Motif Finding Workshop Project

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Motif Finding WorkshopProject
Chaim Linhart January 2008
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Outline

• 1. Some background again
• 2. The project

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1. Background

• Slides with Ron Shamir and Adi Akavia

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Gene from DNA to protein
Pre-mRNA
Mature mRNA
DNA
protein
transcription
translation
splicing
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DNA

• DNA a string over the alphabet of 4 bases
  (nucleotides) A, C, G, T
• Resides in chromosomes
• Complementary strands A-T C-G
• Forward/sense strand AACTTGCG
• Reverse-complement/anti-sense strand
  TTGAACGC
• Directional from 5 to 3
• (upstream) AACTTGCGATACTCCTA
  (downstream)

5 end
3 end
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Gene structure (eukaryotes)
Promoter
DNA
Coding strand
Transcription start site (TSS)
Transcription (RNA polymerase)
Pre-mRNA
Exon
Intron
Exon
Splicing (spliceosome)
5 UTR
3 UTR
Mature mRNA
Stop codon
Start codon
Coding region
Translation (ribosome)
Protein
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Translation

• Codon - a triplet of bases, codes a specific
  amino acid (except the stop codons) many-to-1
  relation
• Stop codons - signal termination of the protein
  synthesis process

http//ntri.tamuk.edu/cell/ribosomes.html
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Genome sequences

• Many genomes have been sequences, including those
  of viruses, microbes, plants and animals.
• Human
• 23 pairs of chromosomes
• 3 Gbps (bps base pairs) , only 3 are genes
• 25,000 genes
• Yeast
• 16 chromosomes
• 20 Mbps
• 6,500 genes

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Regulation of Expression

• Each cell contains an identical copy of the whole
  genome - but utilizes only a subset of the genes
  to perform diverse, unique tasks
• Most genes are highly regulated
• their expression is limited to specific tissues,
  developmental stages, physiological condition
• Main regulatory mechanism transcriptional
  regulation

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Transcriptional regulation

• Transcription is regulated primarily by
  transcription factors (TFs) proteins that bind
  to DNA subsequences, called binding sites (BSs)
• TFBSs are located mainly (not always!) in the
  genes promoter the DNA sequence upstream the
  genes transcription start site (TSS)
• BSs of a particular TF share a common pattern, or
  motif
• Some TFs operate together TF modules

TSS
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TFBS motif models

• Consensus (degenerate) string

AC
CG
ACT
T
gene 1
gene 2
AACTGT
gene 3
CACTGT
gene 4
CACTCT
gene 5
CACTGT
gene 6
gene 7
gene 8
gene 9
AACTGT
gene 10

• Statistical models
• Motif logo representation

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Human G2M cell-cycle genesThe CHR NF-Y module
CDCA3 (trigger of mitotic entry
1) CTCAGCCAATAGGGTCAGGGCAGGGGGCGTGGCGGGAAGTTTGAAAC
T -18 CDCA8 (cell division cycle associated
8) TTGTGATTGGATGTTGTGGGA25bpTGACTGTGGAGTTTGAAT
TGG 23 CDC2 (cell division control protein 2
homolog) CTCTGATTGGCTGCTTTGAAAGTCTACGGGCTACCCGATTG
GTGAATCCGGGGCCCTTTAGCGCGGTGAGTTTGAAACTGCT
0 CDC42EP4 (cdc42 effector protein
4) GCTTTCAGTTTGAACCGAGGA25bpCGACGGCCATTGGCTGCT
GC -110 CCNB1 (G2/mitotic-specific cyclin
B1) AGCCGCCAATGGGAAGGGAG30bpAGCAGTGCGGGGTTTAAA
TCT 45 CCNB2 (G2/mitotic-specific cyclin
B2) TTCAGCCAATGAGAGT15bpGTGTTGGCCAATGAGAAC15
bpGGGCCGCCCAATGGGGCGCAAGCGACGCGGTATTTGAATCCTGGA
10 BSs are short, non-specific, hiding in
both strands and at various locations along the
promoters
TFs NF-Y , CHR
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The computational challenge

• Given a set of co-regulated genes (e.g., from
  gene expression chips)
• Find a motif that is over-represented (occurs
  unusually often) in their promoters
• This may be the TF binding site motif
• Find TF modules over-represented motifs that
  tend to co-occur

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The computational challenge (II)

• Motifs can also be found w/o a given target-set
  genome-wide
• Find a motif that is localized - occurs more
  often neat the TSS of genes
• Find a motif with a strand bias occurs more
  often on the genes coding strand
• Find TF modules with biases in their order /
  orientation / distance

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Motif finding algorithms

• gt100 motif finding algs
• Main differences between them
• Type of analysis input
• Target-set vs. genome-wide
• Single vs. multi-species (conservation)
• Single motifs vs. modules
• Motif model
• Score for evaluating motif
• Motif search technique
• Combinatorial (enumeration) vs. Statistical
  optimization

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Example - Amadeus
Over-represented motifs in the promoters of genes
expressed in the G2 and G2/M phases of the human
cell cycle
CHR
NF-Y
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2. The project
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General goals

• Develop software from A-Z
• Design
• Implementation
• (Optimization)
• Execution analysis of real data
• A taste of bioinformatics
• Have fun
• Get credit

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The computational task

• Given a set of DNA sequences
• Find interesting pairs of motifs
• Order bias
• Other scores
• Main challenges
• Performance (time, memory)
• Output redundancy

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Input

• File with DNA sequences in fasta format
• gtsequence-name1 ltspacegt header1
• ACCCGNNNNTCGGAAATGANN
• CGGAGTAAAATATGCGAGCGT
• gtsequence-name2 ltspacegt header2
• cggattnnnaccgcannnnnnnnaccgtga
• gtsequence-name3 ltspacegt header3
• agtttagactgctagctcgatcgcta
• gcggatnggctannnnnatctag

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Input (II)

• Ignore the header lines
• Sequence may span multiple lines or one long line
• Sequence contains the characters A,C,G,T,N in
  upper or lower case
• N means unknown or masked base
• Sample input files will be supplied

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Input (III)

• Search parameters
• Length of motifs (between 5-10)
• Min. Max. distance between the motifs
• ACGGATTGATNNNTGGATGCCAT
• distance9
• Single vs. two strands search
• Min. number of occurrences (hits) of pair
• GCGGATTCAGTGATGCCANGNATGCCTCAGGATTGNAATGCCA
• hit
  hit hit
• Max. p-value
• Additional parameters

(dont count overlaps, e.g. AAAAAA)
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Output

• A list of the string pairs with the best
  order-bias score (smallest p-values)
• Motif A Motif B A?B B?A
  p-value
• ACGTT GGATT 97 17
  4.3E-15
• ACGTT GATTC 87 16
  2.7E-13
• TTAAC CAGCC 31 114
  1.2E-12
• A non-redundant list of motif pairs (motif
  consensus string)
• logos, of hits, additional scores

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Part A String pairs with order bias

• nA of A?B nB of B?A
• WLOG, nA gt nB
• n nA nB
• H0 random order nA B(n, 0.5)
• p-value prob for at least nA occurrences of A?B
  tail of B(n, 0.5)
• Normal approximation (central limit thm.)
• Fix for multiple testing x2

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Part B Non-redundant list of motif pairs

• Collect similar strings to motif with better
  score (motif consensus)
• String pair (p-value) Motif pair
• ACGTT , GGATT (4.3E-15)
• ACGAT , GGATT (2.4E-11)
• AGGAT , GGTTT (1.7E-5)
• AGGTT , GGTTT (5.9E-5)
• Dont report similar motif pairs
• Motifs that consist of similar strings
• Motif pairs that are small shifts of one another
• Palindromes

, (8.1E-31)
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Part B (cont.) Additional score

• Option I Co-occurrence rate
• N total of sequences
• sA of sequences that contain motif A
• sAB of sequences that contain motifs A and B
• H0 motifs occur independently and randomly
• p-value prob for at least joint occurrences,
  given the number of hits of each single motif
  tail of hypergeometric distribution

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Part B (cont.) Additional score

• Option II Distance bias
• Is the distance between the two motifs uniform
  (H0), or are there specific distances that are
  very common?
• Option III Gap variability
• Are the sequences between the motifs conserved
  (H0),
• or are they highly variable?
• Other options??

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Implementation

• Java (Eclipse) Linux
• GUI Simple graphical user interface for
  supplying the input parameters and reporting the
  results
• Packages for motif logo and statistical scores
  will be supplied
• Time performance will be measured only for part A
• Reasonable documentation
• Separate packages for data-structures, scores,
  GUI, I/O, etc.

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Design document

• Due in 3 weeks (Feb 24)
• 3-5 pages (Word), Hebrew/English
• Briefly describe main goal, input and output of
  program
• Describe main data structures, algorithms, and
  scores for parts AB
• Meet with me before submission

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Fin