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• {"link"=>{"url"=>"http://www.citeulike.org/user/guhjy/article/2715963"}, "post_time"=>"2008-04-25 02:55:48", "tag"=>["image", "jupiter", "ma", "mining", "pathway"], "linkout"=>[{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0001717"}, {"type"=>"Pubmed", "url"=>"http://view.ncbi.nlm.nih.gov/pubmed/18320038"}, {"type"=>"Hubmed", "url"=>"http://www.hubmed.org/display.cgi?uids=18320038"}], "username"=>"guhjy", "article_id"=>"2715963"}
• {"link"=>{"url"=>"http://www.citeulike.org/user/trogdor/article/2715963"}, "post_time"=>"2009-11-11 02:24:22", "tag"=>["correlation_network", "data_mining", "expression_patterns", "gene_regulatory_network", "reconstruction", "regulatory_networks", "transcription"], "linkout"=>[{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0001717"}, {"type"=>"Pubmed", "url"=>"http://view.ncbi.nlm.nih.gov/pubmed/18320038"}, {"type"=>"Hubmed", "url"=>"http://www.hubmed.org/display.cgi?uids=18320038"}], "username"=>"trogdor", "article_id"=>"2715963"}
• {"link"=>{"url"=>"http://www.citeulike.org/user/daforerog/article/2715963"}, "post_time"=>"2009-05-16 22:47:39", "tag"=>["genomics", "mining", "silico"], "linkout"=>[{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0001717"}, {"type"=>"Pubmed", "url"=>"http://view.ncbi.nlm.nih.gov/pubmed/18320038"}, {"type"=>"Hubmed", "url"=>"http://www.hubmed.org/display.cgi?uids=18320038"}], "username"=>"daforerog", "article_id"=>"2715963"}
• {"link"=>{"url"=>"http://www.citeulike.org/user/ptrobajo/article/2715963"}, "post_time"=>"2008-07-11 15:12:13", "tag"=>["compendium", "correlation", "expression", "gene", "meta-analysis"], "linkout"=>[{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0001717"}, {"type"=>"Pubmed", "url"=>"http://view.ncbi.nlm.nih.gov/pubmed/18320038"}, {"type"=>"Hubmed", "url"=>"http://www.hubmed.org/display.cgi?uids=18320038"}], "username"=>"ptrobajo", "article_id"=>"2715963"}
• {"link"=>{"url"=>"http://www.citeulike.org/user/piobyz/article/4003022"}, "post_time"=>"2009-02-03 22:54:55", "tag"=>["analysis", "animals", "array", "biology", "computational", "computer", "databases", "developmental", "expression", "factors", "file-import-09-02-03", "gene", "genetic", "heart", "mice", "oligonucleotide", "profiling", "regulation", "sequence", "simulation", "software", "transcription"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0001717"}, "username"=>"piobyz", "article_id"=>"4003022"}

Mendeley | Further Information

{"title"=>"A visual data mining tool that facilitates reconstruction of transcription regulatory networks", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel C.", "last_name"=>"Jupiter", "scopus_author_id"=>"23489084600"}, {"first_name"=>"Vincent", "last_name"=>"VanBuren", "scopus_author_id"=>"7801675877"}], "year"=>2008, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"0471149993", "scopus"=>"2-s2.0-45849086962", "pui"=>"351894519", "doi"=>"10.1371/journal.pone.0001717", "issn"=>"19326203", "pmid"=>"18320038", "sgr"=>"45849086962"}, "id"=>"f8c14577-efc4-3df7-a8b6-4b07595b4d34", "abstract"=>"BACKGROUND: Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of \"guilt by association\", where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed meta-analysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet, http://vanburenlab.medicine.tamhsc.edu/starnet.html) for visualization of sub-networks of the correlation coefficients built according to user specified parameters.\\n\\nCONCLUSIONS/SIGNIFICANCE: Correlations were calculated across a heterogeneous collection of publicly available microarray data. Users can access this analysis using a new freely available Web-based application for visualizing tractable correlation networks that are flexibly specified by the user. This new resource enables rapid hypothesis development for transcription regulatory relationships.", "link"=>"http://www.mendeley.com/research/visual-data-mining-tool-facilitates-reconstruction-transcription-regulatory-networks-1", "reader_count"=>58, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>23, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>23, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>11, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>1, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>24, "Arts and Humanities"=>1, "Business, Management and Accounting"=>2, "Physics and Astronomy"=>1, "Psychology"=>1, "Chemistry"=>1, "Social Sciences"=>2, "Computer Science"=>18}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>24}, "Computer Science"=>{"Computer Science"=>18}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Unspecified"=>{"Unspecified"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "Iran"=>1, "United States"=>3, "United Kingdom"=>2, "Mexico"=>1, "South Africa"=>1, "Belarus"=>1, "France"=>3, "Switzerland"=>1}, "group_count"=>0}

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• http://doi.org/10.1016/j.drugalcdep.2009.10.001
• http://doi.org/10.1038/srep01011
• http://doi.org/10.1093/nar/gku980
• http://doi.org/10.1161/CIRCEP.108.817320
• http://doi.org/10.1039/b903109j
• http://doi.org/10.3390/ijms16047627
• http://doi.org/10.1016/j.hrthm.2016.01.009
• http://doi.org/10.1093/nar/gkr1265
• http://doi.org/10.1093/nar/gkn213
• http://doi.org/10.1016/j.biosystems.2010.10.017
• http://doi.org/10.3109/10409238.2012.758083
• http://doi.org/10.1177/0192623310382864
• http://doi.org/10.1371/journal.pone.0022843

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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/af34e888-2bf7-4695-831a-9ddea70e75e4", "title"=>"Referee comments: Referee 2 (Gabor Balazsi)", "body"=>"Referee 2's review (Gabor Balazsi):\n\nIn this manuscript, Jupiter and VanBuren 1) present a web-based tool (StarNet) for visualizing cross-correlation based mouse gene networks; 2) compare the distribution of cross-correlation coefficients and genecentric networks for the full dataset and a cardiac cohort.\n\nThe manuscript has no major faults, and StarNet might be a useful tool for biologists looking for candidate genes interacting/regulating a gene of interest. I believe the manuscript could be improved if the authors addressed the following comments:\n\n- Some parts of the manuscript are not clear. For example, the Results start off with a sentence about \"a genecentric distribution\", but such a distribution has not yet been defined. The statistical tests that were used to determine the p-values should be mentioned in parentheses after the p-values. In the Introduction a reference to the source of data should be given. In general, some information from the Methods should appear in the Results too, to make the manuscript clear.\n- On page 4, 3' is followed by \"prime\"\n- On page 4, there should be a reference after Dai et al.\n- The first reference has a typo in it\n- How were the ranges of correlations (indicated by various shades of blue under the network) determined? Was the full range of genecentric correlations equally partitioned?\n- It would be interesting to see if the differences observed for the cardiac cohort hold for another tissue-specific subset\n- It would be interesting to discuss if the difference between the networks in Fig. 3A and 3B could be somehow quantified.\n\n**********\nN.B. These are the comments made by the referee when reviewing an earlier version of this paper. Prior to publication the manuscript has been revised in light of these comments and to address other editorial requirements.", "isRemoved"=>false, "created"=>"2008-03-07T18:03:39Z", "lastModified"=>"2008-03-07T18:03:39Z", "creator"=>{"userId"=>"80539"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>false, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0001717", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2008-03-07T18:03:39Z", "replies"=>[]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/d2422609-89e5-4986-8250-f752520983d7", "title"=>"Referee comments: Referee 3", "body"=>"Referee 3's review:\n\nIn the article, a carefully planned large-scale microarray study involving all mouse experiments and those related to cardiac development was presented. The article was apparently built on rigorous informatics analytical methods, which include careful mapping of genes, normalization of chips, selected grouping of results in many conceivable ways to eliminate bias. For this, the authors did a good job.\n\nHowever, the excitement of possibly interesting findings from data mining of such large data sets carefully prepared in careful ways is met with mediocre scientific data analysis. There is a huge \"disconnect\" between what the data could tell and what the paper actually reported in the study, resulting in a drastic disappointment to the reviewer. In detail, the followings are the major weakness of the paper:\n\n1. While it's good to know that the authors carefully binned the correlation data sets into 20k/40k/100k, full/cardiac cohorts, etc, it's disappointing that the authors did not do further analysis to drill down into the significantly correlated genes from this analysis and report new findings.\n\n2. Of course, the differences between full cohorts and cardiac cohorts are expected to be different. With such large data size in each cohort group, the claim that \"selection of 'cardiac cohort\" biases for genes that are correlated\" should be removed, because we expect the 'cardiac cohort' to be biased in biologically significant way towards genes that are co-regulating under cardiac condition. Therefore, the lack of further investigation to the level of gene groups is inexcusable.\n\n3. The authors only briefly touched on genes that are 100% correlated. Isn't this so easy to identify, even with the simplest informatics method? What's the connection between analysis of these genes and your previously sophisticated informatics method?\n\n4. The negative results found for the 100% correlated genes, while informative, should be put in supplemental materials.\n\n5. Where did the authors get \"Hand1\" to begin the subsequent analysis? Is \"Hand1\" missing from the 20k/40k/100k so that you have to make it up by a genecentric cohort group and \"magically\" come up with Hand1 for discussion? There're lots of unanswered questions.\n\n6. The network analysis related to Hand1 is fine, but not novel. There're many publications already on this topic.\n\nOverall, the paper started ambitiously with great informatics plan, but ended abruptly with little connections between the method and reported results. It shows the general frustrations of the field, in which people with different expertises do not team up in large-scale studies such as this. Suggest authors find collaborators with share similar informatics enthusiasms to guide them in such a study, which could eventually lead to giant steps in scientific breakthrough. As it is presented now, the paper only represent marginal interests to the field.\n\n**********\nN.B. These are the comments made by the referee when reviewing an earlier version of this paper. Prior to publication the manuscript has been revised in light of these comments and to address other editorial requirements.", "isRemoved"=>false, "created"=>"2008-03-07T18:06:06Z", "lastModified"=>"2008-03-07T18:06:06Z", "creator"=>{"userId"=>"80539"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>false, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0001717", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2008-03-07T18:06:06Z", "replies"=>[]}
• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/3010c1c9-7ebd-4fb2-a493-a3534965e1ae", "title"=>"Responses to all referee comments", "body"=>"These are the responses we provided with the submission of our revised manuscript.\n\nDetailed responses to reviewer comments\nOld title: Visual data mining for reconstruction of transcription regulatory networks\nNew title: A visual data mining tool that facilitates reconstruction of transcription regulatory networks\n\nDaniel Jupiter and Vincent VanBuren\n\nAuthor responses are marked.\n\nReviewer #1 (Remarks for the Author):\n\nVisual data mining for reconstruction of transcription regulatory networks\n\nIn this manuscript authors attempt to address on the most important issues in today's life sciences: inability of most experimental biologists to adequately analyze and interpret large datasets (microarray data in this case). Specifically, they show that many analyses utilizing correlation in expression pattern use discrete approach (prersence/absence or correlation) rather than taking into the account the strength of such correlation. \n\nIn my opinion the focus of this paper does not align with its title. My first impression was that the paper is about the StarNet to analyze their dataset, while the webapp is only used for visualization. If this paper is about the software a step-by-step procedure must shown, that would allow the users to reproduce the analyses described in the paper. It must be noted that StarNet is a very good resource for network interpretation and I would encourage authors to re-focuse the manuscript by concentrating on highlighting the usability.\n\nAuthor response:\n\tWe have edited the title, but mainly to clarify that the main thrust of the article is indeed to describe a new tool available to the community. This edit is more in response to Rev #3. A step-by-step guide to reproducing the analysis in the manuscript has been added to the Results; this was trivial as the steps are to type the gene symbol ‘Hand1’ and click ‘Submit’. Despite the simplicity of repeating this analysis, we agree with the reviewer that it is important to include these instructions. We do not think, however, that a detailed description of the Web interface is desirable for this publication. We provide a thorough description in documentation on the Web site, and putting this in the manuscript would curtail further development of the interface by committing the authors to a particular form of the interface. We prefer to allow the form of the interface to be uncommitted so we can quickly respond to user feedback to make improvements to the interface without creating inconsistencies between the interface and our paper. In response to all reviewers’ comments, we have also moved the Methods section to precede the Results. We believe this new ordering of the sections is more natural for presenting this work, and should help to clarify the contribution this work makes.\n\n\nReviewer #2 (Reviewer Name):\n\nGabor Balazsi\n\nReviewer #2 (Remarks for the Author):\n\nIn this manuscript, Jupiter and VanBuren 1) present a web-based tool (StarNet) for visualizing cross-correlation based mouse gene networks; 2) compare the distribution of cross-correlation coefficients and genecentric networks for the full dataset and a cardiac cohort.\n\nThe manuscript has no major faults, and StarNet might be a useful tool for biologists looking for candidate genes interacting/regulating a gene of interest. I believe the manuscript could be improved if the authors addressed the following comments:\n\n- Some parts of the manuscript are not clear. For example, the Results start off with a sentence about \"a genecentric distribution\", but such a distribution has not yet been defined. The statistical tests that were used to determine the p-values should be mentioned in parentheses after the p-values. In the Introduction a reference to the source of data should be given. In general, some information from the Methods should appear in the Results too, to make the manuscript clear.\n\nAuthor response:\t\nThe Methods have been moved to precede the Results, so ‘genecentric’ is now defined the first time it is used. Statistical tests are now mentioned in the Results, and clearer descriptions are given in the Methods. The source of data used in the study (GEO) has been mentioned in the Introduction.\n\n- On page 4, 3' is followed by \"prime\" \n\tThis has been fixed.\n\n- On page 4, there should be a reference after Dai et al.\n\tThe reference has been added.\n\n- The first reference has a typo in it\n\tThe typo has been fixed.\n\n- How were the ranges of correlations (indicated by various shades of blue under the network) determined? Was the full range of genecentric correlations equally partitioned?\n\nAuthor response:\n\tA brief description of how the ranges are determined for the scale bars was added to the Methods as follows:\nNote that the correlation scales were determined on a per network basis. That is, for each network the positive (or negative) scales are redrawn, with the scale drawn by equally partitioning between the minimum and maximum positive (or negative) correlations within that network.\n\n- It would be interesting to see if the differences observed for the cardiac cohort hold for another tissue-specific subset \n\nAuthor response:\n\tWe have not performed this analysis for the present paper, but we have this in mind as a future direction.\n\n- It would be interesting to discuss if the difference between the networks in Fig. 3A and 3B could be somehow quantified. \n\nAuthor response:\n\tWe have added the following passage to the Discussion:\n\nThe full cohort and cardiac cohort networks given here as examples of StarNet’s analysis are not immediately amenable to quantitative comparisons. One obvious obstacle to comparison is that the networks do not have any nodes in common besides the central node. Many networks drawn with StarNet do have several nodes in common, but the common nodes are frequently a minority of the total network nodes. The networks are constructed with arbitrary cutoffs for the highest correlations with a given node, so many biologically important associations may be missing from a particular network. One approach to comparing these networks would then be to create a 'super-network' for each cohort, where all the unique nodes from the full and cardiac network are combined, and a complete network is created from the original distribution (full or cardiac) of correlation coefficients. As every node is connected to every other node in the new networks, analysis of these complete networks using the tools of social network analysis [32] can be achieved for some metrics by modifying the metric to account for the Pearson correlation coefficient assigned to each edge. For example, betweeness, or the degree to which a gene lies between others in the network, may be assessed by determining which is higher for all possible connections between two genes: the direct correlation between the genes or the product of correlations through an intermediate gene. For example, if the correlation between gene X and gene Y is lower than the product of the correlations (X, Z) and (Z, X), then this suggests that any influence between genes X and Y occurs through the intermediate gene Z (thus strengthening the betweeness metric for Z). Such quantitative comparisons between networks remain to be developed in future work.\n\n\n\n \nReviewer #3 (Remarks for the Author):\n\nIn the article, a carefully planned large-scale microarray study involving all mouse experiments and those related to cardiac development was presented. The article was apparently built on rigorous informatics analytical methods, which include careful mapping of genes, normalization of chips, selected grouping of results in many conceivable ways to eliminate bias. For this, the authors did a good job.\n\nHowever, the excitement of possibly interesting findings from data mining of such large data sets carefully prepared in careful ways is met with mediocre scientific data analysis. There is a huge \"disconnect\" between what the data could tell and what the paper actually reported in the study, resulting in a drastic disappointment to the reviewer. In detail, the followings are the major weakness of the paper:\n\nAuthor response:\n\tOur interpretation of this reviewer’s remarks is that we failed to convey the purpose of this work to the reviewer. This manuscript is primarily about a new tool. Our interpretation of the reviewer’s remarks is based on their omission of any comments regarding the new tool StarNet. It appears that the reviewer believes that we were attempting to provide new biological results, but failed. If that were our intent for this manuscript, then the reviewer would certainly be correct. However, the results in our paper are only intended to demonstrate that we have carefully constructed this new tool, and that biomedical scientists may find this tool useful in their research. We have tried to use this reviewer’s comments to make changes in the manuscript to avoid disappointment by other readers. First, we have changed the title to include the word ‘tool’ in a prominent place. Second, we have reordered the Methods to precede the Results, which we believe will also help to clarify the intent of the paper. Third, we added a new section in the Results to explicitly indicate that StarNet is the major contribution of this work. Finally, we added new headers in the Results to more plainly show the main points. Other clarifications were made as indicated below.\n\n1. While it's good to know that the authors carefully binned the correlation data sets into 20k/40k/100k, full/cardiac cohorts, etc, it's disappointing that the authors did not do further analysis to drill down into the significantly correlated genes from this analysis and report new findings.\n\nAuthor response:\n\tWe plan to drill down to report new findings in the future. The present work is meant to reach out to the expertise of an audience with broad interests in transcriptional regulatory networks, or to those who are interested in assessing just a few genes to generate new hypotheses. In this way we can reach a broader audience of collaborators than would otherwise be possible.\n\n2. Of course, the differences between full cohorts and cardiac cohorts are expected to be different. With such large data size in each cohort group, the claim that \"selection of 'cardiac cohort\" biases for genes that are correlated\" should be removed, because we expect the 'cardiac cohort' to be biased in biologically significant way towards genes that are co-regulating under cardiac condition. Therefore, the lack of further investigation to the level of gene groups is inexcusable. \n\nAuthor response:\n\tWe clarify our point here with the following passage:\n\nThere are two main factors that contribute to this observed difference between the cardiac and full cohort sub-distributions: (1) co-regulation is context-specific, meaning that the transcription activity of two genes may be tightly co-regulated in one tissue or milieu, and weakly co-regulated in others; and (2) conditioning the data on a ‘cardiac cohort’, or on any sub-population of tissue types, has a tendency to strengthen measured correlations because of the narrowed range of phenotypes produced by gene activity in those sub-populations. As conditioning increases the average correlation, many gene pairs thus affected that are also highly correlated in other tissues are thus not necessarily specifically corregulated under conditioning.\n\n3. The authors only briefly touched on genes that are 100% correlated. Isn't this so easy to identify, even with the simplest informatics method? What's the connection between analysis of these genes and your previously sophisticated informatics method?\n\nAuthor response:\n\tThe reviewer is correct that the identification of gene pairs with a Pearson correlation of 1 is easy. The result of examining the gene pairs identified in this way is that all such gene pairs are either the same genes, or each of the array probes assay a non-unique sequence from the 3’ end of a gene with multiple transcription start sites. Nothing more needs to be reported about this issue.\n\n4. The negative results found for the 100% correlated genes, while informative, should be put in supplemental materials. \n\nAuthor response:\t\nWe respectfully disagree. There is an anomalous spike in the positive tails of these distributions. This merits an explanation. The explanation shows that correlations of 1 are produced by those array features that do not truly distinguish between two genes or transcripts, and the features are thus measuring the same thing. This explains the spike while reassuring the reader that the technical reproducibility on the selected arrays is consistently good. \n\n5. Where did the authors get \"Hand1\" to begin the subsequent analysis? Is \"Hand1\" missing from the 20k/40k/100k so that you have to make it up by a genecentric cohort group and \"magically\" come up with Hand1 for discussion? There're lots of unanswered questions.\n\nAuthor response:\n\tWe explain this in a new paragraph in the Results as follows:\n\nAn example analysis with StarNet: Hand1\n\nAs a representative example, below we analyze Hand1 with the freely accessible Web-based tool StarNet. We selected Hand1 because its role in cardiac development is well established. The analysis below is intended to illuminate the strengths and weaknesses of StarNet, and is not an attempt to present new results. Analysis was performed with the default settings in StarNet, which includes interrogating the genecentric distribution, and networks are drawn with the highest 5 correlations from the gene of interest (level 1) and the highest 5 correlations for each of those genes (level 2). The genecentric distribution was chosen as a default because this distribution has complete coverage of the array platform. The other parameters were chosen because our testing experience has shown that these parameters produce informative networks that are easily visualized. The StarNet analysis results discussed below may be viewed at http://vanburenlab.tamhsc.edu/Hand1/result.html. Alternatively, these results may also be recreated by typing ‘Hand1’ into the ‘Gene Symbol/Entrez ID’ field of the StarNet interface, and clicking ‘Submit’. With the present implementation, a new analysis takes about two minutes with the default settings in StarNet.\n\n\n6. The network analysis related to Hand1 is fine, but not novel. There're many publications already on this topic.\n\t\nAuthor response:\nThe purpose of the Hand1 analysis is to reference a well-known gene to show how well StarNet identifies associated genes that fit well with what is know about the gene of interest. We did not intend to present this as a new finding. We have modified the manuscript, adding a new introductory paragraph to the Hand1 text in the Results. See # 5 above.\n\nReferee #3, continued:\nOverall, the paper started ambitiously with great informatics plan, but ended abruptly with little connections between the method and reported results. It shows the general frustrations of the field, in which people with different expertises do not team up in large-scale studies such as this. Suggest authors find collaborators with share similar informatics enthusiasms to guide them in such a study, which could eventually lead to giant steps in scientific breakthrough. As it is presented now, the paper only represent marginal interests to the field.\n\t\nAuthor response:\nWe agree that if this paper were to stand alone, that it would not be very interesting. The real impact of this work is the availability of a new research tool for elucidating transcription regulatory networks. We believe this tool will receive broad interest, and is an expression of open collaboration with the biomedical community.\n\n\n\n", "isRemoved"=>false, "created"=>"2008-03-10T20:30:37Z", "lastModified"=>"2008-03-10T20:30:37Z", "creator"=>{"userId"=>"165631"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>false, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pone.0001717", "state"=>"published", "journals"=>{"PLoSONE"=>{"journalKey"=>"PLoSONE", "eIssn"=>"1932-6203", "title"=>"PLOS ONE"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2008-03-10T20:30:37Z", "replies"=>[]}

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Figshare

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• {"files"=>["https://ndownloader.figshare.com/files/936187"], "description"=>"<p>a: The cardiac cohort (dark blue) and full cohort (light blue) Genecentric distributions. b: The largest positive correlations in the cardiac cohort. The highest 335 of these associations have a Pearson correlation of 1 (to 16 decimal places), and these form a notable spike at the tail of the distribution (arrow). c: The 335 associations indicated in panel b are composed of 80 genes in the 22 groups of genes shown here. d: The genomic structure of the Pcdhgb1 family of genes (drawn with the UCSC Genome Browser).</p>", "links"=>[], "tags"=>["distributions"], "article_id"=>606642, "categories"=>["Computational Biology", "Developmental Biology", "Infectious Diseases", "Genetics", "Medicine"], "users"=>["Daniel C. Jupiter", "Vincent VanBuren"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0001717.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Selected_distributions_used_by_StarNet_/606642", "title"=>"Selected distributions used by StarNet.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 10:55:34"}
• {"files"=>["https://ndownloader.figshare.com/files/936300"], "description"=>"<p>a: Full cohort correlation network with <i>Hand1</i> as the central node. b: Cardiac cohort correlation network with <i>Hand1</i> as the central node. c: Known associations involving genes from a (which appear in blue text). d: Known associations involving genes from B (which appear in blue text). In a and b, red text indicates genes with annotation that include “transcription”, and line color represents correlation strength as indicated by the scale bars. In c and d, black edges represent previously known protein-protein interactions, and red arrows represent previously known protein->DNA interactions. Known interactions for all genes in each correlation network are determined by searching Gene RIF interactions supplied by Entrez Gene.</p>", "links"=>[], "tags"=>["graphical", "starnet"], "article_id"=>606742, "categories"=>["Computational Biology", "Developmental Biology", "Infectious Diseases", "Genetics", "Medicine"], "users"=>["Daniel C. Jupiter", "Vincent VanBuren"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0001717.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Representative_graphical_output_from_StarNet_textual_output_not_shown_/606742", "title"=>"Representative graphical output from StarNet (textual output not shown).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 10:56:12"}
• {"files"=>["https://ndownloader.figshare.com/files/936054"], "description"=>"<p>2145 array samples were selected for the Affymetrix whole genome mouse 430 2.0 array platform. Data were normalized and scaled using justRMALite. The resulting distribution of over 130 million Pearson correlation coefficients was filtered to produce various distributions of the strongest relationships. Correlation data and Entrez Gene annotations were used to populate a new database. StarNet was developed to allow users to make database queries to create and draw correlation networks local to their gene of interest on-the-fly, and to provide supporting information about genes in those networks.</p>", "links"=>[], "tags"=>["computational biology/metabolic networks", "computational biology/signaling networks", "computational biology/systems biology", "computational biology/transcriptional regulation", "developmental biology/developmental molecular mechanisms", "developmental biology/molecular development", "developmental biology/organogenesis", "genetics and genomics/bioinformatics", "genetics and genomics/gene expression", "cardiovascular disorders/congenital heart disease"], "article_id"=>606495, "categories"=>["Computational Biology", "Developmental Biology", "Infectious Diseases", "Genetics", "Medicine"], "users"=>["Daniel C. Jupiter", "Vincent VanBuren"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0001717.g001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_pipeline_/606495", "title"=>"Analysis pipeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 10:54:40"}

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• {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"4", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2014", "month"=>"10"}
• {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"2"}
• {"unique-ip"=>"9", "full-text"=>"10", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"11"}
• {"unique-ip"=>"3", "full-text"=>"4", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"12"}
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• {"unique-ip"=>"6", "full-text"=>"3", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2015", "month"=>"11"}
• {"unique-ip"=>"3", "full-text"=>"1", "pdf"=>"2", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2015", "month"=>"12"}
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• {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"4"}
• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"5"}
• {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"6"}
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• {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"8"}
• {"unique-ip"=>"8", "full-text"=>"7", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"9"}
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• {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"12"}
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• {"unique-ip"=>"5", "full-text"=>"16", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2017", "month"=>"2"}
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• {"unique-ip"=>"6", "full-text"=>"3", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2018", "month"=>"3"}
• {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
• {"unique-ip"=>"3", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"4"}
• {"unique-ip"=>"12", "full-text"=>"12", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"5"}
• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
• {"unique-ip"=>"5", "full-text"=>"3", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"7"}
• {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
• {"unique-ip"=>"11", "full-text"=>"15", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
• {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
• {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
• {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
• {"unique-ip"=>"18", "full-text"=>"19", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"2", "year"=>"2019", "month"=>"2"}
• {"unique-ip"=>"10", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"12", "supp-data"=>"0", "cited-by"=>"3", "year"=>"2019", "month"=>"3"}
• {"unique-ip"=>"8", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
• {"unique-ip"=>"22", "full-text"=>"12", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"6", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"5"}
• {"unique-ip"=>"7", "full-text"=>"7", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
• {"unique-ip"=>"5", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
• {"unique-ip"=>"9", "full-text"=>"8", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
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Relative Metric

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F1000Prime