BIIN200: Bioinformatics I

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BIIN200 Bioinformatics I

• Introduction
• Craig A. Struble, Ph.D.
• Department of Mathematics, Statistics, and
  Computer Science
• Marquette University
• Norie Dela Cruz, Ph.D.
• Rat Genome Database
• Medical College of Wisconsin

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Overview

• Introduction to Bioinformatics
• Syllabus
• Student Introductions

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What Is Bioinformatics?

• Bioinformatics is a new subject of genetic data
  collection, analysis and dissemination to the
  research community. Hwa A. Lim (1987)
• Bioinformatics Research, development, or
  application of computational tools and approaches
  for expanding the use of biological, medical,
  behavioral or health data,including those to
  acquire, store, organize, archive, analyze, or
  visualize such data. NIH working definition
  (2000)

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What is Bioinformatics?
Informatics Computer Science Computer
Engineering Information Science
Biology Other Natural Sciences
Bioinformatics
Mathematics Statistics
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Bioinformatics Related Fields

• Computational biology
• Computational molecular biology
• Biomolecular informatics
• Computational genomics

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Biological Data

• Genomes
• DNA Sequences of A, T, C, G
• Annotated with function, interesting features
• Proteins
• Amino Acid Sequences
• Sequences of 20 letters
• Annotated with structure, function, etc.

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Biological Data

• Gene Expression
• Dynamic behavior of genes
• Protein Expression
• Dynamic behavior of proteins
• Structural Features
• RNA and proteins

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Biological Data Sus scrofa agouti-related
protein gene

• 1 ggcacattct cctgttgagc caggctatgc
  tgaccacaat gttgctgagc tgtgccctac
• 61 tgctggcaat gcccaccatg ctgggggccc agataggctt
  ggcccccctg gagggtatcg
• 121 gaaggcttga ccaagccttg ttcccagaac tccaaggtca
  gtgcgggcag gagtgggttg
• 181 ggtggggctt ggacatcctc tggccacaaa gtattctgct
  tgtatgagcc ctttcttccc
• 241 cttcccaatc ccaggcctgg gaggtgggtg ttttgtgcat
  gggtggttct gccctcacat
• 301 catctgtccc agatctaggc ctgcagcccc cactgaagag
  gacaactgca gaacgggcag
• 361 aagaggctct gctgcagcag gccgaggcca aggccttggc
  agaggtaaca gctcagggaa
• 421 agggctgagg ccacaagtct tgagtgggtg tgtcaagcat
  caacctctat ctgtgcttgg
• 481 agttgccact gtggtacaac gggattggcg gtgtcttggg
  agcgctggga cgtggtttca
• 541 tccccggcca gcacaagtgg gttaaggatc tggccttgcc
  atcccttcag cttaggctga
• 601 gactgtggct tggagctgat ctctgaccgg aagctccata
  tgctctgggg tgaccaaaaa
• 661 tggaaaaaca aacatacaaa acacctctac ctgcacttcc
  tgaccccctc acccggggcg
• 721 acactgcaga ccatcccgtt cacgctccac ttccatcctg
  ccttgatctg gcgcattcca
• 781 tgaatgtgct tttggaagtc cttgtttccc aacccttgta
  ggtgctagat cctgaaggac
• 841 gcaaggcacg ctccccacgt cgctgcgtaa ggctgcacga
  atcctgtctg ggacaccagg
• 901 taccatgctg cgacccatgt gctacatgct actgccgttt
  cttcaacgcc ttctgctact
• 961 gccgcaagct gggtactgcc acgaacccct gcagccgcac
  ctagctggcc agccaatgtc
• 1021 gtcg

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Genome Sizes
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Database Growth
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Database Growth
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Database Growth
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Database Growth

• Exponential growth in sequence data
• Not much growth in sequence size
• Expect exponential growth in annotation
  information
• We have lots of data, but its difficult to make
  sense of it.

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Fundamental Problems in Bioinformatics

• Pairwise Sequence Alignment
• Multiple Sequence Alignment
• Phylogenetic Analysis
• Sequence Based Database Searches
• Gene Prediction
• Structure Prediction (RNA and Protein)
• Protein Classification
• Gene Expression
• ...

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Pairwise Sequence Alignment

• Given two DNA or AA sequences, find the best way
  to line them up
• Biology allows for variation
• Gaps, mismatches, etc..

HEAGAWGHEE
PAWHEAE
HEAGAWGHE-E
HEAGAWGHE-E
P-A--W-HEAE
--P-AW-HEAE
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Multiple Sequence Alignment

• Extend pairwise problem to multiple sequences

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Phylogenetic Analysis

• Study relationships between organisms
• Characteristic similarity
• Sequence similarity
• Whole genome comparison

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Phylogenetic Analysis
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Sequence Based Database Searches

• Keyword
• Find all sequences named cytochrome c
• Sequence
• Find all sequences similar to HEAGAWGHEE
• Remember, there are gigabytes to search, and Im
  not about to wait two days for an answer!
• BLAST, FASTA,

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Gene Prediction

• Does the following sequence contain a gene?
• How many introns? Exons? Promoters? Other
  features?

TTGTAATCTCCTCTGTGACTATAATGACTAGTCTCAGGCCTGCCTTCCCC
AGAAACCTCTCTTTTGGCTATTTCTCTTTC TAGTTCTCTGTTTAAACAA
AATTTATTCTATATATCTATCTATCTGTCTATCTATCTATCTATCTATCT
ATCTATCTATC TATCTATCTATCTATCATCTACTTATCATCTGTCTAGC
CATTTGAAGCATCTTTGTGTTTTAGGTCCTGTTAGATTCTCC TTTCAGC
CAGTGGAGGATCTGGACAGAGCTATTTCTTAGCTTCCCCTAAGCCATGTT
GTTAGAACGAATCCCCCACACCT CCTCTGAGTGCTACGTCTCCGTCAAG
AATTATGTATGTGGGATCCAGATGGCCCAGTGGATAAAACTGCAAGTGTC
ATGA CCATGACCTGACTTCAAGGGATTGTGTAGAAAGGGAGTTATCACA
GTGTGAGGGACAGGGCTAAGGACACTAACCCGTAT GTTGAGGGGCACAG
ACGCTAGCAACAACAGTGAAGTGTTTAAAAAGGCAAAAATCATGTTTCTA
GAAGTCAGGAAGAGCC TAACTTGTGGACAAGGACCAACAGGCAGCAGTT
GTAATGGGGCAGGGCAGAGGGAGAGCGGACACGCAGCTTTTGGCATC AA
ACACACCCAGAGTGTGGATAGAGAGTAGGGAAATACTCTAGTCTCTGGCT
AGGATACTCCCCTCTCTTTTTGACATTT CTCATTGGCAGCCCCAAGTGG
TCACTGGAGAGCCAGGAAGCCTAAAGGACACAGTTAGTAGCAGCCAGCTC
CTTTGGTGG AATTTTGGGGACATGGTGGGGTGACTTGGCTCTATCCAGG
CCAGGGCTGGGTGTGAGTATACACTTAGTGACTGGCCTTC
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Gene Prediction
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Structure Prediction (RNA, Protein)

• From sequence, predict 2 and 3D structures.

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Protein Classification

• From sequence, identify characteristics of a
  protein
• Active sites
• Families (e.g. globin)
• Blocks
• Domains
• Folds
• Motifs
• Etc.

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Gene Expression

• Study of gene activity under experimental
  conditions
• Large scale studies with microarrays

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Bioinformatic Based ApplicationVCMAP

• Comparative mapping is a strategy that allows
  cross-organism study of physiological genomics
• Virtual Comparative Map (VCMap) performs homology
  analysis with mathematical predictions to
  construct un-tested (in the wet-lab)
  cross-organism maps between human, rat, mouse and
  zebrafish
• This application provides a highly modular
  investigative environment for the
• Analysis of multiple organisms including
  Zebrafish
• Collection of genetic and radiation hybrid maps
• Prediction of Genes based on homology

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VCMAP

• Homology analysis was based on sequence
  similarity (Altschul, et al 1990) and curated
  homologous genes.
• 85 similarity with 100 bp stretch across all
  species was used to create the maps
• NCBIs UniGene sequence sets, RH and Genetic
  maps were chosen to create anchor objects
  (Kwitek-Black, et al. 2001).
• 1-to-1 homologous objects were used for building
  the virtual comparative maps with a pipeline
  architecture

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VCMAP
Download UniGene data from NCBI
Mask UniGene sequences
Load UniGene data to DB
DB
Format masked sequences
Blast
Map Data
Search UniGene
VC Maps Building
Anchor Report
Generate anchor report
Create Homolog UniGene Object and Scoring
1-to-1 Objects
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VCMAP
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Different perspectives on Bioinformatics

• Bioinformatics is a tool
• Biologists, biochemists, medical professionals,
  etc.
• Obtain meaningful and understandable results
• Bioinformatics is a discipline
• Informaticians, mathematicians, statisticians,
  etc.
• Generate meaningful and understandable results

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Goals of the Course

• Communication between biologists and
  computational scientists
• Access, retrieve, and analyze bioinformatic data
• Know fundamental problems in bioinformatics
• Use standard bioinformatic tools to answer
  biological questions
• Understand theories used to build the tools
• Critically assess solutions to bioinformatic
  problems

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How Are We Going To Get There?

• References
• Cynthia Gibas and Per Jambeck, Developing
  Bioinformatics Computer Skills, OReilly
  Publishers, 2001, ISBN 1-56592-644-1.
• David W. Mount, Bioinformatics Sequence and
  Genome Analysis, Cold Spring Harbor Laboratory,
  2001, ISBN 0879696087.

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How Are We Going To Get There?

• Lab Assignments
• Nine (9) assignments covering the major topics
• Maintain a lab notebook, collected 3 times for
  review
• Bistro Lab
• 368 Cudahy Hall
• Windows workstations, Sun server
• Variety of software
• Lab orientation (when should we have it?)

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How Are We Going To Get There?

• Lab Web Page
• http//bistro.mscs.mu.edu
• For 70 grade
• Post 3 stories with commentary
• Post 3 links to bioinformatic tools, properly
  categorized
• Post 5 comments on others stories
• More posts, writing plug-ins, writing lab HOWTOs,
  etc. will increase grade

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How Are We Going To Get There?

• Exams
• Midterm
• Final
• Intangibles
• Discussion with instructors
• Being engaged in the class
• Suggestions about the lab

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Grading
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Who We Are

• Craig A. Struble, Ph.D.
• Ph.D. in Computer Science, 2000 from Va. Tech
• 3rd year at Marquette
• Interests Microarray data analysis, medical
  literature mining, miRNA,
• Norie Dela Cruz, Ph.D.
• Rat Genome Database

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Who Are You?

• Name
• Where are you from?
• Background
• Why bioinformatics?

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Summary

• Bioinformatics is truly interdisciplinary
• Biology (natural sciences), informatics,
  mathematics statistics
• Databases
• Large, semistructured, incomplete, inaccurate
• Wide-range of problems
• Solutions employ knowledge from sciences with
  algorithms and models from informatics,
  mathematics, and statistics