Chapter 2
Contents
1. 0 80 0 60 frequency 0 40 0 20 0 00 1 500 1000 4000 2000 p value D frequency promoter size bp upstream exon 1 B Min_pos p value 0 7 0 6 0 5 0 44 0 3 5 0 2 0 1 2000 500 1000 4000 promoter size bp upstream exon 1 Min_pos frequency 1 00 0 80 0 60 0 40 0 20 0 00 500 1000 4000 promoter size bp upstream exon 1 2000 Optimal promoter size The p value and frequency of promoters with size 500 1000 2000 and 4000 bp and exon 1 with Match settings to minimize false positives Min_pos or minimize the sum of false positives and negatives Min _sum Overall we see a promoter of A 1000 bp exon 1 works best for Min_sum runs and B 2000 bp exon 1 works best for Min_pos runs As expected C and D frequency of TFBSs hit increases as the promoters become larger For a full color figure see www biomedcentral com content supplementary 1471 2105 9 495 s8 tiff 38 2 9 Additional Files Additional File 2 4 TransFactor LAMA4 MyoD Set up and data analysis of MyoD binding a LAMA4 promoter derived sequence with the TransFactor kit TransFactor confirmation MyoD binds the LAMA4 ENSG00000112769 ENST00000230538 promoter Materials TransFactor Kit Clontech product 631956 Oligos ordered from Operon bring up in TE to 100um LAMA4 MyoD_F biotin tgctttecCACCAGCTGTGCegaccttg LAMA4 MyoD_R caaggtc2. Myf Myoq oPossuM e 2 g 0 40 MYOGENN Q6 CORE TF 2 cubott 1 0 20 2 o 0 00 top20 top50 top100 top200 top20 top50 topl00 tep200 random top expressed genes promoters top expressed genes promoters CORE_TF vs oPOSSUM CORE TF and oPOSSUM binomial test p values for the top 20 50 100 and 200 genes from Cao et al 2006 expression data for over expression A of MyoD or Myog in the appropriately induced cell line We see comparable results in the top 20 50 100 and 200 sets but better overall performance in OPOSSUM for Myog and in CORE_TF for MyoD Frequency B of MyoD or Myog hits was also plotted As expected the smaller more significant lists generally have higher frequency and more significant p values than larger less specific lists Frequency of TFBSs in the promoters was also overall higher in experimental data than random promoters as expected The oPOSSUM MyoD frequency was the only plot that did not seem concordant 42 2 9 Additional Files Additional File 2 10 alignment with MYOD_Q6_01 associated patterns highlighted are identical and show both strands strand both strands strand Identifying MyoD TFBSs conserved in the LAMA4 promoter with ConTra and CORE_TF Many conserved TFBSs were found identically between the two programs Shown here is the most conserved TFBSs found a MyoD TFBS conserved between human chimp and dog in B CORE_TF and also macaque in A ConTra
3. ods have been in development which can identify potential TFs and their binding sites They also tend to target more precise the TFBS instead of just containing a TFBS region However finding TFBSs can be extremely difficult since they may be less than 12 14 bp long and their consensus binding sites may be fairly loose 49 One method to identify TFBSs for known TFs is using position weight matrices PWMs 50 PWMs summarize experimental information on the sequence prefer ence of TFs TRANSFAC 51 52 is the leading PWM database for TFBSs with 834 matrices in total release 11 4 December 2007 compared to 123 in JASPAR 53 54 An additional method to look for new de novo TFBSs is by searching for con servation between orthologous promoters 58 This is based on the presumption that functional elements are evolutionary conserved since mutations to such elements could be detrimental to the organism 58 59 However both the sequence conservation based and the PWM approach alone produce many false positives and false negatives We therefore created CORE TF a program using both methods to reduce false predictions We first look for TFs involved in a biological process of interest relying on the presumption that similarly expressed genes have common TFs as regulators To do this and reduce false predictions with PWMs we search for TFBSs that occur more often in a co regulated set of promoters compared to random promoters This algori
4. Though found by both programs CORE TF also identifies the TFBS is on both strands of the DNA For a full color figure see www biomedcentral com content supplementary 1471 2105 9 495 s10 png A ConTra B CORE_TF 43 2 CORE_TF 44
5. 90 H8 Q 90 AOAN 0 10 90 Ata VO AL S 10 90 PdV 0 IO TdCId H 90 88 90 PdV 0 vO ACH GO AL 90 NINADOAIN 0 9O TUR dNOD 90 417 ZO VCH 0 10 Cd PACA 90 HT 90 FdV 0 GO TdV 90 Ae GO PdV 0 IO Td Aza 0 GO PdV 0 l9O0 IdV 90 A9 T 90 NINADOAIN 0 1090 ldV xlea d 09 GAIN YAAN 4 d_ LS VA AOAN YAAN xlea d 09 COAW ZIOZO xlea d LS VA AOAN ZIOZO diqo uo qIqO AOAN V AL MOO TH Suororp d dryo wo qyy do 9007 Te 39 oeO T QLL 30 2 4 Results and Discussion A p value B Frequency B MYOD_Q6 FAST MYOD_Q6 same GC MYOGENIN_Q6 FAST MYOGENIN_Q6 same GC cut off p value 0 05 ABS log p value frequency o 0 60 top20 top50 top100 top200 top20 top50 top100 top200 top expressed genes promoters top expressed genes promoters Figure 2 5 Significance of myogenic TFBSs in expression data The A signifi cance as the absolute value of the log10 p value and B frequency of MyoD PWM MyoD_Q6 or Myog PWM MYOGENIN_Q6 TFBSs in varying number of promoters from genes with increasingly less significant differences in expression upon MyoD or Myog activation are shown As would be expected the smaller more significant lists generally have higher frequency and more significant p values than larger less specific lists 2 4 3 Orthologous Alignments Versus Genomic Alignments In many CORE_TF runs we assessed the conserved TFBSs using alignments based on homologous Ensembl promot
6. or enter user defined orthologous sequences in fasta format There is also the option to define promoters as was done in page 2 If the user skipped over representation analysis there is a list of TFBSs to chose from for analysis otherwise CORE TF uses TFBS selection from page 3 This is given to page 5 which if necessary retrieves either orthologous IDs and sequences or aligned genomic regions with Ensembl API Aligned genomic regions are pairwise alignments but CORE_TF places them into a multi species viewed align 24 2 8 Implementation ment Sequences are again scanned by Match and TRANSFAC If Ensembl genome alignments were not used the first sequence entered or the ID used for orthologous retrieval is used as the reference to carry out a promoter sequence alignment with BLASTz 65 Alignments are displayed on the screen Tables are shown with each TFBS selected and the following information total score region score number of promoters aligned at that point and the length of the TFBS The region score is defined by taking the sum of 100 times the percent of each nucleotide aligned Figure 2 3A The total score is defined as the region score divided by the pattern length divided by 100 Figure 2 3B More specific details of these region numbers are dis played on additional tables lower in the page The user may select a TF and submit this to the final page A Calculating a region score Example alignment of a 6 bp long TFB
7. second option is to define a promoter sequence as a user specified number of bp before and after the start of exon 1 The pre constructed approximately 3000 promoters and pre retrieved sets to match GC on approximately 10000 promoters of which 3000 are selected are based on 1000 bp upstream of exon 1 and exon 1 sequence If requested page 3 Figure 2 2 uses Ensembl API to retrieve promoters from a locally installed Ensembl database or from the web based Ensembl database depend ing on CORE TF installation If the option to use GC matched random sequences is selected CORE TF matches pre retrieved promoter sequences to the experimental promoter sequences so that at least 3000 similar GC promoters are obtained It then uses Match to scan all sequences for the presence of TRANSFAC Professional note web based CORE TF is still free access to non commercial users vertebrate PWMs passing the PWMs alignment threshold provided on page 1 pre constructed random promoter sets also have pre executed Match runs and initial number of hits counted A binomial test is carried out with the Perl module Math Cephes 64 to identify TFBSs that are over represented in the experimental set over the random set This is displayed on the screen as a sortable table with the TFBSs name p value 10 digits are displayed hits and total number in the experimental and random sets as well as the number of PWM hits in each experimental promoter For clarity p valu
8. 0 06 0 M AHRARNT 01 0 0000564805 85 147 1244 2940 0 42 1 AHRARNT_02 0 221496710981 147 1537 2940 0 52 1 M AHRHIF Q6 0 0000318525 101 147 1553 2940 0 53 2 F AHR 01 0 005052041249 147 714 2940 0 24 0 AHR Q5 0 0611207330115 147 210 2940 0 07 0 PI ZIC3 01 NA 147 147 2924 2940 0 99 9 r ZID 01 0 0966872105130 147 491 2940 0 17 0 r ZNF219 01 0 221264173412 147 204 2940 0 07 0 C ZTA_Q2 0 7314071894121 147 487 2940 0 17 0 Automatically check p values below 0 05 Save table as HTML right click gt save as Save table as TabDelimTxt right click gt save as Select TF Promoter to use in homology analysis Figure 2 2 Page 3 screen shot Page 3 of CORE_TF displays the following columns selection boxes for the next page s analysis all TFBS PWMs with hits the p value the number of experimental promoters hit the number of experimental promoters analyzed the number of random promoters hit the number of random promoters analyzed frequency of hits in the random data as well as a column for each exper imental promoter analyzed indicating the number of TFBSs hit in it Our page is lengthy so for display purposes in this figure we deleted the middle TFBSs as indi cated by the large black bar For a full color figure see www biomedcentral com 1471 2105 9 495 figure F2 genomic regions in a selection of species currently H sapiens P troglodytes M musculus R norvegicus B taurus C familiaris and G gallus
9. 35 2 CORE_TF 2 9 Additional Files O Additional File 2 1 oO 50 40 30 20 10 significantly expressed genes in ChIP 20 50 100 200 most significantly expressed genes verlap of most significant expression genes in ChIP on chip data Indicated are the size of the lists for the top expressed genes and the percent of those contained in the 36 significant ChIP on chip genes true positives There is a trend that the smaller more selective expression gene lists contain a higher percent of true positives 2 9 Additional Files Additional File 2 2 1580 1576 1572 1568 1564 1560 1556 1552 1548 total matrices counted in 3 runs 1544 1540 T E Occur in 3 3 runs I Occur in 2 3 runs E Occur in 1 3 runs 0 5 1 2 4 random set size x1000 Consistency of TF identification in different random set sizes Indicated are the number of TFs that occur in 1 2 or 3 out of 3 total runs As expected the larger the random set size 500 1000 2000 or 4000 promoters the larger the consistency over runs However as indicated by the y axis scale this is not a very large effect 37 2 CORE_TF Additional File 2 3 A Min_sum p value 0 4 MYOD_01 MYOD_Q6 MYOD_Q6_01 MYOGENIN_Q6 0 3 MYOGNF1_01 0 2 p value 2000 o 500 1000 4000 promoter size bp upstream exon 1 C Min_sum frequency
10. Chapter 2 CORE TE a User Friendly Interface to Identify Evolutionary Conserved Transcription Factor Binding Sites in Sets of Co Regulated Genes Matthew S Hestand Michiel van Galen Michel P Villerius Gert Jan B van Ommen Johan T den Dunnen Peter A C t Hoen The Center for Human and Clinical Genetics Leiden University Medical Center Postzone S4 0P PO Box 9600 2300 RC Leiden The Netherlands BMC Bioinformatics 2008 9 495 Parts of this manuscript have been adapted to more appropriately fit this thesis 2 CORE_TF 2 1 Abstract Background The identification of transcription factor binding sites is difficult since they are only a small number of nucleotides in size resulting in large numbers of false positives and false negatives in current approaches Computational methods to reduce false positives are to look for over representation of transcription factor binding sites in a set of similarly regulated promoters or to look for conservation in orthologous promoter alignments Results We have developed a novel tool CORE TF Conserved and Over REpre sented Transcription Factor binding sites that identifies common transcription factor binding sites in promoters of co regulated genes To improve upon existing binding site predictions the tool searches for position weight matrices from the TRANSFAC database that are over represented in an experimental set compared to a random set of promoters and identifies cr
11. POSSUM is limited to only human and mouse both of which are in CORE TF In addition we display our across species TFBSs in a graphical format whereas oPOSSUM presents their data in a less intuitive tabular format 2 4 7 CORE TF Compared to an Existing Program ConTra We also evaluated CORE TF versus ConTra using the LAMA4 promoter for which we had experimental data available as an example ConTra is a website to iden tify and easily view conserved TFBSs in a single cross species promoter alignment but cannot look for over representation in a large promoter set We found that in CORE TF genomic alignment predictions there were three MyoD TFBSs conserved between human and chimp and one TFBS conserved between human chimp and dog Figure 2 4 ConTra found the same TFBSs but also three additional Additional File 2 10 and data not shown Two of the three human chimp CORE TF conserved TFBSs and the human chimp dog CORE_TF conserved TFBS were also found con served in the macaque in ConTra CORE TF did not search for macaque but it is extremely similar to human and chimp so we believe it would not add much informa tion However if a user wanted any Ensembl species added to CORE_TF adding an additional species to the scripts is very simple It is not surprising the same TFBSs were identified since both programs use Ensembl alignments and TRANSFAC PWMs ConTra does have the disadvantage of only using human as a reference genome for automated align
12. S from 4 promoters Promoter seq 1 ACGTGG Promoter seq 2 ACGTGG Promoter seq 3 ACGTGG Promoter seq 4 ACGT Position 123456 number promoters that share conservation 444433 number promoters conserved per position 4 4 4 4 4 4 4 4 3 4 3 4 Percent of each position conserved 100 100 100 100 75 75 Sum of all region score 550 B Calculating a total score Total score Region score Pattern length 100 550 6 100 0 92 Figure 2 3 Formulas for conservation scores Page 6 Figure 2 4 allows for visualization in the alignment by displaying the alignment with selected TFBSs highlighted according to the strand bound blue pos itive strand purple both strands or red negative strand There is also evidence that some TFs may preferentially bind one strand over the other 5 It is up to the user to decide if their TF is strand specific or not 2 3 2 CORE TF Evaluation with Expression and ChIP on chip Data To verify the performance of our algorithms we used expression and ChIP on chip data from Cao et al 2006 66 They studied the promoter binding of two major regula tors of muscle differentiation MyoD and Myog and expression profiles in embryonic fibroblasts from MyoD Myf5 knockout mouse transduced with a MyoD estrogen re ceptor hormone binding fusion protein termed MDER cells These cells have been modified so that they can be studied during differentiation with or without MyoD or 25 2 CORE
13. _TF Complete alignment with MYOD_Q6_01 associated patterns highlighted dentical and show both strands Viewports are identical and strand both strands strand tteeecepetpttpn ap cris tesca tetecsgi tatarpa m m m w w w n m w i j so pbs jase gns jes pers iwe awe 287 205 jno jan pm ny ja is LE Figure 2 4 Page 6 screen shot of a conserved MyoD TFBS in the LAMA4 pro moter Page 6 of CORE TF displays two identical boxes containing aligned pro moters with conserved TFBSs highlighted by color blue if on the positive strand purple if on both strands and red if on the negative strand For a full color figure see www biomedcentral com 1471 2105 9 495 figure F 4 If requested in the previous page to show run details not shown in this figure boxes with score construction for all conserved TFBSs are also displayed as well as the patterns of all selected PWMs hit Here we show an example of a MyoD TFBS PWM MyoD_Q6_01 in the LAMA4 promoter conserved in human chimp and dog on both strands Myog present Promoter binding was also studied in a common mouse myoblast cell line C2C12 ChIP on chip is a technique using a TF targeting antibody that is used to pull down TF bound DNA fragments which are then amplified labeled and hybridized to a promoter or tiling microarray As a positive control set for TF binding we took those promoters from the ChIP on chip data that showed enrichment for MyoD or Myog
14. binding sites p value lt 0 001 We re analyzed the Affymetrix expression data by applying a RMA summarization and normalization and using the R package limma 67 68 to fit a linear model containing the following factors MyoD expression yes no Myog expression yes no and time of differentiation 0 24 48 and 96 h As a positive control set for MyoD or Myog induced regulation of gene expression we took the top 200 or less genes based on the effect of MyoD or Myog expression respectively When needed accession numbers were converted to Ensembl gene IDs using Idconverter 69 For the 200 most significantly induced genes we evaluated whether their promoters contained MyoD or Myog TFBSs according to the ChIP on chip data We expect that the smaller more specific lists would have a higher percent of promoters with true TFBSs significant on the ChIP on chip platform and therefore likely to contain more significantly over representated TFBSs in our predictions We found that as a general trend this is true that the smaller more specific expression lists contain a higher percent of true positives significant ChIP on chip genes Additional File 2 1 2 3 3 Random Data Size Evaluation We evaluated what would be an appropriate number of random promoters by running a set of 14 experimental promoters against several random set sizes 500 1000 2000 and 4000 For this the Match cutoff was set to minimize the sum of false positives and negativ
15. e results of one page into the next Figure 2 1 Page one allows a user to select run options and input criteria including a p value cut off for highlighting data see below 6 different Match the program that aligns TRANSFAC PWMs to nucleotide sequences 51 55 settings minimize false positives minimize false negatives minimize the sum of both error rates and non redundant sets of these 3 settings and data input type for a set of experimental promoters and a set of random promoters The experimental promoter lists are en tered as sequences in fasta format or Ensembl gene IDs Five options are available for the random promoter list input sequences in fasta format an Ensembl gene ID list randomly retrieve Ensembl promoters pre constructed promoter sets and pre retrieved sequence sets that are matched to the experimental set based on percentage of GC content There is also an option to skip the over representation analysis and go directly to page 4 Depending on the selections from page 1 page 2 presents text boxes to paste in lists of fasta format sequences or Ensembl gene IDs or radio buttons to select a certain number of random promoters for the appropriate species or species based check boxes for pre constructed runs or GC matched runs If CORE_TF must retrieve promoters there are two options to define promoter sequences The first option is to call a promoter as exon 1 plus a user defined number of base pairs bp upstream The
16. ely 9 26 06 0 01 0 021 0 008 0 019 0 007 0 017 0 007 0 015 Count 48 48 48 SS 95 4319 45 0561 140 488 Measurements over 5 time points 0 008 0 034 0 007 0 029 0 006 0 024 0 006 0 02 Sum 3 756 72 96 df 141 143 9 29 06 0 003 0 026 0 001 0 024 0 001 0 019 0 0 003 0 019 Average 0 07825 1 5 2 MS 47 7160 0 31955 that the target sequence is a TFBS for MyoD 40 0 005 0 03 0 003 0 024 0 001 0 02 0 017 0 019 Variance 0 02247 0 25532 0 68085 F 149 324 10 6 06 0 027 0 612 0 023 0 48 0 017 0 387 0 017 0 322 P value 0 028 0 455 0 022 0 355 0 02 0 292 0 246 F critical 1 5E 35 3 06029 2 9 Additional Files Additional File 2 5 Cao_et_al_2006_ChIP_CORE_TF CORE TF run results to identify over represented TFBSs in MyoD Myog ChIP on chip data http www ncbi nlm nih gou pmc articles PMC2613159 bin 1471 2105 9 495 S5 als Additional File 2 6 A C2C12 MyoD B C2C12 Myog 1 p 0 8 v 2 06 s a 04 0 2 0 24 96 168 240 312 384 456 24 96 168 24 312 384 456 sorted TFBS CORE_TF Setting sorted TFBS Similar GC 2o Fast C MDER MyoD D MDER Myog 1 0 8 w w 3 2 06 s g 04 0 2 o 25 100 175 250 325 400 475 25 100 175 250 325 400 475 sorted TFBS sorted TFBS CORE TF using random FAST runs vs runs with similar GC It is visible that in all ChIP on chip data tested the
17. erforms similar to their Fisher test Additional File 2 8 Unlike our frequency observations the frequency identified by oPOSSUM of TFBS hits in the MyoD induced set did not show the expected high to low pat tern Additional File 2 9 When comparing p values from the binomial tests for the predictions by the two programs we see similar patterns between the two programs across the top 20 50 100 and 200 genes but CORE TF has more significant MyoD predictions and oPOSSUM has more significant Myog predictions Additional File 2 9 It must be noted that we are only comparing over represented TFBSs whereas oPOSSUM has already taken conservation into their program at this point which may explain higher sensitivity for Myog promoters We instead do this on individ ual promoters and display it graphically in the next step We believe this graphical representation to be more interpretable Since we can do better in one out of two tested TFs without our orthologous promoter conservation we believe CORE TF to be a superior tool The two programs differ on several other levels OPOSSUM only takes Ensembl IDs as input whereas we also accept nucleotide sequences We also offer a larger choice of random data sets and conservation methods as well as the choice to account for GC content In addition our number of vertebrate species available is six all of which can be compared together oPOSSUM only accepts two species comparisons at a time For vertebrates O
18. ers as well as Ensembl genomic alignments Ensembl pairwise alignments can be considered syntenic they are grouped to make the actual Ensembl synteny blocks 71 Ensembl orthologs are identified using protein tree calculations 62 The number of promoters aligning and the quality of the alignment to the reference promoter varies tremendously amongst different promoters for both methods data not shown but we did not find one method outperforming the other Synteny does not imply the start of one gene corresponds to the start of a gene in another species Therefore this could give poor predictions for TFs that bind and function close to the transcription start site However due to many incorrect exon 1 annotations it is also possible that using orthologous promoter alignments may align regions that are not corresponding regions if an annotation missed exon 1 exon 2 would be annotated as exon 1 and we would instead align to it Therefore there is not one alignment method that outperforms another to predict conserved TFBSs 2 4 4 TFBSs Conserved in Orthologous Alignments The top 10 ranked genes of the Myog induced genes were inspected for the presence of MYOGENIN _Q6 motifs To this end all available orthologs for the mouse genes were retrieved All conserved TFBSs and their conservation scores are reported in Table 2 2 There are seven promoters which appear to have conserved TFBSs Four of these promoters Chrng Myog Acta1 and Tnnc1 had h
19. es For this test we used a promoter size of 1000 bp before exon 1 and all of exon 1 The larger the random size used the more consistent the number of TFBSs 26 2 8 Implementation that were identified Additional File 2 2 but also the longer the run time We found a random size of 2000 promoters to be the best trade off between accuracy and speed 2 3 4 Promoter Size Evaluation We evaluated an appropriate promoter size for our TFs of interest by taking the Cao et al 2006 expression data top 50 MyoD or Myog responsive promoters for the appropriate stimulation MyoD or Myog compared to 2000 purely random mouse Ensembl promoters We varied the promoter size to include exon 1 plus an additional number of bp upstream 500 1000 2000 and 4000 Analysis showed that with a Match setting to minimize false positives a promoter size of 2000 bp exon 1 was best whereas with a Match setting to minimize the sum of false positives and negatives a promoter size of 1000 bp exon 1 was preferable Additional File 2 3 We continued with a Match setting to minimize the sum of false positives and negatives setting using 1000 bp upstream exon 1 as our promoter size 2 3 5 Evaluation of GC Content To evaluate the effect of GC content we ran purely random Ensembl promoters the FAST setting of CORE_TF on all Cao et al ChIP data We then compared that to runs with the option to get random promoters of approximately equal GC content compared to t
20. es below a defined threshold from page 1 are highlighted in blue The table can be downloaded as an HTML file or a tab delimited text file The user can select a number of TFBSs plus a promoter of interest and continue to the next page There is also a Java script with a button to automatically select all TFBSs with a p value 22 2 8 Implementation PAGE 1 Input PAGE 6 A select experimental set type View alignment with B select random set type highlighted TFBS or skip to conservation kip to conservation PAGE 5 Output A Promoter alignment B All selected TFBS promotors hit conservation scores B input random data or generation parameters Input Select TFBS Get sequences if needed suma API Calculate TFBS overlap in I Identify TFBS Match T i Identify TFBS pi Match Transfac Binomial Test Get homologous sequences and BLASTz align or genomic alignments if needed PAGE 3 Output ENSEMBL API All TF matrices p val promoter hits PAGE 4 Input Input Ensembl gene ID and additional A Select Promoter species choices or homologous B Select TF sequences Figure 2 1 Flowchart of CORE TF runs CORE TF runs linearly through 6 web pages Pages 1 and 2 take as input experimental gene promoter lists and random gene promoter lists or requests to create random lists Depending on format se quences are retrieved with Ensembl API or random lists generated before identi
21. fying TFBSs with Match TRANSFAC A binomial test is run to identify over represented TFBSs in the experimental set compared to the random set and displayed in page 3 as a table In the table TFs and a promoter can be selected which are sent to page 4 If requested homologs and sequences or genomic alignments are retrieved from Ensembl for the selected promoter If not already a genomic alignment input sequences or retrieved sequences are aligned with BLASTz TFBSs are identified with Match TRANSFAC overlapping TFBSs are identified and scores calculated and the data is displayed in page 5 Conserved TFBSs can be selected and displayed as highlights in the alignment in page 6 below the defined threshold Page 4 gives the user the opportunity to use Ensembl defined orthologs or aligned 23 2 CORE_TF TFBS output Match cut off was minimize the sum of both error rates These are high quality vertebrate V matrix p value high light has cut off below 0 05 NA the frequency of the TFBS in the experimental or random promoters was 0 or 1 Select in the table below the TF and promoter of interest for the homology analysis Automatically check p values below 0 05 exp exp random random freq hits in x Name of Matrix P values promoters Prom otersPTomoters Prom SA ome NSMUSG00000031077 C r ACAAT B 0 80012577398 147 225 2940 0 08 1 r AFP1 _Q6 0 94685808574 147 178 2940
22. gcacagctggtggaaacga Neg MyoD_F biotin tgctttcCTCGAGGAGTGCezaccttg Neg _MyoD_R caaggtcgcactcctcgaggaaagca Nucleotides are the mutated nucleotides from the original target sequence Antibodies Primary Santa Cruz MyoD M318 sc760 Secondary goat anti rabbit IgGHRP from TransFactor Kit Protein Recombinant MyoD protein Plate Reader BIOTEK Synergy HT Methods Oligo preparation done as mix 10yl forward 101 reverse oligo place 95 C heat block 10 minutes cool on desktop 30 minutes mix 20ul with 198ul Mg to make 1uM concentration vortex briefly The TransFactor Kit User Manual V Colorimetric TransFactor ELISA Procedure is followed with the following additions changes dilute MyoD antibody 1 100 dilute goat anti rabbit antibody 1 1000 step F1 after adding the TMB substrate place directly into the reader plate reader protocol 1 Kinetic 13x5 minute intervals 2 Absorbance 3 Wavelength 655nm 4 Shake 30s read 39 2 CORE_TF Results slope Tn T n 1 T2 T1 sample Neg_MyoD LAMA4_MyoD T3 T2 sample Neg_MyoD LAMA4 MyoD T4 T3 sample Neg MyoD LAMA4_MyoD T5 T4 sample Neg_MyoD LAMA4 MyoD Gnumeric spreadsheet Anova single factor results Groups measurements sample day ANOVA Source of Variation Between Groups Within Groups Total Conclusion With a p value of 1 5E 35 there is a very significant difference in MyoD binging between the negative and target oligos It is therefore highly lik
23. he experimental set the Similar GC option 2 3 6 Wet lab Verification of a CORE TF Predicted Conserved TFBS To give wet lab confirmation to the results of the CORE_TF conservation predic tions we used the TransFactor kit with double stranded DNA designed on a LAMA4 ENSG00000112769 MyoD predicted TFBS conserved between human chimp and dog Figure 2 4 This was an Ensembl genomic alignment run with a Match setting to minimize the sum of false positives and false negatives The promoter size was defined as 3000 bp upstream of exon 1 and including exon 1 We also included a neg ative control of the same DNA sequence with four mutations Recombinant MyoD protein was used to test for binding For more details on the TransFactor run see the additional material Additional File 2 4 2 3 7 CORE TF Compared to an Existing Program oPOS SUM To evaluate our script with existing technology we ran the Cao et al 2006 expression data most significant 20 50 100 and 200 genes through the oPOSSUM website 60 We chose oPOSSUM for comparison since it performs similar analysis and is freely available We used their custom single site analysis page Other than setting to mouse vertebrate JASPAR PWMs retrieving 1000 bp up and 433 bp downstream using Ensembl API we calculated this as the average size of exon 1 of the transcription start site and showing all results all settings used their defaults It must be noted that JASPAR only has a PWM for M
24. ion level increased upon MyoD or Myog activation were en riched for MyoD or Myog TFBSs We ran sets consisting of the 20 50 100 and 200 genes most significantly affected by MyoD or Myog activation versus a random set of approximately equal GC content Additional File 2 7 We found significant enrich ment of the MyoD_Q6 PWM in all MyoD enriched sets We also found MYOD_Q6_01 enriched in the top 50 and top 100 MyoD enriched sets MYOGENIN_Q6 was found enriched in the top 20 Myog enriched set only Other PWMs for MyoD or Myog and other sets of promoters were not significant or considered NA due to 100 of promoters hit in the experimental data The same data was also run through with the CORE_TF FAST setting We found that the two settings perform similar with slightly higher frequencies but slightly less significant p values when matching on GC Figure 2 5 Additionally as expected the smaller more specific lists generally have higher frequencies and lower p values than larger less specific lists Figure 2 5 29 2 CORE_TF SSAAL Jueoyrusts GT dog oy ut you oem mq GQ Q gt s n ea d pey 10 9M AGOAN pue 9M AOAN sun LSVaA GO N 94 ut 93oN D91991105 319q u5onl turuefuog ore sonfea d ploq ur poquoseid ore sog yy 33I eyep dryo uo gyyO 9007 Te 39 OVD uo suorprpoid FL AYOO IO HZ I ZOOAWO 60 479 1090 FdV 20 8 TO 90 HS 90 GOAN I0 HZ I 10 DUVdd 0 90 7ASO Z0 H0 Q 10 94LV 90 H9 9 10 VLVD IO HTI 10
25. its in three or more orthologs We also inspected the MyoD induced genes presence of MyoD_01 motifs using the same approach and identified two promoters with conserved TFBSs Table 2 2 Only one promoter was found conserved over three or more orthologs Rgs16 In addition of the nine across species conserved TFBSs all except Tnnc1 not on the array Tnnc2 31 2 CORE_TF Rgs16 and Nptx1 were found significant in the ChIP on chip data Literature was examined to see if predictions were correct We found evidence for binding of Myog to Myog 72 Tnnil 73 and Chrng 74 We also found evidence for MyoD binding Nptx1 also called NP1 75 Table 2 2 Orthologous conservation of target TFBSs in target genes A Gene GeneID TF_Name Tot Score Promos Length Name Score Chrng ENSMUSG00000026253 MYOGENIN_Q6 1 1000 5 10 Chrng ENSMUSG00000026253 MYOGENIN_Q6 1 1000 5 10 Tnnt3 ENSMUSG00000061723 MYOGENIN_Q6 1 1000 2 10 Tnnc ENSMUSG00000017300 MYOGENIN_Q6 1 800 2 8 Tnnil ENSMUSG00000026418 MYOGENIN_Q6 1 800 2 8 Myog ENSMUSG00000026459 MYOGENIN_Q6 0 83 666 7 5 8 Actal ENSMUSG00000031972 MYOGENIN_Q6 0 8 640 4 8 Tnnc ENSMUSG00000021909 MYOGENIN_Q6 0 72 720 4 10 Actal ENSMUSG00000031972 MYOGENIN Q6 0 6 480 3 8 B Gene GeneID TF_Name Tot Score Promos Length Name Score Rgs16 ENSMUSG00000026475 MYOD 01 1 1200 4 12 Rgs16 ENSMUSG00000026475 MYOD 01 0 5 600 2 12 Nptz1 ENSMUSG00000025582 MYOD_01 0 4 840 2 21 Npt
26. ment retrievals whereas CORE TF can do this for all six species currently installed Additionally CORE TF does not use an Ensembl multi species defined alignment but combines many Ensembl pair wise alignments into one allow ing any number of Ensembl species to be included in one alignment ConTra does not 33 2 CORE_TF display strand specific binding which CORE_TF does by color coding Additionally Con Tra does not search for over represented TFBSs in a group of promoters 2 4 8 Future Efforts An item that can be improved in the future is our evolutionary scoring algorithm e g by taking into account the confidence of each nucleotide in the PWM An additional improvement will be to analyze combinations of TFBSs 2 5 Conclusion We have developed a tool for identifying over represented TFBSs in promoters from co expressed genes aided by the evaluation of cross species conservation CORE_TF is easy to use and displays results in tables or graphically allowing for easy interpretation of the results Our method seems to correctly predict the presence of experimentally verified TFBSs as shown by our extensive analysis on Cao et al 2006 expression and ChIP on chip data and wet lab confirmation of a MyoD predicted TFBS in the LAMA4 promoter We also show improvements over two existing programs oPOS SUM and ConTra with greater flexibility in input data coverage of a larger number of species more intuitive output and the option to acc
27. oss species conservation of the predicted transcription factor binding sites The algorithm has been evaluated with expression and chromatin immunoprecipitation on microarray data We also implement and demonstrate the importance of matching the random set of promoters to the experimental promoters by GC content which is a unique feature of our tool Conclusion The program CORE_TF is accessible in a user friendly web interface at http www LGTC nl CORE_TF It provides a table of over represented transcrip tion factor binding sites in the users input genes promoters and a graphical view of evolutionary conserved transcription factor binding sites In our test data sets it successfully predicts target transcription factors and their binding sites 20 2 2 Background 2 2 Background There are both experimental and computational approaches to identify transcription factors TFs and their relevant binding sites In the wet lab hypothesis driven techniques such as deletion constructs with luciferase reporter assays and chromatin immunoprecipitation on microarrays ChIP on chip can be used to identify TF bind ing site TFBS regions Luciferase assays can prove that a specific region has reg ulatory function but they are laborious and time consuming ChIP on chip is more global but requires prior knowledge of which TF to target using a specific antibody and is laborious time consuming and expensive Faster and cheaper in silico meth
28. ount for GC content Our tool is provided as a web service free to all non commercial users 2 6 Availability and Requirements Project name CORE_TF Project home page hitp www LGTC nl CORE_TF Operating system s Linux Programming language Perl we used 5 8 4 Other requirements TransFac Professional we used 11 2 BLASTz sorttable js Math Cephes Perl module Apache we used 1 3 33 License GNU General Public License v3 http www gnu org licenses Any restrictions to use by non academics none for website use TransFac Profes sional license for a local install 2 7 Authors contributions MH JD GO and PH conceived of the primary concepts of the software MH and MG did the primary programming and debugging MV performed all primary installations on the web server and helped in debugging code MH MG and PH performed the software evaluation on expression and ChIP on chip data Wet lab work was done by MH Manuscript drafting was done by MH MG JD GO and PH All authors read and approved the final manuscript 34 2 8 Acknowledgements 2 8 Acknowledgements We would like to thank Renee de Menezes and Maarten van Iterson for their statistical comments and Ivo Fokkema for his programming and implementation assistance This work was funded by the Center for Biomedical Genetics in the Netherlands PH was supported by a VENI grant from the Dutch Organization for Scientific Research NWO grant 2005 03808 ALW
29. runs on purely random Ensembl promoters FAST runs has a bias towards high and low p values while the random set with a similar GC follows a more normal distribution This could account for false positives in the FAST runs Additional File 2 7 Cao_et_al_2006_expression_ CORE_TF CORE TF run results to identify over represented TFBSs in expression array data http www ncbi nim nih gou pmc articles PMC2613159 bin 1471 2105 9 495 S7 als 41 2 CORE_TF Additional File 2 8 2 0 07 0 06 0 05 0 04 0 03 0 02 0 01 w _ Fisher score zscore o 10 20 50 100 200 100 200 promoters promoters g 0 025 0 02 0 015 0 01 0 005 0 60 0 40 0 20 binomial test p val Q frequency 0 ii 0 00 7 i 10 20 50 100 200 10 20 50 100 200 random promoters promoters oPOSSUM runs on expression data Custom oPOSSUM runs using the top 10 20 50 100 and 200 genes from Cao et al 2006 expression data OPOSSUM supplies A Fisher and B z scores C We also used their hits in the experimental and background data to generate a binomial test p value similar to our program D Frequency of TFBS hits overall declines as we stray from the top hits as expected but this is not an entirely smooth curve Additional File 2 9 A p value B Frequency 5 1 00 s lt gt 9 80 neni C c me SB BE My Myo opossum 43 0 60 W roo Q6 CORE TI 3
30. tched GC content as controls similar GC option Using both sets of random promoters CORE_TF found a significant 28 2 4 Results and Discussion over representation p value lt 0 05 after applying multiple test correction with Ben jamini Hochberg in R 70 for the MyoD PWM MYOD_Q6 in the MyoD bound promoters and the Myog PWM MYOGENIN_Q6 in the Myog bound promoters in both C2C12 and MDER cells Additional File 2 5 The MyoD PWM MYOD Q6 01 was also significant in all MyoD targeted runs except the MDER MyoD with random promoters matched on GC content Strikingly by ranking TFBSs on p value we demonstrate that the target TFs were higher ranked with the GC matched promoters as control rather than with the purely random set of control promoters Table 2 1 indicating that improper matching of GC content leads to false positive identification of TFBSs By evaluating the distribution of p values for all TFs using both random sets we observed purely random promoters yield more high and low p values than a random set of promoters matched on GC content Additional File 2 6 Since our target ChIP TFs remained significant when using GC matched promoters resulting in a smaller list of significant TFBSs we believe this method to yield less false positives To demonstrate that our algorithm is able to find shared regulatory sites in co regulated genes identified in expression microarray data we evaluated whether genes for which the express
31. tday 0 IOCHAN Z0 HT 959 Ved 90 H I vO ACH ZO AL 2 90 dv 0 IO90 TAN Z0 HZ IOO4Z LO Ae s 1090 FdV ZO A8 S IO OUZ 0 90 NINADOAW C0 HZ VOTdV 20 H9 T 90 THA dNOO ZO AL S 10 VHdTVZdV 0 lO9O0 AVI Z0 H6 1 10 rdV 60 AL P 10 Gddrazca c0 AS T 90 TVIN3MOO TTO 0 90 Idad1 ZO dV I 90 TdT 0 90 ZASO 0 H9 8 10 90 GOAW 0 O90 ACA ZO AP T 10 90 GOAW 0 TO CHAN 0 HT O VH 0 FO HZH 0 H6 9 10 98 v 0 1090 714N 90 HT9 90 GOAN 0 IO ZddPrAcH F10 H0 6 ZO Ve 0 90 NINADOAW 90 H0 8 90 TddT 0 TO TdAIA H SO AT T 1090 VdV 0 10 90 AVI 90 497 10 90 FdV 0 9O TUT dNOD 90 H0 90 GOAN 0 IO Td Aza 0 90 NINADOAIN 0 90 PdV 0 90 NINADOAW 0 90 PdV 0 90 FdV 0 GO VdV 0 90 FdV 0 GO VdV 0 GO tdv 0 10 90 IdV 0 GO PdV 0 10 90 IdV xea d 99 304W YAAN xed LS Va 804 YAAN xIea d OD5 30AN ZTOZO slPA d_ LS Wa SOAN IOO dryo wo qi yD SoAq g 10 0 10 PINH 80 99 9OTdAT 0 AP 6 ZOLA 80 9 10 90 FdV 10 40 VOTdV 60 AF E IO VLVD Z0 48 S pO IdV 80 489 90 FdV 10 H6 IO VSOITAH 60 HGS 9D FdV Z0 ASP ZO LATAV 80 A 90 TddT 10 467 90 JAH 0 9O CHSO Z0 HyF F TO 90 GOAW 60 464 90 NINADOAIN 10 AL T 10 90 AOAN 0 90 GMAN 20 AL 1090 GVWNS 60 H6 2 GO dV ZO A9 L 10 LVAVLAATTIVIE 0 IO CHAN 0 A9 F 90 Vea 0 90 ZASO ZO A9 L ZO INAH 0 IO90 TAN 0 HO0 P 90 TddT 0 10 CHAN ZO AE Z 90 TddT 0 lO90 AVI SOPI 90 214 0 10 90 TAN 0 HZ S O Vd 0 1090 AZA 0 HT I 10 27 0 10 90 AVN 0 AS P 90 AOAN 0 PO ACA SO AT S 10 90 dY 0 IO VLVD 0 HZ I IO TdV 0 IO ZddvaAcA
32. thm in analogy to the work of Elkon et al 2003 57 implements a binomial test to evaluate for this over representation Some PWMs have a bias towards certain nucleotides such as T s and A s for a TATA box binding TF and would therefore likely be over represented if an experimental set had high numbers of T s and A s and the random set had equal content of all four nucleotides We therefore also offer the option to exclude biases based on GC content by matching random promoters with approximately equal GC content to the experimental promoters To identify individual TFBSs with increased precision and add additional support for the relevant TFs we subsequently scan individual promoters for cross species conservation again employing TRANSFAC matrices All steps are flexible allowing for a multitude of input types Ensembl 62 gene IDs nucleotide sequences or selected by CORE TF We also compared CORE_TF to two existing programs oPOSSUM 60 and ConTra 61 CORE_TF is accessible as a web page In this paper we present and evaluate the performance of our web based tool for identification of TFBSs 21 2 CORE_TF 2 3 Implementation 2 3 1 CORE TF Construction Format The main script is written in Perl and presented in HTML on an Apache web server Input and table sorting is done using an edited Java script sorttable js 63 By default following the title page there are 6 pages that are run in a linear fashion feeding th
33. tify specific TFBSs by looking for across species conservation of TFBSs selected from the TFBSs in page 3 and the promoters of page 3 This is done on Ensembl genomic alignments or BLASTz alignments of orthologous promoters provided by Ensembl or the user Across species conserved TFBSs are displayed in tables calculations as in Figure 2 3 in page 5 and as aligned promoters in a graphical format Figure 2 4 in page 6 Alternatively if a user did not wish to look at a list of promoters but just a single promoter they could look purely for cross species conserved TFBSs by skipping straight to page 4 from page 1 They must then provide which promoter they want to search and a set of TFBSs from a web displayed list In theory they could paste the sequences conserved in the alignments back into the over representation pages to find TFBSs over represented in conserved regions as opposed to the normal order of looking for conservation with over represented TFBSs 2 4 2 Prediction of Over Represented TFBSs To evaluate the performance of our tool we first used the Cao et al 2006 ChIP on chip data as a positive control We tested whether the promoters in the ChIP pull down were enriched for the TFBSs for the TFs targeted in the ChIP experiments compared to arandom set of promoters To evaluate the effect of matching promoters for GC content CORE_TF was run with a purely random selected set of promoters FAST option and a random set of promoters with ma
34. yf which represents a TF family including 27 2 CORE_TF MyoD and Myog We also used their number of hits in their background and target genes to run a binomial test in the statistical package R to match our data 2 3 8 CORE TF Compared to an Existing Program ConTra We also chose to evaluate CORE_TF versus an additional easily viewable cross species conservation program ConTra 61 As a test promoter for comparison we used the LAMA4 ENSG00000112769 promoter for which we had a lab verified MyoD TFBS The ConTra website was run on all default parameters selecting transcript ENST00000230538 except for looking at 3000 bp upstream instead of 2000 bp up stream giving a promoter the same size as the CORE_TF run We looked at the PWM MyoD Q6 01 This was the only PWM for MyoD available at the ConTra website and the best performing for CORE_TF with this promoter 2 4 Results and Discussion 2 4 1 CORE_TF Work Flow and Function We have developed a series of web pages to identify TFBSs in two sequential processes First pages 1 to 3 allow a user to predict TFs that regulate a set of co regulated genes This is done by identifying TFBSs that are over represented in the promoters of an experimental e g similar expressed genes from microarray data compared to a random data set taking GC content into account if requested These results are displayed in a sortable table in page 3 Figure 2 2 Secondly pages 4 to 6 allow a user to iden
35. z1 ENSMUSG00000025582 MYOD 01 0 4 480 2 12 Conserved TFBSs for A Myog PWM MYOGENIN_Q6 and B MyoD PWM MYOD 01 from target genes promoters in expression data Total score represents a score of conservation from 0 to 1 over the conserved TFBS length Score represents an additive score over the TFBS Promos is the number of promoters with the conserved TFBS Length is the length of the TFBS 2 4 5 Wet lab Confirmation of a CORE TF Predicted Con served TFBS To confirm a CORE TE conserved TFBS in the lab we looked at a MyoD predicted TFBS in the LAMA4 promoter Using Ensembl defined genomic alignments we found the matrix MyoD_Q6_01 conserved in human chimp and dog Figure 2 4 Using a recombinant MyoD protein and the TransFactor kit we found significant p value 1 5E 35 binding to our target TFBS compared to a mutated one Additional File 2 4 2 4 6 CORE TF Compared to Existing Programs oPOSSUM We compared the performance of CORE_TF using a random set with similar GC to oPOSSUM a webtool with similar objectives as ours OPOSSUM looks for over represented JASPAR PWMs in pre defined species alignments but is limited to spe 32 2 4 Results and Discussion cific species alignments e g human mouse and use of the smaller JASPAR PWM database We used the previously mentioned expression microarray datasets for the evaluation of both programs performances Our runs on the oPOSSUM web site showed that our binomial test p
Download Pdf Manuals
Related Search