The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Timothy L Bailey - One of the best experts on this subject based on the ideXlab platform.
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The Meme Suite
Nucleic acids research, 2015Co-Authors: Timothy L Bailey, Charles E Grant, James R. Johnson, William Stafford NobleAbstract:The Meme Suite is a powerful, integrated set of web-based tools for studying sequence motifs in proteins, DNA and RNA. Such motifs encode many biological functions, and their detection and characterization is important in the study of molecular interactions in the cell, including the regulation of gene expression. Since the previous description of the Meme Suite in the 2009 Nucleic Acids Research Web Server Issue, we have added six new tools. Here we describe the capabilities of all the tools within the suite, give advice on their best use and provide several case studies to illustrate how to combine the results of various Meme Suite tools for successful motif-based analyses. The Meme Suite is freely available for academic use at http://Meme-suite.org, and source code is also available for download and local installation.
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motif based analysis of large nucleotide data sets using Meme chip
Nature Protocols, 2014Co-Authors: William Stafford Noble, Timothy L BaileyAbstract:Meme-ChIP is a web-based tool for analyzing motifs in large DNA or RNA data sets. It can analyze peak regions identified by ChIP-seq, cross-linking sites identified by CLIP-seq and related assays, as well as sets of genomic regions selected using other criteria. Meme-ChIP performs de novo motif discovery, motif enrichment analysis, motif location analysis and motif clustering, providing a comprehensive picture of the DNA or RNA motifs that are enriched in the input sequences. Meme-ChIP performs two complementary types of de novo motif discovery: weight matrix-based discovery for high accuracy; and word-based discovery for high sensitivity. Motif enrichment analysis using DNA or RNA motifs from human, mouse, worm, fly and other model organisms provides even greater sensitivity. Meme-ChIP's interactive HTML output groups and aligns significant motifs to ease interpretation. This protocol takes less than 3 h, and it provides motif discovery approaches that are distinct and complementary to other online methods.
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Meme chip
Bioinformatics, 2011Co-Authors: Philip Machanick, Timothy L BaileyAbstract:Motivation: Advances in high-throughput sequencing have resulted in rapid growth in large, high-quality datasets including those arising from transcription factor (TF) ChIP-seq experiments. While there are many existing tools for discovering TF binding site motifs in such datasets, most web-based tools cannot directly process such large datasets. Results: The Meme-ChIP web service is designed to analyze ChIP-seq ‘peak regions’—short genomic regions surrounding declared ChIP-seq ‘peaks’. Given a set of genomic regions, it performs (i) ab initio motif discovery, (ii) motif enrichment analysis, (iii) motif visualization, (iv) binding affinity analysis and (v) motif identification. It runs two complementary motif discovery algorithms on the input data—Meme and DREME—and uses the motifs they discover in subsequent visualization, binding affinity and identification steps. Meme-ChIP also performs motif enrichment analysis using the AME algorithm, which can detect very low levels of enrichment of binding sites for TFs with known DNA-binding motifs. Importantly, unlike with the Meme web service, there is no restriction on the size or number of uploaded sequences, allowing very large ChIP-seq datasets to be analyzed. The analyses performed by Meme-ChIP provide the user with a varied view of the binding and regulatory activity of the ChIP-ed TF, as well as the possible involvement of other DNA-binding TFs. Availability: Meme-ChIP is available as part of the Meme Suite at http://Meme.nbcr.net. Contact: t.bailey@uq.edu.au Supplementary information:Supplementary data are available at Bioinformatics online.
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Meme suite tools for motif discovery and searching
Nucleic Acids Research, 2009Co-Authors: Timothy L Bailey, Mikael Boden, Fabian A Buske, Martin C Frith, Charles E Grant, Luca Clementi, Jingyuan Ren, Wilfred W Li, William Stafford NobleAbstract:The Meme Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular Meme motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by Meme and GLAM2. Transcription factor motifs (including those discovered using Meme) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. Meme output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://Meme.nbcr.net.
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Meme discovering and analyzing dna and protein sequence motifs
Nucleic Acids Research, 2006Co-Authors: Timothy L Bailey, Nadya Williams, Chris MislehAbstract:Meme (Multiple EM for Motif Elicitation) is one of the most widely used tools for searching for novel 'signals' in sets of biological sequences. Applications include the discovery of new transcription factor binding sites and protein domains. Meme works by searching for repeated, ungapped sequence patterns that occur in the DNA or protein sequences provided by the user. Users can perform Meme searches via the web server hosted by the National Biomedical Computation Resource (http://Meme.nbcr.net) and several mirror sites. Through the same web server, users can also access the Motif Alignment and Search Tool to search sequence databases for matches to motifs encoded in several popular formats. By clicking on buttons in the Meme output, users can compare the motifs discovered in their input sequences with databases of known motifs, search sequence databases for matches to the motifs and display the motifs in various formats. This article describes the freely accessible web server and its architecture, and discusses ways to use Meme effectively to find new sequence patterns in biological sequences and analyze their significance.
William Stafford Noble - One of the best experts on this subject based on the ideXlab platform.
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The Meme Suite
Nucleic acids research, 2015Co-Authors: Timothy L Bailey, Charles E Grant, James R. Johnson, William Stafford NobleAbstract:The Meme Suite is a powerful, integrated set of web-based tools for studying sequence motifs in proteins, DNA and RNA. Such motifs encode many biological functions, and their detection and characterization is important in the study of molecular interactions in the cell, including the regulation of gene expression. Since the previous description of the Meme Suite in the 2009 Nucleic Acids Research Web Server Issue, we have added six new tools. Here we describe the capabilities of all the tools within the suite, give advice on their best use and provide several case studies to illustrate how to combine the results of various Meme Suite tools for successful motif-based analyses. The Meme Suite is freely available for academic use at http://Meme-suite.org, and source code is also available for download and local installation.
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motif based analysis of large nucleotide data sets using Meme chip
Nature Protocols, 2014Co-Authors: William Stafford Noble, Timothy L BaileyAbstract:Meme-ChIP is a web-based tool for analyzing motifs in large DNA or RNA data sets. It can analyze peak regions identified by ChIP-seq, cross-linking sites identified by CLIP-seq and related assays, as well as sets of genomic regions selected using other criteria. Meme-ChIP performs de novo motif discovery, motif enrichment analysis, motif location analysis and motif clustering, providing a comprehensive picture of the DNA or RNA motifs that are enriched in the input sequences. Meme-ChIP performs two complementary types of de novo motif discovery: weight matrix-based discovery for high accuracy; and word-based discovery for high sensitivity. Motif enrichment analysis using DNA or RNA motifs from human, mouse, worm, fly and other model organisms provides even greater sensitivity. Meme-ChIP's interactive HTML output groups and aligns significant motifs to ease interpretation. This protocol takes less than 3 h, and it provides motif discovery approaches that are distinct and complementary to other online methods.
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Meme suite tools for motif discovery and searching
Nucleic Acids Research, 2009Co-Authors: Timothy L Bailey, Mikael Boden, Fabian A Buske, Martin C Frith, Charles E Grant, Luca Clementi, Jingyuan Ren, Wilfred W Li, William Stafford NobleAbstract:The Meme Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular Meme motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by Meme and GLAM2. Transcription factor motifs (including those discovered using Meme) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. Meme output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://Meme.nbcr.net.
Dino Ienco - One of the best experts on this subject based on the ideXlab platform.
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Meme ranking to maximize posts virality in microblogging platforms
Journal of Intelligent Information Systems, 2013Co-Authors: Francesco Bonchi, Carlos Castillo, Dino IencoAbstract:Microblogging is a modern communication paradigm in which users post bits of information, or “ Memes ” as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a Meme, it become visible to the user community. When a user finds a Meme of another user interesting, she can eventually repost it, thus allowing Memes to propagate virally trough the social network. In this paper we introduce the Meme ranking problem , as the problem of selecting which k Memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of Meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
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The Meme Ranking Problem: Maximizing Microblogging Virality
Journal of Intelligent Information Systems, 2013Co-Authors: Francesco Bonchi, Carlos Castillo, Dino IencoAbstract:Microblogging is a modern communication paradigm in which users post bits of information, or "Memes" as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a Meme, it become visible to the user community. When a user finds a Meme of another user interesting, she can eventually repost it, thus allowing Memes to propagate virally trough the social network. In this paper we introduce the Meme ranking problem, as the problem of selecting which k Memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of Meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
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the Meme ranking problem maximizing microblogging virality
International Conference on Data Mining, 2010Co-Authors: Dino Ienco, Francesco Bonchi, Carlos CastilloAbstract:Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a “Meme” of another user interesting, she can eventually repost it, thus allowing Memes to propagate virally trough a social network. In this paper we introduce the Meme ranking problem, as the problem of selecting which k Memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of Meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
Francesco Bonchi - One of the best experts on this subject based on the ideXlab platform.
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Meme ranking to maximize posts virality in microblogging platforms
Journal of Intelligent Information Systems, 2013Co-Authors: Francesco Bonchi, Carlos Castillo, Dino IencoAbstract:Microblogging is a modern communication paradigm in which users post bits of information, or “ Memes ” as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a Meme, it become visible to the user community. When a user finds a Meme of another user interesting, she can eventually repost it, thus allowing Memes to propagate virally trough the social network. In this paper we introduce the Meme ranking problem , as the problem of selecting which k Memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of Meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
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The Meme Ranking Problem: Maximizing Microblogging Virality
Journal of Intelligent Information Systems, 2013Co-Authors: Francesco Bonchi, Carlos Castillo, Dino IencoAbstract:Microblogging is a modern communication paradigm in which users post bits of information, or "Memes" as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a Meme, it become visible to the user community. When a user finds a Meme of another user interesting, she can eventually repost it, thus allowing Memes to propagate virally trough the social network. In this paper we introduce the Meme ranking problem, as the problem of selecting which k Memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of Meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
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the Meme ranking problem maximizing microblogging virality
International Conference on Data Mining, 2010Co-Authors: Dino Ienco, Francesco Bonchi, Carlos CastilloAbstract:Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a “Meme” of another user interesting, she can eventually repost it, thus allowing Memes to propagate virally trough a social network. In this paper we introduce the Meme ranking problem, as the problem of selecting which k Memes (among the ones posted their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of Meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
Charles E Grant - One of the best experts on this subject based on the ideXlab platform.
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The Meme Suite
Nucleic acids research, 2015Co-Authors: Timothy L Bailey, Charles E Grant, James R. Johnson, William Stafford NobleAbstract:The Meme Suite is a powerful, integrated set of web-based tools for studying sequence motifs in proteins, DNA and RNA. Such motifs encode many biological functions, and their detection and characterization is important in the study of molecular interactions in the cell, including the regulation of gene expression. Since the previous description of the Meme Suite in the 2009 Nucleic Acids Research Web Server Issue, we have added six new tools. Here we describe the capabilities of all the tools within the suite, give advice on their best use and provide several case studies to illustrate how to combine the results of various Meme Suite tools for successful motif-based analyses. The Meme Suite is freely available for academic use at http://Meme-suite.org, and source code is also available for download and local installation.
-
Meme suite tools for motif discovery and searching
Nucleic Acids Research, 2009Co-Authors: Timothy L Bailey, Mikael Boden, Fabian A Buske, Martin C Frith, Charles E Grant, Luca Clementi, Jingyuan Ren, Wilfred W Li, William Stafford NobleAbstract:The Meme Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular Meme motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by Meme and GLAM2. Transcription factor motifs (including those discovered using Meme) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. Meme output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://Meme.nbcr.net.
