Computational Biology

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Computational Biology

• The advent of genomic sequencing has led to a new
  era in biological studies, one involving aspects
  of informational technologies to expedite
  analysis of biological data.

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BIOS 480 Goals

• Provide a comprehensive understanding of current
  methods in biological sequence analysis
• Assess challenges and approaches in new
  bioinformatics-related disciplines
• Support in-depth, hands-on experience in design
  and implementation of bioinformatics tools (BIOS
  482)

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Grades

• The primary assessment tool in this course will
  be participation as exhibited by in-class
  discussion (40) and performance on assigned
  homework (30).
• A final exam testing your comprehensive knowledge
  of the material will count 20 of your final
  grade.
• www.uwp.edu/barber/bioinformatics/BIOS480.htm
  has lectures and important materials for this
  class and BIOS 482

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Introduction to

• Sequence alignments
• Gene prediction
• Motifs
• Phylogeny
• Genomics
• Proteomics
• Metabolomics

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The advent of genome sequencing brought
bioinformatics into its own

• Yeah, but now that the human genome is done,
  isnt genomics done
• No

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Capillary and Slab gel electrophoresis use a
modified Sanger technology with
fluorescent dyes
Typical reads of 500-750 nt on an hour
timescale. Variation depending on sequencer.
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Microfabricated Capillary Arrays

• Etch a glass chip with T-shaped channels that are
  7 cm long, and mM in depth and width, can devise
  a 96 well chip that would be capable of 150,000
  bases/h
• Miniaturization is one booming field driving
  bioinformatics

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Free Solution Electrophoresis

• Possibly will improve separation time (no matrix)
  without losing read length
• Label DNA molecules with friction increasing
  molecule such as streptavidin
• Currently can read 100 bp, a long way to go

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Who needs electrophoresis?

• Pyrosequencing
• MALDI-TOF Mass Spectrometry
• Sequencing by Hybridization
• Massively Parallel Signature Sequencing
• A testimony to innovative molecular biology
• Single molecule methods

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Pyrosequencing

• Real-time sequencing measuring release of PPi
  during DNA synthesis
• Has been of particular use for SNP analysis

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Put the sequencing reactions through a mass
spectrometer
Spectra of the C- and G- terminated
oligonucleotides
Current limit 100 bp, Facilitated by sensitivity
and high-throughput loading
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Potential innovations in DNA sequencing

• Sequencing by hybridization
• Cot-based analysis
• http//www.msstate.edu/research/mgel/cbcs/cbcs.htm
  
• Chip-based analysis
• http//www.hyseq.com/content/131.php
• http//citeseer.nj.nec.com/context/471959/0
• Linear Read
• http//www.usgenomics.com/about/index.shtml

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Cot analysis
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Growth in genomic technology

• U.S. Genomics's technology platform, the
  GeneEngine, has two components, (1)
  nanotechnology systems for positioning DNA so
  that it can be read linearly
  (broadly termed DNA Delivery
  Mechanism(s)) and (2) detection technologies
  that allow the reading of information from the
  DNA Delivery Mechanism(s). (FRET-based??)

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The future looks bright, but what about right now?
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Overview of Genomic Sequencing
Original DNA

• Break DNA into random fragments (8-10X Coverage)
• Amplify fragments in a vector and sequence
  500-700 bases
• in from each end

Base calling performed by Phred software
http//www.phrap.org/ http//www.genome.org/c
gi/reprint/8/3/175.pdf
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Overview of Genomic Sequencing
Original DNA

• Break DNA into random fragments (8-10X Coverage)
• Amplify fragments in a vector and sequence
  500-700 bases
• in from each end
• Assemble fragments of sequence that have been
  read

Contig 1
Contig 2
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Phred Software

• Calls bases in four phases
• Predicting peaks (ideal locations)
• Locating observed peaks
• Matching observed to predicted
• Finding missing peaks
• http//www.genome.org/cgi/reprint/8/3/186.pdf
• http//www.genome.org/cgi/reprint/8/3/175.pdf

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Errors in Sequencing Reads

• Each base call is assigned a quality score
• q -10 x log10(p) Higher quality scores
  correspond to low error probabilities
• Errors are associated with peak vicinity, use the
  following parameters in error probability
  determination on a TRAINING SET
• Peak spacing
• Uncalled/called ration (two window sizes)
• Peak resolution
• Result in a look-up table inherent to Phred
  software

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Common Sources of Sequencing Errors

• The first fifty or so peaks of a trace are noisy
  and unevenly spaced due to anomalous migration of
  short DNA fragments, and unreacted dye-primer and
  dye-terminator molecules.
• Near the end of the trace, peaks become less
  evenly spaced due to less accurate trace
  processing, less well resolved as diffusion
  effects increase, and also labeled molecules
  decrease.
• Compressions most common in GC-rich regions
  when bases near the end of a single-stranded
  fragment bind to a complementary region forming a
  hairpin (migrates more rapidly than expected)
• Dye-terminator sequencing method helps resolve
  compressions, but has own problems About 85
  of high quality dye terminator errors resulted
  from a missing G peak following an A, or a
  missing A folling a T, Ewing and Green, 1998.

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Assembly of large DNA sequences

• Several assembly programs exist and can be run
  with different degrees of success Phrap, TIGR
  Assembler, CAP, STROLL, etc.

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Overlap-layout-consensus

• Most fragment assembly algorithms include the
  following three steps
• Overlap. Finding potentially overlapping
  fragments.
• Layout. Finding the order of fragments.
• Consensus. Deriving the DNA sequence from the
  layout.

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Overlap

• The overlap problem is to find the best match
  between the suffix of one sequence and the prefix
  of another.
• If no sequencing errors, simply find the longest
  suffix of one string that exactly matches the
  prefix of another string.
• Since errors are small, the common practice is to
  use filtration method and to filter out pairs of
  fragments that do not share a significantly long
  common substring.

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Layout

• Many algorithms select a pair of fragments with
  the best overlap at every step.
• The score of overlap is either the similarity
  score or a more involved probablilistic score.
• The selected pair of fragments with the best
  overlap score is checked for consistency.
• If this check is accepted, the two fragments are
  merged.

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Layout

• At later stages of the algorithm the collections
  of fragments (contig) rather than individual
  fragments are merged.
• The difficulty with the layout step is deciding
  whether two fragments with a good overlap really
  overlap (i.e. their differences are caused by
  sequencing errors) or represent a repeat in a
  genome (i.e. their differences are caused by
  mutations).
• Use additional scaffolding measures physical
  mapping, optical mapping, http//schwartzlab.biote
  ch.wisc.edu/omm/omm.html

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Consensus

• The simplest way to build the consensus is to
  report the most frequent character in the
  substring layout that is (implicitly) constructed
  after the layout step is completed.

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The Human Touch

• Consed A Graphical Tool for Editing Phrap
  Assemblies.

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Some definitions

• Heuristics A term in computer science that
  refers to 'guesses" made by a program to obtain
  approximately accurate results. Typically, these
  are used to increase the speed of a program
  greatly at the cost of potentially yielding
  suboptimal results. BLAST and FASTA use
  heuristics based on knowledge of how sequences
  evolve.
• Greedy algorithm The idea behind a greedy
  algorithm is to perform a single procedure in the
  recipe over and over again until it can't be done
  any more and see what kind of results it will
  produce.