MSCS282: Bioinformatics I

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MSCS282 Bioinformatics I

• Introduction
• Craig A. Struble, Ph.D.
• Department of Mathematics, Statistics, and
  Computer Science
• Marquette University
• Michael A. Thomas, Ph.D.
• Bioinformatics Research Center
• Medical College of Wisconsin

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Michael A. Thomas, Ph.D.
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Overview

• Welcome
• Syllabus
• Student Introductions
• Introduction to Bioinformatics

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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 is sometimes called

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

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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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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
• What are we to do with all this data?

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Challenges of Large Databases

• Storage
• Indexing, physical layout, memory management
• Modeling
• Relational, hierarchical, semi-structured
• Efficiency
• Update, query, analysis
• Interpretation
• Visualization

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Problems in Bioinformatics

• Consider just sequence analysis
• Sequence alignment
• Gene discovery
• Promoter discovery
• Intron splice sites
• Protein and RNA structure prediction

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Applications of Bioinformatics

• VCMAP
• DORR and ASAP
• miRNA

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VCMAP

• 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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Disease Oriented Research Resource

• The major goal of the RGD Disease Oriented
  Research Resource is to create collaborative
  relationships between RGD and 20 particular
  disease rat research communities to identify,
  collect, and integrate disease-specific
  components of data and information all the way
  down to specific genes of interest into RGD
  Disease portals.

Specific Goals for RGD

• Prioritize data for curation and addition to RGD
  based on targeted disease areas
• Effectively combine automated and manual data
  acquisition and curation methods
• Provide a way to integrate Rat Genome Sequencing
  Project results with RGD activities
• Help RGD incorporate tools developed in BRC to
  add focus of data mining and analysis to
  traditional curation and database functions

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DORR Workflow
Genes
Curation
Strains
ASAP
QTLs
VCMap
Biomedical Literature
Identify Disease
GROIs
Microarray
In Development
Pathways
Phenotypes
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Data Acquisition
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Automated Sequence Analysis Pipeline
Markers
seqs mapped in ROI Seqs predicted in ROI
Seq(Fasta/Trace)
VCMap
input sequences
RepeatMask
Search extra genomic sequence(HTGS, TraceDB,nr)
RepeatMask
Assembly Seq
AssembledSeqUnassembledSeq
MetaGene
UniGene Search
ePCR
RepeatMask
Homolog Search
RepeatMask
Hot Zone
BLAST (nrpir)
UniGene Report
Repeat Report
Marker Report
Homolog Report
MetaGene Report
Gene Report
Visualization (Clickable Image Map)
Additional Reports
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miRNA Gene and Target Prediction

• microRNA genes (miRNAs) were recently recognized
  as a class of functional non-coding genes
• 70nt precursor which has a hairpin fold
• 20nt RNA molecule from Dicer cutting the stem
  loop
• First identified were lin-4 and let-7
• Developmental role in C. Elegans

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miRNA Examples
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miRNA Gene Prediction

• Can we predict where miRNA genes might be?
• Microscan (Burge Lab)

Scan C. Elegans for 70nt hairpin folds (structure
prediction)
Compare with C. Briggsae (sequence alignment)

• Score alignments
• 3 and 5 conservation
• Overall conservation
• Size of loop, etc.

Select sequences with score gt threshold
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miRNA Target Prediction

• Lin-4 and let-7 interact with 3-UTR
• Idea look for conserved 3-UTR regions which are
  complementary to discovered miRNA genes

Find conserved sequences of 20nt in length from
3-UTR database
Align with miRNA genes

• Score alignment
• 3 and 5 matches
• Overall matching

Select high scoring matches
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Goals of the Course

• For everyone
• Communication
• For the biologist
• Incorporate bioinformatics into research
• Understand computational modeling
• For the computational scientist
• Develop tools for biological research
• Create new algorithms for mining biological data
• Understand how to find biologically meaningful
  information

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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

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Biological Data

• DNA and Protein Sequences are annotated
• Source
• Organism
• Function
• Updates
• Etc.

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Classic examples

• Sequence alignment
• Multiple sequence alignment
• Examples from Setubal/Meidanis (1997)