Promoter Analysis

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Promoter Analysis

• Goals, Problems Solutions

Chaim Linhart Rani Elkon Dec. 03
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Outline

• Background
• Questions
• Some answers
• PRIMA

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Regulation of Expression

• Each cell contains a copy of the whole genome
• BUT utilizes only a subset of the genes
• Most genes are highly regulated
• their expression is limited to specific tissues,
  developmental stages, physiological condition

How is the expression of genes regulated?
One way is through transcriptional regulation
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Regulation of Transcription

• The conditions in which a gene is transcribed are
  mainly encoded in the DNA in a region called
  promoter
• Each promoter contains several short DNA
  subsequences, called binding sites (BSs) that
  are bound by specific proteins called
  transcription factors (TFs)

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Regulation of Transcription (II)
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Regulation of Transcription (III)

• By binding to a genes promoter, TFs can either
  promote or repress the recruitment of the
  transcription machinery
• The conditions in which a gene is transcribed are
  determined by the specific combination of BSs in
  its promoter

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Regulation of Transcription (III)

• Assumption
• Co-expression
• ?
• Transcriptional co-regulation
• ?
• Common BSs

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DNA chips
? Data analysis (normalization,
clustering) ? Co-expression
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WH-questions

• So we know why were looking for common BSs
• What exactly are we trying to find?
• Where should we look for it?
• How can we find it?

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Promoter Region (Where?)

• What is the promoter region?
• Upstream Transcription Start Site (TSS)
• Too short ? miss many real BSs (false negatives)
• Too long ? lots of wrong hits (false positives)
• Length is species dependent (e.g., yeast 600bp,
  thousands in human)
• Common practice 500-2000bp
• Mask-out repetitive sequences?
• Common practice Yes
• Consider both strands?
• Common practice Yes

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Promoter Region II

• Additional problems
• Where exactly is the TSS?
• What about 1st exon, intron?
• Multiple transcripts
• Answers actually depend on the TF

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The What? question

• Computational tasks
• New BSs of known TFs
• New motifs (BSs of unknown TFs)
• Modules combinations of TFs

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BSs Models

• Exact string(s)
• Example
• BS TACACC , TACGGC
• CAATGCAGGATACACCGATCGGTA
• GGAGTACGGCAAGTCCCCATGTGA
• AGGCTGGACCAGACTCTACACCTA

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BSs Models (II)

• String with mismatches
• Example
• BS TACACC 1 mismatch
• CAATGCAGGATTCACCGATCGGTA
• GGAGTACAGCAAGTCCCCATGTGA
• AGGCTGGACCAGACTCTACACCTA

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BSs Models (III)

• Degenerate string
• Example
• BS TASDAC (SC,G DA,G,T)
• CAATGCAGGATACAACGATCGGTA
• GGAGTAGTACAAGTCCCCATGTGA
• AGGCTGGACCAGACTCTACGACTA

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BSs Models (IV)

• Position Weight Matrix (PWM)
• Example BS

Need to set score threshold

• ATGCAGGATACACCGATCGGTA 0.0605
• GGAGTAGAGCAAGTCCCGTGA 0.0605
• AAGACTCTACAATTATGGCGT 0.0151

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BSs Models (V)

• More complex models
• PWM with spacers (e.g., for p53)
• Markov model (dependency between adjacent columns
  of PWM)
• Hybrid models, e.g., mixture of two PWMs

And we also need to model the non-BSs sequences
in the promoters
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How to find novel motifs

• Degenerate string
• YMF - Sinha Tompa 02
• String with mismatches
• WINNOWER Pevzner Sze 00
• Random Projections Buhler Tompa 02
• MULTIPROFILER Keich Pevzner 02
• PWM
• MEME Bailey Elkan 95
• AlignACE Hughes et al. 98
• CONSENSUS - Hertz Stormo 99

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How to find TF modules

• BioProspector Liu et al. 01
• Co-Bind GuhaThakurta Stormo 01
• MITRA Eskin Pevzner 02
• CREME Sharan et al. 03
• MCAST Bailey Noble 03

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PRIMAPRomoter Integration in Microarray Analysis

• Goal Identify TFs whose BSs are abundant
  (statistically over-represented) in promoters of
  co-expressed genes
• Limited to known TFs
• Uses PWM to model BSs
• Allows multiple BSs per promoter
• Integrated into Expander

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PRIMA input-output

• Input
• Promoter sequences (typically 1200bp) of
• Background (BG) set, typically all genes
• Target set, i.e., co-expressed genes
• PWMs of known TFs, e.g., TRANSFAC
• Output
• p-values of over-represented TFs

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PRIMA algorithm

• For each PWM
• Compute a threshold score for declaring hits of
  the PWM (hit subsequence that is similar to the
  PWM hypothetical BS)
• Scan BG and target-set promoters for hits
• Apply a statistical test to decide whether the
  number of hits in the target-set is significantly
  higher than expected by chance, given the
  distribution of hits in the BG
• (Find co-occurring pairs of TFs)

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PRIMA results on HCC

• We ran PRIMA on 568 genes that are
• periodically expressed in the human cell-cycle
• (data from Whitfield et al. 02)

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PRIMA results on HCC (II)

• Locations
• of hits

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PRIMA results on HCC (III)

• Co-occurring pairs of TFs

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PRIMA future directions

• More information to utilize
• Distribution of hits locations
• Modules co-occurrence of TFs, possibly with
  distance and/or strand bias
• Homology BSs are more conserved than rest of
  promoter

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PRIMA in EXPANDER
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PRIMA in EXPANDER (II)
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Acknowledgements

• PRIMA
• Rani Elkon
• Roded Sharan
• Ron Shamir
• Yossi Shiloh

• Expander
• Adi Maron-Katz
• Amos Tanay
• Israel Steinfeld
• Naama Arbili
• Roded Sharan

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Questions?