1. Algorithms in Bioinformatics: A Practical Introduction
Project:
Motif finding using ChIP-seq peak data

2. Transcriptional Control (I)

3. Transcriptional Control (II)
TATAAT is the motif!

4. Motif model
TTGACA
TCGACA
TTGAAA
ATGACA
GTGACA
TTGACT
TTGACC
Consensus
Pattern
TTGACA
Positional
Weight
Matrix (PWM)
Motif can be described in two ways based on the binding sites discovered

5. ChIP experiment Chromatin immunoprecipitation experiment
Detect the interaction between protein (transcription factor) and DNA.

6. Peak data
Peak data represents the locations where a particular TF binding.
The data tells us the locations and intensities.
(Note that due to experimental error, peaks of low intensity may be noise.)
ChIP-seq data for
Human (MCF7)
E2 treatment at 45min
chr1:883, ,485

7. Our aim
Given the DNA sequences of those peaks, find motifs which occur in those peak regions.
For the example below, we have two motifs: TTGACA and GCATC.
Note that each instance has at most 1 mutation.
GCACGCGGTATCGTTAGCTTGACAATGAAGAATCCCCCCGCTCGACAGT
GCATACTTTGACACTGACTTCGCTTCTTTAATGTTTAATGAAACATGCG
CCCTCTGGAAATTAGTGCGGCATCTCACAACCCGAGGAATGACCAAATG
GTATTGAAAGTAAGGCAACGGTGATCCCCATGACACCAAAGATGCTAAG
CAACGCTCAGGCAACGTTGACAGGTGACACGTTGACTGCGGCCTCCTGC
GTCTCTTGACCGCTTAATCCTAAAGGCCTCCTATTAGTATCCGCAATGT
GAACAGGAGCGCGAGCCATCAATTGAAGCGAAGTTGACACCTAATAACT

8. Input (I) From every peak, we get approximately +/-200 DNA sequence
>cmyc_1_chr1_ _ _range_chr1_ _ _intensity_20
CCTCCATACCAGCCCCAATGTTCTGCGTTCCCGAATGAAAGACACACAACACAGCCTTTATATTTTGATATGCCTAAAACTGCTCAATGGCTGGGCCACTTCCTAGCTAGTATCCACGTGGCTATCCCACCTCTCTCTGATATTCCCAAGTCATTACTTACTAAAATCTGTAATTACATCTTTGCTGCCCTAGGCCCAATCTGGCAGCCCTCCTGTGGCCCCTCAGGCTACTACATGGCAGCTAAGCTCTCTGACCCACATCTTCTCAGGCACCGTGCCTCCTCTTCTCCACCTTATTCAAACATGGTGGCTCTCCTTCCTCCTTCTTCCTGTCTGTCCCCAGCCTGGGAATTCTAAAAGTCCCACCTCTGTCTGCCCTGTTCAGCCATTGGCTGTCGGCATCTTTATTTACGAG
>cmyc_2_chr1_ _ _range_chr1_ _ _intensity_15
GGTCATAAACCAAGCTTCTTCAAAGATTTTTGGCTTTTTGGCACCAGTGGCCTGCAGGGTGGCGAGCTCTGCCAGTTTGAAGTGACCAAGTTAAGTGGCCTGGGAAAGGCCATTTGGTGCGCGGTCCAGCAGTTTTGGGCGCTCTCGGCTTCCGCCCTCAGCTGCGGTCACGTGCGGCTGCTCACGTGCCAGACGCTGCTGTCACTTCGTAGCTGTTCCGGCTTCCTCTGAGTGAGGCTCGCAACGTCTCCCACGGAGTCGCCTTCGTTCTGCTCTGGGTCTCCCGTGGCCACTGAGACCTCGGAGCTCGACCGGCGCCTGCCCGCCCGTGCGGCCCTCACTCCCCGAGGCTATCCAGGTGAGGCCGCCTGGGGTCCCTCCCCGGCTCCGGAGAGCCGACTGGTTTCCCTGCCG
>cmyc_3_chr1_ _ _range_chr1_ _ _intensity_36
GTAGTCCCAACCAGGTCCTGAGCTGGTTAGCCAACCCTCAGCGCCAGTCGGGCCAACATCCGGTGACGAATCCAAGTCCCGCCTCTAAGCCCATCTGCTGTCCAATGCCGCCCTCTGCCGGTCTTTACCTCCCCGCCTAGCTGTGAGCCGCTTCCAGACAACCCGGAAGTGATCTTTCCTCTTCCGGATTACGGGTCCGGACGTCCGCACGTGGTTGCCGGTTTAGGGTGCTGCTGTAGTGGCGATACGTCCCGCCGCTGTCCCGAAGTGAGGGATCCGAGCCGCAGCGAGAGCCATGGAGGGCCAGCGCGTGGAGGAGCTGCTGGCCAAGGCAGAGCAGGAGGAGGCGGAGAAGCTGCAGCGCATCACGGTGCACAAGGAGCTGGAGCTGGAGTTCGACCTGGGCAACC
……………

9. Input (II) A set of sequences which are likely containing no motif.
AACAAGGGAAAGAGTAGTGAGTGCTTCTTTCTATTCAGAGGGAGGGGAAGTTGCTGTTAGCTAAGACAGTCAGGACTGAGAAGGGGGGGGGGGGTTTAACTCTCCTGGAGGGAGCTGAGAGGTAAAGGGAGGGGCGTGAGGTAGAACAAGCCGAGAACACAGGGCAGGTTGGTCTGACTCCAGAGCACAGTGCAGGAGCCCGGAAGTTGACTCAGTTCAGTTAGCAAGTATTTTCACACAAGGCGTGAACACTGAAGACAAAAGCAAGAGACACAGCTCTATCTCTAAGAAGATTTTCAGAGCCAAGATCGATGGGGCACACCTGTTAATCCCAGCACTTAGGAGGCTGAGGCAGGAGGATCCCAAGTTCAAAACCAGCCTGGACTTGTTTTAAGGAAAA
>SEQ_2
AAAAAAAAAAAAAAGACTTCCAGTTTAATAAATGACCAATTCAGGAATGGAGATTAGGGCTGGATGACAAGTTTTTAATTGTCAAGGACTCAATTCTGTTTATCAGTTGGTATGGAATTATGTAAGCTTTTAGCGATATGACCGCACGGAGCAGTGTAGAGAGTGATCTGAGAGACGCTTGGGGGTCAGGATGGAGATAGAACTCCCTCTCTATTAGAAGGTGTTTGGTGGTAGGTAACCCTGGGCTAGCATGGTGGGTCTCTTCTTACTTAGGCTTCCATCTTTGTGGTTCAAATCCAAGAAGGACCTGCGTTCCCTCCCTCCTTGTGATCAGCTGATTGCTAGAGCATAACTCATCTTAACTTCTCATGTACTCTCCGGGTACAGGAAGGGAGGGGGC
>SEQ_3
CCACTGCTGACAGTGGAGCATGAAACGACCGGCTTCCTGACTATGTTGGTACCCTTTCAGGAGCCTAAAACAGTGCTTTCAATACTTGTGTCTATGTCTGTTAGCCACAACTTTCTAGTTTCCCAGAGAGATTTTGAAGTGTAGTTTTGTATTTGCTCAAATATATATTCATATGGTGAGGTGCACATTTTTTATATTATATTTTTATTCATTTATTTTTGGTGCTTGGGAATTATACTCTAGGAATAAAGCGCCTGGTAGAAAGTGGCACACATCTTTAATCCCAGCACTCAGGAAGCAGAGGCAGACAAATCTCTGCGTTCCAGGACAGCCTGGTCTATAGAGCAAGGTCCAAGCCAGCCAGGTTTACACAAAGAAACCTAGTGTGGAAAAGACAAAA
……………

10. Output You need to output a list of candidate (ranked) motifs.
You can model the motif as PWM or consensus sequence.
If you model the motif as a PWM, one of the answer for the previous dataset is
You may also return other significant motifs.

11. Aim of the project Given a sample file and a background file,
you need to implement a method which output a list of motifs.
You need to take advantage of the fact that this is a ChIP-seq dataset
Hint: Read papers on ChIP-seq and understand its properties.