The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Kana Shimizu - One of the best experts on this subject based on the ideXlab platform.
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Poodle: tools predicting intrinsically disordered regions of amino acid sequence.
Methods in molecular biology (Clifton N.J.), 2014Co-Authors: Kana ShimizuAbstract:Protein intrinsic disorder, a widespread phenomenon characterized by a lack of stable three-dimensional structure, is thought to play an important role in protein function. In the last decade, dozens of computational methods for predicting intrinsic disorder from amino acid sequences have been developed. They are widely used by structural biologists not only for analyzing the biological function of intrinsic disorder but also for finding flexible regions that possibly hinder successful crystallization of the full-length protein. In this chapter, I introduce Prediction Of Order and Disorder by machine LEarning (Poodle), which is a series of programs accurately predicting intrinsic disorder. After giving the theoretical background for predicting intrinsic disorder, I give a detailed guide to using Poodle. I then also briefly introduce a case study where using Poodle for functional analyses of protein disorder led to a novel biological findings.
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Poodle-I: disordered region prediction by integrating Poodle series and structural information predictors based on a workflow approach.
In silico biology, 2010Co-Authors: Shuichi Hirose, Kana Shimizu, Tamotsu NoguchiAbstract:Under physiological conditions, many proteins that include a region lacking well-defined three-dimensional structures have been identified, especially in eukaryotes. These regions often play an important biological cellular role, although they cannot form a stable structure. Therefore, they are biologically remarkable phenomena. From an industrial perspective, they can provide useful information for determining three-dimensional structures or designing drugs. For these reasons, disordered regions have attracted a great deal of attention in recent years. Their accurate prediction is therefore anticipated to provide annotations that are useful for wide range of applications. Poodle-I (where "I" stands for integration) is a web-based disordered region prediction system. Poodle-I integrates prediction results obtained from three kinds of disordered region predictors (Poodles) developed from the viewpoint that the characteristics of disordered regions change according to their length. Furthermore, Poodle-I combines that information with predicted structural information by application of a workflow approach. When compared with server teams that showed best performance in CASP8, Poodle-I ranked among the top and exhibited the highest performance in predicting unfolded proteins. Poodle-I is an efficient tool for detecting disordered regions in proteins solely from the amino acid sequence. The application is freely available at http://mbs.cbrc.jp/Poodle/Poodle-i.html.
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Poodle-S
Bioinformatics (Oxford England), 2007Co-Authors: Kana Shimizu, Shuichi Hirose, Tamotsu NoguchiAbstract:Summary: Protein disorder is characterized by a lack of a stable 3D structure, and is considered to be involved in a number of important protein functions such as regulatory and signalling events. We developed a web application, the Poodle-S, which predicts the disordered region from amino acid sequences by using physicochemical features and reduced amino acid set of a position-specific scoring matrix. Availability: Poodle-S is available from http://mbs.cbrc.jp/Poodle/Poodle-s.html and can be used by both academic and commercial users. Contact: [email protected]
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Poodle-L
Bioinformatics (Oxford England), 2007Co-Authors: Shuichi Hirose, Kana Shimizu, Satoru Kanai, Kuroda Yutaka, Tamotsu NoguchiAbstract:Motivation: Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications. Results: We developed Prediction Of Order and Disorder by machine LEarning (Poodle-L; L stands for long), the Support Vector Machines (SVMs) based method for predicting long disordered regions using 10 kinds of simple physico-chemical properties of amino acid. Poodle-L assembles the output of 10 two-level SVM predictors into a final prediction of disordered regions. The performance of Poodle-L for predicting long disordered regions, which exhibited a Matthew's correlation coefficient of 0.658, was the highest when compared with eight well-established publicly available disordered region predictors. Availability: Poodle-L is freely available at http://mbs.cbrc.jp/Poodle/Poodle-l.html Contact: hirose-shuichi@aist.go.jp Supplementary information: Supplementary data are available at Bioinformatics online.
Kathryn M. Meurs - One of the best experts on this subject based on the ideXlab platform.
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myxomatous mitral valve disease in the miniature Poodle a retrospective study
Veterinary Journal, 2019Co-Authors: Kathryn M. Meurs, Darcy B Adin, K Odonnell, Bruce W Keene, Clarke E Atkins, Teresa C Defrancesco, Sandra TouAbstract:Myxomatous mitral valve disease (MMVD) is the most common cardiovascular disease in the dog. The natural history of the disease is wide ranging and includes patients without clinical signs as well as those with significant clinical consequences from cardiac arrhythmias, pulmonary hypertension and/or congestive heart failure. The factors that determine which dogs remain asymptomatic and which develop clinical disease are not known. Disease characteristics could be breed or family related; some breeds of dogs, particularly the Cavalier King Charles spaniels, develop MMVD at an early age. The purpose of this study was to retrospectively characterize MMVD in the miniature Poodle, a commonly affected breed in which MMVD has not been well characterized. Thirty-two miniature Poodles met the inclusion criteria. Mean age was 11±three years. Clinical signs included exercise intolerance, syncope and coughing. Eighteen dogs were classified as ACVIM Stage B1, 12 as stage B2, and two as stage C. Mean vertebral heart scale (VHS) was 10.2 (±standard deviation of 0.9); 15 of 28 dogs had a VHS <10.3. One dog had radiographic evidence of congestive heart failure. Mean diastolic left ventricle dimension normalized to body weight was 1.6 (±0.4) and mean systolic was 0.8 (±0.3). Mitral valve prolapse was subjectively classified as mild or moderate in 19 dogs and severe in two. In the miniature Poodles reported here, MMVD appears to be a fairly late onset disease and often is a mild phenotype.
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Myxomatous mitral valve disease in the miniature Poodle: A retrospective study
Veterinary journal (London England : 1997), 2018Co-Authors: Kathryn M. Meurs, Darcy B Adin, Bruce W Keene, Clarke E Atkins, Teresa C Defrancesco, K. O’donnell, Sandra TouAbstract:Myxomatous mitral valve disease (MMVD) is the most common cardiovascular disease in the dog. The natural history of the disease is wide ranging and includes patients without clinical signs as well as those with significant clinical consequences from cardiac arrhythmias, pulmonary hypertension and/or congestive heart failure. The factors that determine which dogs remain asymptomatic and which develop clinical disease are not known. Disease characteristics could be breed or family related; some breeds of dogs, particularly the Cavalier King Charles spaniels, develop MMVD at an early age. The purpose of this study was to retrospectively characterize MMVD in the miniature Poodle, a commonly affected breed in which MMVD has not been well characterized. Thirty-two miniature Poodles met the inclusion criteria. Mean age was 11±three years. Clinical signs included exercise intolerance, syncope and coughing. Eighteen dogs were classified as ACVIM Stage B1, 12 as stage B2, and two as stage C. Mean vertebral heart scale (VHS) was 10.2 (±standard deviation of 0.9); 15 of 28 dogs had a VHS
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Evaluation of artificial selection in Standard Poodles using whole-genome sequencing.
Mammalian Genome, 2016Co-Authors: Steven G. Friedenberg, Kathryn M. Meurs, Trudy F. C. MackayAbstract:Identifying regions of artificial selection within dog breeds may provide insights into genetic variation that underlies breed-specific traits or diseases—particularly if these traits or disease predispositions are fixed within a breed. In this study, we searched for runs of homozygosity (ROH) and calculated the di statistic (which is based upon FST) to identify regions of artificial selection in Standard Poodles using high-coverage, whole-genome sequencing data of 15 Standard Poodles and 49 dogs across seven other breeds. We identified consensus ROH regions ≥1 Mb in length and common to at least ten Standard Poodles covering 0.6 % of the genome, and di regions that most distinguish Standard Poodles from other breeds covering 3.7 % of the genome. Within these regions, we identified enriched gene pathways related to olfaction, digestion, and taste, as well as pathways related to adrenal hormone biosynthesis, T cell function, and protein ubiquitination that could contribute to the pathogenesis of some Poodle-prevalent autoimmune diseases. We also validated variants related to hair coat and skull morphology that have previously been identified as being under selective pressure in Poodles, and flagged additional polymorphisms in genes such as ITGA2B, CBX4, and TNXB that may represent strong candidates for other common Poodle disorders.
Trudy F. C. Mackay - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of artificial selection in Standard Poodles using whole-genome sequencing.
Mammalian Genome, 2016Co-Authors: Steven G. Friedenberg, Kathryn M. Meurs, Trudy F. C. MackayAbstract:Identifying regions of artificial selection within dog breeds may provide insights into genetic variation that underlies breed-specific traits or diseases—particularly if these traits or disease predispositions are fixed within a breed. In this study, we searched for runs of homozygosity (ROH) and calculated the di statistic (which is based upon FST) to identify regions of artificial selection in Standard Poodles using high-coverage, whole-genome sequencing data of 15 Standard Poodles and 49 dogs across seven other breeds. We identified consensus ROH regions ≥1 Mb in length and common to at least ten Standard Poodles covering 0.6 % of the genome, and di regions that most distinguish Standard Poodles from other breeds covering 3.7 % of the genome. Within these regions, we identified enriched gene pathways related to olfaction, digestion, and taste, as well as pathways related to adrenal hormone biosynthesis, T cell function, and protein ubiquitination that could contribute to the pathogenesis of some Poodle-prevalent autoimmune diseases. We also validated variants related to hair coat and skull morphology that have previously been identified as being under selective pressure in Poodles, and flagged additional polymorphisms in genes such as ITGA2B, CBX4, and TNXB that may represent strong candidates for other common Poodle disorders.
Munawar Hafiz - One of the best experts on this subject based on the ideXlab platform.
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ESSoS - Poodles, More Poodles, FREAK Attacks Too: How Server Administrators Responded to Three Serious Web Vulnerabilities
Lecture Notes in Computer Science, 2016Co-Authors: Benjamin Fogel, Shane Farmer, Hamza Alkofahi, Anthony Skjellum, Munawar HafizAbstract:We present an empirical study on the patching characteristics of the top 100,000 web sites in response to three recent vulnerabilities: the Poodle vulnerability, the Poodle TLS vulnerability, and the FREAK vulnerability. The goal was to identify how the web responds to newly discovered vulnerabilities and the remotely observable characteristics of websites that contribute to the response pattern over time. Using open source tools, we found that there is a slow patch adoption rate in general; for example, about one in four servers hosting Alexa top 100,000 sites we sampled remained vulnerable to the Poodle attack even after five months. It was assuring that servers handling sensitive data were more aggressive in patching the vulnerabilities. However, servers that had more open ports were more likely to be vulnerable. The results are valuable for practitioners to understand the state of security engineering practices and what can be done to improve.
Tamotsu Noguchi - One of the best experts on this subject based on the ideXlab platform.
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Poodle-I: disordered region prediction by integrating Poodle series and structural information predictors based on a workflow approach.
In silico biology, 2010Co-Authors: Shuichi Hirose, Kana Shimizu, Tamotsu NoguchiAbstract:Under physiological conditions, many proteins that include a region lacking well-defined three-dimensional structures have been identified, especially in eukaryotes. These regions often play an important biological cellular role, although they cannot form a stable structure. Therefore, they are biologically remarkable phenomena. From an industrial perspective, they can provide useful information for determining three-dimensional structures or designing drugs. For these reasons, disordered regions have attracted a great deal of attention in recent years. Their accurate prediction is therefore anticipated to provide annotations that are useful for wide range of applications. Poodle-I (where "I" stands for integration) is a web-based disordered region prediction system. Poodle-I integrates prediction results obtained from three kinds of disordered region predictors (Poodles) developed from the viewpoint that the characteristics of disordered regions change according to their length. Furthermore, Poodle-I combines that information with predicted structural information by application of a workflow approach. When compared with server teams that showed best performance in CASP8, Poodle-I ranked among the top and exhibited the highest performance in predicting unfolded proteins. Poodle-I is an efficient tool for detecting disordered regions in proteins solely from the amino acid sequence. The application is freely available at http://mbs.cbrc.jp/Poodle/Poodle-i.html.
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Poodle-S
Bioinformatics (Oxford England), 2007Co-Authors: Kana Shimizu, Shuichi Hirose, Tamotsu NoguchiAbstract:Summary: Protein disorder is characterized by a lack of a stable 3D structure, and is considered to be involved in a number of important protein functions such as regulatory and signalling events. We developed a web application, the Poodle-S, which predicts the disordered region from amino acid sequences by using physicochemical features and reduced amino acid set of a position-specific scoring matrix. Availability: Poodle-S is available from http://mbs.cbrc.jp/Poodle/Poodle-s.html and can be used by both academic and commercial users. Contact: [email protected]
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Poodle-L
Bioinformatics (Oxford England), 2007Co-Authors: Shuichi Hirose, Kana Shimizu, Satoru Kanai, Kuroda Yutaka, Tamotsu NoguchiAbstract:Motivation: Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications. Results: We developed Prediction Of Order and Disorder by machine LEarning (Poodle-L; L stands for long), the Support Vector Machines (SVMs) based method for predicting long disordered regions using 10 kinds of simple physico-chemical properties of amino acid. Poodle-L assembles the output of 10 two-level SVM predictors into a final prediction of disordered regions. The performance of Poodle-L for predicting long disordered regions, which exhibited a Matthew's correlation coefficient of 0.658, was the highest when compared with eight well-established publicly available disordered region predictors. Availability: Poodle-L is freely available at http://mbs.cbrc.jp/Poodle/Poodle-l.html Contact: hirose-shuichi@aist.go.jp Supplementary information: Supplementary data are available at Bioinformatics online.