Algorithmic Analysis of Human DNA Replication Timing from Discrete Microarray Data

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Algorithmic Analysis of Human DNA Replication
Timing from Discrete Microarray Data

• Christopher Taylor
• Gabriel Robins Anindya Dutta

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Thesis Statement

• The DNA replication timing profile can be
  reconstructed efficiently and accurately from
  discrete time points.

(Glossary)
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Presentation Outline

• Biology background
• Microarray technology
• Experimental data
• Challenges
• Algorithms
• Research Plans
• Replication timing
• Origins
• Scale up

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Why Study DNA Replication?

• Natural Science
• DNA is the blueprint for organisms
• It must be passed on (organism, cell)
• Engineering
• Gene therapy
• Insertion, deletion, modification
• Cancer is unchecked replication

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... A G G T C G A C A C ... ... T C C
A G C T G T G ...

• Human genome gt 3 billion bp
• Replication rate 1000 bp/min
• Serial replication ? 5.7 years
• 6 to 10 hours (speedup gt 5000)

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Background

• Prokaryotes
• E. Coli
• DnaA binds to oriC
• Eukaryotes ORC
• S. Cerevisiae (yeast)
• ARS 11 bp consensus
• Mapping of origins
• Human
• No known consensus
• Few origins characterized

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Genome Tiling Microarrays

• Interrogation at genomic scale
• Large increase in data
• Microarray data analysis
• Array of probes tiles genome

ATGGACTACGGATCAGTAAATCGATTAGGCACCAGATCAAGTACGATCCA
GAGTACATAGCATACCATGACTAGATACCTGATGCCTAGTCATTTAGCTA
ATCCGTGGTCTAGTTCATGCTAGGTCTCATGTATCGTATGGTACTGATCT
GAGTACATAGCATACCATGACTAGACTCATGTATCGTATGGTACTGATCT

• Cross-hybridization
• Repeats not tiled
• Gaps in genome

PM probe
MM probe
GAGTACATAGCATACCATGACTAGA
A
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Image analysis computes intensity of each array
probe
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The Cell Cycle
Start of S-phase (0 hour)
S-Phase
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Profiling DNA Replication Timing

• Ideal f(chr, bp) rtime
• Isolate DNA replicated in discrete parts
  of S-phase
• One cell is not enough
• Synchronize S-phase entry
• Apply drugs
• Release together
• Synchronization error
• Label in two hour intervals
• Allelic Variation
• mf(chr, bp) rtime1, rtime2,

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Allelic Variation
0hr
0hr
2hr
2hr

• Fluorescent in-situ Hybridization (FISH)
• Replication timing at a given site

4hr
4hr
Temporally non-specific replication (TNS)
6hr
6hr
Temporally specific replication (TS)
8hr
8hr
10hr
10hr
11
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What is the Problem?

• Reconstruct a continuous replication profile
• Temporally (time points)
• Spatially (probes)
• from noisy data
• Biological experiments
• Synchronization error
• Microarray artifacts
• efficiently
• Genomic data (gt 3 billion bp)

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Initial Analysis

• Tiling Analysis Software (TAS)
• Wilcoxon Rank Sum test in sliding window
• Assess enrichment of treatment over control

• Window slides to get p-value for each probe
• O(kn) time complexity
• n probes on array
• k probes in a window
• k scales linearly with window size

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New Analysis

• Thesis Statement (revisited)
• The DNA replication timing profile can be
  reconstructed efficiently and accurately from
  discrete time points.
• Incorporate information from all time points
• Continuous view of replication timing (TR50)
• Address temporally non-specific replication
• Scale up to the whole genome efficiently

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Allelic Variation Examples
Temporally specific replication
0 0
1/1 0
0
5
0
2
4
6
8
10
TR50
Temporally non-specific replication
1/6 1/6
1/3 0 1/3
0
2
4
6
8
10
5
TR50
Challenge From distribution of array signal,
determine replication category.
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Temporal Specificity Algorithm

• // Is there evidence that all alleles are
  replicating together?
• If (max sum of two adjacent time points 5/6
  total sum) then probe is temporally specific
• // Is at least one allele replicating apart from
  the majority?
• Else If (max sum of two adjacent time points not
  including the maximum time point 1/3 total
  sum) then probe is temporally
  non-specific
• // Isolated signal is not strong enough to be an
  allele.
• Else
  probe is
  temporally specific

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Plotting TR50
8 6 4 2 TR50 (hours)
33 33.5
34 Chromosomal Position
(in millions of bp)

• Smoothed TR50 curve recovers replication pattern
• Local minima ? Possible locations of replication
  origin

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Segregation Algorithm

• Sliding window passes over probes to generate
  intervals
• Ratio of TSP to TNSP determines temporal
  specificity
• Average TR50 determines timing category

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Research Plan Profile Generation

• Parameters to evaluate
• Segregation Algorithm sliding window size,
  minimum probe density
• Join Intervals minimum interval size

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Evaluation

• Concordance of biological phenomena
• Segregation intervals ? FISH
• STR50 local minima ? Other origin methods
• Correlation with other biological data
• Gene density ? Early replication
• AT content ? Late replication
• Gene expression ? Early replication
• Activating acetylation/methylation ? Early
  replication
• Performance on random data
• Large quantity of TNS replication

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Research Plan Replication Origins

• Drive DNA replication pattern
• Smoothed TR50 local minima
• Cleaned up with new profiles
• Other biological assays
• Early labeling fragments
• Nascent strands
• Bubble trapping
• ORC binding

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Approach and Evaluation

• Correlation between methods
• Consensus sets
• Motif analysis
• Positional attributes
• Replication timing
• Proximity to genes
• Evaluation is difficult (few validated origins)
• Agreement between methods
• Testing proposed correlations
• Paper in preparation

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Scaling Up to Whole Genome

• Pilot 1 100 of human genome
• Algorithms developed with scalability in mind
• Incremental update sliding windows ? Linear time
• Performance based evaluation
• If 100 data available
• Profile multiple runs
• Else
• Profile many 1 runs

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Implementation Details

• Java
• Class representation of proprietary microarray
  files
• Algorithms to process raw microarray data
• Diagnostic tools
• Perl
• Scripts to process intermediate and final data
• Correlations, data transformation, quality
  assurance
• R statistical language
• Smoothing, statistical plots, correlation studies
• Shell scripts
• Automated processing of microarray sets

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Current/Expected Contributions

• Algorithms, Software Infrastructure, Analysis
• Probe-by-probe TR50 analysis
• Temporal Specificity Algorithm
• Combinatorial analysis of allele locations
• Segregation Algorithm
• TNS, Early, Mid, Late replicating areas
• Used to design validation experiments
• Smoothed TR50 profile
• Local minima provide candidate origin set
• Linear algorithms enable scale up
• Randomness testing

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Publications

• Completed
• ENCODE Project Consortium. The ENCODE
  (ENCyclopedia Of DNA Elements) Project. Science.
  2004 Oct 22 306(5696)636-40.
• ENCODE Project Consortium. Identification and
  analysis of functional elements in 1 of the
  human genome by the ENCODE pilot project.
  Nature. In Press, to appear in June 14,
  2007 issue
• Karnani N., Taylor C., Malhotra A., Dutta A.
  Pan-S replication patterns and chromosomal
  domains defined by genome tiling arrays of encode
  genomic areas. Genome Research.
  In Press, to appear in June
  2007 issue
• UCSC Browser Tracks
  TR50, Smoothed TR50, Local Minima,
  Segregation
• In Progress
• Multi-million dollar NIH grant for scale up to
  full human genome
• Paper detailing origin methods, correlations, etc.

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Timeline

• Spring 2007 (present to June 20)
• Implement proposed replication profile
  generation algorithms
• Generate new profiles for existing data and
  evaluate against FISH
• Collect new origin sets and continue analysis for
  paper completion
• Summer 2007 (June 21 to September 21)
• Explore correlations of new profiles with other
  data sets
• Submit paper to PSB 2008 based on new method and
  results
• Develop random data sets to test profile
  generation algorithms
• Fall 2007 (September 22 to December 21)
• Evaluate performance for scale up to whole
  genome
• Tie up loose ends and begin writing the
  dissertation
• Winter 2007-2008 (December 22 to March 19)
• Finish dissertation and schedule defense before
  May 2008

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Acknowledgements

• Advising
• Anindya Dutta, Gabriel Robins
• Biological Experiments
• Neerja Karnani, Patrick Boyle, Larry Mesner,
  Jamie Teer, Hakkyun Kim
• Collaborative Analysis
• Ankit Malhotra
• Discussions of Analysis
• Stefan Bekiranov

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THE END
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Why is this work computer science?

• Fred Brooks The Computer Scientist as Toolsmith
  II
• Hitching our research to someone elses driving
  problems, and solving those problems on the
  owners terms, leads us to richer computer
  science research.
• Not an incremental improvement
• Algorithmic techniques and analysis used to solve
  a problem previously addressed inadequately with
  a statistical approach that performed poorly
• Collaboration outside of engineering disciplines
  enhances visibility, funding opportunities, and
  demand for CS work
• Developed algorithms, time complexity analysis,
  combinatorial analysis, feedback to experimental
  design

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Will this work lead to any CS publications?

• The Nature article focused on analysis of the
  biological data and includes descriptions of some
  of my algorithms
• The Genome Research paper and origins paper will
  also contain writeups of my algorithms and
  analysis techniques
• The Pacific Symposium on Biocomputing focuses on
  algorithms and computational techniques

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Isn't your approach too simple?

• The approach isnt simple
• Combinatorial analysis
• Temporal specificity algorithm (many iterations)
• Probewise computation to deal with binding
  affinity
• Incremental updating sliding windows
• Cross-hybridiztion
• Synchronization error
• Smoothing
• Parameterization
• Linear algorithms for scale up

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Can't your algorithm be replaced by a well-known
statistical method?

• HMMs were used for segregation of intervals
• Performed poorly in comparison to my algorithm
• Less accurate categorization of replication
  intervals
• Prone to rapid oscillation, producing tiny
  intervals
• Parameterization was difficult
• Lowess smoothing is a statistical method
• Parameterization was not easy

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What are the biggest challenges in this work?

• Noise!
• The data to analyze comes from biological
  experiments with several sources of noise that
  compound upon one another
• Biology
• I havent had a course in biology since 10th
  grade
• Microarrays
• New, evolving technology were still learning to
  deal with
• Data size
• Hundreds of GB of data to process
• Replicates, failed experiments
• Algorithms must be efficient

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What kind of career are you aiming for after
graduation, and why?

• Teaching Computer Science (Small College)
• I enjoyed learning in my undergraduate curriculum
  with meaningful interactions with professors
• I taught Discrete Math at UVa in Fall 02 and
  Spring 03
• Enjoyable, but 60-70 students too large
• Post-doctoral (Biological Computing)
• Many opportunities around the world
• Further exploration of the field

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How will you know when your work/thesis is done?

• Research is never really done, but you have to
  declare victory at some point
• The replication profiling algorithms Ive
  developed already perform quite well
• I have concrete plans to improve and finalize
  them