Bioinformatics 101 BimCore www.bimcore.emory.edu

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Bioinformatics 101BimCore www.bimcore.emory.edu

• Bioinformatics is the computational management,
  analysis and dissemination of biological
  information.
• 3 main types of data genome sequences, protein
  sequences and structure, and functional genomics
  (expression data).
• Genomics mapping, sequencing and analysis of
  genomes (sequences, structural and functional).
• Proteomics qualitative and quantitative
  comparison of a proteome (complete set of
  proteins of an organism) under different
  conditions to unravel biological processes.

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Information available

• Databases
• Genome sequences - Flybase, GeneBank, EMBL,
• SwissProt, PDB, ESTs, BACs, etc.
• Experimental information
• Published information
• PubMed, MedLine

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How locate relevant information

• Text searches
• Query matches
• Sort by relevance
• gtgt sequence to sequence comparisons

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Definitions

• Homolog Sequences that share a common
  ancestor may have similar function.
• Paralogue Similar sequence within species, may
  have similar function.
• Orthologue Same sequence separated by a
  speciation event, probably same function.
• Analog Non-homolog proteins that have similar
  folding or similar functional sites, which arose
  through convergent evolution.

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Evolution and Alignment

• Similarity is an observable quantity (
  identity).
• Homology is a conclusion drawn from the data,
  thus two genes share a common evolutionary
  history.
• Alignments reflect amino acid substitutions, and
  insertions and deletions.
• Certain regions are more conserved than others.
• In biomolecular sequences (DNA, RNA, AA seqs),
  high sequence similarity usually implies
  significant structural and / or functional
  similarity.

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Sequence alignment based onEdit Distance

• Transforming one string into another by a series
  of edit operations on the individual characters.
• The operations allowed are insertion of a
  character in the first string, deletion of a
  character from the first string, or the
  substitution of a character in the first string
  with a character in the second string.
• To limit the transforming operations to one
  string, an insertion in one string can be
  considered a deletion in the other string and
  visa-versa.

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Edit Distance Example

• v-intner- vintner
• RIMDMDMMI RRRMRRR
• wri-t-ers writers
• Edit Distance 5 Edit Distance 6
• (R)eplacements 1, (I)nsertions/(D)eletions1,
  (M)atches0
• Edit Distance (sometimes referred to as
  Levenshtein distance, Sov. Phys. Dokl. 10707-10,
  1966) is the minimum number of edit operations to
  transform one string into the other.

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Similarity Example

• v-intner- vintner
• RIMDMDMMI RRRMRRR
• wri-t-ers writers
• Similarity 4 Similarity 1
• (R)eplacements 0, (I)nsertions/(D)eletions0,
  (M)atches1
• Similarity is value of the alignment that
  maximizes the total alignment value.

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DNA Scoring Scheme

• Default Scoring Matrix (Smith Waterman) for DNA
• A B C D G H K M N R
  S T U V W X Y
• A 10 -9 -9 10 -9 10 -9 10 10
  10 -9 -9 -9 10 10 10 -9
• B -9 10 10 10 10 10 10 10 10
  10 10 10 10 10 10 10 10
• C -9 10 10 -9 -9 10 -9 10 10
  -9 10 -9 -9 10 -9 10 10
• D 10 10 -9 10 10 10 10 10 10
  10 10 10 10 10 10 10 10
• G -9 10 -9 10 10 -9 10 -9 10
  10 10 -9 -9 10 -9 10 -9
• H 10 10 10 10 -9 10 10 10 10
  10 10 10 10 10 10 10 10
• K -9 10 -9 10 10 10 10 -9 10
  10 10 10 10 10 10 10 10
• M 10 10 10 10 -9 10 -9 10 10
  10 10 -9 -9 10 10 10 10
• N 10 10 10 10 10 10 10 10 10
  10 10 10 10 10 10 10 10
• R 10 10 -9 10 10 10 10 10 10
  10 10 -9 -9 10 10 10 -9
• S -9 10 10 10 10 10 10 10 10
  10 10 -9 -9 10 -9 10 10
• T -9 10 -9 10 -9 10 10 -9 10
  -9 -9 10 10 -9 10 10 10
• U -9 10 -9 10 -9 10 10 -9 10
  -9 -9 10 10 -9 10 10 10
• V 10 10 10 10 10 10 10 10 10
  10 10 -9 -9 10 10 10 10
• W 10 10 -9 10 -9 10 10 10 10
  10 -9 10 10 10 10 10 10
• X 10 10 10 10 10 10 10 10 10
  10 10 10 10 10 10 10 10

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Amino Acid Scoring Schemes

• Identity scoring
• Genetic code scoring
• Chemical similarity
• Observed substitutions

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Amino acid substitution matrix

• Align protein structures or sequences of known
  functionally similar proteins.
• Look at the frequence of amino acid
  substitutions.
• Compute a log-odds ration matrix
• M(ai,aj) log(observed freq (ai,aj) /expected
  freq (ai,aj))
• is more likely than random
• - is less likely than random
• 0 is at the random base rate

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PAM Percent Accepted Mutations

• Globally align protein sequences (at least 85
  identity)
• Calculate the frequency of amino acid
  substitutions
• Divide by the normalized frequency of amino acids
  -gt
• log ratios.
• A 1 PAM matrix specifies a unit of evolutionary
  change 1 accepted point mutation per 100
  residues.
• Create a higher PAM matrix by multiplying the 1
  PAM by itself.

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PAM 250 matrix

• A B C D E F G H I K L
  M N P Q R S T V W Y Z
• A 2 0 -2 0 0 -4 1 -1 -1 -1 -2
  -1 0 1 0 -2 1 1 0 -6 -3 0
• B 0 2 -4 3 2 -5 0 1 -2 1 -3
  -2 2 -1 1 -1 0 0 -2 -5 -3 2
• C -2 -4 12 -5 -5 -4 -3 -3 -2 -5 -6
  -5 -4 -3 -5 -4 0 -2 -2 -8 0 -5
• D 0 3 -5 4 3 -6 1 1 -2 0 -4
  -3 2 -1 2 -1 0 0 -2 -7 -4 3
• E 0 2 -5 3 4 -5 0 1 -2 0 -3
  -2 1 -1 2 -1 0 0 -2 -7 -4 3
• F -4 -5 -4 -6 -5 9 -5 -2 1 -5 2
  0 -4 -5 -5 -4 -3 -3 -1 0 7 -5
• G 1 0 -3 1 0 -5 5 -2 -3 -2 -4
  -3 0 -1 -1 -3 1 0 -1 -7 -5 -1
• H -1 1 -3 1 1 -2 -2 6 -2 0 -2
  -2 2 0 3 2 -1 -1 -2 -3 0 2
• I -1 -2 -2 -2 -2 1 -3 -2 5 -2 2
  2 -2 -2 -2 -2 -1 0 4 -5 -1 -2
• K -1 1 -5 0 0 -5 -2 0 -2 5 -3
  0 1 -1 1 3 0 0 -2 -3 -4 0
• L -2 -3 -6 -4 -3 2 -4 -2 2 -3 6
  4 -3 -3 -2 -3 -3 -2 2 -2 -1 -3
• M -1 -2 -5 -3 -2 0 -3 -2 2 0 4
  6 -2 -2 -1 0 -2 -1 2 -4 -2 -2
• N 0 2 -4 2 1 -4 0 2 -2 1 -3
  -2 2 -1 1 0 1 0 -2 -4 -2 1
• P 1 -1 -3 -1 -1 -5 -1 0 -2 -1 -3
  -2 -1 6 0 0 1 0 -1 -6 -5 0
• Q 0 1 -5 2 2 -5 -1 3 -2 1 -2
  -1 1 0 4 1 -1 -1 -2 -5 -4 3
• R -2 -1 -4 -1 -1 -4 -3 2 -2 3 -3
  0 0 0 1 6 0 -1 -2 2 -4 0
• S 1 0 0 0 0 -3 1 -1 -1 0 -3
  -2 1 1 -1 0 2 1 -1 -2 -3 0
• T 1 0 -2 0 0 -3 0 -1 0 0 -2
  -1 0 0 -1 -1 1 3 0 -5 -3 -1

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BLOSUM Block Substitution Matrix

• Align ungapped protein sequences from the BLOCKS
  database
• Look at the the frequency of amino acid
  substitutions.
• Compute a log-odds ratio matrix (ie PAM
  calculations)
• Higher BLOSUM Cluster groups by varying
  similarity and recalculate matrix.
• Number following indicates percent identity
  within the set, BLOSUM62 62 identity.

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BLOSUM62 matrix

• A B C D E F G H I K L
  M N P Q R S T V W X Y Z
• A 4 -2 0 -2 -1 -2 0 -2 -1 -1 -1
  -1 -2 -1 -1 -1 1 0 0 -3 -1 -2 -1
• B -2 6 -3 6 2 -3 -1 -1 -3 -1 -4
  -3 1 -1 0 -2 0 -1 -3 -4 -1 -3 2
• C 0 -3 9 -3 -4 -2 -3 -3 -1 -3 -1
  -1 -3 -3 -3 -3 -1 -1 -1 -2 -1 -2 -4
• D -2 6 -3 6 2 -3 -1 -1 -3 -1 -4
  -3 1 -1 0 -2 0 -1 -3 -4 -1 -3 2
• E -1 2 -4 2 5 -3 -2 0 -3 1 -3
  -2 0 -1 2 0 0 -1 -2 -3 -1 -2 5
• F -2 -3 -2 -3 -3 6 -3 -1 0 -3 0
  0 -3 -4 -3 -3 -2 -2 -1 1 -1 3 -3
• G 0 -1 -3 -1 -2 -3 6 -2 -4 -2 -4
  -3 0 -2 -2 -2 0 -2 -3 -2 -1 -3 -2
• H -2 -1 -3 -1 0 -1 -2 8 -3 -1 -3
  -2 1 -2 0 0 -1 -2 -3 -2 -1 2 0
• I -1 -3 -1 -3 -3 0 -4 -3 4 -3 2
  1 -3 -3 -3 -3 -2 -1 3 -3 -1 -1 -3
• K -1 -1 -3 -1 1 -3 -2 -1 -3 5 -2
  -1 0 -1 1 2 0 -1 -2 -3 -1 -2 1
• L -1 -4 -1 -4 -3 0 -4 -3 2 -2 4
  2 -3 -3 -2 -2 -2 -1 1 -2 -1 -1 -3
• M -1 -3 -1 -3 -2 0 -3 -2 1 -1 2
  5 -2 -2 0 -1 -1 -1 1 -1 -1 -1 -2
• N -2 1 -3 1 0 -3 0 1 -3 0 -3
  -2 6 -2 0 0 1 0 -3 -4 -1 -2 0
• P -1 -1 -3 -1 -1 -4 -2 -2 -3 -1 -3
  -2 -2 7 -1 -2 -1 -1 -2 -4 -1 -3 -1
• Q -1 0 -3 0 2 -3 -2 0 -3 1 -2
  0 0 -1 5 1 0 -1 -2 -2 -1 -1 2
• R -1 -2 -3 -2 0 -3 -2 0 -3 2 -2
  -1 0 -2 1 5 -1 -1 -3 -3 -1 -2 0
• S 1 0 -1 0 0 -2 0 -1 -2 0 -2
  -1 1 -1 0 -1 4 1 -2 -3 -1 -2 0
• T 0 -1 -1 -1 -1 -2 -2 -2 -1 -1 -1
  -1 0 -1 -1 -1 1 5 0 -2 -1 -2 -1

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Which matrix

• Blosum finds short, highly similar sequences.
• Blosum usually best for local similarity
  searches.
• Blosum62 is default for blast.
• BLOSUM80 BLOSUM62 BLOSUM45
• PAM1 PAM120 PAM250
• Less divergent More divergent

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Which matrix

• If sequences are thought to be related, a PAM250
  is best.
• When sequences are not known to be related,
  PAM120 tends to give more sensitivity.
• To pick up short segments of highly similar
  sequences, PAM40 is a good choice.

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Global Alignment

• Optimal Alignment over the entire length of both
  sequences.

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Global Alignment

• Needleman Wunsch Algorithm
• Every residue of the two sequences has to
  participate.
• Guaranteed to calculate an Optimal similarity
  score.
• Cannot detect domains.

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Local Alignment

• Locates the highest scoring alignment regardless
  of position and length.

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Local Alignment

• Smith Waterman Algorithm
• Guaranteed to find all significant matches to a
  given query.
• Can find regions of strong similarity domains.
• Computationally expensive.

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Blast Basic Local Alignment Sequence Tool

• Objective find all local regions of similarity
  distinguishable from random.
• Local alignments
• Gaps permitted
• Statistically sound, but no guarantee of
  optimality
• Fast
• Less sensitive for (shorter) sequences.

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BLAST Three step algorithm

• Compile a list of high scoring words of length w
• (w4 for proteins, w12 for nucleic acids)
• Scan the word hits of score greater than
  threshold, T
• Extend word hit in both directions to find High
  Scoring Pairs with scores greater than S.

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BLAST Word size

• A word is any short sequence lt 6 letters
• Protein (1 - 2), nucleotide (1 - 6)
• High word size results in
• Faster
• Less sensitive
• More selective

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Other BLAST programs

• Query Database
• BLASTN nucleic acid query nucleic acid
• BLASTP protein query protein
• BLASTX translated NA query protein
• TBLASTN protein query translated NA
• TBLASTX translated NA query translated NA

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Other BLAST Programs
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Other BLAST Programs
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Other BLAST Programs
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Blast Databases

• Nucleotide Sequence Databases nr  All
  GenBankRefSeq NucleotidesEMBLDDBJPDB
  sequences (but no EST, STS, GSS, or phase 0, 1 or
  2 HTGS sequences). No longer "non-redundant". 
  est Database of GenBankEMBLDDBJ sequences from
  EST Divisions est_human, est_mouse, est_others
  gss  Genome Survey Sequence, includes
  single-pass genomic data, exon-trapped sequences,
  and Alu PCR sequences. htgs  Unfinished High
  Throughput Genomic Sequences phases 0, 1 and 2
  (finished, phase 3 HTG sequences are in nr) pat
  Nucleotides from the Patent division of GenBank.
  mito  Database of mitochondrial sequences
  vector  Vector subset of GenBank(R), NCBI, in
  ftp//ftp.ncbi.nih.gov/blast/db/ pdb  Sequences
  from the 3-dimensional structure from Brookhaven
  Protein Data Bank GENOMES (yeast, E. coli,
  Drosophila genome  month  All new or revised
  GenBankEMBLDDBJPDB sequences released in the
  last 30 days. alu  Select Alu repeats from
  REPBASE, suitable for masking Alu repeats from
  query sequences.
• dbsts Database of GenBankEMBLDDBJ sequences
  from STS Divisions . chromosome Searches
  Complete Genomes, Complete Chromosome, or contigs
  form the NCBI Reference Sequence project..

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Blast Databases

• Peptide Sequence Databases
• nr All non-redundant GenBank CDS
  translationsRefSeq ProteinsPDBSwissProtPIRPRF
  swissprot Last major release of the SWISS-PROT
  protein sequence database (no updates) pat
  Proteins from the Patent division of GenPept.
  pdb  Sequences derived from the 3-dimensional
  structure from Brookhaven Protein Data Bank
  Yeast yeast (Saccharomyces cerevisiae) genomic
  CDS translations ecoli  Escherichia coli
  genomic CDS translations Drosophila genome
  Drosophila genome proteins provided by Celera
  and Berkeley Drosophila Genome Project (BDGP).
  month All new or revised GenBank CDS
  translationPDBSwissProtPIRPRF released in the
  last 30 days.

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Other Blast Programs

• MegaBlast - optimized for aligning sequences that
  differ slightly as a result of sequencing or
  other similar "errors".
• Uses a larger word size (16)
• Is up to 10 times faster than more common
  sequence similarity programs
• able to efficiently handle much longer DNA
  sequences
• non-affine gapping parameters (open 0,
  extension variable

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Other Blast Programs

• TraceBlast - optimized for cross species
  comparisons.
• word size (11)
• Expect value (10)

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Other BLAST programs

• Gapped BLAST (BLAST 2.0)
• extends words from no-gap to gap, generate gapped
  alignments
• PSI-BLAST
• Position specific iterated BLAST, use gapped
  BLAST, generate a Profile from multiple
  iterations used instead of the input and Distance
  Matrix.

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Limitations of BLAST

• Needs islands of strong homology
• Limits on the combination of scoring and penalty
  values
• Variants (blastx, tblastn, tblastx) use 6-frame
  translation, yet does miss sequences with frame
  shifts, etc.
• Finds and reports only local alignments.

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Rules of Thumb

• For short amino acid sequences (20 - 40), 50
  identity happens by chance.
• (Increase expect value and decrease word size.)
• If A and B are homologous, and B and C are
  homologous, then A and C are homologous, even if
  you can not see it.
• Locate and filter regions of low complexity.
  Leads to false positive alignment (similarity
  without homology).

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FASTA Fast Alignment

• Rapid global alignment
• allows alignment to shift frames
• not a strong mathmatical basis

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FASTA

• Show diagrams.

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LALIGN

• A FASTA derivative for local alignments
• Compares two protein sequences to identify
  regions of similarity
• Will report several sequence alignments within a
  given sequence
• Works for internal repeats that are missed by
  FASTA because of gaps.

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Precomputed Alignments

• Related sequences, related structures, related
  articles, summaries, etc.
• InterPro
• Pfam
• ProDom
• Smart
• etc.

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BimCoreBioMolecular Computing Resource

• Founded in 1992 as a subscription based
  computing support service for Emory
  bioinformatics based reasearch.
• Bioinformatics is the MIS for molecular
  biology.
• Our mission is to serve as a human interface
  between researchers and computing technology
  enabling investigators to refine scope of
  biological investigations and accelerate
  discovery.

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BimCoreBioMolecular Computing Resource

• Sequence Analysis Molecular Modeling
• Microarray Analysis
• BimCore provides
• Software and computational hardware (evaluate
  software,
• purchase Emory site licenses)
• Training (workshops, courses and online
  tutorials)
• Collaborations (evaluation, direct approach,
  training
• and interpretation)
• Computer expertise (programming, evaluate
  needs,
• direct purchase and set-up)

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The Biology of Molecular BiologyRobert J. Huskey
http//www.people.virginia.edu/
rjh9u/humbiol.html
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Sequence Analysis Facility

• ...ATATAA...GTA...ATGCTAGGCGCTTCTATCTTC
• ..................UACGAUCCGCGAAGAUAGAAG

DNA
UAC GAU CCG CGA AGA UAG AAG...
RNA protein
M L G A S I F ..
protein
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Sequence Analysis Facility

• Given a sequence (DNA or protein) search against
  
• biological databases for similarities in type
  and function.
• Generate and review sequence alignments.
• Discover evolutionary path of a DNA or protein
• sequence (phylogeny).
• Given a sequence of DNA (eg. ACGTGTGGG)
• locate genes and mutations.
• Assemble sequence fragments into larger
  component sequence.

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Molecular Modeling Center

• Analyze protein structure, interpret the
  mechanism of action.
• Generate a model structure by Homology
  Modeling.
• Mutate a protein structure, predict affect on
  the protein.
• Design inhibition of protein function, drug
  design and molecular docking. Emerson

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Microarray Data Analysis Facility

• Determine the expression level of every gene in
  an organism,
• tissue or cell culture.
• Monitor the changes in expression levels in
  response to
• environmental changes, such as stress,
  disease, drug.
• Lead to a study and understanding of the
  biological response.

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• Sequence Analysis Facility
• Given a sequence (DNA or protein) search against
  biological databases for
• similarities in type and function.
• Generate and review sequence alignments.
• Discover evolutionary path of a DNA or protein
  sequence (phylogeny).
• Given a sequence of DNA (eg. ACGTGTGGG) locate
  genes and mutations.
• Assemble sequence fragments into larger
  component sequence.
• Molecular Modeling Center
• Generate a model structure by Homology Modeling.
• Mutate a protein structure, predict affect on
  the protein.
• Design inhibition of protein function, drug
  design and molecular docking.
• Microarray Data Analysis Facility
• Determine the expression level of every gene in
  an organism, tissue or cell
• culture.
• Monitor the changes in expression levels in
  response to environmental
• changes, such as stress, disease, drug.
• Lead to a study and understanding of the
  biological response.