Basic Overview of Bioinformatics Tools and Biocomputing Applications II

1
Basic Overview of Bioinformatics Tools and
Biocomputing Applications II

• Dr Tan Tin Wee
• Director
• Bioinformatics Centre

2
Common Computational Analyses

• Sequence Assembly
• Simple sequence analysis
• Translation and reverse Complement ORF
• Composition statistics (protein DNA)
• Molecular mass
• Total charge and pI local hydropathy
• Simple determination of secondary structures
• Restriction site analysis
• Internal repeat analysis
• Detection of active sites functional residues
  characteristic structures substrates and
  processing signals

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Common Computational Analyses

• Database sequence search
• Multiple alignment
• 2 and 3 Structure prediction transmembrane
  helix detection
• Structure modeling
• Docking prediction and design
• Hidden Markov model searches

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Database Searching

• Text-based Database Searching -using a text
  string to match an annotation in a sequence
  database record ie. Keyword search
• Sequence-based Database Searching -using a
  biological sequence to match its whole or parts
  of its sequence to the sequences of every
  sequence database records

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Text-Based Database Searching

• Examples Entrez SRS DBGET AceDB- common
  integrated database systems
• Search Concepts
• Boolean Search - AND OR NOT
• Broadening Search
• Narrowing the Search
• Proximity searching soundex
• Wild Card Stemming eg. Thala for thalasemia
  thalassemia thalassemic
• Use standard string search algorithms and boolean
  operations vocabulary matches

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Text-based Database Searching

• Example To find the human homolog of the
  Drosophila per gene
• Procedure
• Web to Entrez
• All Fields enter human per
• Hits returned irrelevant - broaden search
• human period - more hits
• check every one find the human RIGUI gene
• Hit and miss clever guess work free form or
  controlled vocabulary (MeSH terms)Use Boolean
  searches

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Sequence-based Database Searching

• Homology Search
• Global or Local Sequence Alignment
• Needleman-Wunch Algorithm
• Smith-Waterman Algorithm
• Lipman - Pearson FASTA
• Altschuls BLAST
• Take a sequence pairwise comparison with each
  sequence in the database

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Sequence-based Database Searching

• Basic Assumptions
• Sequences of homologous Genes/Protein diverge
  over time even though structure and/or function
  change little
• Significant sequence similarity inferred as
  potential structural /functional similarity or
  common evolutionary origin
• Based on well-characterised protein infer the
  function of an unknown sequence at gene or
  protein sequence level.

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Sequence-based Database Searching

• Global Alignmentforces complete alignment of the
  pairwise comparison of the two input sequences
• Local Alignmentlooks for local stretches of
  similarity and tries to align the most similar
  segments
• Algorithms used may be similar but output
  different statistics needed to assess results

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Sequence-based Database Searching

• Alignment Scoring
• Substitution score and substitution matrixPAM
  BLOSUM
• affine gap costs/gap penalty and gap scores
• Optimal alignments dynamic programmingNeedleman-
  Wunsch algorithmSmith-Waterman algorithm
  (SSEARCH)
• Additional heuristics to speed up the search -
  FASTA BLAST

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Some definitions

• Affine gap costs - scoring system for gaps within
  alignments which charges a penalty for gap
  formation and additional per-residue penalty
  proportional to size of gap
• Alignment score - numerical value indicating the
  overall quality of an alignment the higher the
  better the alignment.
• Algorithm - fixed procedure embodied in a
  computer program
• Heuristics - a computer science term referring to
  guesses made by the program to approximate
  results usually based on arbitrary or predefined
  rules.
• Gapped Alignment - alignment of sequences where
  gaps are permitted

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Computational Genefinding

• Major challenge in genome project
• Given a DNA sequence where does a gene begin and
  stop - ORF
• Where are the exons and introns
• Where are the transcription elements
• Gene structure and other regulatory elements

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Genomic Elements

• Intron-exon splice sites
• Start-Stop codons
• Branch Points
• Promoters and terminators of transcription
• Polyadenylation sites
• ribosomal binding sites
• Topoisomerase II binding sites
• Topoisomerase I cleavage sites
• Transcription factor binding sites

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Detecting Genomic Elements

• Local sites and motifs/patterns for such element
  - signals and signal sensors
• Extended variable-length regions eg exons and
  introns- contents and content sensors
• Linguistic technique - gene structure described
  in formal grammar - GeneLang genefinding program

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Signal sensors

• Simple consensus sequenceUse of Pattern matching
  algorithms
• Weight matricesallow for weighted score for each
  weight matrix sensors to be summed
• Use of Artificial Neural Networks (ANN)

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Content Sensors

• Long ORF for bacteria
• Statistical models eg. Markov models -
  GeneMarkstatistical models of nucleotide
  frequencies and dependencies in codon structure
• Neural Nets eg Grailexon detection by neural
  network combined with signal sensors for
  exon-intron splice sites

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Some Definitions

• Artificial Neural Nets - statistical pattern
  recognition method - a type of nonlinear
  regression
• Markov Models - statistical models for sequences
  in which the probability of each residue depends
  on the residues preceding it.
• Dynamic Programming - type of algorithm widely
  used for constructing sequence aligments and for
  evaluating all posible candidate gene structure

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Other Genefinding methods

• Use of dynamic programmingLinguistic rules for
  functional featuresParameters of a Markov
  Process on hidden variables - hidden Markov
  Models (HMM)
• HMM genefinder - EcoParse Xpound GeneMark HMM
  Veil HMMgene GenScan