GRAPES: A Software for Parallel Searching on Biological Graphs Targeting Multi-Core Architectures
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{"title"=>"GRAPES: A Software for Parallel Searching on Biological Graphs Targeting Multi-Core Architectures", "type"=>"journal", "authors"=>[{"first_name"=>"Rosalba", "last_name"=>"Giugno", "scopus_author_id"=>"6506371678"}, {"first_name"=>"Vincenzo", "last_name"=>"Bonnici", "scopus_author_id"=>"36611214400"}, {"first_name"=>"Nicola", "last_name"=>"Bombieri", "scopus_author_id"=>"8904473200"}, {"first_name"=>"Alfredo", "last_name"=>"Pulvirenti", "scopus_author_id"=>"34977484800"}, {"first_name"=>"Alfredo", "last_name"=>"Ferro", "scopus_author_id"=>"57190518739"}, {"first_name"=>"Dennis", "last_name"=>"Shasha", "scopus_author_id"=>"7005430203"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "sgr"=>"84886012840", "doi"=>"10.1371/journal.pone.0076911", "scopus"=>"2-s2.0-84886012840", "pui"=>"370089751", "pmid"=>"24167551"}, "id"=>"4f0a2dc5-21ee-3600-bb78-101607b61995", "abstract"=>"Biological applications, from genomics to ecology, deal with graphs that represents the structure of interactions. Analyzing such data requires searching for subgraphs in collections of graphs. This task is computationally expensive. Even though multicore architectures, from commodity computers to more advanced symmetric multiprocessing (SMP), offer scalable computing power, currently published software implementations for indexing and graph matching are fundamentally sequential. As a consequence, such software implementations (i) do not fully exploit available parallel computing power and (ii) they do not scale with respect to the size of graphs in the database. We present GRAPES, software for parallel searching on databases of large biological graphs. GRAPES implements a parallel version of well-established graph searching algorithms, and introduces new strategies which naturally lead to a faster parallel searching system especially for large graphs. GRAPES decomposes graphs into subcomponents that can be efficiently searched in parallel. We show the performance of GRAPES on representative biological datasets containing antiviral chemical compounds, DNA, RNA, proteins, protein contact maps and protein interactions networks.", "link"=>"http://www.mendeley.com/research/grapes-software-parallel-searching-biological-graphs-targeting-multicore-architectures", "reader_count"=>12, "reader_count_by_academic_status"=>{"Researcher"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>2, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>3, "Other"=>1, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Agricultural and Biological Sciences"=>2, "Chemistry"=>1, "Computer Science"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Computer Science"=>{"Computer Science"=>7}}, "reader_count_by_country"=>{"Italy"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1254654"], "description"=>"<p>List of the new GRAPES components together with the components of GRAPES taken from GraphGrepSX <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076911#pone.0076911-Bonnici1\" target=\"_blank\">[16]</a> and SING <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076911#pone.0076911-DiNatale1\" target=\"_blank\">[15]</a>.</p>", "links"=>[], "tags"=>["graphgrepsx"], "article_id"=>829901, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.t001", "stats"=>{"downloads"=>3, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_GRAPES_vs_GraphGrepSX_and_SING_/829901", "title"=>"GRAPES vs GraphGrepSX and SING.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254653"], "description"=>"<p>It shows building, matching and total (building+macthing) time of the compared tools. Comparisons show that GRAPES outperforms the other tools by using 1 or 6 threads. However, parallelism helps to decrease the computation costs. We found that indexing induces efficiency when the index is built once and re-used for multiquery. Indeed, GRAPES results faster than non-indexing based methods such as VF2 by running one or more queries (see Multiquery running plot, run on 6 threads).</p>", "links"=>[], "tags"=>[], "article_id"=>829900, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g009", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_results_on_the_PPI_database_/829900", "title"=>"Experimental results on the <i>PPI</i> database.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254650"], "description"=>"<p>It shows building, matching and total (building+macthing) time of the compared tools. The <i>AIDS</i> dataset contains a large number of small, simply structured graphs. Here, querying is not an expensive task. Comparisons show that GRAPES outperforms the other tools by using 1 thread. Parallelism due to the simple structure of data does not help. However, indexing induces efficiency when the index is built once and re-used for multiquery. Indeed, GRAPES results faster than non-indexing based methods such as VF2 by running two o more queries (see Multiquery running plot, run on 6 threads).</p>", "links"=>[], "tags"=>[], "article_id"=>829897, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_results_on_the_AIDS_database_/829897", "title"=>"Experimental results on the <i>AIDS</i> database.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254648"], "description"=>"<p>Each node in the trie links to (i) the list of graphs Gi containing the feature associated with that node, (ii) the number of times the feature occurs in each Gi, and (iii) the starting vertices vi of each such path.</p>", "links"=>[], "tags"=>["stored", "node", "grapes", "graphs"], "article_id"=>829895, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_information_stored_in_a_node_of_a_GRAPES_index_trie_for_graphs_of_Figure_1_/829895", "title"=>"The information stored in a node of a GRAPES index (trie) for graphs of Figure 1.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254649"], "description"=>"<p>The graphs in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076911#pone-0076911-g001\" target=\"_blank\">Figure 1</a> are reduced to the following components after filtering. and derive from and from . This reduction of search space makes GRAPES fast and suitable to parallelization. The combinatorial subgraph isomorphism algorithm (applied in the Matching phase) will be run only on these components.</p>", "links"=>[], "tags"=>[], "article_id"=>829896, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g005", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Filtering_in_GRAPES_/829896", "title"=>"Filtering in GRAPES.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254647"], "description"=>"<p>The index for the graph of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076911#pone-0076911-g001\" target=\"_blank\">Figure 1</a>. A trie stores all features of length 2 of . Each node is associated to a feature, that is the path in the trie up to this node. For each node in the trie, a list of information such as the number of times the feature appears in (equally the number of paths in mapped to it) and the identifications of the first nodes of such paths is stored.</p>", "links"=>[], "tags"=>["trie", "indexing"], "article_id"=>829894, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g003", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_trie_data_structure_used_as_indexing_in_GRAPES_/829894", "title"=>"The trie data structure used as indexing in GRAPES.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254655", "https://ndownloader.figshare.com/files/1254656"], "description"=>"<div><p>Biological applications, from genomics to ecology, deal with graphs that represents the structure of interactions. Analyzing such data requires searching for subgraphs in collections of graphs. This task is computationally expensive. Even though multicore architectures, from commodity computers to more advanced symmetric multiprocessing (SMP), offer scalable computing power, currently published software implementations for indexing and graph matching are fundamentally sequential. As a consequence, such software implementations (i) do not fully exploit available parallel computing power and (ii) they do not scale with respect to the size of graphs in the database. We present GRAPES, software for parallel searching on databases of large biological graphs. GRAPES implements a parallel version of well-established graph searching algorithms, and introduces new strategies which naturally lead to a faster parallel searching system especially for large graphs. GRAPES decomposes graphs into subcomponents that can be efficiently searched in parallel. We show the performance of GRAPES on representative biological datasets containing antiviral chemical compounds, DNA, RNA, proteins, protein contact maps and protein interactions networks.</p></div>", "links"=>[], "tags"=>["parallel", "searching", "graphs", "targeting", "multi-core"], "article_id"=>829902, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0076911.s001", "https://dx.doi.org/10.1371/journal.pone.0076911.s002"], "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_GRAPES_A_Software_for_Parallel_Searching_on_Biological_Graphs_Targeting_Multi_Core_Architectures_/829902", "title"=>"GRAPES: A Software for Parallel Searching on Biological Graphs Targeting Multi-Core Architectures", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254652"], "description"=>"<p>It shows building, matching and total (building+macthing) time of the compared tools. Comparisons show that GRAPES outperforms the other tools by using 1 or 6 threads. However, parallelism helps to decrease the computation costs. We found that indexing induces efficiency when the index is built once and re-used for multiquery. Indeed, GRAPES results faster than non-indexing based methods such as VF2 by running one or more queries (see Multiquery running plot, run on 6 threads).</p>", "links"=>[], "tags"=>[], "article_id"=>829899, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_results_on_the_PCM_database_/829899", "title"=>"Experimental results on the <i>PCM</i> database.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254651"], "description"=>"<p>It shows building, matching and total (building+macthing) time of the compared tools. Comparisons show that GRAPES outperforms the other tools by using 1 or 6 threads. However, parallelism helps to decrease the computation costs. We found that indexing induces efficiency when the index is built once and re-used for multiquery. Indeed, GRAPES results faster than non-indexing based methods such as VF2 by running one or more queries (see Multiquery running plot, run on 6 threads).</p>", "links"=>[], "tags"=>[], "article_id"=>829898, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Experimental_results_on_the_PDBS_database_/829898", "title"=>"Experimental results on the <i>PDBS</i> database.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254646"], "description"=>"<p>Schema of GRAPES.</p>", "links"=>[], "tags"=>[], "article_id"=>829893, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schema_of_GRAPES_/829893", "title"=>"Schema of GRAPES.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1254645"], "description"=>"<p>The database is composed by four graphs , , and . is the query. The occurrences of in the graphs are shown by colored vertices and bold edges.</p>", "links"=>[], "tags"=>[], "article_id"=>829892, "categories"=>["Uncategorised"], "users"=>["Rosalba Giugno", "Vincenzo Bonnici", "Nicola Bombieri", "Alfredo Pulvirenti", "Alfredo Ferro", "Dennis Shasha"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0076911.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graph_database_and_query_/829892", "title"=>"Graph database and query.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-10-22 03:01:06"}

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Relative Metric

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