ChIP Sequencing
Contents
1. QIAGEN Tutorial ChIP Sequencing November 27 2015 Sample to Insight CLC bio a QIAGEN Company Silkeborgvej 2 Prismet 8000 Aarhus C Denmark Telephone 45 70 22 32 44 www clicbio com support clcbio qiagen com 66606 00000 QIAGEN Tutorial ChIP Sequencing 2 ChIP Sequencing This tutorial takes you through a complete ChIP sequencing workflow using the CLC Genomics Workbench This tutorial makes use of the peak shape based Transcription Factor ChiP Seq tool present in CLC Genomics Workbench 7 5 and higher ChIP Sequencing is used to analyze the interactions of proteins with genomic DNA After a cross linking step that covalently links proteins and DNA ChiP seq uses chromatin immunoprecipitation ChIP to fish out the relevant pieces of genomic DNA By subsequent massive parallel DNA sequencing and mapping to the reference genome it is possible to identify binding sites of DNA associated proteins It can be used to accurately map global binding sites for any protein of interest when specific antibodies are available A natural next step bioinformatics analysis is to extract the binding regions and perform pattern discovery to learn about any conserved binding motif in the DNA Usually a control experiment is performed where the immuno precipitation step is left out This control data is typically used to correct for sequencing biases e g genomic regions that are more accessible repeated regions or copy number2. Search terms Annotation types Peak Flanking Flanking upstream residues 0 Flanking downstream residues 0 Naming of result sequences _ Include annotation name _ Include annotation type Vv Include annotation region vA Include annotation chromosome Include sequence track name Previous Next Finish Cancel 13 Figure 18 Options for the Extract Annotations tool 00000 00000 QIAGEN Tutorial Bibliography Bailey et al 2006 Bailey T L Williams N Misleh C and Li W W 2006 MEME discovering and analyzing DNA and protein sequence motifs Nucleic Acids Res 34 suppl 2 W369 W3 73 Landt et al 2012 Landt S G Marinov G K Kundaje A Kheradpour P Pauli F Batzoglou S Bernstein B E Bickel P Brown J B Cayting P Chen Y DeSalvo G Epstein C Fisher Aylor K l Euskirchen G Gerstein M Gertz J Hartemink A J Hoffman M M Iyer V R Jung Y L Karmakar S Kellis M Kharchenko P V Li Q Liu T Liu X S Ma L Milosavljevic A Myers R M Park P J Pazin M J Perry M D Raha D Reddy T E Rozowsky J Shoresh N Sidow A Slattery M Stamatoyannopoulos J A Tolstorukov M Y White K P Xi S Farnham P J Lieb J D Wold B J and Snyder M 2012 ChlP seq guidelines and practices of the ENCODE and modENCODE consortia Genome Res 22 9 1813 31 Marinov et al 2014 Mar
3. 16 3 83E 271 SYNJ1 5355 NC_000021 38208454 38208647 Peak 38208551 194 29 06 5 47E 186 DYRKIA 398904 KCNJ6 0 NC 000021 39822927 39823121 Peak 39823024 195 28 59 4 61E 180 RPS26P4 37112 B3GALTS 128002 NC_000021 43567226 43567423 Peak 43567323 198 26 92 5 89E 160 CRYAA 101243 C21lorf136 5582 NC 000021 42269166 42269371 Peak 42269275 206 25 81 3 20E 147 C2CD2 22097 ZNF295 10637 als Create Track from Selection als He Ef Figure 17 A very strong peak near the gene SYNJ1 Most external sequence based analysis tools require an input in fasta file format To export the sequence as fasta you can run the Export tool File Export 2 Then choose the fasta format select the file Extracted Annotations and finally select the output file name You have now performed a complete ChlP seq Analysis on a small dataset using the CLC Genomics Workbench starting from raw sequencing data and ending with the inspection of the called peaks and the extraction of the sequences within peak regions The ChiP seq workflow described here does not require any tweaking of parameters and can be readily applied to larger ChiP seq datasets 00000 ChIP Sequencing 96690 QIAGEN Tutorial 1 Choose where to run 2 Select annotated sequences OR annotation or variant tracks 3 Set parameters Extract Annotations Set parameters Select a reference sequence track Reference sequence track NC_000021 Genome Refine extracted annotations
4. Cancel Figure 8 A stringent read matching is desired for ChIP seq Map Reads to Reference Result handling 1 Choose where to run Output options 2 Select sequencing reads eWercstehesde cack 3 Batch overview Create stand alone read mappings 4 References V Create report _ Collect un mapped reads 5 Mapping options 6 Result handling Result handling Open e Save Into separate folders Log handling _ Open log 1 tt Ay Previous Next Finish Cancel Figure 9 Select Create reads track Create report and Save Toolbox Epigenomics Analysis i Transcription Factor ChIP Seq This opens a dialog where you select the nrsf chr21 Reads and click Next You can now choose control chr21 Reads gt as control data see figure 10 You can leave the Maximum P value for peak calling to the default value of 0 10 A smaller P value can be specified to obtain a smaller number of high quality peaks while a higher P value threshold can be set to obtain a higher number of peaks ChIP Sequencing 8 QIAGEN Tutorial Transcription Factor ChlP Seq Peak shape parameters man Choose where to run N Select one or more read mapping Controls Control data control chr21 Reads pa Ww Peak shape parameters Peak calling Maximum P value for peak calling 0 1 GQ Previous Next Cancel 2 Figure 10 Choose control data After clicking Next you can choose the output da
5. aberrations For further information see the Wikipedia entry at http en wikipedia org wiki Chip Seq The workflow consists of five parts e Importing the raw sequencing data e Mapping the reads to a reference genome e Calling peaks e Visualizing the results e Extracting the DNA sequences of the peak regions In this tutorial we will focus on how to run the analysis and we will not go through the technical details of how the Transcription Factor ChIP Seq analysis is implemented The user manual already explains the details of the algorithm Click the Help button in the dialog see below to read this or go to http clcsupport com clcgenomicsworkbench current index php manual ChIP_Seq_Analysis html We will look at a subset of a ChliP seq dataset for the transcription factor NRSF Neural Restrictive Silencer Factor on the human cell line Gm12878 Also known as REST RE1 Silencing Transcription factor NRSF is a transcription factor involved in the repression of neural genes in non neuronal cells such as the lymphoblastoid cell line Gm12878 We therefore expect NRSF ChIP seq peaks to be associated with genes involved in neural activity The data was collected by the Myers Lab at the HudsonAlpha Institute for Biotechnology This dataset is well studied and has been often used to evaluate the performances of ChIP seq algorithms Rye et al 2011 In addition to the NRSF ChIP seq dataset we will also use a control experiment wh
6. The settings are not important for the result of this tutorial but when you work with your own data this may be important For more information about the other settings please click the Help button After clicking Next the dialog shown in figure 9 now appears Select Create reads track to create track based results Check Create report to obtain a detailed report about the read mapping and leave Collect un mapped reads unchecked since we are not interested in those reads Select the output options Click Next and Finish You can follow the progress of the mapping both in the status bar at the bottom left corner and under the Processes tab There is also a log showing the progress Because of the quite big reference sequence Human chromosome 21 with a size of 47 Mbp it may take a few minutes to map the data Calling peaks The results of the read mapping are now used as input to the Transcription Factor ChIP Seq tool to detect significant peaks ChIP Sequencing T QIAGEN Tutorial Map Reads to Reference Mapping options 1 Choose where to run i Read alignment 2 Select sequencing reads Mismatch cost 2 3 Batch overview Insertion cost 3 4 References Deletion cost 3 5 Mapping options Length fraction 0 5 Similarity fraction 0 8 _ Global alignment Color space alignment 5 Color error cost 3 Auto detect paired distances Non specific match handling Map randomly e Ignore L S Previous Next Finish
7. ate for your data This dataset consists of two fastq files obtained using an lllumina sequencer so the Illumina importer should be chosen Select the nrsf chr21 fastg and control chr21 fastgq files and make sure the Paired reads checkbox is not checked The option to discard read names and quality scores are not significant in this context and can be safely set to false because of the relatively small amount of reads Click Next Save the imported reads list and click Finish After a short while the raw reads from both files have been imported Next import the reference genome sequence that was also included in the zip file In this tutorial we will use only the human chromosome 21 as reference The reference is provided in genbank format in the file NC_000021 gbk Since this file contains both sequence and annotations we first import it using the Standard Import tool and later we extract separate tracks to store sequence and gene annotations To import the genbank file drag and drop the NC_000021 gbk into the CLC Genomics Workbench or use the Standard Import tool QIAGEN Tutorial ChIP Sequencing 4 File Import 24 Standard Import Locate NC_000021 gbk Select Select the default option Automatic import The CLC Genomics Workbench will correctly recognize that the file is in genbank format Then press Next and choose a folder where the result will be saved Next we want to extract the sequence and gene informatio
8. control dataset figure 13 For each of the 3 measures the table provides the name the value notes to better understand the meaning of the measure and a status which can assume the value OK if the value is reflective of sufficient quality and Low or Very Low if the value is lower than the quality threshold For QIAGEN Tutorial ChIP Sequencing 9 a of reads C 047 Very low For mammalian cells this value should be at least 10 million reads For smaller organisms e g worm and fly this value should be at least 2 million reads Relative strand correlation 1 012 OK The relative strand correlation describes the ratio between the fragment length peak and the read length peak in the cross correlation plot This value should be greater than 0 8 for transcription factor binding sites but can be lower for ChIP seq input or for histone marks Normalized strand coefficient 2 488 OK The normalized strand coefficient describes the ratio between the fragment length peak and the background cross correlation values This value should be greater than 1 05 for ChIP seq experiments Figure 12 Table of quality measures for the NRSF ChIP seq dataset more details on how the quality thresholds were determined see Landt et al 2012 and Marinov et al 2014 In figure 12 the values for the relative strand correlation and the normalized strand coefficient are OK while the number of reads is classified as Very Low This should not be
9. d now have the files depicted in figure 4 vE control chr21 E nrsf chr21 NC_000021 NC_000021 Genome gt NC _ 000021 Gene Figure 4 The files created after the importing step is done Mapping the reads to the reference genome Once the data has been imported the next step in the analysis is to map the reads to the reference genome Toolbox NGS Core Tools Map Reads to Reference z The dialog shown in figure 5 allows you to choose the files containing the raw reads Since we want to map two lists we check the Batch option to enable the batch mode and select the folder where the sequence lists are stored ChIP seq_ NRSF_chr21 in figure 5 Map Reads to Reference 1 choos a Select sequencing reads Navigation Area Selected elements 1 2 Select sequencing reads ve H ChiP seq_NRSF_chr21 control chr21 nrsf chr21 C2 NC_000021 4 ih Q lt enter search term gt Vv Batch Previous Next Finish Cancel Figure 5 Select sequence list containing the reads Since we want to map two lists we choose the batch mode We then press Next and check that only the two lists control chr21 and nrsf chr21 are selected figure 6 Clicking Next will allow you to select a reference sequence as shown in figure 7 At the top you select NC_000021 Genome 7 by clicking the Browse and select element acy button You can select either single Sequences or a list of Sequences as refer
10. ence sequences but in this tutorial we are using only chromosome 21 Note that both the sequence track NC_000021 Genome 7 that we just selected and the sequence NC_000021 3 could be used as reference Click Next and set mapping options as shown in figure 8 For ChIP seq we recommend stringent mapping settings Setting the length fraction to 0 5 specifies the minimum length fraction of a read that must match the reference sequence and setting the similarity fraction to 0 8 specifies the minimum fraction of similarity between the read and the reference sequence The mismatch insertion and deletion costs are here set at 2 3 ChIP Sequencing 6 QIAGEN Tutorial Map Reads to Reference Batch overview Units Contents 2 Select sequencing reads control chr21 nrsf chr2 1 1 Choose where to run 3 Batch overview Only use elements containing Exclude elements containing 2 elements in total Previous Next Finish Cancel Figure 6 Check that all reads are used as input for the mapping Map Reads to Reference References 1 Choose where to run References 2 Select sequencing reads References NC_000021 Genome 3 Batch overview Reference masking 4 References e No masking Exclude annotated Include annotated only Masking track ana Previous Next Finish Cancel Figure 7 Specifying the reference sequences and masking and 3 Next select to ignore the non specific matches
11. ere the immuno precipitation step is left out In this tutorial we will look at only a subset of the data namely only the reads of the NRSF and control experiments mapping to human chromosome 21 Importing the raw sequencing data First download the data set from our web site http download clcbio com testdata raw_data ChIP segq NRSF_chr21 zip Unzip the file somewhere on your computer e g the Desktop ChIP Sequencing 3 Start the CLC Genomics Workbench and import the sequencing data File Import 24 Illumina This will bring up the dialog shown in figure 1 Illumina Set parameters 1 Choose where to run ChIP seq_NRSF_chr21 z 2 Import files and options DEVICES ChiP seq SF_chr21 _ control chr21 fastq A Preview E vault H ChiP seq_N _chr21 zip NC_000021 gbk Macintosh HD nrsf chr21 fastq PLACES 2 items Kind 2 documents Size 67 2 MB II II il 70 497 715 bytes General options Paired reads Paired read information _ Discard read names Paired end forward reverse Mate pair reverse forward Discard quality scores _ i Minimum distance 180 Maximum distance 250 Illumina options v Remove failed reads Quality scores NCBI Sanger or Illumina Pipeline 1 8 and later MiSeq de multiplexing Trim reads 1 JIA Previous Next Finish Cancel A A Figure 1 Import raw reads When analyzing your own data you should select the sequencing technology appropri
12. g the results The best way to visualize the results is to create a Track List Toolbox Track Tools F Create Track List 23 Select the tracks we created so far as shown in figure 16 and then press Finish Once the Track list is created the easiest way to explore peaks is to make a split view of the table and the peak annotation track by double clicking on the label nrsf chr21 Reads Peaks Annotated You will then be able to browse through the peaks by clicking in the table as the peak annotation track and the table are connected As a result the view will jump to the position of the peak selected in the table You can browse through all the 144 peaks found ChIP Sequencing 11 Create Track List Select tracks from same genome 1 Select tracks from same oe genome Navigation Area Selected elements 6 gt FF cubi 737 NC_000021 Genome Y E ChiP seq_NRSF_chr21 NC_000021 Gene XE NC 000021 Genome nrsf chr21 Reads i control chr21 Reads is NC_000021 Gene nrsf chr21 Reads Peak shape score allt tll Sa 238 F z control chr2 1 Reads gt J nrsf chr21 Reads Peaks Annotated nrsf chr21 Reads gt nrsf chr21 Reads Peaks 4 lx nrsf chr21 Reads Peak shape score a Q lt enter search term gt Previous Next Finish _ Cancel Figure 16 Create a Track List to visualize the results for this sample by selecting in the table Next we sort the table accord
13. ing to P value so that we can look at the top peak figure 17 The strongest peak is close to the gene SYNJ1 synaptojanin 1 This gene encodes a phospho inositide phosphatase that regulates levels of membrane phosphatidylinositol 4 5 bisphosphate The expression of this enzyme affects synaptic transmission and thus it is not a surprise that this gene is inhibited by NRSF whose function is to repress neural genes in non neuronal cells Note the nicely distributed green forward and red reverse reads for this peak this is a typical Shape for transcription factors Extracting the DNA sequences of the peak regions A common step in the analysis of ChIP seq data is to extract the DNA sequences associated with peaks in the ChIP seq data These sequences are typically enriched with respect to some DNA motif especially when the protein under examination is a transcription factor such as NRSF This tutorial only covers the step of extracting the sequences The motif discovery can be then performed using external applications such as MEME Bailey et al 2006 http meme nbcr net meme intro html1 or TRANSFAC Matys et al 2006 http www biobase international com product transcription factor binding sites You can then use the Extract Annotations tool to extract the sequences related to the peak regions Toolbox Classical Sequence Analysis General Sequence Analysis gp Extract Annotations m After selecting the peak file n
14. inov G K Kundaje A Park P J and Wold B J 2014 Large scale quality analysis of published ChIP seq data G3 Bethesda 4 2 209 23 Matys et al 2006 Matys V Kel Margoulis O V Fricke E Liebich l Land S Barre Dirrie A Reuter l Chekmenev D Krull M Hornischer K Voss N Stegmaier P Lewicki Potapov B Saxel H Kel A E and Wingender E 2006 TRANSFAC and its module TRANSCompel transcriptional gene regulation in eukaryotes Nucleic Acids Res 34 Database issue D108 10 Rye et al 2011 Rye M B S trom P and Drabl s F 2011 A manually curated ChIP seq benchmark demonstrates room for improvement in current peak finder programs Nucleic Acids Res 39 4 e25 14
15. n for chromosome 21 and store these as tracks Toolbox Track Tools Convert to Tracks 2 Select NC_000021 t as input press Next and in the next window figure 2 select both the options Create sequence track and Create annotation tracks Then press on the green plus icon F and from the list of possible annotations add Gene by selecting it and pressing the right arrow button ij as shown in figure 3 Convert to Tracks Select tracks to create 1 Choose where to run Tracks 2 Select sequences or read mappings V Create sequence track V Create annotation tracks 3 Select tracks to create Annotation types Gene GF Create reads track Genome V Sort sequences by name ara Previous Next Finish Cancel Figure 2 Extract sequence and gene annotations to tracks Select Annotation types Available Selected CDS Gene Exon Gap Misc RNA Misc binding PolyA signal Source 4i TATA box pms Unsure V Segment mRNA Done Figure 3 Select the annotation type Gene The option Sort sequences by name is irrelevant as we are only looking at one chromosome here Click Next Save the tracks and click Finish The output will be the sequence track 00000 00000 QIAGEN Tutorial ChIP Sequencing 5 NC_000021 Genome which stores the sequence of chromosome 21 and the anno tation track NC_000021 Gene which stores gene annotations for chromosome 21 You shoul
16. rsf chr21 Reads Peaks Annotated H as input the dialog shown in figure 18 will appear Select the sequence track NC_000021 Genome and choose the annotation type Peak by clicking the green plus icon 4 Choose Include annotation region and Include annotation chromosome to give informative names to the resulting sequences then Click Next choose to Save the sequences and click Finish After few seconds the sequences will be exported to a file named Extracted Annotations 00000 ChIP Sequencing 12 00000 QIAGEN Tutorial a e p Oo BBE Ol E FUR how New Save Import Export Graphics Print Undo Redo Cut Copy Paste Delete Workspace Plugins Download Workflows I tw Track List x 4 000 33 022 200 34022400 33 022 600 34 022 800 33 023 000 33 023 200 ar i NC_000021 Genome NC_000021 Gene Gene annotations 352 S 1 0S SS _ Ss gt TO T feist rae CC ee a ar Se 486 047 reads SSS SSS 24 ee ee aa 2190 SS SSS control chr21 Reads Sannn E 307 787 reads SS 20 _ 35 nrsf chr21 Reads Peak shape score Graph 0 nrsf chr21 Reads Peaks Annotated Peak annotations 144 C mey Fa F E nrsf chr21 R xX l Rows 144 Table view Genome Filter F Chromosome Region Name Center of peak Length Peak shape score P value 5 gene 5 distance 3 gene 3 distance NC 000021 33022530 33022725 33022627 196 35
17. seq datasets is acceptable the next step is to annotate them with information about their nearest upstream and downstream genes This can be done using the Annotate with Nearby Gene Information tool Toolbox Epigenomics Analysis Ss Annotate with Nearby Gene Information 5 Select first the track to annotate nrsf chr21 Reads Peaks and after clicking Next the dialog shown in figure 14 will appear Annotate with Nearby Gene Information Parameters Choose where to run a 2 Select an annotation Selecte gene track track Gene track NC_000021 Gene Bl W Parameters 2J Previous Next Finish Cancel Figure 14 Select the annotation track to be used as gene reference Choose NC_000021 Gene as the reference gene track then click Next and Save the result The file nrsf chr21 Reads Peaks Annotated H will be generated You should now have the files depicted in figure 15 vE E control chr21 nrsf chr21 X NC_000021 NC 000021 Genome NC_000021 Gene control chr21 Reads k control chr21 mapping summary report nrsf chr21 Reads m nrsf chr21 mapping summary report nrsf chr21 Reads Peaks m nrsf chr21 Reads QC Report nrsf chr21 Reads Peak shape filter lw nrsf chr21 Reads Peak shape score nrsf chr21 Reads Peaks Annotated Figure 15 All files created after the Transcription Factor ChIP Seq analysis is done Visualizin
18. surprising or worrisome because the data used in this tutorial is a small subset of a ChIP seq experiment In fact the full datasets consists of about 16 millions reads which is significantly higher than the threshold value However in normal circumstances a small number of reads would be a strong indicator that the ChIP seg experiment is of low quality Number of reads 307 787 Very low For mammalian cells this value should be at least 10 million reads For smaller organisms e g worm and fly this value should be at least 2 million reads Relative strand correlation 1 192 OK The relative strand correlation describes the ratio between the fragment length peak and the read length peak in the cross correlation plot This value should be greater than 0 8 for transcription factor binding sites but can be lower for ChIP seq input or for histone marks Normalized strand coefficient 2 3751 OK The normalized strand coefficient describes the ratio between the fragment length peak and the background cross correlation values This value should be greater than 1 05 for ChIP seq experiments Figure 13 Quality measures for the control ChIP seq dataset The quality measures table for the control experiment figure 13 can be interpreted in a similar fashion We note that since this is a control experiment the value of the relative strand correlation is not important and the status would be OK also for low values As for NRSF the fact that
19. ta to be generated see figure 11 Transcription Factor ChiP Seq Result handling Choose where to run N Select one or more read mapping Output options v Create QC report Ww Peak shape parameters v Save peak shape filter Result handlin 9 v Save peak shape score graph track Result handling Open Save Log handling Open log 2711S Previous Next Cancel Figure 11 Select the output data to be generated In this tutorial we select all the output which the Transcription Factor ChIP Seq tool can generate After a few minutes the analysis will complete and the following results will appear e nrsf chr21 Reads Peaks the list of all called peaks e nrsf chr21 Reads QC Report The quality control reports The QC report contains metrics about the quality of the ChiIP seq experiment e nrsf chr21 Reads Peak shape filter l The peak shape filter contains the peak shape that was learned during the ChIP seq analysis e nrsf chr21 Reads Peak shape score lm A graph track containing the peak Shape score The track shows the peak shape score for each genomic position Before continuing the analysis or looking at the results we recommend to look at the quality control report The most important sections of the report are the tables containing Quality measures The report nrsf chr21 Reads QC Report will contain one table for the NRSF dataset figure 12 and one for the
20. the number of reads is very low is due to the fact that only a small subset of the data was used The quality report contains additional information that could be used for troubleshooting For ex ample if the relative strand correlation or the normalized strand coefficient were classified as low the cross correlation plots should be examined in more details More information regarding the cross correlation plots and the Transcription Factor ChIP Segq tool can be found in the user manual Click the Help 7 button or go to http clcsupport com clcgenomicsworkbench current index php manual Running_ChIP_Seq Analysis_tool html tin this tutorial we used only the subsets of the data mapping to chromosome 21 The com plete datasets can be found at the UCSC website The complete NRSF dataset is available at http ngdownload test cse ucsc edu goldenPath hg18 encodeDCC wgEncodeHudsonalphaChipSeg releasel wgEncodeHudsonalphaChipSeqRawDataReplK562Nrsf fastq gz The complete control dataset is available at http hgdownload test cse ucsc edu goldenPath hg18 encodeDCC wgEncodeHudsonalphaChipSeg releasel wgEncodeHudsonalphaChipSegRawDataRep1K562Control fastq gz You can download the human reference genome for hg18 from the CLC Genomics Workbench using the command Download Download Genome Data Animal mammals Homo sapiens hg18 00000 ChIP Sequencing 10 96690 QIAGEN Tutorial After having verified that the quality of the ChIP
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