2009 Ohio Collaborative Conference on Bioinformatics最新文献

{"title":"Computation of Putative Targets for Human and Mouse snoRNAs, Responsible for Prader-Willi Syndrome","authors":"A. Prakash, D. Rearick, Samuel S. Shepard, A. Fedorov","doi":"10.1109/OCCBIO.2009.36","DOIUrl":"https://doi.org/10.1109/OCCBIO.2009.36","url":null,"abstract":"Abstract—This Small nucleolar RNA (snoRNA) are a group of non-protein-coding RNA molecules among hundreds of others in the human genome. These molecules bind specifically to other cellular RNA targets via base pairing to form short, double-stranded structures. This binding causes the snoRNA targets to undergo specific chemical modifications. There are a number of (orphan) snoRNAs whose targets are still unknown; yet they clearly seem to play an important cellular function as their removal seems to cause genetic diseases like Prader-Willi Syndrome. In this project we aim to computationally predict targets for a specific group of orphan snoRNA of human and mouse (known as HBII-85 and MBII-85 respectively) that are known to be associated directly in the development of Prader-Willi Syndrome [1]. We started off by modifying our previously published snoTARGET program [2], to search for targets in the entire set of human and mouse genomic sequences. Then we generated a computational pipeline to characterize targets common to these two species. This resulted in the discovery of dozens of putative HBII-85/MBII-85 targets within the evolutionarily conserved segments of mRNAs, introns, and intergenic regions. Several of these targets have been found to be very well conserved evolutionarily among other mammals, and seem to have distinctive secondary structures detected by Evofold program [3]. Hence these targets can form the primary objects for further experimental validation. This could enhance the understanding of the function and clinical relevance of this group of snoRNA and could pave novel modes of intervention for arresting or alleviating the Prader-Willi Syndrome. The human genome contains hundreds of small non-protein-coding RNA molecules of which one group are the snoRNA (small nucleolar RNA). These molecules bind specifically to other cellular RNA targets via base pairing to form short, double-stranded structures. This binding causes the snoRNA targets to undergo specific chemical modifications. There are a number of (orphan) snoRNAs whose targets are still unknown; yet, because their removal causes genetic diseases such as Prader-Willi Syndrome, they clearly seem to play an important cellular function. In this project we aimed to computationally predict targets for a specific group of orphan snoRNA of human and mouse (known as HBII-85 and MBII-85 respectively) that are known to be directly involved in the development of Prader-Willi Syndrome. To fulfill this task we modified our previously published snoTARGET program, to search for targets in the entire set of human and mouse genomic sequences. Then we generated a computational pipeline to characterize targets common for these two species. This approach resulted in the discovery of dozens of putative HBII-85/MBII-85 targets within the evolutionary conserved segments of mRNAs, introns, and intergenic regions. Several of these targets are located within mammalian-wide evolutionary conserved sequenc","PeriodicalId":231499,"journal":{"name":"2009 Ohio Collaborative Conference on Bioinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126840576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Modularity in Biological Systems","authors":"Xin Li, Sinan Erten, G. Bebek, M. Koyuturk, Jing Li","doi":"10.1109/OCCBIO.2009.29","DOIUrl":"https://doi.org/10.1109/OCCBIO.2009.29","url":null,"abstract":"In systems biology, comparative analysis of molecular interactions across diverse species indicates that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. In this paper, we investigate the evolution of modularity in biological networks through phylogenetic analysis of network modules. Namely, we develop a computational framework, which identifies modules in networks of diverse species independently and projects these modules into the networks of other species, with aview to capturing the evolutionary trajectories of functional modules. These trajectories can then be used to reconstruct modular phylogenies and whole-network phylogenies, or to enhance identification of functional modules. In the context of phylogeny reconstruction, our experiments on a comprehensive collection of simulated and real networks show that comparison of networks based on module trajectories is more informative than other measures of network similarity. These results demonstrate the key role of modularity in the functional evolution of biological systems and motivate further investigation of the evolution of functional modules.","PeriodicalId":231499,"journal":{"name":"2009 Ohio Collaborative Conference on Bioinformatics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115223907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binary Classification Based on Potentials","authors":"E. Boczko, Andrew DiLullo, Todd R. Young","doi":"10.1109/OCCBIO.2009.31","DOIUrl":"https://doi.org/10.1109/OCCBIO.2009.31","url":null,"abstract":"We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard SupportVector Machine methods.","PeriodicalId":231499,"journal":{"name":"2009 Ohio Collaborative Conference on Bioinformatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122045186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Binary Classification Based on Signed Distance Functions with Support Vector Machines","authors":"E. Boczko, Todd R. Young, Minhui Zie, Di Wu","doi":"10.1109/OCCBIO.2009.30","DOIUrl":"https://doi.org/10.1109/OCCBIO.2009.30","url":null,"abstract":"We compare methods based on the Signed Distance Function (SDF) a new tool for binary classification with standard Support Vector Machine (SVM) methods. We demonstrate on several sets of micro-array data that the performance of the SDF based methods can match or exceed that of SVM methods.","PeriodicalId":231499,"journal":{"name":"2009 Ohio Collaborative Conference on Bioinformatics","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116172347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}