Transcrip:on factor binding mo:fs

Transcription

1 Transcrip:on factor binding mo:fs BMMB- 597D Lecture 29 Shaun Mahony

2 Transcrip.on factor binding sites Short: Typically between 6 20bp long Degenerate: TFs have favorite binding sequences but don t require a perfect match to bind. Belong to families: Related TFs oken bind to similar sequences. Likes to bind: CCGGAA TGACCT..TGACCT ATTA CACGTG

3 We can represent a TF s binding preference as a degenerate string or probabilis.cally Defini.on: A mo:f is a panern of conserva:on in a sequence alignment. TF: NFκB Favorite sequence: GGGAATTTCC Known NFκB binding sites: GGGAATTTCC GGGAATTTCC GGGAATTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGAAATTCC GGGACTTCCC GGGGATTTCC GGGGATTTCC GGGGATTTCC GGGATTTTCC GGGGATTCCC GGGATTTCCC GGGAATTCAC GGGGCTTTCC GGGGCTTTCC GGGAAGTCCC Consensus sequence GGGRNWTYCC Weight matrix

4 Consensus sequences Defini.on: A consensus sequence represents a mul:ple sequence alignment using a degenerate alphabet to represent variable posi:ons. Extended DNA Alphabet (IUPAC) A C G T W = A or T S = C or G M = A or C K = G or T R = A or G Y = C or T N = any base (A/C/G/T) Alignment GGGAATTTCC GGGAATTTCC GGGAATTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGAAATTCC GGGACTTCCC GGGGATTTCC GGGGATTTCC GGGGATTTCC GGGATTTTCC Consensus GGGAATTTCC GGGAMTTTCC GGGAMWTTCC GGGAMWTYCC GGGRNWTYCC GGGGTTTCCC?

5 Posi.on weight matrices Defini.on: A posi:on weight matrix (PWM) is a matrix of log likelihood values that gives a weighted match to fixed length strings. Also known as posi:on specific scoring matrix (PSSM). GGGAATTTCC GGGAATTTCC GGGAATTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGACTTTCC GGGAAATTCC GGGACTTCCC GGGGATTTCC GGGGATTTCC GGGGATTTCC GGGATTTTCC GGGGATTCCC GGGATTTCCC GGGAATTCAC GGGGCTTTCC A C G T Count matrix A C G T Rela.ve frequency matrix p i,j : probability of observing letter i at position j

6 Posi.on weight matrices A C G T Posi.on weight matrix: m i,j = log 2 (p i,j / b i ) where b i is the background probability of letter i Informa:on content: LeNer height in logo: Sequence logo T IC j = 2 ( p i, j log 2 (p i, j )) i=a Height i, j = p i, j IC j

7 Mo.f scanning: finding instances of a known mo.f >Test Sequence TTACGTTTGTCGATTTATGGGACTTTCCTCTTCGTATTTATTAGGCT T T A C G T T T G T C T C A C G T Score = A T G G G A C T T T C C T C A C G T Score = 17.57

8 Databases of known mo.fs TRANSFAC: hnp:// regula:on.com/pub/databases.html Public version is old (2005), private version is expensive. Best coverage, not necessarily highest quality mo:fs. Jaspar: hnp://jaspar.genereg.net Open access. High quality mo:fs, medium coverage. UniProbe: hnp://the_brain.bwh.harvard.edu/uniprobe Based on in vitro protein binding microarray experiments. Good coverage of many TF families.

9 Jaspar

10 Mo.f scanning tools Mo:fViz: hnp://biowulf.bu.edu/mo:fviz/ Various mo:f scanning tools included. FIMO: hnp://meme.nbcr.net/meme/cgi- bin/fimo.cgi Part of the MEME suite of mo:f analysis tools. MATCH: Kel, et al. Nucleic Acids Res (2003) TAMO: Gordon, et al. Bioinforma:cs (2005) Mo:fScanner/TOUCAN: Aerts, et al. Nucleic Acids Res (2005)

11 FIMO: Finding Individual Mo.f Occurrences >Seq1 AATGTTCAGCTAAAATGTATTATTTTTTGTCTATACATAGAACGTGGGAATTTCCCC AGGGTTCTAAATAGACTCAAGTTAGCCTCTTATCAGAGCAGAA >Seq2 TGCCTCAGACTTTCAAATTATATCTGGGTGGATCATTCAAAGGCCGGGAAATTCCAA TAGCTGCCTACATAGATATGGAGTATTGCCATACTTTCTGGCC >Seq3 CCTCACAGGAACAGGAGCAGAGAAATTTTGAACAAATCAAATCTTGGGATTTCCCCT GGTGCTGTACCTGCAATAGCCTGTCCCAACTAAGGGTATAACA

12 FIMO: Finding Individual Mo.f Occurrences

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14 What if we don t know the mo.f? Defini.on: Mo:f- finding is the process by which short, degenerate, sta:s:cally- overrepresented panerns are discovered in a set of sequences. When do we use mo.f scanning vs. mo.f- finding? Mo.f scanning: We know TF x binds somewhere in a sequence (e.g. we know it directly regulates a gene) AND we know the mo:f for TF x. Beware of similarity thresholds! Mo.f- finding: We hypothesize that a set of genes are bound by the same (unknown) TF. The set of genes may be defined by a similar gene expression profiles. Mo.f- finding: We performed high- throughput Chroma:n Immunoprecipita:on assays (ChIP- chip/chip- seq) on TF y, and we want to characterize TF y s binding preference from that data.

15 Mo.f- finding approaches Expecta:on- Maximiza:on MEME, Improbizer Gibbs Sampling AlignACE, GLAM, Mo:fSampler Clustering SOMBRERO Exhaus:ve enumera:on Weeder, YMF Evalua:on of various methods in: Tompa, et al. Nature Biotech. (2005)

16 Mo.f- finding with Expecta.on Maximiza.on (EM) Define a mixture model with two components: Mo:f & Background, where mo:f is ini:alized randomly Simple EM algorithm: 1. Expecta>on: Find expected likelihood of sequence data given current model. i.e. Probabilis:cally assign each k- mer to mo:f & background components. 2. Maximiza>on: Update model parameters to maximize expected- likelihood func:on. i.e. Update the mo:f and background component parameters to reflect the assignments in 1) 3. Repeat E- M un:l no change in likelihood.

17 Problem: We added galactose to the growth media of yeast (S. cerevisiae), and observed the up- regula:on of four genes: GAL7, GAL10, GAL1 & GAL2. We hypothesize that these four genes are under the control of the same transcrip:on factor. To test this hypothesis, we perform mo:f- finding on the promoter sequences of these genes. >YBR018C GAL7 GACGGTAGCAACAAGAATATAGCACGAGCCGCGGAGTTCATTTCGTTACTTTTGATATCACTCACAACTATTGCGAAGCGCTTCAGTGAAAAAATC ATAAGGAAAAGTTGTAAATATTATTGGTAGTATTCGTTTGGTAAAGTAGAGGGGGTAATTTTTCCCCTTTATTTTGTTCATACATTCTTAAATTGCTT TGCCTCTCCTTTTGGAAAGCTATACTTCGGAGCACTGTTGAGCGAAGGCTCATTAGATATATTTTCTGTCATTTTCCTTAACCCAAAAATAAGGGAA AGGGTCCAAAAAGCGCTCGGACAACTGTTGACCGTGATCCGAAGGACTGGCTATACAGTGTTCACAAAATAGCCAAGCTGAAAATAATGTGTAGC TATGTTCAGTTAGTTTGGCTAGCAAAGATATAAAAGCAGGTCGGAAATATTTATGGGCATTATTATGCAGAGCATCAACATGATAAA >YBR019C GAL10 ATCGCTTCGCTGATTAATTACCCCAGAAATAAGGCTAAAAAACTAATCGCATTATCATCCTATGGTTGTTAATTTGATTCGTTAATTTGAAGGTTTGT GGGGCCAGGTTCTGCCAATTTTTCCTCTTCATAACCATAAAAGCTAGTATTGTAGAATCTTTATTGTTCGGAGCAGTGCGGCGCGAGGCACATCTGC GTTTCAGGAACGCGACCGGTGAAGACGAGGACGCACGGAGGAGAGTCTTCCGTCGGAGGGCTGTCGCCCGCTCGGCGGCTTCTAATCCGTACTT CAATATAGCAATGAGCAGTTAAGCGTATTACTGAAAGTTCCAAAGAGAAGGTTTTTTTAGGCTAAGATAATGGGGCTCTTTACATTTCCACAACATA TAAGTAAGATTAGATATGGATATGTATATGGTGGTAATGCCATGTAATATGATTATTAAACTTCTTTGCGTCCATCCAAAAAAAAAGT >YBR020W GAL1 ACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTC TTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTC TCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGC TTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGAT TAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTA ACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATA >YLR081W GAL2 AGGTTGCAATTTCTTTTTCTATTAGTAGCTAAAAATGGGTCACGTGATCTATATTCGAAAGGGGCGGTTGCCTCAGGAAGGCACCGGCGGTCTTTC GTCCGTGCGGAGATATCTGCGCCGTTCAGGGGTCCATGTGCCTTGGACGATATTAAGGCAGAAGGCAGTATCGGGGCGGATCACTCCGAACCGA GATTAGTTAAGCCCTTCCCATCTCAAGATGGGGAGCAAATGGCATTATACTCCTGCTAGAAAGTTAACTGTGCACATATTCTTAAATTATACAACAT TCTGGAGAGCTATTGTTCAAAAAACAAACATTTCGCAGGCTAAAATGTGGAGATAGGATAAGTTTTGTAGACATATATAAACAATCAGTAATTGGA TTGAAAATTTGGTGTTGTGAATTGCTCTTCATTATGCACCTTATTCAATTATCATCAAGAATAGTAATAGTTAAGTAAACACAAGATTA

18 MEME has a lot of op.ons, and good documenta.on

19 MEME Galactose example

20 MEME Galactose example

21 >YBR018C GAL7 GACGGTAGCAACAAGAATATAGCACGAGCCGCGGAGTTCATTTCGTTACTTTTGATATCACTCACAACTATTGCGAAGCGCTTCAGTGAAAAAATC ATAAGGAAAAGTTGTAAATATTATTGGTAGTATTCGTTTGGTAAAGTAGAGGGGGTAATTTTTCCCCTTTATTTTGTTCATACATTCTTAAATTGCTT TGCCTCTCCTTTTGGAAAGCTATACTTCGGAGCACTGTTGAGCGAAGGCTCATTAGATATATTTTCTGTCATTTTCCTTAACCCAAAAATAAGGGAA AGGGTCCAAAAAGCGCTCGGACAACTGTTGACCGTGATCCGAAGGACTGGCTATACAGTGTTCACAAAATAGCCAAGCTGAAAATAATGTGTAGC TATGTTCAGTTAGTTTGGCTAGCAAAGATATAAAAGCAGGTCGGAAATATTTATGGGCATTATTATGCAGAGCATCAACATGATAAA >YBR019C GAL10 ATCGCTTCGCTGATTAATTACCCCAGAAATAAGGCTAAAAAACTAATCGCATTATCATCCTATGGTTGTTAATTTGATTCGTTAATTTGAAGGTTTGT GGGGCCAGGTTCTGCCAATTTTTCCTCTTCATAACCATAAAAGCTAGTATTGTAGAATCTTTATTGTTCGGAGCAGTGCGGCGCGAGGCACATCTGC GTTTCAGGAACGCGACCGGTGAAGACGAGGACGCACGGAGGAGAGTCTTCCGTCGGAGGGCTGTCGCCCGCTCGGCGGCTTCTAATCCGTACTT CGTTTCAGGAACGCGACCGGTGAAGACGAGGACGCACGGAGGAGAGTCTTCCGTCGGAGGGCTGTCGCCCGCTCGGCGGCTTCTAATCCGTACT CAATATAGCAATGAGCAGTTAAGCGTATTACTGAAAGTTCCAAAGAGAAGGTTTTTTTAGGCTAAGATAATGGGGCTCTTTACATTTCCACAACATA TCAATATAGCAATGAGCAGTTAAGCGTATTACTGAAAGTTCCAAAGAGAAGGTTTTTTTAGGCTAAGATAATGGGGCTCTTTACATTTCCACAACAT ATAAGTAAGATTAGATATGGATATGTATATGGTGGTAATGCCATGTAATATGATTATTAAACTTCTTTGCGTCCATCCAAAAAAAAAGT >YBR020W GAL1 ACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTC TTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTC TCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGC TTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGAT TAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTA ACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATA >YLR081W GAL2 AGGTTGCAATTTCTTTTTCTATTAGTAGCTAAAAATGGGTCACGTGATCTATATTCGAAAGGGGCGGTTGCCTCAGGAAGGCACCGGCGGTCTTTC GTCCGTGCGGAGATATCTGCGCCGTTCAGGGGTCCATGTGCCTTGGACGATATTAAGGCAGAAGGCAGTATCGGGGCGGATCACTCCGAACCGA GATTAGTTAAGCCCTTCCCATCTCAAGATGGGGAGCAAATGGCATTATACTCCTGCTAGAAAGTTAACTGTGCACATATTCTTAAATTATACAACAT TCTGGAGAGCTATTGTTCAAAAAACAAACATTTCGCAGGCTAAAATGTGGAGATAGGATAAGTTTTGTAGACATATATAAACAATCAGTAATTGGA TTGAAAATTTGGTGTTGTGAATTGCTCTTCATTATGCACCTTATTCAATTATCATCAAGAATAGTAATAGTTAAGTAAACACAAGATTA We found a mo.f! Now what?

22 Which TFs could bind our mo.fs? Match discovered mo:fs against known mo:f databases. Mo:f alignment sokware: STAMP hnp:// Mo:f alignment, mul:ple alignment, clustering TOMTOM hnp://meme.nbcr.net/meme/cgi- bin/tomtom.cgi Integrated with MEME suite

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25 Transcrip.on factor binding mo.fs come in families Elk4 NR2F1 Antp Max Eip74EF PPARg Lim3 Myc FEV Esrrb Yox1 NHLH1

26 The fu.lity theorem Fu.lity theorem*: essen:ally all TF binding mo:f occurrences will have no func:on. How can we focus on mo:f instances that are bound & func:onal? Conserva:on Nucleosome posi:oning Cis- regulatory modules (i.e. clusters of sites) Accessibility (DNaseI hypersensi:vity) Chroma:n marks (H3K4me1, H3K27ac) * Wasserman & Sandelin, Nature Reviews Gene:cs (2004)

27 Priors for mo.f- finding Posi:onal priors: Analyzing ChIP- seq data, mo:fs are more likely to be centered under the peak. Structural priors: Mo:fs are more likely to be similar to known mo:fs and to have par:cular informa:on content shapes.

28 Further reading Protein- DNA binding assays: Determining the specificity of protein DNA interac:ons, Stormo & Zhao, Nature Rev Gene:cs (2010) Mo:f scanning & mo:f- finding: Prac:cal strategies for discovering regulatory DNA sequence mo:fs, MacIsaac & Fraenkel, PLoS Comp Bio (2006) Applied bioinforma:cs for the iden:fica:on of regulatory elements, Wasserman & Sandelin, Nature Rev Gene:cs (2004) MEME: Fiung a mixture model by expecta:on maximiza:on to discover mo:fs in biopolymers, Bailey & Elkan, Proc. ISMB (1994) Gene regula:on & Gal4 system: Genes & Signals, Ptashne & Gann (2002)