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1. 50 pu 40 purity 30 purity 10 purity ty ty ty ty rity ty ty 20 purity ty 10 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 True fold change at 100 sample purity Figure 2 5 Low level amplifications and deletions The required fold change cutoff to detect amplifications and deletions of different magnitudes as a function of sample purity 2 2 Outputs of the Copy Number Variant Detection tool The Copy Number Variant Detection tool calls CNVs that are both global outliers on the target level and locally consistent on the region level The tool produces several outputs which are described below 2 2 1 Region level CNV track The algorithm will produce a region level annotation track which contains the CNV regions detected by the algorithm Every annotation in this track joins one more more targets from the input target track to produce contiguous CNVs Each CNV in the region level tracks is characterized in terms of the following properties Minimum CNV length The minimum CNV length is the length of the region level CNV annotation This number should be interpreted as the lowest bound for the size of the CNV The true CNV can extend into the adjacent genomic regions that have not be targeted P value The p value corresponds to the probability that an observation identical to the CNV or even more of an outlier would occur by chance under the null hypothesis T2. User manual for Copy Number Variant Detection Plugin beta Windows Mac OS X and Linux January 8 2015 This software is for research purposes only CLC bio a QIAGEN Company Silkeborgvej 2 Prismet DK 8000 Aarhus C Denmark CC big A QIAGEN company Contents 1 Introduction to the Copy Number Variant Detection Plugin 2 Running the Copy Number Variant Detection Plugin 2 1 Algorithm and parameter description 2 000 2 eee ee es How to set the fold change cutoff when the sample purity is not 100 2 2 Outputs of the Copy Number Variant Detection tool 2 2 1 Region level CNV track 4k 6 aw ee 6 ee ae Ea ewe ae Ee we How to interpret fold changes when the sample purity is not 100 2 2 2 lTargetlevel CNV track n noaoo a a 2 23 Genelevi annotation TaK es saos ewa a ea a ee RN a N AAA RODO 6b es ceca ee ee bet eee eee ERB Eee eee ee ESS Target level log2 ratioS n ww a a CI SSS gc oe BR a a a ee oe ee a E 2 2 9 Algorithm report a 4 oe ee hea ee ee ee hea ao ee ee Ea Normalization and chromosome analysis 1 eee ee et Prediction of target level CNVS 0 0 0 2 eee ee ee ee Prediction of region level CNVS 1 ee ee a 3 Installation 4 Uninstall SI eia Tea UT 10 10 11 12 14 15 15 15 16 16 16 1 7 18 20 22 Chapter 1 Introduction to the Copy Number Variant Detection Plugin The Copy Number Variant Detection Plugin is designed to detect copy
3. Input and reference Parameters Algorithm parameters Result handling Copy Number Variant Detection Result handling Output options Create algorithm report Create targettevel CNV track Result handling Open C Save Log handling _ Open log Previous Next X Cancel Figure 2 3 Specifying whether an algorithm report and a target level CNV track should be created Figures 2 4 shows the required fold change cutoffs in order to detect a particular degree of amplification deletion at different sample purities Figure 2 5 zooms in for low level amplifications and deletions Fold change cutoff Figure 2 4 The required fold change cutoff to detect amplifications and deletions of different 10 0 7 9 575 9 05 8 57 6 57 6 07 5 57 5 07 4 57 4 07 3 575 Sample purity 100 purity 90 purity 80 purity 70 purity 60 purity 50 purity 40 purity 30 purity 20 purity 10 purity L T T T T T T T T T T T T T T T T 1 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 10 0 True fold change at 100 sample purity magnitudes as a function of sample purity CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN M M N Fold change cutoff N o 4 wo 1 1 Sample purity 100 purity 90 purity 80 purity 70 purity 60 purity
4. function of sample purity Observed fold change 1 077 T T T T T T T T T T T T T T T T T i 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 20 95 10 0 3 0 294 25 2 775 l 26 4 257 K Sample purity 245 71 gt 100 purity a 223 4 A o gt Fd 90 purity E22 l A 80 purity co z2 4 70 puri 24 E purity bal a 60 purity 9 F w 50 purity 2197 Pa 2a i 40 purity 91 87 l L l 30 purity 81774 x 5 20 purity 1 67 J 10 purity 1 57 j 1 475 l 1 34 1 275 i a f Yi 1 17 Hig Observed fold change f T T T T T T T T T T T T T T T T T T T 1 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 12 Figure 2 7 Low level amplifications and deletions the true fold change in the 100 pure sample for different observed fold changes as a function of sample purity 2 2 2 Target level CNV track The algorithm will produce a target level CNV track if you ve chosen to create one when running the algorithm The target level CNV track is an annotation track containing one annotation for every target in the input data Inspection of the target level CNV track can give you additional information about both the CNVs called in the region level results and those regions that have not been called Each target is annotated with the following information CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION
5. 313 Niu and Zhang 2012 Niu Y S and Zhang H 2012 The screening and ranking algorithm to detect dna copy number variations Ann Appl Stat 6 3 1306 1326 23
6. PLUGIN 13 Target number Targets are numbered in the order in which they occur in the genome This information is used by the results report see section 2 2 4 Case coverage The normalized coverage of the target in the case sample Baseline coverage The normalized coverage of the target in the baseline Length The length of the target region P value The p value corresponds to the probability that an observation identical to the CNV or even more of an outlier would occur by chance under the null hypothesis The null hypothesis is that of no CNVs in the data The p value in the target level output reflects the global evidence for a CNV at that particular target The target level p values are combined to produce the region level p values in the region level CNV output FDR corrected p value The FDR corrected p values correct for false positives arising from car rying out a very high number of statistical tests The FDR corrected p value will therefore always be larger than the uncorrected p value Fold change raw The fold change of the normalized case coverage compared to the normalized baseline coverage The normalization corrects for the effects of different library sizes between the different samples Negative fold changes indicate deletions and positive fold changes indicate amplifications A fold change of 1 0 represents identical coverages Fold change adjusted As observed by Li et al 2012 Li et al 2012 the fold cha
7. a sequence from list of annotations found in a GFF file Located in the Toolbox CLC Microbial Genome Finishing Module CLC bio support dcbio com Version 1 3 2 Build 140318 1029 Various tools for genome finishing aimed to dose and produce high quality genomes in sequencing projects CLC Workbench Client Plugin Q CLC bio suppor t dcbio com Version 6 0 Build 140207 0940 105889 Client plugin for connecting to a CLC Genomics Server CLC Science Server CLC Drug Discovery Server or Bioinformatics Database The plug in also includes Grid Engine Integration Proxy Settings Check for Updates Install from Fie dose _ Figure 4 1 The plugin manager with plugins installed The installed plugins are shown in this dialog To uninstall Click the Copy Number Variant Detection Plugin Uninstall If you do not wish to completely uninstall the plugin but you don t want it to be used next time you start the Workbench click the Disable button When you close the dialog you will be asked whether you wish to restart the workbench The plugin will not be uninstalled until the workbench is restarted 22 Bibliography Li et al 2012 Li J Lupat R Amarasinghe K C Thompson E R Doyle M A Ryland G L Tothill R W Halgamuge S K Campbell I G and Gorringe K L 2012 Contra copy number analysis for targeted resequencing Bioinformatics 28 10 1307 1
8. ar fold changes 5 The expected fold change variation in region is determined using the statistical model for target level coverages Region level CNVs are identified as the regions with fold changes significantly different from 1 0 CHAPTER 1 INTRODUCTION TO THE COPY NUMBER VARIANT DETECTION PLUGIN 6 If chosen in the parameter steps gene level CNV calls are also produced Chapter 2 Running the Copy Number Variant Detection Plugin 2 1 Algorithm and parameter description To start the Copy Number Variant Detection tool click Toolbox Resequencing Analysis c Copy Number Variant Detection Select the case read mapping and click Next You are now presented with choices regarding the data to use in the CNV prediction method as shown in figure 2 1 Copy Number Variant Detection Input and reference parameters p Choose where to run N Select read mappings Ww Input and reference Parameters Input parameters Target regions track Control mappings D Gene track Read filters v Ignore non specific matches v Ignore broken pairs G Previous ext Finis X Cancel Figure 2 1 The first step of the CNV detection tool Target regions track An annotation track containing the regions targeted in the experiment must be chosen This track must not contain overlapping regions or regions made up of several intervals because the algorithm is designed to operate on simple genomi
9. c regions CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN T Control mappings You must specify one or more read mappings which will be used to create a baseline by the algorithm For the best results the controls should be matched with respect to the most important experimental parameters such as gender and technology If using non matched controls the CNVs reported by the algorithm may be less accurate Gene track Optional If you wish you can provide a gene track which will be used to produce gene level output as well as CNV level output ignore non specific matches If checked the algorithm will ignore any non specifically mapped reads when counting the coverage in the targeted positions Note If you are interested in predicting CNVs in repetitive regions this box should be unchecked Ignore broken pairs If checked the algorithm will ignore any broken paired reads when counting the coverage in the targeted positions Copy Number Variant Detection Algorithm parameters p Choose where to run N Select read mappings Ww Input and reference Parameters gt Algorithm parameters Statistics Threshold for significance 0 05 Minimum fold change absolute value 1 5 Low coverage cutoff 30 Graining level Coarse L 7 Intermediate Enhance single target sensitivity A Previous gt Next Finis X Cancel Figure 2 2 The second step of the CNV detection tool Click Next to set the
10. d and installed If the Copy Number Variant Detection Plugin is not shown on the server and you have it on your computer e g if you have downloaded it from our web site you can install it by clicking the Install from File button at the bottom of the dialog This will open a dialog where you can browse for the plugin The plugin file should be a file of the type cpa When you close the dialog you will be asked whether you wish to restart the CLC Workbench The plugin will not be ready for use until you have restarted tin order to install plugins on Windows the Workbench must be run in administrator mode Right click the program shortcut and choose Run as Administrator Then follow the procedure described below 20 CHAPTER 3 INSTALLATION Manage Plugins and Resources mE support ckbio com Version 1 5 1 Build 131211 2142 102901 Perform alignments with ClustalO ClustalW and MUSCLE ak a udiecaniaeaiacs annotations found in a GFF file Located in the Toolbox Batch Rename CLC bio support ckbio com Version 1 3 1 Build 131211 2144 102901 Rename files in batch by adding a prefix or a number Biobase Genome Vre Annotate CLC bio su Version 2 0 11 Build aes aa 103719 Create tracks with various data from Biobase Genome Trax Biobase Genome Trax Download CLC bio support cicbio com Version 2 0 11 Build 140103 1322 103719 Create tracks with various data from Biobase Genome Trax Plugin requires regi
11. direction of change detected for the region Note however that the change detected for the region may be inconsistent with the fold change for a single target in the region The reason for this is typically statistical noise at the single target Regional effect size The effect size of a target level CNV reflects the magnitude of the observed fold change of the CNV region in which the target was found The effect size of a CNV is classified into the following categories Strong or Weak The effect size is Strong if CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 14 the fold change exceeds the fold change cutoff specified in the parameter steps Otherwise the effect size will be Weak Note however that Weak CNV calls will be filtered from the region level output Comments The comments can include useful information for interpreting the CNV calls Possible comments in the target level output are 1 Low coverage target If the target had a coverage under the specified coverage cutoff it will be classified as low coverage Low coverage targets were not used in calculating the statistical models and will not have p values 2 Disproportionate chromosome coverage If the target occurred on a chromosome that was detected to have disproportionate coverage In this case the target was not used to set up the statistical models 3 Atypical fold change in region If there is a discrepancy between the direction of fold cha
12. efore based on evidence from just one target and may be less accurate than p values for larger regions 2 Disproportionate chromosome coverage If a region is found on a chromosome that was determined to have disproportionate coverage this will be noted in the comments This means that the targets constituting this region were not used to set up the statistical models Furthermore the size and fold change value of this CNV region may explain why the chromosome was detected to have disproportionate coverage 3 Low coverage If all targets inside a region had low coverage then the region will be classified as a low coverage region and will be given a p value of 1 0 You will only see these regions in the results if you set the significance cutoff to 1 0 These properties can be found in separate columns when viewing the tracks in table view Note The region level calls do not guarantee that a single larger CNV will always be called in just one CNV region This is because adjacent region level CNV calls are not joined into a single region if their average fold changes are sufficiently different For example if a 2 fold gain is detected in a region and a 3 fold gain is detected in an immediately adjacent region of equal size then these may appear in the results as two separate CNVs or one single CNV with a 2 5 fold gain depending on your chosen graining level and the fold changes observed in the rest of the data How to interpret fo
13. hance single target sensitivity box This will increase the sensitivity of detection of very small CNVs and has the greatest effect in the case of the coarser graining levels Note however that these small CNV calls are much more likely to be false positives If this box is unchecked only larger CNVs supported by several targets will be reported and the false positive rate will be lower Clicking Next you are presented with options about the results see figure 2 3 In this step you can choose to create an algorithm report by checking the Create algorithm report box Furthermore you can choose to output results for every target in your input by checking the Create target level CNV track box When finished with the settings click Next to start the algorithm How to set the fold change cutoff when the sample purity is not 100 Given a sample purity of X and a desired detection level absolute value of fold change in 100 pure sample of 7 the following formula gives the required fold change cutoff X rya U ana cutoff 2 1 X x 100 For example if the sample purity is 40 and you want to detect 6 fold amplifications or deletions e g 12 copies instead of 2 or 2 copies instead of 12 then the cutoff should be 0 40 toff cutoft oo lt O Tope 3 0 2 2 CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN Choose where to run Select read mappings
14. he null hypothesis is that of no CNVs in the data The p value for a CNV region is calculated by combining the p values of its constituent targets discarding any low coverage targets Fold change adjusted The fold change of the adjusted case coverage compared to the base line Negative fold changes indicate deletions and positive fold changes indicate amplifi cations A fold change of 1 0 or 1 0 represents identical coverages The fold changes are adjusted for statistical differences between targets with different sequencing depths Note if your sample purity is less than 100 you need to take that into account when interpreting the fold change values This is described in more detail in section 2 2 1 CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 11 Consequence The consequence classifies statistically significant CNVs as Gain or Loss Number of targets The total number of targets forming the minimal CNV region Targets A list of the names of the targets forming the minimal CNV region Note however that the list is truncated to 100 characters If you want to see all the targets that constitute the CNV region you can use the target level output Section 2 2 2 Comments The comments can include useful information for interpreting individual CNV calls The possible comments are 1 Small region If a region only consists of 1 target it is classified as a small region The p value of this region is ther
15. ibly other genes P value The p value of the entire CNV region affecting this gene and possibly other genes Number of targets The total number of targets forming the entire CNV region affecting this gene and possibly other genes Comments If the CNV region affecting this gene had any comments as described in section 2 2 1 this will be present in the gene level results as well CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 15 Targets A list of the names of the targets forming the minimal CNV region forming the entire CNV region affecting this gene and possibly other genes Note however that the list is truncated to 100 characters If you want to know the full list of targets inside the CNV region you can use the target level output track 2 2 4 Report The report contains information about the results of the Copy Number Variant Detection tool The sections of this report are described individually below Normalization The Normalization section gives information about the sample level and chromosome level coverages Any chromosomes with disproportionate coverages are noted targets on these chromosomes were ignored when setting up the statistical models Target level log2 ratios The target level coverage log ratios are presented as a graph An example is shown in figure 2 8 On the horizontal axis the targets are placed in the order in which they appear in the genome On the vertical axis the adjusted c
16. ld changes when the sample purity is not 100 If your sample purity is less than 100 it is necessary to take that into account when interpreting the fold change values Given a sample purity of X and an observed fold change of F following formula gives the actual fold change that would be seen if the sample were 100 pure fold ch in 100 C old change in o pure sample X 100 a 2 3 For example if the sample purity is 40 and you have observed a fold change of 3 then the fold change in the 100 pure sample would have been 3 0 1 fold ch in 100 pure sample __ 1 6 0 2 4 old change in 6 pu p 10 100 E 2 4 Figures 2 6 shows the true fold changes for different observed fold changes at different sample purities Figure 2 zooms in for low level amplifications and deletions CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 10 07 T 9 575 9 075 8 57 l 8 07 Ed 754 A Sample purity yY i 100 purity I lt n o 7 0 Z 2 7 90 purity a 6 575 A al l 80 purity ke R eog 70 purity o A G Z 5575 J 60 purity o e J a 50 purity 2 5 0 S 40 purity 3 45 30 purity 2 407 20 purity i 3 55 1 4 10 purity 3 0 7 2 575 Wy WW 2 0 l Vy WA 1 5 Wie f S Figure 2 6 The true fold change in the 100 pure sample for different observed fold changes as a
17. levels produce longer CNV calls and less noise and the algorithm will run faster However smaller CNVs consisting of only a few targets may be missed at a coarser graining level e Coarse prefers CNVs consisting of many targets The algorithm is most sensitive to CNVs spanning over 10 targets This is the recommended setting if you expect large scale deletions or insertions and want a minimal false positive rate e Intermediate prefers CNVs consisting of an intermediate number of targets The algorithm is most sensitive to CNVs spanning 5 or more targets This is the recommended setting if you expect CNVs of intermediate size e Fine prefers CNVs consisting of fewer targets The algorithm is most sensitive to CNVs spanning 3 or more targets This is the recommended setting if you want to detect CNVs that span just a few targets but the false positive rate may be increased Note The CNV sizes listed above are meant as general guidelines and are not to be interpreted as hard rules Finer graining levels will produce larger CNVs when the signals for this are sufficiently clear in the data Similarly the coarser graining levels will also be able to predict shorter CNVs under some circumstances although with a lower sensitivity Enhance single target sensitivity All of the graining levels assume that a CNV spans more than one target If you are also interested in very small CNVs that affect down to a single target in your data check the En
18. lgorithm report in the output handling step of the wizard an algorithm report will also be produced This contains information about the statistical models of the algorithm and can be used to evaluate how well the assumptions of the model were fulfilled We will now present the different sections of this report Normalization and chromosome analysis This section of the report is related to the first step of the Copy Number Variant Detection tool where the chromosome level coverages are analyzed to detect any outliers The total coverages of the case chromosomes are plotted against the total coverages of the baseline and the detected outliers are indicated Chromosome coverages identified as disproportionate are marked with red crosses see figure 2 9 1 1 Chromosome coverage regression model Chromosome coverages 120000000 110000000 100000000 90000000 80000000 70000000 60000000 50000000 Chromosomal coverage case 40000000 30000000 Abnormal x chromosomes 20000000 10000000 _ Normal chromosomes 0 0 50000000 100000000 Chromosomal coverage baseline Figure 2 9 An example graph showing the coverages of the chromosomes in the case versus the baseline In this example three chromosomes are marked as abnormal Two of these chromosomes are significantly amplified and log ratios of coverages of many targets on these chromosome are significantly higher than for targets on other chromosomes The third outlier chromo
19. nd the corresponding BIC score is indicated in the End BIC column A large reduction in the number of local maximizers indicates that it was possible to join many smaller CNV regions into larger ones Note The segmentation process only produces regions of similar adjusted coverage log ratios Each segment is tested afterwards to identify if it represents a CNV Therefore the number of segments shown in this table does not correspond to the number of CNVs actually predicted by the algorithm Chapter 3 Installation The Copy Number Variant Detection Plugin is installed as a plugin Plugins are installed using the plugin manager Help in the Menu Bar Plugins and Resources 59 1 or Plugins 4 in the Toolbar The plugin manager has three tabs at the top e Manage Plugins This is an overview of plugins that are installed e Download Plugins This is an overview of available plugins on CLC bio s server e Manage Resources This is an overview of resources that are installed To install a plugin click the Download Plugins tab This will display an overview of the plugins that are available for download and installation see figure 3 1 Clicking a plugin will display additional information at the right side of the dialog This will also display a button Download and Install Click the Copy Number Variant Detection Plugin and press Download and Install A dialog displaying progress is now shown and the plugin is downloade
20. nge detected for the target and the direction of fold change detected for the region then the fold change of the target is atypical compared to the region This is usually due to statistical noise and the regional fold change is likely to be more accurate in the interpretation especially for large regions 2 2 3 Gene level annotation track If you have specified a gene track in the input parameters you will get a gene level CNV track as well The gene level CNV track is an annotation track which is obtained by intersecting the region level CNV track with the gene track in the input ignoring any genes that do not overlap with the targets Note that a single CNV may be reported several times in different genes and a single gene may also be reported several times if it is affected by more than one CNV In addition to the annotations on the gene track supplied in the input parameters the gene level CNV track contains the following annotation columns Region length The length of the actual annotation That is the length of the CNV region intersected with the gene CNV region The entire CNV region affecting this gene and possibly other genes CNV region length The length of the entire CNV region affecting this gene and possibly other genes Consequence The consequence classifies statistically significant CNVs as Gain or Loss Fold change adjusted The adjusted fold change of the entire CNV region affecting this gene and poss
21. nges raw depend on the coverage Therefore the fold changes have to be adjusted for statistical differences between targets with different sequencing depths before the statistical tests are carried out The results of this adjustment are found in the Fold change adjusted column Note that sometimes this will mean that a change that appears to be an amplification in the raw fold change column may appear to be a deletion in the adjusted fold change column or vice versa This is simply because for a given coverage level the raw fold changes were skewed towards amplifications or deletions and this effect was corrected in the adjustment Note if your sample purity is less than 100 you need to take that into account when interpreting the fold change values This is described in more detail in section 2 2 1 Region joined targets The region to which this target was classified to belong The region may or may not have been predicted to be a CNV Regional fold change The adjusted fold change of the region to which this target belongs This fold change value is computed from all targets constituting the region Regional p value The p value of the region to which this target belongs This is the p value calculated from combining the p values of the individual targets inside the region Regional consequence lf the target is included in a CNV region this column will show Gain or Loss depending on the
22. number variations CVNs from targeted resequencing experiments The current release is a beta version The tool takes read mappings and target regions as input and produces amplification and deletion annotations The annotations are generated by a depth of coverage method where the target level coverages of the case and the controls are compared in a statistical framework The algorithm implemented in Copy Number Variant Detection Plugin is inspired by the following papers e Li et al CONTRA copy number analysis for targeted resequencing Bioinformatics 2012 28 10 1307 1313 Li et al 2012 e Niu and Zhang The screening and ranking algorithm to detect DNA copy number variations Ann Appl Stat 2012 6 3 1306 1326 Niu and Zhang 2012 The Copy Number Variant Detection tool identifies CNVs regions where the normalized coverage is statistically significantly different from the controls The algorithm carries out the analysis in several steps 1 Base level coverages are analyzed for all samples and a robust coverage baseline is generated using the control samples 2 Chromosome level coverage analysis is carried out on the case sample and any chromo somes with unexpectedly high or low coverages are identified 3 Sample coverages are normalized and a global target level statistical model is set up for the variation in fold change as a function of coverage in the baseline 4 Each chromosome is segmented into regions of simil
23. overage log ratio of each target is plotted The black line represents the actually observed mean adjusted log ratio of coverage for each target The cyan and red lines represent the 95 confidence intervals of the expected mean adjusted log ratios of coverages based on the statistical model Chromosome boundaries are indicated as vertical lines 2 1 Coverage log2 ratios by target Adjusted log2 ratios all targets PTUs al TATEA E Ua ahe ICR SP i U I h T Nh ft uA i U al Adjusted log2 ratio Expected 95 Cl minimum Expected 95 Cl maximum Observed Chromosome boundaries 0 50000 100000 150000 Target number Note missing values indicate targets with low coverage Figure 2 8 An example graph showing the mean adjusted log ratios of coverages in the report produced by the Copy Number Variant Detection tool In this example the second and ninth chromosomes are amplified and the log ratios of coverages of targets on these chromosome are significantly higher than for targets on other chromosomes The black line in these regions is outside the boundaries defined by the cyan and red lines CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 16 CNV statistics The last section in the report provides some information about the number of CNVs called in the region level prediction results The number of uncalled or filtered regions are also shown 2 2 5 Algorithm report If you have chosen to produce an a
24. parameters related to the target level and region level CNV detection as shown in as shown in figure 2 2 Threshold for significance P values lower than the threshold for significance will be considered significant The higher you set this value the more CNVs will be predicted Minimum fold change absolute value You must specify the minimum fold change for a CNV call If the absolute value of the fold change of a CNV is less than the value specified in this parameter then the CNV will be filtered from the results even if it is otherwise statistically significant For example if a minimum fold change of 1 5 is chosen then the adjusted coverage of the CNV in the case sample must be either 1 5 times higher or 1 5 times lower than the coverage in the baseline for it to pass the filtering step If you do not want to filter on the fold change enter 0 O in this field Note If your sample purity is less than 100 it is necessary to take that into account when you adjust the fold change cutoff This is described in more detail in section 2 1 CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 8 Low coverage cutoff If the average coverage of a target is below this value it will be considered low coverage and it will not be used to set up the statistical models and p values will not be calculated for it in the target level CNV prediction Graining level The graining level is used for the region level CNV prediction Coarser graining
25. respondence between the expected distribution and the observations Prediction of region level CNVs The final section of the algorithm report is related to the region level CNV prediction In this part of the algorithm the chromosomes are segmented into regions of similar adjusted mean log ratios More segments lead to a reduced variance per segment in the extreme where every target forms its own segment the variance is zero However more segments also mean that the model contains more free parameters and is therefore potentially over fitted A value known as the Bayesian Information Criterion BIC gives an indication of the balance of these two effects CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 19 for any potential segmentation of a chromosome The segmentation process aims to minimize the BIC producing the best balance of accuracy and overfitting in the final segments The segmentation begins by identifying a set of potential breakpoints known as local maximizers The number of potential breakpoints at the start of the segmentation is shown in the local maximizers at start column and the corresponding BIC score is indicated in the Start BIC column Breakpoints are removed strategically one by one and the BIC score is calculated after each removal When enough breakpoints have been removed for the BIC score to reach its minimum the final number of breakpoints is shown in the local maximizers at end column a
26. s a function of log2 coverage Adjusted log2 ratio of target 4 5 6 T 8 9 10 Log coverage of target Figure 2 10 An example graph showing the mean adjusted log ratios of coverages plotted against the log coverages of targets in the algorithm report of the Copy Number Variation Detection plugin Here the adjusted mean log ratios are centered around 0 0 for most coverages and the variation decreases with increasing log coverage This indicates a good fit of the model However at very high coverages the adjusted log ratios are centered higher than 0 0 which indicates that for these coverages the model is not a perfect fit But only very few targets are affected by this as the points are very sparse at these high coverage levels Statistical model for adjusted log2 ratios In this section of the algorithm report you can see how well the algorithm was able to model the statistical variation in the log ratios of coverages An example is shown in figure 2 11 A good fit of the model to the data points indicates that the variance has been modelled accurately To make the points more visible double click the figure to open it in a separate editor Here you can select how to visualize the data points and the fitted model For example you can choose to highlight the data points in the sidepanel MA Plot Settings Dot properties Dot type Dot Distribution of adjusted log2 ratios in bins One of the assumptions of the statistical model
27. some had zero coverage in both the case and the baseline The graph is followed by a table where the detailed chromosome coverages are shown after normalization Chromosomes with disproportionate coverage and chromosomes without any targets are marked in the Comment column These chromosomes are the ones marked with red crosses in the graph in section 1 1 of the algorithm report and these chromosomes were not used in the coverage normalization step CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 17 Prediction of target level CNVs This section of the algorithm report gives information about the statistical models used to predict target level CNVs Adjustment of log2 ratios The first two graphs in this section are related to the adjustment of the log ratios of coverages as a function of log coverage The log ratio of coverages for targets depends on the level of coverage of the target as observed by Li et al Bioinformatics 2012 who also proposed that a linear correction should be applied Li et al 2012 In the first of the two graphs the non adjusted log ratios of target coverages are plotted against the log coverage of the targets In the second graph the mean log ratios are plotted after adjustment figure 2 10 If the model fits the data we expect to see that the adjusted mean log ratios are centered around O for all log coverages and the variation decreases with increasing log coverage Adjusted log2 ratio a
28. stration Blast2GO PRO Q BioBam Bioinformatics pluginsupport blast2go com GD additional Alignments This module allows for use of two other alignment methods which are otherwise not distributed with the CLC Workbench When the plug in is installed you will see the new alignment methods in the Toolbox under Alignments and Trees gt Additional Alignments When you run the alignments there are a number of parameters that can be set You can also specify command line instructions S a EE Create Alignment Join Alignments BB Create Pairwise Comparison at Create Tree The mern alignments in the toolbox Allignment methods Three different alignment methods are included in this extension ClustalW ClustalO and Muscle For more detailed information on each of Figure 3 1 The plugins that are available for download 21 Chapter 4 Uninstall Plugins are uninstalled using the plugin manager Help in the Menu Bar Plugins and Resources 2 or Plugins 4 in the Toolbar This will open the dialog shown in figure 4 1 L g Download Plugins Manage Resources E Manage Plugins and Resources TH CLC bio suppor t dcbio com Version 1 5 1 Guild 131211 2142 102901 Perform alignments with ClustalO ClustalW and MUSCLE mg Daal Annotate with GFF file Q CLC bio suppor t dcbio com Version 2 2 6 Build 131211 2143 102901 Using this plug in it is possible to annotate
29. used by the CNV detection tool is that the coverage log ratios of targets are normally distributed with a CHAPTER 2 RUNNING THE COPY NUMBER VARIANT DETECTION PLUGIN 18 Log2 coverage vs standard deviation o o o o o o L E L on oa a Ln Oo on Qo Ln Qo on io oO Standard deviation of adjusted log2 ratio x Model z SRR719299_1 paired Reads nN mn ho So 2 3 4 5 6 7 Log2 coverage Figure 2 11 An example graph showing how the variance in the target level mean log ratios was modelled in the algorithm report of the Copy Number Variation Detection plugin Here the data points are very close to the fitted model indicating a good fit of the model to the data mean of zero and the variance only depends on the log coverage of each target in the baseline The bar charts in this section of the algorithm report show how well this assumption of the model fits the data An example is shown in figure 2 12 A good fit of the model to the data points indicates that the variance has been modelled accurately Adjusted log2 ratios in bin 1 120 110 100 90 80 70 60 Density 50 40 30 20 Expected 10 Gaussian model Observed L E Z LC 29 v 7o v Adjusted log2 ratio Figure 2 12 An example bar chart from the algorithm report of the Copy Number Variation Detection plugin showing how well the normal distribution assumption was fulfilled by the adjusted coverage log ratios Here there is a good cor
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