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Bioinformatic Analysis of Chromatin Genomic Data Giulio Pavesi University of Milano

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Presentazione sul tema: "Bioinformatic Analysis of Chromatin Genomic Data Giulio Pavesi University of Milano"— Transcript della presentazione:

1 Bioinformatic Analysis of Chromatin Genomic Data Giulio Pavesi University of Milano

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4 “Nucleosome” The nucleosome core particle consists of approximately 147 base pairs of DNA wrapped in 1.67 left- handed superhelical turns around a histone octamer The nucleosome core particle consists of approximately 147 base pairs of DNA wrapped in 1.67 left- handed superhelical turns around a histone octamer Octamer: 2 copies each of the core histones H2A, H2B, H3, and H4 Octamer: 2 copies each of the core histones H2A, H2B, H3, and H4 Core particles are connected by stretches of "linker DNA", which can be up to about 80 bp long Core particles are connected by stretches of "linker DNA", which can be up to about 80 bp long

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6 Epigenetics Modern experimental techniques and technologies allow for the genome- wide study of different types of histone modifications, shedding light on the role of each one Modern experimental techniques and technologies allow for the genome- wide study of different types of histone modifications, shedding light on the role of each one

7 The histone code Example H3K4me3 Example H3K4me3 H3 is the histone H3 is the histone K4 is the residue that is modified and its position (K lysine in position 4 of the sequence) K4 is the residue that is modified and its position (K lysine in position 4 of the sequence) me3 is the modification (three- methyl groups attached to K4) me3 is the modification (three- methyl groups attached to K4) If no number at the end like in H3K9ac means only one group If no number at the end like in H3K9ac means only one group

8 Different chromatin states Chromatin structure (and thus, gene expression) depend also on the post-translational modifications associated with histones forming nuclesomes

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10 “ChIP” If we have the “right” antibody, we can extract (“immunoprecipitate”) from living cells the protein of interest bound to the DNA If we have the “right” antibody, we can extract (“immunoprecipitate”) from living cells the protein of interest bound to the DNA And - we can try to identify which were the DNA regions bound by the protein And - we can try to identify which were the DNA regions bound by the protein Can be done for transcription factors Can be done for transcription factors But can be done also for histones - and separately for each modification But can be done also for histones - and separately for each modification

11 TF ChIP Histone ChIP ChIP- Seq

12 Many cells- many copies of the same region bound by the protein

13 After ChIP Identification of the DNA fragment bound by the protein Sequencing Size selection: only fragments of the “right size” (200 bp) are kept

14 So - if we found that a region has been sequenced many times, then we can suppose that it was bound by the protein, but…

15 Only a short fragment of the extracted DNA region can be sequenced, at either or both ends (“single” vs “paired end” sequencing) for no more than 35 (before) / 50 (now) / 75 (now) bps Thus, original regions have to be “reconstructed” …and, once again, bioinformaticians can be of help…

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18 Read Mapping Each sequence read has to be assigned to its original position in the genome Each sequence read has to be assigned to its original position in the genome A typical ChIP-Seq experiment produces from 6 (before) to 100 million (now) reads of and more base pairs for each sequencing “lane” (Solexa/Illumina) A typical ChIP-Seq experiment produces from 6 (before) to 100 million (now) reads of and more base pairs for each sequencing “lane” (Solexa/Illumina) Research in read alignment algorithms is booming (who is going to be the next BLAST?) Research in read alignment algorithms is booming (who is going to be the next BLAST?) There exist efficient “sequence mappers” against the genome for NGS read There exist efficient “sequence mappers” against the genome for NGS read

19 Read Mapping “Typical” (actual sequence) TTTGAATATATTGAGAAAATATGACCATTTTT +12_10_2007_SequencingRun_3_1_119_647 (“quality” scores)

20 Read Mapping Reads mapping more than once (repetitive regions) can be discarded: Reads mapping more than once (repetitive regions) can be discarded: Never, use all matches everywhere Never, use all matches everywhere If they map more than a given maximum number of times If they map more than a given maximum number of times If they do not map uniquely in the best match If they do not map uniquely in the best match If they do not map uniquely with 0, 1, or 2 substitutions If they do not map uniquely with 0, 1, or 2 substitutions

21 Read Mapping Sequence quality tends to be lower toward the 3’end of sequence reads Sequence quality tends to be lower toward the 3’end of sequence reads Trick: if too few read map, “trim” the reads: Trick: if too few read map, “trim” the reads: Map reads with standard parameters (two substitutions will do) Map reads with standard parameters (two substitutions will do) Take all the reads that haven’t been mapped, and re- map them trimming away the first and the last nucleotides Take all the reads that haven’t been mapped, and re- map them trimming away the first and the last nucleotides Repeat until no significant improvement/increase in mapped reads is obtained Repeat until no significant improvement/increase in mapped reads is obtained Discard reads mapping on different locations of the genome Discard reads mapping on different locations of the genome

22 “Peak finding” The critical part of any ChIP-Seq analysis is the identification of the genomic regions that produced a significantly high number of sequence reads, corresponding to the region where the protein (nucleosome) of interest was bound to DNA The critical part of any ChIP-Seq analysis is the identification of the genomic regions that produced a significantly high number of sequence reads, corresponding to the region where the protein (nucleosome) of interest was bound to DNA Since a graphical visualization of the “piling” of read mapping on the genome produces a “peak” in correspondence of these regions, the problem is often referred to as “peak finding” Since a graphical visualization of the “piling” of read mapping on the genome produces a “peak” in correspondence of these regions, the problem is often referred to as “peak finding” A “peak” then marks the region that was enriched in the original DNA sample A “peak” then marks the region that was enriched in the original DNA sample

23 “Peak finding” Peaks: How tall? How wide? How much enriched?

24 “Peak finding” The main issue: the DNA sample sequenced (apart from sequencing errors/artifacts) contains a lot of “noise” The main issue: the DNA sample sequenced (apart from sequencing errors/artifacts) contains a lot of “noise” Sample “contamination” - the DNA of the PhD student performing the experiment Sample “contamination” - the DNA of the PhD student performing the experiment DNA shearing is not uniform: open chromatin regions tend to be fragmented more easily and thus are more likely to be sequenced DNA shearing is not uniform: open chromatin regions tend to be fragmented more easily and thus are more likely to be sequenced Repetitive sequences might be artificially enriched due to inaccuracies in genome assembly Repetitive sequences might be artificially enriched due to inaccuracies in genome assembly Amplification pushed too much: you see a single DNA fragment amplified, not enriched Amplification pushed too much: you see a single DNA fragment amplified, not enriched As yet unknown problems, that anyway seem to produce “noisy” sequencings and screw the experiment up As yet unknown problems, that anyway seem to produce “noisy” sequencings and screw the experiment up

25 ChIP-Seq histone data Histone modifications tend to be located at preferred locations with respect to gene annotations/transcribed regions Histone modifications tend to be located at preferred locations with respect to gene annotations/transcribed regions Hence, enrichment can be assessed in two ways Hence, enrichment can be assessed in two ways Enrichment with respect a the control experiment and peak identification Enrichment with respect a the control experiment and peak identification “Local” enrichment in given regions with respect to gene annotations “Local” enrichment in given regions with respect to gene annotations Promoters (active/non active) Promoters (active/non active) Upstream of transcribed/non transcribed genes Upstream of transcribed/non transcribed genes Within transcribed/not transcribed regions Within transcribed/not transcribed regions Enhancers, whatever else Enhancers, whatever else

26 Esperimento Eseguire una ChIP-Seq per diverse modificazioni istoniche, partendo da quelle più “classiche” Eseguire una ChIP-Seq per diverse modificazioni istoniche, partendo da quelle più “classiche” Verificare: Verificare: Se ciascuna modifica ha una sua localizzazione “preferenziale” sul genoma o rispetto ai geni (es. nel promotore, nella regione trascritta, etc.) Se ciascuna modifica ha una sua localizzazione “preferenziale” sul genoma o rispetto ai geni (es. nel promotore, nella regione trascritta, etc.) Se ciascuna modifica è “correlata” in qualche modo alla trascrizione/espressione dei geni Se ciascuna modifica è “correlata” in qualche modo alla trascrizione/espressione dei geni

27 Genome wide histone modifications maps through ChIP-Seq Barski et.al - Cell , 2007 Barski et.al - Cell , histone lysine and arginine methylations in CD4+ T cells 20 histone lysine and arginine methylations in CD4+ T cells H3K27 H3K27 H3K9 H3K9 H3K36 H3K36 H3K79 H3K79 H3R2 H3R2 H4K20 H4K20 H4R3 H4R3 H2BK5 H2BK5 Plus: Plus: Pol II binding Pol II binding H2A.Z (replaces H2A in some nucleosomes) H2A.Z (replaces H2A in some nucleosomes) insulator-binding protein (CTCF) insulator-binding protein (CTCF)

28 Genome wide histone modifications maps through ChIP-Seq

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30 Esperimento ChIP-Seq associata a una particolare modificazione (es, H3K4me3) ChIP-Seq associata a una particolare modificazione (es, H3K4me3) Domanda: la modificazione è “correlabile” alla trascrizione dei geni? Domanda: la modificazione è “correlabile” alla trascrizione dei geni? Ovvero, la modificazione “marca” particolari nucleosomi rispetto all’inizio della trascrizione, o alla regione trascritta Ovvero, la modificazione “marca” particolari nucleosomi rispetto all’inizio della trascrizione, o alla regione trascritta Esempio: potrebbero esserci modificazioni che: Esempio: potrebbero esserci modificazioni che: Marcano l’inizio della trascrizione Marcano l’inizio della trascrizione Marcano tutta e solo la regione trascritta Marcano tutta e solo la regione trascritta “Silenziano” particolari loci genici impedendo la trascrizione “Silenziano” particolari loci genici impedendo la trascrizione

31 Esperimento Sequenze ottenute da ChIP-Seq per la modificazione studiata Sequenze ottenute da ChIP-Seq per la modificazione studiata Input: coordinate genomiche delle posizioni in ciascuna delle sequenze mappa (vedi file di esempio) Input: coordinate genomiche delle posizioni in ciascuna delle sequenze mappa (vedi file di esempio) Input: coordinate genomiche dei geni RefSeq annotati Input: coordinate genomiche dei geni RefSeq annotati Un nucleosoma marcato dalla modificazione dovrebbe corrispondere a un “mucchietto” di read che si sovrappongono (“picco”) Un nucleosoma marcato dalla modificazione dovrebbe corrispondere a un “mucchietto” di read che si sovrappongono (“picco”) Andiamo a contare, nucleosoma per nucleosoma, quanto alto è il “mucchietto”, ovvero quanti read sono associabili al nucleosoma Andiamo a contare, nucleosoma per nucleosoma, quanto alto è il “mucchietto”, ovvero quanti read sono associabili al nucleosoma

32 Nucleosoma Esempio: se si trovasse la modifica nel nucleosoma a monte del TSS dei geni trascritti, troveremmo un “mucchietto” così Modificazione

33 Nucleosoma Esempio: se si trovasse la modifica nei nucleosomi associati alle regioni trascritte, troveremmo “mucchietti” così Modificazione

34 Analisi: primo esempio Input Input Lista ordinata delle coordinate genomiche dei TSS associati ai geni trascritti Lista ordinata delle coordinate genomiche dei TSS associati ai geni trascritti Lista ordinata delle coordinate genomiche dei TSS associati ai geni NON trascritti Lista ordinata delle coordinate genomiche dei TSS associati ai geni NON trascritti Lista ordinata delle coordinate genomiche dove mappa ciascuna sequenza della ChIP-Seq Lista ordinata delle coordinate genomiche dove mappa ciascuna sequenza della ChIP-Seq Output: calcolare la distribuzione (i “mucchietti”) rispetto ai TSS delle due categorie: Output: calcolare la distribuzione (i “mucchietti”) rispetto ai TSS delle due categorie: Geni trascritti Geni trascritti Geni NON trascritti Geni NON trascritti

35 TSS Dato ciascun TSS, calcolare quante sequenze mappano tra e bp rispetto al TSS Contare quante sequenze mappano a -1000, -999, ,0 +1,+2, ,+999,+1000 Sommare per tutti i TSS i conteggi a ciascuna distanza (-1000, -999, -998,...,-1,0,+1,+2, ,+999,+1000) Algoritmo!

36 TSS Attenzione! TSS Le coordinate rispetto al TSS dipendono dalla direzione della trascrizione!!

37 Output: histone modifications at TSS Distance from TSS Read count (peak height)

38 I risultati!

39 PolII is found bound to DNA at the TSS of transcribed genes

40 H3K4me3 is found just before and after the TSS of transcribed genes

41 H3K4me2 (not me3!) is found just before and after the TSS of transcribed genes, but farther away than H3K4me3

42 H3K4me1 is found just before and after the TSS of transcribed genes, but farther away than H3K4me3 and H3K4me2

43 H3K27me3 covers the whole locus of “silent” genes - no transcription here

44 H3K27me1 (not me3!) is vice versa associated before and after loci of transcribed genes

45 H3K36me3 is found within the transcribed region - a bit downstream of the TSS - as if it “lets” polymerase proceed with transcription

46 H3K9me1 is similar in profile to H3K4me3

47 Barski et. al. High-Resolution Profiling of Histone Methylations in the Human Genome, Cell 129(4)

48 Histone modifications at transcribed regions Expression level Read count (peak height) HighLow


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