Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling
Publication Date
September 23, 2010
Journal
PLOS Computational Biology
Authors
Juan A. G. Ranea, Ian Morilla, Jon G. Lees, Adam J. Reid, et al
Volume
6
Issue
9
Pages
e1000945
DOI
https://dx.plos.org/10.1371/journal.pcbi.1000945
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1000945
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/20885791
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944794
Europe PMC
http://europepmc.org/abstract/MED/20885791
Web of Science
000282372600013
Scopus
77957806312
Mendeley
http://www.mendeley.com/research/finding-dark-matter-human-yeast-protein-network-prediction-modelling
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CiteULike | Further Information

Mendeley | Further Information

{"title"=>"Finding the \"dark matter\" in human and yeast protein network prediction and modelling", "type"=>"journal", "authors"=>[{"first_name"=>"Juan A.G.", "last_name"=>"Ranea", "scopus_author_id"=>"7801487448"}, {"first_name"=>"Ian", "last_name"=>"Morilla", "scopus_author_id"=>"36523269400"}, {"first_name"=>"Jon G.", "last_name"=>"Lees", "scopus_author_id"=>"9636068500"}, {"first_name"=>"Adam J.", "last_name"=>"Reid", "scopus_author_id"=>"15760716800"}, {"first_name"=>"Corin", "last_name"=>"Yeats", "scopus_author_id"=>"55991503600"}, {"first_name"=>"Andrew B.", "last_name"=>"Clegg", "scopus_author_id"=>"36146926700"}, {"first_name"=>"Francisca", "last_name"=>"Sanchez-Jimenez", "scopus_author_id"=>"7003273730"}, {"first_name"=>"Christine", "last_name"=>"Orengo", "scopus_author_id"=>"7006781333"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"77957806312", "issn"=>"1553734X", "scopus"=>"2-s2.0-77957806312", "pmid"=>"20885791", "pui"=>"359882378", "doi"=>"10.1371/journal.pcbi.1000945"}, "id"=>"79d41da3-80fd-3ae2-bb93-997e69dfdb1b", "abstract"=>"Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or \"dark matter\" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.", "link"=>"http://www.mendeley.com/research/finding-dark-matter-human-yeast-protein-network-prediction-modelling", "reader_count"=>52, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>17, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>17, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>1, "Student > Master"=>4, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>6, "Mathematics"=>1, "Agricultural and Biological Sciences"=>34, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemistry"=>1, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>34}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "United States"=>2, "Japan"=>2, "Brazil"=>1, "United Kingdom"=>3, "Mexico"=>2, "Australia"=>1, "India"=>1}, "group_count"=>1}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/828496"], "description"=>"<p>Panels A and B correspond to the KG and PG networks respectively, the legend for these panels show the correlation coefficients and exponents corresponding to the linear regression fit of the data. The corresponding randomised networks are shown below for KG (panels C, E) and PG (panels D, F) networks respectively. Panels C and D are from network randomisations by the adjacency method (see the section: Network randomisation). Panels E and F randomisations are from the evidence and p-value shuffling respectively.</p>", "links"=>[], "tags"=>["networks"], "article_id"=>498865, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Yeast_degree_distribution_for_the_various_networks_analysed_/498865", "title"=>"Yeast degree distribution for the various networks analysed.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:27:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/828641"], "description"=>"<p>Panels A and B correspond to the KG and PG networks respectively, the legend for these panels show the correlation coefficients and exponents corresponding to the linear regression fit of the data. The corresponding randomised networks are shown below for KG (panels C, E) and PG (panels D, F) networks respectively. Panels C and D are from network randomisations by the adjacency method (see the section: Network randomisation). Panels E and F randomisations are from the evidence and p-value shuffling respectively.</p>", "links"=>[], "tags"=>["networks"], "article_id"=>499010, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.g003", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Human_degree_distribution_for_the_various_networks_analysed_/499010", "title"=>"Human degree distribution for the various networks analysed.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:30:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/412437"], "description"=>"<div><p>Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.</p></div>", "links"=>[], "tags"=>["yeast"], "article_id"=>141486, "categories"=>["Biological Sciences", "Cancer", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945", "stats"=>{"downloads"=>11, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Finding_the_Dark_Matter_in_Human_and_Yeast_Protein_Network_Prediction_and_Modelling/141486", "title"=>"Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 00:24:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/828783"], "description"=>"<p>These plots present the precision value (y-axis) versus specific bits similarity score between the interaction profiles of the protein pairs (x-axis in plots A and C) and versus Recall (# of pairs predicted, x-axis in plots B and D) in yeast (plots A and B) and human (plots C and D) PG <sub>0.001&0.0014</sub> networks. The gold standard dataset used, KG≥2 evidences, is described in the section: Validation of the second order predictions for the PG networks.</p>", "links"=>[], "tags"=>["networks", "validation", "evidences"], "article_id"=>499147, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PG_networks_functional_context_validation_by_the_KG_8805_2_evidences_dataset_/499147", "title"=>"PG networks functional context validation by the KG≥2 evidences dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:32:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/828924"], "description"=>"<p>From left to right: <b><i>Prot. Acc. N.</i></b>, Protein accession number in Uniprot database; <b><i>KGk<sub>i</sub></i></b>, protein connection degree (k<sub>i</sub>) in the KG network; <b><i>PGk<sub>i</sub></i></b>, protein ki in the PG network; <b><i>PGk<sub>i</sub>_er</i></b>, protein k<sub>i</sub> enrichment ratio in PG compared to KG network; <b><i>R.</i></b>, rank in the PGk<sub>i</sub>_er list; <b><i>Gene name</i></b> in Uniprot and <b><i>Uniprot</i></b> functional <b><i>description</i></b>.</p>", "links"=>[], "tags"=>["proteins", "yeast", "ranked"], "article_id"=>499289, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.t002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Ten_top_proteins_in_the_yeast_and_human_PGk_i_er_ranked_lists_/499289", "title"=>"Ten top proteins in the yeast and human PGk<sub>i</sub>_er ranked lists.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 02:34:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/828382"], "description"=>"<p>A and C plots are from Yeast datasets and B and D are for Human results. A and B plots show precision versus p-values and C and D graphs show precision versus recall. Inset to the C plot shows an enlargement to visualize the improvements obtained by using the Fisher integration in yeast.</p>", "links"=>[], "tags"=>["benchmark", "studies", "methods"], "article_id"=>498743, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_of_the_benchmark_studies_for_the_individual_prediction_methods_and_the_integrated_methods_/498743", "title"=>"Results of the benchmark studies for the individual prediction methods and the integrated methods.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-09-23 02:25:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/828889"], "description"=>"<p>For the <b><i>Biological processes</i></b> and <b><i>Molecular functions</i></b> GO categories, from left to right: <b><i>GO term name</i></b>, name of the enriched GO term; <b><i>GO code</i></b>, the term's code in the GO database; <b><i>P-value</i></b>, is the enrichment p-value computed according to the GOrilla server <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000945#pcbi.1000945-Eden1\" target=\"_blank\">[45]</a>; <b><i>N</i></b>, is the total number of genes in the ranked list; <b><i>B</i></b>, is the total number of genes associated with the specific GO term in the whole ranked list; <b><i>n</i></b>, is the total number of genes in the selected top of the list; <b><i>b</i></b>, is the number of genes in the selected top of the list associated with the specific GO term; <b><i>E.</i></b>, Enrichment (N, B, n, b) = (b/n)/(B/N). Parent-child relationships between GO terms are indicated with the “>” symbol.</p>", "links"=>[], "tags"=>["ranked", "enrichment"], "article_id"=>499251, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.t003", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Human_PGk_i_er_ranked_list_enrichment_analysis_in_the_GO_database_/499251", "title"=>"Human PGk<sub>i</sub>_er ranked list enrichment analysis in the GO database.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 02:34:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/828959"], "description"=>"<p>From left to right: the <i>yeast</i> and <i>human</i> data division; <i>Type</i>, real data or random model; <i>Networks</i>, intersections of network models; <i># Edges</i>, number of protein pairs in the intersections; <i># Nodes</i>, number of different proteins (nodes) in the intersections; <i>#Ed./#Nod.</i>, or network density is ratio of the number of edges divided by the number of nodes; <i>%PGe</i>, percentage of the PG model's edges backed by the KG model. <i>R. 1</i>, <i>p-values random model</i> and <i>R. 2</i>, <i>adjacency random model</i> (see the section: Network randomisation) the randomisation process was realized over the matrix of possible binary associations of all the proteins (nodes) in the PG and KG models. Calculation of intersections for the random models went through 1,000 iterations. For further statistics see Table S2 in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000945#pcbi.1000945.s001\" target=\"_blank\">Text S1</a>.</p>", "links"=>[], "tags"=>["kg", "networks", "intersection"], "article_id"=>499321, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Juan A. G. Ranea", "Ian Morilla", "Jon G. Lees", "Adam J. Reid", "Corin Yeats", "Andrew B. Clegg", "Francisca Sánchez-Jiménez", "Christine Orengo"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000945.t001", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PG_and_KG_networks_intersection_analysis_/499321", "title"=>"PG and KG networks intersection analysis.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-09-23 02:35:21"}

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