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1. Tuschl T Sander C and Marks D 2003 MicroRNA targets in Drosophila Genome Biology 5 R1 De Hoon M J L Imoto S and Miyano S The C Clustering Library The University of Tokyo Institute of Medical Science Human Genome Center 30 May 2009 Snedecor G W and Cochran W G Statistical methods Ames Iowa Iowa State University Press 1989 De Hoon M J L Imoto S and Miyano S Statistical analysis of a small set of time ordered gene expression data using linear splines Bioinformatics 18 11 1477 1485 2000 19 miR AT User s Manual v1 6 8 Acknowlegements amp Permissions Special thanks to Hasan Jamil of the Integration Informatics Laboratory in the Department of Computer Science Wayne State University and Anupam Bhattacharjee for technical support in web hosting Thanks to Brad Sherman for assistance with DAVID integration Thanks to Drs Anton Enright and Stijn van Dongen for help regarding the use of the MicroCosm Targets database miR AT uses a variety of databases software tools and libraries Our thanks to their maintainers and developers MicroCosm Targets A web resource developed by the Enright Lab at the EMBL EBI European Molecular Biology Laboratory European Bioinformatics Institute containing computationally predicted targets for microRNAs across many species Our main microRNA database source www ebi ac uk enright srv microcosm htdocs targets v5 National Center for Biote2. This will calculate the distribution of individual miRNA scores across all results as well as the distribution of cumulative scores for genes The browser will automatically reposition viewing to the bottom of the page 1 e below the results once the distributions have been calculated and displayed Figure 8 Individual Target score distribution Cumutative score distribution 0 6 DAT 0 5 0 4 0 3 0 2 0 1 100 120 140 160 180 200 220 10 0 300 500 700 900 1100 130 0 0 0 2 0 0 0 20 0 binwidth binwidth Figure 8 miRNA target score distribution histograms 3 4 Functional Annotation using DAVID To automatically feed the entire results list of genes for the chosen organism to DAVID click on the FUNCTIONAL ANNOTATION button at the top of the Results page Figure 9 FUNCTIONAL ANNOTATION Figure 9 Display functional annotations for the resulting list of genes using DAVID For more information about using and interpreting DAVID Functional Annotations visit http david abcc ncifcrf gov miR AT User s Manual v1 6 3 5 Gene Expression Upload miR AT search results can be optionally matched with gene expression data Click on the EXPRESSION button to expand the expression upload option Figure 10 wv EXPRESSION CworkiWwSUimiR AN i Upload DATA Figure 10 Expand EXPRESSION upload option and select a file to upload Use the BROWSE button to select a gene expression text file to upload The first column of
3. Documents and Settings user name java policy Linux user home java policy If you do not find any user specific policy file then create a new one using File New from the policytool toolbar menu Click on the Add Policy Entry button to create a new policy entry for miR AT 22 miR AT User s Manual v1 6 Policy Tool DER File KeyStore Policy File Edit Policy Entry Remove Policy Entry Figure 19 Java JDK Policy Tool application 4 Add permissions to the policy entry for miR AT Enter the CodeBase i e website of miR AT by typing the following exactly including final hyphen http cptweb cpt wayne edu miR AT Java TreeView Applet permission to Read Files Click on the Add Permission button see Figure 20 to open the Permissions dialog see Figure 21 From the Permission drop down select FilePermission From the Target Name drop down select lt lt ALL FILES gt gt From the Actions drop down select read Click on the OK button to save the permission Java TreeView Applet permission to Write Files Click on the Add Permission button to open the Permissions dialog From the Permission drop down select FilePermission From the Target Name drop down select lt lt ALL FILES gt gt From the Actions drop down select write Click on the OK button to save the permission Java TreeView Applet permission to Delete Files Click on the Add Permission button to open the Permissions di
4. Version 1 6 it is no longer necessary to log in using a username password all sessions are anonymous The miR AT application Search screen is displayed Figure 1 To initiate a search enter a list of miRNA identifiers into the Enter a list of miRNA textbox This list can be either one ID per line a comma separated list or a tab separated list Use the List separator buttons to match the list format the default is one miRNA ID per line To see an example of a valid miRNA identifier simply click on the example above the textbox BB miIRAT miRNA combinatorial Analysis of Targets Introduction miR AT is a computational tool for the identification of all transcripts that are targets of a list of input miRNAs Targets are identified using the Sanger MicroCosm Targets database and the output provides a list of all computationally predicted targets the number of sites in each transcript and the cumulative score Filters can be applied to restrict target site selection Links for each transcript in the output provide additional information from the NCBI and Ensembl databases as well as miR target site locations miR AT also enables the automated submission of output target lists to DAVID for functional annotation of ontologies and pathways Enter a list of miRNA Example WG hsa let 7e List separator NewLine Comma Tab Select organism MicroCosm Targets Version 5 Minimum number of miRNA sites per transcript Minimum
5. data vectors Weights cannot be suitably applied to the data if the Spearman rank correlation is used especially since the weights are not necessarily integers The calculation of the Spearman rank correlation in the C Clustering Library therefore does not take any weights into consideration As in the case of the Pearson correlation a distance measure can be defined corresponding to the Spearman rank correlation as d El where rs is the Spearman rank correlation 4 1 6 Kendall s tau Kendall s t is another example of a non parametric similarity measure It is similar to the Spearman rank correlation but instead of the ranks themselves only the relative ranks are used to calculate t 10 As in the case of the 11 miR AT User s Manual v1 6 Spearman rank correlation the weights are ignored in the calculation We can define a distance measure corresponding to Kendall s t as As Kendall s t is defined such that it will lie between 1 and 1 the corresponding distance will be between 0 and 2 4 1 7 Euclidean Distance The Euclidean distance is a true metric as it satisfies the triangle inequality The C Clustering Library package defines the Euclidean distance as n gat x y n i l Only those terms are included in the summation for which both x and y are present The denominator n is chosen accordingly In this formula the expression data x and y are subtracted directly from each other Therefore make sure that
6. database reveals a near normal distribution with a mean score of 16 398 and a standard deviation of 0 883 Based on the score distribution the default minimum score of 15 00 eliminates the lowest 5 of scores from the MicroCosm database Maximum individual site p value Specifies the maximum MicroCosm miRNA binding site score p value Effectively with a default value of 1 00 miR AT does not limit binding sites by p value To more tightly constrain your search choose an appropriate p value Once the search parameters have been set initiate the search by clicking on the Search button A new search can be initiated at any time by clicking Search on the navigation bar at the top of the page to return to this page miR AT User s Manual v1 6 3 Results Depending on the number of input miRNAs and search parameters a search may take anywhere from a fraction of a second to a couple minutes Once the the search is complete the Results page is displayed Figure 2 By default results are sorted by number of sites and cumulative site score Results can be sorted by any column e g Gene Name Ensembl ID simply by clicking on the column header B miRAT miRNA Combinatorial Analysis of Targets Help Contact Other Integra Tools 6 results found 0 4375 gt CLUSTERING gt EXPRESSION FUNCTIONAL ANNOTATION DISTRIBUTION RESULTS txt NCBI GENE INPUT CUMULATIVE ENSEMBL ID TRANSCRIPT NAME DESCRIPTION miRNAs SCORE Putative guan
7. number of input miRNAs per transcript Minimum individual site score Maximum individual site p value Search Clear Figure 1 miR AT Search screen miR AT User s Manual v1 6 miR AT currently supports three organisms from the MicroCosm Targets database human rat and mouse Select the desired organism for your search using the Select organism radio buttons Searches can be constrained in four different ways Minimum number of miRNA sites per transcript Specifies the minimum number of miRNA binding sites on a given transcript Note that one miRNA may bind at more than one site Each unique site is counted For example setting the minimum to three will match a transcript with three different binding sites for a single miRNA as well as a transcript with single binding sites for three different miRNA To specify a minimum number of miRNAs use the next parameter Minimum number of input miRNAs per transcript Specifies the minimum number of unique miRNAs from the input list that must bind to the transcript Note that one miRNA may bind at more than one site For example a transcript may have five binding sites but only two miRNA where one miRNA binds at two sites and the other miRNA binds at three To specify a minimum number of sites use the previous parameter Minimum individual site score Specifies the minimum MicroCosm score for miRNA binding sites Analysis of nearly 900 000 target scores in the MicroCosm
8. the expression data is properly normalized when using the Euclidean distance for example by converting the measured gene expression levels to log ratios Unlike the correlation based distance functions the Euclidean distance takes the magnitude of the expression data into account It therefore preserves more information about the data and may be preferable See 11 for an example of the use of the Euclidean distance for k means clustering 4 1 8 City Block Distance The city block distance alternatively known as the Manhattan distance is related to the Euclidean distance Whereas the Euclidean distance corresponds to the length of the shortest path between two points the city block distance is the sum of distances along each dimension As gene expression data tend to have missing values the city block distance is defined as the sum of distances divided by the number of dimensions n 1 d 2 i l Xi yi This is equal to the distance one would have to walk between two points in a city where you have to walk along city blocks The city block distance is a metric as it satisfies the triangle inequality As for the Euclidean distance the expression data are subtracted directly from each other and care should be taken that they are properly normalized 12 miR AT User s Manual v1 6 4 2 Viewing Clustering Results with Java TreeView Once clustering calculations are complete and results are available 1 e the Run CLUST
9. the file must contain a list of identifiers miR AT currently accepts either Ensembl Transcript identifiers or Entrez Gene identifiers These two types may be mixed in the same file An optional second column contains the associated expression level or may be blank All columns beyond the first two are simply ignored Columns may be separated either by comma or tab miR AT will ignore all spaces in the file Identifiers are case insensitive In the event that two identifiers refer to the same output result the expression level of the last identifier takes precedence Once uploaded the Search results are updated Figure 11 to show any genes with matching expression data The first column EXPRESSED indicates a match with a checkmark If expression level data is provided it is displayed in the second column EXPRESSION LEVEL Both columns are sortable Note that expression data must be reloaded after each new search NCBI ID ENSGOO000133136 ENSTO0000372054 GNGS5P2 Putative guanine nucleotide binding protein G I G S G O gamma 5 like subunit Source Uniprot SWISSPROT Acc Q9Y3K8 Phosphatidylinositol 4 5 bisphosphate v 2 0124 ENSGOO000185133 ENSTOO000331075 27124 PIBSPA 5 phosphatase A EC 3 1 3 56 d 1 Source Uniprot SWISSPROT Acc Q15735 Guanine nucleotide binding protein G I G S G O subunit gamma 5 precursor Source Uniprot SWISSPROT Acc P63218 v 1 1003 ENSGOO000174021 ENSTOO000370641 2787 GNG5 Fi
10. transcript identifier hyperlink 3 2 Binding Sites Detail miRNA binding site details for each match can be viewed by clicking on the blue Number of Sites link Figure 5 NUMBER NCBI GENE INPUT CUMULATIVE ENSEMBL ID TRANSCRIPT NAME DESCRIPTION aes miRNAs SCORE Putative guanine nucleotide binding protein G I G S G O gamma 5 like subunit Source Uniprot SWISSPROT Acc Q9Y3K8 N ENSG00000133136 ENSTOO000372054 GNG5P2 3 1 53 2131 Figure 5 Search results binding site details hyperlink A detailed list of miRNAs their exact binding site on the transcript orientation p value and binding site score is displayed Figure 6 In addition a complete list of all miRNAs that target this gene are given as an aid to the researcher Input miRNAs that target the gene chrx 10947 6407 10947 6428 0 0000964222 18 6473 hsa let 7e 3 chrX 109476431 109476453 0 0000964222 17 0555 chrX 109476454 109476475 E 0 0000964222 17 5103 All miRNAs that target the gene 1 hsa miR 10a 2 hsa miR 144 hsa miR 192 4 hsa miR 184 Figure 6 Input miRNAs and other miRNAs that target the gene transcript To return to the main Results page use the browser back button miR AT User s Manual v1 6 3 3 Score Distributions To display distribution statistics across all Results click on the DISTRIBUTION button Figure 7 DISTRIBUTION Figure 7 Display target score distribution histograms
11. 901 Beaubien Detroit MI 48201 2119 USA domski at wayne edu miR AT User s Manual v1 6 1 Whatis miR AT microRNAs are short endogenous oligonucleotides that have been implicated in a wide variety of diseases The mature form of microRNAs miRNAs are approximately 22 nucleotides in length and anneal to complementary sites in the 3 untranslated region UTR of target transcripts as part of an RNA induced silencing complex RISC complex While microRNAs appear to act through multiple mechanisms two general modes of action have been identified transcript degradation and inhibition of protein translation 1 3 The former is associated with near perfect base complementarity between an miRNA and its target sequence while non perfect miRNA target matches result in inhibition of protein translation and is the dominant mode of miRNA activity reported in eukaryotes However a number of recent reports have shown that non perfect complementarity can also result in mRNA degradation through poly A deadenylation 4 5 Therefore the effect of microRNA activity may be reflected by changes of either mRNA or protein levels depending on the microRNA and target transcript involved There are currently over 700 known miRNAs in the human genome and each miRNA may regulate dozens to hundreds of target transcripts Many reports have shown numerous miRNAs aberrantly expressed in a variety of diseases including cancer and indicate that hundreds and
12. ERING button has been clicked and no errors have been detected the View RESULTS button becomes visible Figure 15 CLUSTERING Spearman Rank Correlation Y Figure 15 View RESULTS button Click on the View RESULTS button to launch the Java TreeView applet with the current clustering results Java TreeView divides the clustering results into three main panels see Figure 16 1 a Heat Diagram of microRNA versus transcripts a ahierarchical clustering dendrogram of microRNAs is displayed to the top b ahierarchical clustering dendrogram of genes is displayed to the left 2 amagnification of a selected region of the Heat Diagram 3 alistof gene names from the selected region A region of interest magnification is selected in panel 1 by dragging the mouse over the heat diagram to created a bounded rectangle 13 miR AT User s Manual v1 6 File Settings Analysis Export Window Help Dendrogram Usage Hints Click and drag to scroll Figure 16 Clustering results displayed in Java TreeView application 14 miR AT User s Manual v1 6 4 3 Saving Clustering Results with Java TreeView IMPORTANT By default miR AT cannot save a security feature of the Java lan clustering results to a local disk drive This is guage to keep applets running in your browser from accessing your private information or maliciously writing files to yo
13. S Templates A free website template source on which many of our CSS templates have been based www FreeCSS Templates org 21 miR AT User s Manual v1 6 9 Appendix A Configuring Java to Allow Java Treeview Export amp Save For security reasons by default Java prevents applets from reading or writing to the local hard drive Java can be configured to grant access to specific trusted applications The following procedure must be done by a user with Administrator privileges on the target computer 1 Download and install the Java JDK The latest release of the Java Platform Standard Edition SE Java Development Kit JDK can be found at http java sun com javase downloads index jsp Click on Download JDK and follow the installation instructions you must choose the download appropriate for your platform computer 2 Start Java policytool Navigate to the JDK installation folder Common locations are Windows C Program Files Java jdk1 6 0_20 Linux usr java jdk1 6 0 20 The exact version number of the JDK may vary Navigate to the bin folder From this folder run policytool e g by double clicking on policytool exe in Windows 3 Open the user specific policy file or Create a new policy file Open your user specific policy file java policy using File Open from the policytoo1 toolbar menu see Figure 19 This file should be located as follows Vista C Users username java policy Windows C
14. alog From the Permission drop down select FilePermission From the Target Name drop down select lt lt ALL FILES gt gt From the Actions drop down select delete Click on the OK button to save the permission 23 miR AT User s Manual v1 6 Java TreeView Applet permission to Read Property Values Click on the Add Permission button to open the Permissions dialog From the Permission drop down select PropertyPermission Next to Target Name drop down type user home From the Actions drop down select read Click on the OK button to save the permission Click the Done button to complete the Policy Entry Policy Entry CodeBase hitp lintegra cs wayne edu 8080 miR AT SignedBy Add Principal Edit Principal Remove Principal Principals Add Permission Edit Permission Remove Permission java io FilePermission ALL FILES read java io FilePermission ALL FILES write java io FilePermission ALL FILES delete java util PropertyPermission user home read Done Cancel Figure 20 Policy Entry for miR AT B Permissions Add New Permission Permission v Target Name A Actions b Signed By ox Cancel Figure 21 Add Permissions dialog 24 miR AT User s Manual v1 6 Save the policy file Save the policy file using File Save from the policytool toolbar menu see Figure 19 If this is a new policy file the
15. as written at the Laboratory of DNA Information Analysis Human Genome Center Institute of Medical Science University of Tokyo 4 6 1 Shirokanedai Minato ku Tokyo 108 8639 Japan Contact mdehoon AT gsc riken jp Permission to use copy modify and distribute this software and its documentation with or without modifications and for any purpose and without fee is hereby granted provided that any copyright notices appear in all copies and that both those copyright notices and this permission notice appear in supporting documentation and that the names of the contributors or copyright holders not be used in advertising or publicity pertaining to distribution of the software without specific prior permission THE CONTRIBUTORS AND COPYRIGHT HOLDERS OF THIS SOFTWARE DIS CLAIM ALLWARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY SPECIAL INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHAT SOEVER RESULTING FROM LOSS OF USE DATA OR PROFITS WHETHER IN AN ACTION OF CONTRACT NEGLIGENCE OR OTHER TORTIOUS ACTION ARIS ING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE Python The programming language Used as the CGI interface to the Cluster 3 0 library www python org sorttable DHTML Javascript and DOM to make a TABLE sortable www kryogenix org code browser sorttable Free CS
16. chnology Information The national resource for molecular biology information We use Entrez Gene for detailed information about individual genes ncbi nlm nih gov Ensembl The Ensembl project produces genome databases for vertebrates and other eukaryotic species and makes this information freely available online We use Ensembl for detailed information about individual genes and their transcripts www ensembl org DAVID Bioinformatics Resources A comprehensive set of functional annotation tools for investigators to understand biological meaning behind large lists of genes For analyzing gene list results from miR AT david abcc ncifcrf gov Integration Informatics Laboratory Our hosting service The Integration Informatics Laboratory research project laboratory at Wayne State University s Computer Science Department integra cs wayne edu Java Java Servlets JavaServer Pages The principle programming platform of miR AT Java sun com Apache Tomcat Our Java server environment tomcat apache org 20 miR AT User s Manual v1 6 Java TreeView An open source extensible viewer for microarray data in the PCL or CDT format jtreeview sourceforge net Cluster 3 0 The open source clustering software library we use with Java TreeView bonsai ims u tokyo ac jp mdehoon software cluster 30 May 2009 The C Clustering Library for cDNA microarray data Copyright 2002 2005 Michiel Jan Laurens de Hoon This library w
17. cient is defined as 1 N XX YY nial o e in which are the sample mean of x and y respectively and ox oy are the sample standard deviation of x and y The Pearson correlation coefficient is a measure for how well a straight line can be fitted to a scatterplot of x and y If all the points in the scatterplot lie on a straight line the Pearson correlation coefficient is either 1 or 1 depending on whether the slope of line is positive or negative If the Pearson correlation coefficient is equal to zero there is no correlation between x and y The Pearson distance is then defined as As the Pearson correlation coefficient lies between 1 and 1 the Pearson distance lies between 0 and 2 Note that the Pearson correlation automatically centers the data by subtracting the mean and normalizes them by dividing by the standard deviation While such normalization may be useful in some situations e g when clustering gene expression levels directly instead of gene expression ratios information is being lost in this step In particular the magnitude of changes in gene expression is being ignored This is in fact the reason that the Pearson distance does not satisfy the triangle inequality 4 1 2 Pearson Absolute Correlation Centered By taking the absolute value of the Pearson correlation we find a number between zero and one If the absolute value is one all the points in the scatter plot lie on a straight line with either a positi
18. empty results list e fixed unique output file naming per user e support for all major browsers Firefox IE Chrome Safari e various interface and navigation improvements e separate links and credits page e separate history page Version 1 1 0 25 Jun 2008 e initial web release 18 miR AT User s Manual v1 6 7 References 10 11 Jackson R J and Standart N 2007 How Do MicroRNAs Regulate Gene Expression Sci STKE 2007 rel Wu L and Belasco J G 2008 Let Me Count the Ways Mechanisms of Gene Regulation by miRNAs and siRNAs Molecular Cell 29 1 7 Bartel D P 2004 MicroRNAs Genomics Biogenesis Mechanism and Function Cell 116 281 297 Wu L Fan J and Belasco J G 2006 MicroRNAs direct rapid deadenylation of mRNA Proceedings of the National Academy of Sciences of the United States of America 103 4034 4039 Giraldez A J Mishima Y Rihel J Grocock R J Van Dongen S Inoue K Enright A J and Schier A F 2006 Zebrafish MiR 430 Promotes Deadenylation and Clearance of Maternal mRNAs Science 312 75 79 Doench J G and Sharp P A 2004 Specificity of microRNA target selection in translational repression Genes amp Development 18 504 511 Krek A Grun D Poy M N Wolf R Rosenberg L Epstein E J MacMenamin P da Piedade I Gunsalus K C Stoffel M et al 2005 Combinatorial microRNA target predictions 37 495 500 Enright A John B Gaul U
19. es not appear Clicking the DISTRIBUTION button doesn t appear to do anything Especially the first time it may take a minute or more to load the Java applet Be patient If the distribution histograms do not appear at the bottom of the page try clicking DISTRIBUTION again If the problem persists try restarting your browser or possibly trying a different browser Unable to export save clustering images or results Clicking on Export or Save from Java Treeview does nothing See Appendix A 17 miR AT User s Manual v1 6 6 Version History Version 1 6 0 04 Aug 2011 e removed requirement for login Version 1 5 3 19 Jul 2010 e added message to results for missing microRNAs Version 1 5 2 11 Apr 2010 e fixed page timeout recovery Version 1 5 1 17 Feb 2010 e added miR AT publication and citation info Version 1 5 0 18 Nov 2010 e new gene expression upload option e updated documentation Version 1 4 0 10 Nov 2010 e moved application to new server e updated documentation Version 1 3 0 20 Jan 2010 e fixed download of results in TXT format e restored sortable columns to output page e added busy icon while waiting for clustering e changed default Minimum number of miRNA sites per transcript to 2 e changed default Minimum individual site score to 15 0 e updated documentation Version 1 2 0 21 Sep 2009 e integration of clustering library and heat map display applet e new export of results in CSV format e error checking for
20. gure 11 Search results updated with gene expression data miR AT User s Manual v1 6 3 6 Results Download To download results as either a plain text file tab separated or as a CSV file comma separated for use in a spreadsheet click on the appropriate RESULTS button at the top of the Results page Figure 12 RESULTS txt RESULTS csv Figure 12 Download the results as either plain text or a CSV file Depending on your computer operating system a dialog may open to allow you to either open or save the file Figure 13 Some browsers download the file directly e g Chrome without a dialog Opening dbcraig 00004EE3 csv You have chosen to open E dbcraig 00004EE3 csv which is a Microsoft Office Excel Comma Separated Values File from http integra cs wayne edu 8080 What should Firefox do with this File Microsoft Office Excel default Save File Figure 13 Open Save results dialog miR AT User s Manual v1 6 4 Clustering miR AT employs standard hierarchical clustering techniques in a novel way Whereas typical clustering would have gene transcripts on the vertical axis and samples e g expression arrays on the horizontal with clustering results representing gene expression profiles miR AT clusters transcripts with respect to the input set of microRNAs on the horizontal with clustering results predicting transcripts with similar miRNA target sites profiles Cluste
21. ine nucleotide birding protein G I G S G O garnma 5 like subunit Source Unipro SWISSPROT Acc Q9Y3K8 ENSGOO000133136 ENSTO0000372054 GNGBP2 3 1 53 2131 Phosphatidylincsitol 4 5 bisphosphate ENSGOO000185133 ENSTOOO00331075 27124 PIB5PA 5 phosphatase 4 EC 3 1 3 56 a ii 52 3035 Source Uniproz S WISSPROT Acc Q15735 Figure 2 miR AT Search results 3 1 Gene and Transcript Identifiers To view gene details for a particular match click on the blue Ensembl ID Figure 3 This opens a new browser window with search results from the Ensembl database NUMBER NCBI GENE INPUT CUMULATIVE ENSEMBL ID TRANSCRIPT NAME DESCRIPTION ern miRNAs SCORE Putative guanine nucleotide binding protein G I G S G O zamma 5 like subunit Source Uniprot SWISSPROT Acc Q9Y3K8 piros 36 ENSTOO000372054 GNG5P2 3 1 53 2131 Figure 3 Search results gene identifier hyperlink miR AT User s Manual v1 6 To view gene transcript details for a particular match click on the blue Ensembl Transcript ID Figure 4 This opens a new browser window with search results from the Ensembl database NUMBER NCBI GENE INPUT CUMULATIVE ENSEMBL ID TRANSCRIPT NAME DESCRIPTION E miRNAs SCORE Putative guanine nucleotide binding protein G I G S G O garmma 5 like subunit Source Uniprot SWISSPROT Acc Q9Y3K8 ENSGOOO0001 33136 prea serie GNGS5P2 3 1 53 2131 Figure 4 Search results
22. ined as dy l ry where ry is the uncentered correlation As the uncentered correlation coefficient lies between 1 and 1 the corresponding distance lies between 0 and 2 The uncentered correlation is equal to the cosine of the angle of the two data vectors in n dimensional space and is often referred to as such From this viewpoint it would make more sense to define the distance as the arc cosine of the uncentered correlation coefficient 4 1 4 Pearson Absolute Correlation Uncentered As for the regular Pearson correlation we can define a distance measure using the absolute value of the uncentered correlation day 1 r where ry is the uncentered correlation coefficient As the absolute value of the uncentered correlation coefficient lies between 0 and 1 the corresponding distance lies between 0 and 1 as well Geometrically the absolute value of the uncentered correlation is equal to the cosine between the supporting lines of the two data vectors i e the angle without taking the direction of the vectors into consideration 4 1 5 Spearman Rank Correlation The Spearman rank correlation is an example of a non parametric similarity measure It is useful because it is more robust against outliers than the Pearson correlation To calculate the Spearman rank correlation replace each data value by its rank from ordering each vector by its value The Pearson correlation is then calculated between the two rank vectors instead of the
23. lli User s Manual Version 1 6 Douglas B Craig Kazi Zakia Sultana Alan A Dombkowski Wayne State University School of Medicine Division of Clinical Pharmacology amp Toxicology miR AT User s Manual v1 6 Table of Contents WHAE MIRAN neem dea pe cer er ota er rere amc tn Eo rta rere eer rere eee errr 1 Z SHAR CHING visesssscisesinssissesisesinesinesinesinesiinesinesiioesivesiinesiinesinesinesitnesinesisonstnesitnesitonsteesteesitnasitensseastensteastens bees teesmesssdiess 2 D RESULT a E O I O 4 3 1 Gene and Transcript Identifiers 3 2 Binding Sites Detail 3 3 Score Distributions 3 4 Functional Annotation using DAVID 3 5 Gene Expression Upload 3 6 Results Download 4 1 Similarity Metrics 4 2 Viewing Clustering Results with Java TreeView 4 3 Saving Clustering Results with Java TreeView 8 ACKNOWLEGEMENTS amp PERMISSIONS sesssressrsesssescorsrecsesestorcreesterseorsroesecestorcrcesrsestorsrorsesesrorcrecsrerrsere 29 9 APPENDIX Aissssissesisssincsiassivesinssincsinnsnvesbansincsivassnsedbnssivnesinesbeedbneseees aes beedbnesenesbansdsvednadbeed nadbved nad rneddasssesecesesess 26 miR AT User s Manual v1 6 4 August 2011 2011 Wayne State University Contact Alan Dombkowski Ph D Director Functional Genomics and Bioinformatics Facility Division of Clinical Pharmacology and Toxicology Wayne State University Room 3N47 Children s Hospital of Michigan 3
24. n save the file as follows Vista C Users username java policy Windows C Documents and Settings user name java policy Linux user home java policy If you receive an error e g Access is denied check the file access security settings for any existing java policy file The user you are logged in as should have modify write permission in order to save changes IMPORTANT Close and restart any open browser for the new Java security policy to take effect rok ok ok 25
25. ojloajo GENES503X ENSTOOOO GID TRANSCRIPT NAME GWEIGHT Export To cidatatresults cdlt Save Cancel Figure 18 Gene Text Export dialog in Java TreeView Field s to print Expression Data Header Line For additional information about using Java TreeView consult the complete documentation at http jtreeview sourceforge net 16 miR AT User s Manual v1 6 5 Troubleshooting HTTP Status 500 If your session has timed out you may see an HTTP Status 500 type Exception report error page Simply go to the miR AT home page http mir at org Clustering Errors Clustering will give an popup error if clustering is run with only one miRNA Clustering only makes sense if there is more than one thing to cluster Clustering takes too long Occasionally the applet will hang This happens most often the first time clustering is used in a session In most cases clicking the Run CLUSTERING button again will correct the problem If the problem persists try restarting your browser or possibly trying a different browser View RESULTS button does not appear Occasionally the applet will appear to complete clustering and then not display the View RESULTS button This problem is similar to the one above In most cases clicking the Run CLUSTERING button again will correct the problem If the problem persists try restarting your browser or possibly trying a different browser Distribution do
26. perhaps thousands of transcript targets could potentially be affected in neoplastic tissue The 3 UTR in a single messenger RNA may contain binding sites for a number of microRNAs and a transcript can be concurrently repressed by multiple microRNA species 6 Thus the genome wide complexity of miRNA induced regulation presents a formidable challenge particularly when assessing the impact of multiple dysregulated miRNAs Mounting evidence reveals that microRNAs can exert a cooperative effect on target gene repression Since a gene product may be simultaneously repressed by multiple microRNAs and a number of microRNAs may be differentially expressed in a given disease condition it is essential to consider their combinatorial interactions 1 3 7 8 miR AT microRNA Combinatorial Analysis of Targets leverages and integrates several existing high quality databases to enable combinatorial microRNA target characterization and functional analysis Among the features of miR AT are the ability to predict combinatorial targets of multiple microRNAs user specified parameters for minimum number of sites and number of unique microRNAs required in each target minimum score criteria an integrated link for functional annotation of predicted targets and the ability to cluster microRNAs and their target genes based on target site patterns in the transcripts miR AT User s Manual v1 6 2 Searching miR AT can be found at http mir at org As of
27. ring distance using this method then is simply a function of microRNA transcript target site scores To begin cluster miR AT Search results click on the CLUSTERING button to expand clustering options Figure 14 gt CLUSTERING N v CLUSTERING Spearman Rank Correlation Run CLUSTERING Figure 14 Expand CLUSTERING options Select the similarity metric to use for clustering from the dropdown menu then click the Run CLUSTERING button Similarity metrics are described in the next section An animated graphic wheel will display while clustering results are calculated When the clustering is complete the graphic will be replaced by the View RESULTS button A delay of 30 seconds or more is common the first time clustering is run 4 1 Similarity Metrics In order to cluster miRNA transcripts and the genes they interact with the user must select a metric by which similarity is to be measured To cluster search results miR AT uses the C Clustering Library developed by Michiel de Hoon et al at the Laboratory of DNA Information Analysis Human Genome Center Institute of Medical Science University of Tokyo Eight similarity metrics are available with this library All are available from the CLUSTERING dropdown menu The descriptions given below are taken from the C Clustering Library documentation 9 miR AT User s Manual v1 6 4 1 1 Pearson Correlation Centered The Pearson correlation coeffi
28. ur computer To allow the Java TreeView applet to save results to your local machine see Appendix A eek To save Java TreeView clustering results to an image file click on Export Export to Image The Export to Image dialog Figure 17 will open if it does not then see Appendix A By default the applet points to the miR AT server Files cannot be saved to the server Instead enter a valid path to your local disk drive e g C data results png in the Export To text box Select a image file format Click on the Save button Export to Image Gene Headers Include Array Headers GID EWEIGHT Total Size _ Below Tree Preview Selection Only Check Box to Display Preview Gene Tree v Array Tree v Data Matrix x scale 12 0 y scale 12 0 Border 0 0 Use apple key to select multiple headers 2940 x 8930 pixels arama Export To c idatatresults png Browse Image Format png x v Append Extension Save Cancel Figure 17 Export to Image dialog in Java TreeView Depending on how many genes are in the results it may be necessary to disable display of individual gene names This can be done by holding the Ctrl key down and clicking on NAME in the Gene Headers list to de select this header Cell pixel size of individual heat diagram points can be sized using the x scale miRNA axis and
29. ve or a negative slope If the absolute value is equal to zero there is no correlation between x and y The distance is defined as usual as d 1 r where r is the Pearson correlation coefficient As the absolute value of the Pearson correlation coefficient lies between 0 and 1 the corresponding distance lies between 0 and 1 as well In the context of gene expression experiments note that the absolute correlation is equal to one if the gene expression data of two genes microarrays have a shape that is either exactly the same or exactly opposite The absolute correlation coefficient should therefore be used with care 4 1 3 Pearson Correlation Uncentered In some cases it may be preferable to use the uncentered correlation instead of the regular Pearson correlation coefficient The uncentered correlation is defined as I x Yi m A U n o og x y where 10 miR AT User s Manual v1 6 This is the same expression as for the regular Pearson correlation coefficient except that the sample means are set equal to zero The uncentered correlation may be appropriate if there is a zero reference state For instance in the case of gene expression data given in terms of log ratios a log ratio equal to zero corresponds to the green and red signal being equal which means that the experimental manipulation did not affect the gene expression The distance corresponding to the uncentered correlation coefficient is def
30. y scale gene axis values 2415 miR AT User s Manual v1 6 To save Java TreeView clustering results to a text data file click on Export Save Data The Gene Text Export dialog Figure 18 will open if it does not then see Appendix A By default the applet points to the miR AT server Files cannot be saved to the server Instead enter a valid path to your local disk drive e g C data results txt in the Export To text box Choose the fields to save by Ctrl clicking on the field names e g GID TRANSCRIPT NAME GWEIGHT Choose to include expression data and or header lines by checking unchecking the boxes Click on the Save button Data is saved in a tab delimited format which can be easily imported into any spreadsheet programs e g Microsoft Excel E Gene Text Export Genes from ENST00000373515 to ENSTO00000380304 A B C D E F GID TRANSCRI NAME GWEIGHT hsa mir 122 hsa mir 2086 EWEIGHT 1 000000 1 000000 GENE115x ENSTOOOO APOM 0 0 0 0 GENE269xX ENSTOOO SASHT 0 0 0 0 GENE270X ENST DO00 ZMF 354A 0 0 0 0 GENE338x ENSTOO00 SLC9A11 0 0 0 0 GENE441X ENSTO000 LRRC45 0 0 0 0 GENE46 7x ENSTOO00 MTX2 0 0 GENE4 4x ENSTOOOO RWDD2A 0 0 0 0 0 0 GENE481X ENSTOO00 0 0 0 0 0 0 plolojlojlojojloj
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