Algorithms in Bioinformatics: A Practical Introduction

Project:Motif finding using ChIP-seq pea

k data

Transcriptional Control (I)

Transcriptional Control (II)

TATAAT is the motif!

Motif model

Motif can be described in two ways based on the binding sites discovered

ConsensusPattern

PositionalWeightMatrix (PWM)

TTGACA

TTGACATCGACATTGACATTGAAAATGACATTGACAGTGACATTGACTTTGACCTTGACA

nucleotide 1 2 3 4 5 6A 0.1 0 0 1 0.1 0.8C 0 0.1 0 0 0.9 0.1G 0.1 0 1 0 0 0T 0.8 0.9 0 0 0 0.1

alignment position

ChIP experiment Chromatin immunoprecipitation experimen

t Detect the interaction between protein (transcri

ption factor) and DNA.

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.)

chr1:883,686-958,485

ChIP-seq data forHuman (MCF7)E2 treatment at 45min

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.

GCACGCGGTATCGTTAGCTTGACAATGAAGAATCCCCCCGCTCGACAGTGCATACTTTGACACTGACTTCGCTTCTTTAATGTTTAATGAAACATGCGCCCTCTGGAAATTAGTGCGGCATCTCACAACCCGAGGAATGACCAAATGGTATTGAAAGTAAGGCAACGGTGATCCCCATGACACCAAAGATGCTAAGCAACGCTCAGGCAACGTTGACAGGTGACACGTTGACTGCGGCCTCCTGCGTCTCTTGACCGCTTAATCCTAAAGGCCTCCTATTAGTATCCGCAATGTGAACAGGAGCGCGAGCCATCAATTGAAGCGAAGTTGACACCTAATAACT

Input (I) From every peak, we get approximately +/-200 DNA sequence

>cmyc_1_chr1_4842133_4842148_range_chr1_4841934_4842348_intensity_20CCTCCATACCAGCCCCAATGTTCTGCGTTCCCGAATGAAAGACACACAACACAGCCTTTATATTTTGATATGCCT

AAAACTGCTCAATGGCTGGGCCACTTCCTAGCTAGTATCCACGTGGCTATCCCACCTCTCTCTGATATTCCCAAGTCATTACTTACTAAAATCTGTAATTACATCTTTGCTGCCCTAGGCCCAATCTGGCAGCCCTCCTGTGGCCCCTCAGGCTACTACATGGCAGCTAAGCTCTCTGACCCACATCTTCTCAGGCACCGTGCCTCCTCTTCTCCACCTTATTCAAACATGGTGGCTCTCCTTCCTCCTTCTTCCTGTCTGTCCCCAGCCTGGGAATTCTAAAAGTCCCACCTCTGTCTGCCCTGTTCAGCCATTGGCTGTCGGCATCTTTATTTACGAG

>cmyc_2_chr1_5073201_5073215_range_chr1_5073002_5073415_intensity_15GGTCATAAACCAAGCTTCTTCAAAGATTTTTGGCTTTTTGGCACCAGTGGCCTGCAGGGTGGCGAGCTCTGCCA

GTTTGAAGTGACCAAGTTAAGTGGCCTGGGAAAGGCCATTTGGTGCGCGGTCCAGCAGTTTTGGGCGCTCTCGGCTTCCGCCCTCAGCTGCGGTCACGTGCGGCTGCTCACGTGCCAGACGCTGCTGTCACTTCGTAGCTGTTCCGGCTTCCTCTGAGTGAGGCTCGCAACGTCTCCCACGGAGTCGCCTTCGTTCTGCTCTGGGTCTCCCGTGGCCACTGAGACCTCGGAGCTCGACCGGCGCCTGCCCGCCCGTGCGGCCCTCACTCCCCGAGGCTATCCAGGTGAGGCCGCCTGGGGTCCCTCCCCGGCTCCGGAGAGCCGACTGGTTTCCCTGCCG

>cmyc_3_chr1_9530642_9530652_range_chr1_9530443_9530852_intensity_36GTAGTCCCAACCAGGTCCTGAGCTGGTTAGCCAACCCTCAGCGCCAGTCGGGCCAACATCCGGTGACGAATCC

AAGTCCCGCCTCTAAGCCCATCTGCTGTCCAATGCCGCCCTCTGCCGGTCTTTACCTCCCCGCCTAGCTGTGAGCCGCTTCCAGACAACCCGGAAGTGATCTTTCCTCTTCCGGATTACGGGTCCGGACGTCCGCACGTGGTTGCCGGTTTAGGGTGCTGCTGTAGTGGCGATACGTCCCGCCGCTGTCCCGAAGTGAGGGATCCGAGCCGCAGCGAGAGCCATGGAGGGCCAGCGCGTGGAGGAGCTGCTGGCCAAGGCAGAGCAGGAGGAGGCGGAGAAGCTGCAGCGCATCACGGTGCACAAGGAGCTGGAGCTGGAGTTCGACCTGGGCAACC

……………

Input (II) A set of sequences which are likely containing no motif.

>SEQ_1AACAAGGGAAAGAGTAGTGAGTGCTTCTTTCTATTCAGAGGGAGGGGAAGTTGCTGTTAGCTAAGACAGTC

AGGACTGAGAAGGGGGGGGGGGGTTTAACTCTCCTGGAGGGAGCTGAGAGGTAAAGGGAGGGGCGTGAGGTAGAACAAGCCGAGAACACAGGGCAGGTTGGTCTGACTCCAGAGCACAGTGCAGGAGCCCGGAAGTTGACTCAGTTCAGTTAGCAAGTATTTTCACACAAGGCGTGAACACTGAAGACAAAAGCAAGAGACACAGCTCTATCTCTAAGAAGATTTTCAGAGCCAAGATCGATGGGGCACACCTGTTAATCCCAGCACTTAGGAGGCTGAGGCAGGAGGATCCCAAGTTCAAAACCAGCCTGGACTTGTTTTAAGGAAAA

>SEQ_2AAAAAAAAAAAAAAGACTTCCAGTTTAATAAATGACCAATTCAGGAATGGAGATTAGGGCTGGATGACAAGT

TTTTAATTGTCAAGGACTCAATTCTGTTTATCAGTTGGTATGGAATTATGTAAGCTTTTAGCGATATGACCGCACGGAGCAGTGTAGAGAGTGATCTGAGAGACGCTTGGGGGTCAGGATGGAGATAGAACTCCCTCTCTATTAGAAGGTGTTTGGTGGTAGGTAACCCTGGGCTAGCATGGTGGGTCTCTTCTTACTTAGGCTTCCATCTTTGTGGTTCAAATCCAAGAAGGACCTGCGTTCCCTCCCTCCTTGTGATCAGCTGATTGCTAGAGCATAACTCATCTTAACTTCTCATGTACTCTCCGGGTACAGGAAGGGAGGGGGC

>SEQ_3CCACTGCTGACAGTGGAGCATGAAACGACCGGCTTCCTGACTATGTTGGTACCCTTTCAGGAGCCTAAAACA

GTGCTTTCAATACTTGTGTCTATGTCTGTTAGCCACAACTTTCTAGTTTCCCAGAGAGATTTTGAAGTGTAGTTTTGTATTTGCTCAAATATATATTCATATGGTGAGGTGCACATTTTTTATATTATATTTTTATTCATTTATTTTTGGTGCTTGGGAATTATACTCTAGGAATAAAGCGCCTGGTAGAAAGTGGCACACATCTTTAATCCCAGCACTCAGGAAGCAGAGGCAGACAAATCTCTGCGTTCCAGGACAGCCTGGTCTATAGAGCAAGGTCCAAGCCAGCCAGGTTTACACAAAGAAACCTAGTGTGGAAAAGACAAAA

……………

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.

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.