ComiRNet User Guide (v. 1.2)
Contents
1. SVM separating hyperplane Density and distance based merging o SVM based identification of common objects between each pair of biclusters Figure 4 HOCCLUS2 Second step of the algorithm execution Overlap identification and merging of biclusters The stopping criterion is based on a cohesiveness threshold Figure 5 HOCCLUS2 Hierarchies of overlapping biclusters The hierarchical structure of biclusters as provided by HOCCLUS2 helps to detect multiple alternative co targeting of different miRNAs on specific groups of genes 3 Ranking of the extracted biclusters Figure 6 Ranking is based on the p value obtained by Student s t test through which we compare the average intra bicluster similarity to the average inter bicluster similarity among miRNA target genes Figure 6 HOCCLUS2 Third step of the algorithm Ranking of biclusters Red edges represent intra bicluster similarities blue dashed edges represent inter bicluster similarities The similarities between miRNA targets belonging to the same and to different biclusters respectively are pairwise computed according to the simGIC similarity on the gene classification provided in Gene Ontology Query Functions ComiRNet provides two main modules for querying the database that are Search Interactions and Search Biclusters Each module is equipped with a web interface for the retrieval and visualization of dat2. The higher the compactness value the higher the probability that objects in the bicluster are involved in the same pathway or in strictly related pathways The intra bicluster biological coherence This quantity is expressed by the value of two parameters i e pBP and pMF which measure the similarity of target genes in the bicluster with respect to genes in other biclusters on the basis of the biological process BP in which they are involved or of their molecular function MF The lower the p values the higher the probability that 1 genes in the bicluster are involved in the same biological process or that many of them have related molecular functions 11 miRNAs in the bicluster work together as a regulatory module The level of the hierarchy to which the bicluster belongs to The lower the hierarchy level to which a bicluster belongs the lower the number of objects in the bicluster but the higher the percentage of them with direct interactions The biclusters compactness gives a measure of this feature On overall biclusters belonging to lower levels of hierarchies are the most useful to detect pathway specific activities of miRNAs whereas biclusters at higher levels are much more informative about inter pathway functional correlations Biclusters Source ComiRNet stores 15 different hierarchies defined as Source in the search form obtained by varying the threshold values of two parameters of HOCCLUS2 1 e alpha and beta A
3. be dynamically sorted according to each column and can be exported as plain text or XML file Clicking on column headers values in each column are dynamically sorted cI icking on the Show button it s is possible to openandvisualise the bicluster s card Export Data Plain Text Export Data XML Displaying 1 to 3 of 3 4 a h s A i Level Biclusters Name Compactness gene miRNA pBP pMF Action 379 1 0 4 3 0 0001 0 0001 Show 379405 0 73 11 4 iii ad Show 348_356_379_405 0 6 17 6 0 0001 1 0 Show Displaying 1 to 3 of 3 Export Data Plain Text Export Data XML k 4 Clicking on the Export Data buttons results can be exported in plain text or in XML format Figure 9 Results table of the Search biclusters module The figure shows the results obtained by searching for biclusters satisfying the criteria specified in Figure 8 In the rightmost column a Show button opens a new window reporting the summary of the bicluster properties Figure 10 panel A a dynamic graph based visualization of the predicted miRNA gene interactions network Figure 10 panel B and a comprehensive view of the bicluster hierarchy 1 e parent and child biclusters Figure 10 panel C Biclusters 379 Options 0 0 Filter Interactions Hide Isolated Objects Panel A Details Level 1 Compactness 1 0 gene 4 miRNA 3 pBP lt 0 0001 pMF lt 0 0001 gene EE eee ee BMPR2 PTEN SMAD4 TGFBR2 miRNA ln
4. biclusters are provided Detailed properties of each bicluster can be visualized clicking on the Show button Tutorial Click here to open a video tutorial showing dynamic functions described in this guide
5. values of the algorithm HOCCLUS2 that is compactness Fier pP vave E pha which represents the minimum cohesiveness value that a bicluster must satisfy after min max Fier pMF Value ga min max of the hierarchy levels and the number of 7 8 Set the threeshould or the range of values Of the value of aipha the lower the number pBP and or pMF for biclusters to be browsed EO ayaa Renee x ore the predicted interaction networks are 9 Select the OR check box to enable searching in OR mode reliable but the less is their number Search in OR into 11 Click the Search but ton in order to start the Options Resuts Page 4 search L J Show EREA among different levels indo 10 Additional options to custom the results view nie Search Figure 8 Details on the options available in the Search Biclusters module The figure shows a search in hierarchy 15 box 1 using as search criteria the gene SMAD4 and the miRNA hsa mir 17 boxes 2 3 Filter applied are biclusters compactness box 6 with a min value 0 3 and pBP lt 0 05 Box 1 source allows the user to select the source hierarchy and it is mandatory After selecting the desired hierarchy two types of queries can be performed 1 the retrieval of all the biclusters in the hierarchy or 11 the exploration of only those biclusters containing miRNA s and or gene s of interest boxes 2 3 In the latter case simila
6. ComiRNet User Guide v 1 2 e Introduction e About ComiRNet o Method e Query Functions o Search Interactions o Search Biclusters e Tutorial Introduction This document provides an overview of ComiRNet content and utilities This is not a comprehensive guide but should provide users with enough information to properly browse the database and use its principal tools for data analysis Please read through it and contact us at gianvito pio_AT_uniba it with any comments or questions About ComiRNet ComiRNet Co clustered miRNA Regulatory Networks is a database specifically designed to provide biologists and clinicians with user friendly and effective tools for the study of miRNAs The database stores automatically mined and non redundant data of miRNA gene target interactions MTIs and miRNA gene regulatory networks MGRNs in the form of biclusters Data are produced by exploiting miRNAs target predictions from 10 different prediction databases stored in mirDIP and validated MTIs extracted from miRTarBase Based on the principles of the ComiRNet approach genes in a bicluster are likely to function together as a network and miRNAs in the same bicluster are likely to cooperatively target groups of networked genes The use of computational predictions in place of only experimentally validated interactions offers the possibility to detect single interactions and regulatory modules that would be otherwise impossible to reconstruct by c
7. Results can be ordered by gene name miRNA ID and score value by clicking on the column headers q 4 4 4 4 4 8 Clicking on hyperlinked items allows the user to access complete gene and miRNA information from reference databases and to get information on experimentally validated interactions from miRTarBase Figure 7 Details on the options available in the Search Interaction module Boxes 1 2 3 MTIs can be queried by specifying one or more search items separated by commas Gens have to be specified by using Gene official symbols e g CDOKNIA whereas miRNAs have to specified by using miRNA identifiers e g hsa mir 17 boxes 1 2 The system searches with the AND condition by default As an alternative the user can perform the query by enabling the OR condition check box box 3 Boxes 4 5 6 A filter on the interaction score in the interval 0 1 box 4 allows users to perform the query at different levels of stringency We recommend to filter interactions with the scores lower then 0 2 0 3 Indeed lower score values would return too many interactions with a low significance Additional options are provided in the Options box box 5 which allows users to choose how many results 1 e MTIs have to be shown per page and the inclusion exclusion of interaction scores Finally the search button allows the user to start the query with the specified options box 6 Boxes 7 8 9 The result tabl
8. a Several filtering criteria can be used to refine the query to satisfy specific user needs Search Interactions The Search Interaction module allows users to extract MTIs on the 3 UTR of all known human genes Currently ComiRNet stores about 5 million predicted interactions between 934 human miRNAs and 30 875 gene transcripts mRNAs Results are not redundant and are shown with the score 1 e probability identified by the approach described in Method Step A Details of the available query options are shown in Figure 7 The output consists of an interactive table visible see the bottom of the figure Numbered boxes help to underline step by step all the available options and filters that can be used to refine the query and to export the results ComiRNet The Database of Predicted miRNAs Regulatory Networks Hom ne Search Interactions Search Bichusters 1 Search by gene symbol Search Interactions m E3 2 Search by miRNA ID Kraini tesenions ontein 1a Combes were podoaba e redo UTR of gees targeted by mits 10 mah are pron datas 6 Click the Search but miRNA E i AT Targets TargetScen a elena RNA22 3 ton in order to start the 3 The OR check box enables searching in OR lt I search mode sanon E Options Reet F srov Seon 9 Click on the Export t Z Data button to 7 co download results txt options to customize the results view export owa forriat L 7
9. e shows the list of MTIs retrieved In particular it shows the gene symbol the gene s ENTREZ ID the miRNA ID a green check symbol if the interaction is validated in miRTarBase and the interaction score Results can be ordered by clicking on the column header with respect to gene symbols ENTREZ IDs miRNA IDs and interaction scores box 7 Complete information on genes and miRNAs are provided throughout the hyperlink to the their own entry in reference databases GeneCards and NCBI for target genes miRBase for miRNAs box 8 If an interaction is validated in miRTarBase by clicking on the green check symbol the user is brought to the relevant entry in the reference database Finally it is possible to export and download the query results by clicking on the Export Data button box 9 Search Biclusters ComiRNet also stores MGRNSs predicted by HOCCLUS2 on the basis of the identified MTIs that can be queried through the Search Bicluster module Bicluster Properties Each MGRN represented as a bicluster is characterized by several properties that help the user in the selection of the most significant MGRNs on the basis of different criteria that are The bicluster compactness This value can vary in the interval 0 1 and measures the bicluster cohesiveness The compactness of a bicluster represents the weighted percentage of direct interactions in the bicluster normalized by the number of all the possible interactions
10. is of the values of alpha and beta the number of hierarchical levels and the number of significant biclusters per level may vary in a sensible manner Hence the selection of one hierarchy to analyze is fundamental for the type of results the user can get As for a first and general exploration of the biclusters we suggest to start from the hierarchy 1 which is the less stringent among all the hierarchies Once the user detects the bicluster of interest a search in hierarchies with higher values of alpha and beta parameters can help in the retrieval of more significant results Details of the available query options are shown in Figure 8 Numbered boxes help to underline step by step all the available options and filters that can be used to refine the query and to export the results 2 Search by gene symbol ee radi Mean E aa Search Biclusters Sourc 1 alpha 0 1 beta 0 3 filend y 1 Select the Source Source i on z i e bicluster hierarchy i ae m 2 alpha 0 1 beta 0 4 filename semisupervised_mirDIP D mens EB ip 3 Search by miRNA ID E p rsm Ip DIP Bictuster Name iit K HP z E F oeer SUT EAA aa ee prea 5 Set the range of hierarchy s opel levels to browse Fiter Hierarchy Level irDIi 6 Setthe threshold or the Far Congacione alee SOURCE 2 F gt min max ComiRNet can perform queries on 15 different hierarchies identified with different range of values of biclusters parameter
11. litating the users in keeping only those objects in the bicluster that are particularly interesting for further analysis Panel B contains the graph based representation of the interaction network Nodes represent miRNAs and target genes whereas edges represent the miRNA gene target interactions By hovering the mouse pointer on a miRNA or on a gene the user can highlight all the predicted targets or all the miRNAs targeting the gene respectively This allows user awareness of the impact of a single miRNA on the whole set of genes involved in the bicluster or alternatively of which are the miRNAs among all those in the bicluster that co target a specific gene This is particularly important when the user is exploring biclusters which do not belong to the first level of the hierarchy Indeed in this case biclusters do not necessarily represent fully connected networks and the identification of co targeting entities becomes important When the user hovers the mouse pointer on an edge the system shows the predicted interaction score enabling a quick evaluation of the reliability of a specific interaction in the overall context of the network Panel C contains the Hierarchy Browser which allows the user to browse the hierarchy the considered bicluster belongs to by analyzing the details of its parent and child biclusters Similarly to the interface used to show the results of queries on MGRNs also in this case some information about listed
12. lpha is the minimum cohesiveness value that a bicluster must satisfy after performing a merging The value of this parameter implicitly influences the number of the hierarchy levels and the number of biclusters at each hierarchy level i e the higher the value of alpha the lower the number of biclusters per level Beta is the minimum score that an interaction must have to be considered as reliable The higher its value the more the predicted interaction networks are reliable but the less 1s their number Table 1 shows some statistics about the hierarchies identified by HOCCLUS2 considering the number of hierarchy levels and the number percentage of significant biclusters p value lt 0 05 per hierarchy for each combination of alpha and beta thresholds 1861 576 30 95 515 27 67 2 0 1 0 4 1229 377 30 67 349 28 39 3 0 1 0 5 866 309 35 68 260 30 02 4 0 2 0 3 2172 654 30 11 639 29 41 5 0 2 0 4 1399 443 31 66 408 29 16 6 0 2 0 5 966 350 36 23 287 29 71 7 0 3 0 3 2469 755 30 57 674 27 29 8 0 3 0 4 1570 485 30 89 459 29 23 9 0 3 0 5 1181 425 35 98 398 33 70 10 0 4 0 3 3115 873 28 02 787 25 26 11 0 4 0 4 1863 608 32 63 532 28 55 12 0 4 0 5 1371 494 36 03 444 32 38 13 0 5 0 3 3415 851 24 91 735 21 52 14 0 5 0 4 2039 623 30 55 541 26 53 15 0 5 0 5 1329 453 34 08 391 29 42 Table 1 Some statistics about hierarchies stored in ComiRNet On the bas
13. mentally validated interactions positively labelled examples and miRNA gene target predictions MMTIs returned from several prediction algorithms unlabelled examples This classifier acts as a meta classifier of unlabelled examples As a result of the first step a unique meta prediction score 1s available for all possible interactions In the second step these prediction scores are used to identify miRNA gene regulatory networks MGRNs through the biclustering algorithm HOCCLUS2 see 2 in Publications A B positive unlabeled miRTarBase mirDIP RNA22 3 UTR Figure 2 ComiRNet computational approach Step A The semi supervised ensemble based classifier learns to combine predictions referred to 3 UTR of genes targeted by miRNAs extracted from DIANA microT micro Cosm miRanda picTar 4 way and picTar 5 way PITA All Targets and PITA Top Targets TargetScan Conserved and TargetScan Non Conserved RNA22 3 UTR The algorithm is three stepped 1 Train an SVM classifier which outputs the probability of an instance to be labelled 2 Assign a weight to each instance on the basis of its probability of being a labelled instance 3 Train a SVM classifier which takes into account instance weights and outputs the probability of an instance to be positive Both steps 1 and 3 are performed by resorting to an ensemble based learning approach In particular K subsets of instances are identified consisting of the whole se
14. most significant results on the basis of the p values pBP and pMEF respectively measuring the biological significance of the genes in the biclusters The use of these filters with a max value of 0 05 is suggested in order to reduce the retrieval of too many results with a poor significance In any case we suggest to avoid using both the filters together in the same query because they do act on different biological properties of the biclusters Indeed the selection of the most significant biclusters on the basis of pMF can hide highly significant biclusters on the basis of pBP Additional options are available in the Options section box 10 which allows users to choose how many results 1 e biclusters per page have to be shown and the inclusion exclusion of biclusters duplicate Biclusters duplicates can be generated at different level of the hierarchy We suggest to keep the default option in order to avoid redundancy in the results Finally the search button allows the user to start the query with the specified options box 11 The results obtained from the query are shown in a table placed on the bottom of the search form see Figure 9 The table includes the list of biclusters matching the search criteria and reports for each bicluster the hierarchy level to which it belongs the bicluster name identifier the compactness value the number of genes and miRNAs involved and the pBP and pMF values The results in the table can
15. onsidering only experimentally validated interactions which are strictly dependent on the cell type and experimental conditions used This paves the way to the systematic use of ComiRNet for 1 acomprehensive analysis of cooperative targeting of miRNAs of interest Figure 1a 2 the discovery of unknown miRNA and gene functions on the basis of the ComiRNet biclustering Figure 1b 3 the discovery of unknown miRNA targets which could be worth to be experimentally validated This possibility is due to the ComiRNet ability to associate objects that are apparently not related Figure Ic a b c Discovered Network Possible high similarity among Possible unknown interactions bicluster objects of the same type among objects of different types O miRNA E mRNA Figure 1 a Biclusters extracted by ComiRNet suggest interaction networks between objects of different types i e miRNA and mRNA black edges indicate interactions among miRNAs and target genes in the bicluster b red dashes edges underline the putative functional similarity among object of the same type suggested by the ComiRNet biclustering c green dashes edges indicate putative unknown functional interactions between miRNAs and genes suggested by the ComiRNet biclustering Method ComiRNet is based on a two stepped computational approach see Figure 1 In the first step a semi supervised ensemble based classifier see 1 in Publications is learned from both experi
16. rly to the Search Interaction form a list of gene symbols and or miRNAs IDs can be provided both as single search criterion or in combination The system searches with the AND condition by default As an alternative the user can perform the query by enabling the OR condition check box box 9 The Bicluster name search field box 4 lets users search for a single bicluster This feature is useful to quickly retrieve biclusters that were considered interesting in a previous analysis The filter on the hierarchical level box 5 allows the user to select a range of hierarchical levels that the system has to consider This filter is useful to discard useless results once the user has already analyzed the full hierarchy and identified the levels with the most interesting results If you are using the database for the first time we suggest do not use this filter to avoid to discard some potentially interesting results The filter on the bicluster compactness box 6 in the interval allows the user to run the query at different levels of stringency with respect the compactness of the biclusters that have to be selected We recommend the use of this filter at min value 0 2 0 3 and max value not more than 0 5 Indeed lower score values would return biclusters with too much low significance whereas score higher than 0 5 may exclude very significant results The filters on the pBP and pMF values boxes 7 8 allow the user to select the
17. sa mir 17 hsa mir 19a hsa mir 20a ote if you experience problems while viewing the networks please reset the browser zoom ith CTRL 0 Reset View Export Data Plain Text Export Data XML Panel C Hierarchy Browser Biclusters Name Compactness gene miRNA 348_356 379 405 0 6 Parent Biclusters 379 405 0 73 379 1 0 Figure 10 Details about a selected bicluster In panel A the user can see bicluster properties where the searched items gene official symbols and or miRNA ID are underlined in red The Filter interactions slider placed on the top left side allows users to dynamically customize a threshold on the minimum score of interactions in the interval 0 1 to be shown in the network graph visualization panel B Moving the slider from left to right the system dynamically redraws the graph excluding all those miRNA gene interactions with a score below the selected threshold Moreover a check box allows the user to hide isolated nodes i e miRNAs and genes that are not connected to any other nodes in the bicluster according to the selected threshold This option is particularly useful for an easier interpretation of predicted MGRNs in which a large number of miRNAs and genes is involved e g biclusters belonging to high levels of the hierarchy The application of this filter contextually modifies the list of miRNAs and genes of the bicluster reported in the summary of bicluster properties thus faci
18. t of positive instances 1 e experimentally validated interactions and a subset of the unlabelled instances 1 e predicted interactions randomly sampled with replacement Step B The biclustering algorithm HOCCLUS2 Hierarchical and Overlapping Co CLUStering 2 exploits the set of non redundant interactions predicted by the semi supervised ensemble based classifier with the associated probabilities to identify overlapping and hierarchically organized biclusters each one representing putative MGRNs HOCCLUS2 consists of three steps 1 Extraction of a set of non hierarchically organized biclusters in form of bicliques Figure 3 through an iterative bottom up strategy This step exploits statistical properties of data ri r c O c oO o Faj r2 F r2 A 4 4 Pa p E re eee OD Ci _ i 7 a e a r3 nn a LR Ts a O m eee a N r y e r4 i r4 Eine ha w ae x O an T4 N M i ls N r6 O O O Figure 3 Identification of biclusters in form of bicliques 2 An iterative process in which at each iteration two operations are performed see Figure 4 a overlap identification in which miRNAs or mRNAs belonging to a bicluster can be added to another bicluster by exploiting an SVM based classification algorithm b merging in which biclusters are merged when some distance and density based heuristic criteria are satisfied Merging implicitly defines a hierarchy of biclusters see Figure 5 a b
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