can "predict" next best options for User
  
  
• Analysis: DNA seq --> gene --> protein --> motifs --> 3D structure
  
  
• Automate this as completely as possible
  
  
• "smart" Analysis Packages --> deduce function
  
  
• Extend Analysis to Metabolic and Genetic Pathways

C. "Training" of "smart" Analysis Packages:

• Use Current Information: Sequence, Protein Structure, sites, motifs
  
  
• Use Physical and Genetic Maps, Pathway information
  
  
• Use new Information: Expression Arrays, Polymorphisms, Pathways
  
  
• Also need Additional Information:
  • Kinetic information - order of formation of complexes
  • Concentration info - key control molecule concentrations
    
    
• Integrate Sequence DBs, Analysis Tools, Organism DBs
  
  
• Combine with "smart" Algorithms that can "learn" from the DB info to provide User with Options, new Information
  
  
• => High Predictive Value - ask "what if" questions
  
  
• Basic Problem with Biology becoming a "Predictive Science"
  • Large number of Different Molecules, eg Proteins
    • Large Variety per Cell
    • Variety Changes with Type of Cell in Organism
  • Often a Small number of Each Molecule
  • Thus: Statistical Analysis is often not Appropriate

VI. Additional Materials:

A. Books:

43 hits to "books" and "bioinformatics" at "amazon.com" in March, 2002 ...

1. "Bioinformatics: Sequence and Genome Analysis." David W. Mount. Cold Spring Harbor Press, 2001.

Recent, authoritative, most emphasis on Sequence Analysis, some on genomics and organismal databases.

2. "Bioinformatics : A Practical Guide to Analysis of Genes and Proteins". Second Edition. Ed., Andreas Baxevanis and B.F.Francis Ouellette. John Wiley, 2000.

Recent text emphasizing how to use Web ... database searches ... software tools available.

3. "Bioinformatics Basics: Applications in Biological Science and Medicine." Hooman Rashidi and Lukas Buehler. 1999.

Recent text providing introduction to many topics, by two UCSD Biology personnel.

4. "Computational Molecular Biology: An Algorithmic Approach." Pavel A. Pevzner. 1999.

Advanced computational approach to algorithms used in bioinformatics and molecular biology, by UCSD CSE professor.

5. "Biological Sequence Analysis." R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Cambridge University Press, 1998.

Recent text emphasizing automata and hidden Markov model (HMM) approaches

6. "Bioinformatics: The Machine Learning Approach." Pierre Baldi and Soren Brunak. MIT Press, 1998.

Another recent text emphasizing automata and HMMs

7. "Sequence Analysis Primer." Ed., Michael Gribskov and John Devereux. Oxford University Press, 1992.

Bit dated, but still good ... GCG program emphasis ... detailed sequence analysis example ... Gribskov now at SDSC.

New Bioinformatics books are now coming out regularly
Check at Amazon.com under "Books" and "bioinformatics"
for latest ... (amazon.com lists 5 books about to come out ...)

Others:

"Intro to Computational Molecular Biology." Joao Meidanis and Joao Carlos Setubal. PWS Publishing, Boston, 1997.

"Computational Methods in Molecular Biology." Ed., S. Salzberg, D. Searls, and S. Kasif. Elsevier Science, 1999.

"Intro to Computational Biology: Maps, Sequences, and Genomes." Michael S. Waterman. Chapman and Hall, 1997. Math and algorithm intensive.

"Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology." Dan Gusfield. Cambridge Univ Press, 1997. Definitive algorithm text ... very math intensive.

"The Secrets of Life: A Mathematician's Introduction to Molecular Biology." Ed., Eric S. Lander and Michael S. Waterman. Natl Acad Sci Press, 1998. Very readable ...

"DNA Sequencing : From Experimental Methods to Bioinformatics." Luke Alphey. Springer Verlag, 1997.

"Computer Methods for Macromolecular Sequence Analysis." Ed., Russell F. Doolittle. Methods of Enzymology, Vol 266. 1996.

"Computer Analysis of Sequence Data", parts I and II. Ed, Annette M. Griffin and Hugh G. Griffin. Humana Press, 1994.

"Biocomputing: Bioinformatics and Genome Projects." Ed, Douglas W. Smith. Academic Press, 1993.

"Molecular Evolution: Computer Analysis of Protein and Nucleic Acid Sequences." Ed., Russell F. Doolittle. Methods of Enzymology, Vol 183. 1990.

"Of URFs and ORFs: A Primer on How to Analyze Derived Amino Acid Sequences." Russell F. Doolittle. University Science Books. 1986.

B. Selected Recent Review Articles on Bioinformatics:

446 at PubMed for 'bioinformatics AND review',

101 for 'bioinformatics AND review AND 2002'
257 for 'bioinformatics AND review AND 2001'
144 for 'bioinformatics AND review AND 2000'
67 for 'bioinformatics AND review AND 1999'
52 for 'bioinformatics AND review AND 1998'
18 for 'bioinformatics AND review AND 1997'
13 for 'bioinformatics AND review AND 1996'
6 for 'bioinformatics AND review AND 1995'
7 for 'bioinformatics AND review AND 1994'
1 for 'bioinformatics AND review AND 1993'
2 for 'bioinformatics AND review AND 1992'
0 for 'bioinformatics AND review AND 1991'

"Molecular Biologist's Guide to Proteomics." Graves, PR, and Haystead, TA. 2002. Microbiol Mol Biol Rev 66: 39-63. [PubMed]

"A Genomic Regulatory Network for Development." Davidson, EH, et al (26 authors). 2002. Science 295:1669-1678. [PubMed]

"Systems Biology: A Brief Overview." Kitano, H. 2002. Science 295: 1662-1664. [PubMed]

"Biological data becomes computer literate: new advances in bioinformatics." Goodman, N. 2002. Curr Opin Biotechnol 13: 68-71. [PubMed]

"Insights into Protein Function through Large-Scale Computational Analysis of Sequence and Structure." Weir, M., Swindells, M., and Overington, J. 2001. Trends Biotechnol 19(10Suppl): S1-S6. [PubMed]

"Exploring the Protein Interactome using Comprehensive Two-Hybrid Projects." Ito, T., Chiba, T., and Yoshida, M. 2001. Trends Biotechnol 19(10Suppl): S23-S27. [PubMed]

"A Genomic View of Alternative Splicing." Modrek, B., and Lee, C. 2001. Nat Genet 30: 13-19. [PubMed]

"Recent Advances in Computational Genomics." Claverie, JM., Abergel, C., Audic, S., and Ogata, H. 2001. Pharmacogenomics 2: 361-372. [PubMed]

"The Impact of Microbial Genomics on Antimicrobial Drug Development." Tang, CM., and Moxon, ER. 2001. Annu Rev Genomics Hum Genet 2: 259-269. [PubMed]

"Bioinformatics Tools for Whole Genomes." Searls, DB. 2000. Annu Rev Genomics Hum Genet 1: 251-279. [PubMed]

"Of Mice and Genome Sequence." Hamilton, BA, and Frankel, WN. 2001. Cell 107: 13-16. [PubMed]

"A Tour of Structural Genomics." Brenner, SE. 2001. Nat Rev Genet 2: 801-809. [PubMed]

"Gene Expression Data Analysis." Brazma, A., and Vilo, J. 2001. Microbes Infect 3: 823-829. [PubMed]

"Analysing Gene Expression Data from DNA Microarrays to Identify Candidate Genes." Wu, TD. 2001. J Pathol 195: 53-65. [PubMed]

"What is Bioinformatics? A Proposed Definition and Overview of the Field." Luscombe, NM., Greenbaum, D., and Gerstein, M. 2001. Methods Inf Med 40: 346-358. [PubMed]

"Sequencing the entire genomes of free-living organisms: the foundation of pharmacology in the new millennium." Broder, S, and Venter, JC. 2000. Annu Rev Pharmacol Toxicol 40: 97-132. [PubMed]

"Protein function in the post-genomic era." Eisenberg, D, Marcotte, EM, Xenarios, I, and Yeates, TO. 2000. Nature 405: 823-826. [PubMed]

"Who's your neighbor? New computational approaches for functional genomics." Galperin, MY, and Koonin, EV. 2000. Nature Biotech 18: 609-613. [PubMed]

"Structural genomics: beyond the human genome project." Burley SK, et al. 1999. Nat Genet. 23: 151-157. [PubMed]

"Computational methods for the identification of differential and coordinated gene expression." Claverie JM. 1999. Hum Mol Genet. 8: 1821-1832. [PubMed]

"Multiple sequence alignment: algorithms and applications." Gotoh O. 1999. Adv Biophys. 36: 159-206. [PubMed]

"Mapping regulatory networks in microbial cells." VanBogelen RA, et al. 1999. Trends Microbiol. 7: 320-328. [PubMed]

"How will bioinformatics influence metabolic engineering?" Edwards JS and Palsson B. 1998. Biotechnol Bioeng. 58: 162-169. [PubMed] Bernhard Palsson group in Bioengineering.

"Computational aspects of expression data." Vingron M, et al. 1999. J Mol Med. 77: 3-7. [PubMed]

"Functional genomics: going forwards from the databases." Rastan S and Beeley LJ. 1997. Curr Opin Genet Dev. 7: 777-783. [PubMed]

"Informatics--genome and genetic databases." Ashburner M and Goodman N. 1997. Curr Opin Genet Dev. 7:750-756. [PubMed]

"Bioinformatics: from genome data to biological knowledge." Andrade MA and Sander C. 1997. Curr Opin Biotechnol. 8: 675-683. [PubMed]

"Functional Genomics - Bioinformatics is Ready for the Challenge." T.F. Smith. 1998. Trends Genet. 14: 291-293. [PubMed]

"Bioinformatics in a post-genomics age." Gershon et al. 1997. Nature 389: 417-422. [PubMed]

"Bioinformatics." Boguski MS. 1994. Curr Opin Genet Dev. 4: 383-388. [PubMed]

... and many more ...

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