2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)最新文献

{"title":"Poster: Randomized algorithms for planted Motif Search","authors":"J. C. Merlin, Hieu T. Dinh","doi":"10.1109/ICCABS.2012.6182654","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182654","url":null,"abstract":"Planted (l, d) Motif Search is one of the well studied problems in computational biology. Two kinds of algorithms for solving this problem can be found in the literature, namely, exact and approximate. We introduce a third kind, namely, randomized algorithms with an estimate on the probability of identifying the planted motif. We present one such algorithm. An experimental study of this algorithm has yielded promising results.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115364025","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":"Workshop: Bioinformatics methods for reconstruction of Infectious Bronchitis Virus quasispecies from next generation sequencing data","authors":"R. O’Neill, I. Măndoiu, Mazhar I. Khan, C. Obergfell, Hongjun Wang, Andrew Bligh, Bassam Tork, Nicholas Mancuso, A. Zelikovsky","doi":"10.1109/ICCABS.2012.6182674","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182674","url":null,"abstract":"Viral infections cause a significant burden on animal health, reducing yields and increasing production costs due to expensive control programs. Vaccination is a vital part of control programs; however, its effectiveness is reduced by the quick evolution of escape viral quasispecies in animal hosts. Existing techniques for studying quasispecies evolution and response to vaccines have severely limited sensitivity and often require prior knowledge of sequence polymorphisms. By generating millions of short reads per run, with no need for culture or cloning, next-generation sequencing (NGS) technologies enable comprehensive identification of viral quasispecies infecting an animal. However, analysis of NGS data is challenging due to the huge amount of data on one hand, and to the short read lengths and high error rates on another. As a consequence, many tools developed for Sanger reads do not work at all or have impractical runtimes when applied to NGS data. Even newly developed algorithms for de novo genome assembly from NGS data are tuned for reconstruction of haploid genomes, and work poorly when the sequenced sample contains a large number of closely related sequences, as is the case in viral quasispecies. To address these shortcomings we have developed computational methods for reconstructing quasispecies sequences and estimate their frequencies from both shotgun and amplicon NGS data. Preliminary analysis of 454 sequencing reads generated from synthetic pools of Infectious Bronchitis Virus (IBV) clones and field isolates collected at various intervals after vaccination with attenuated live IBV vaccine will be presented.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724971","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":"Poster: Image registration and visualization tool for in-situ gene expression images","authors":"Ernur Saka, E. Rouchka","doi":"10.1109/ICCABS.2012.6182658","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182658","url":null,"abstract":"In the age of high-throughput molecular biology techniques, scientists have incorporated the methodology of in-situ hybridization to map spatial patterns of gene expression. In order to compare expression patterns within a common tissue structure, these images need to be “registered” or organized into a common coordinate system for alignment to a reference or atlas image. In this work we present three different image registration methodologies (manual; correlation based; mutual information based) to determine the common coordinate system for the reference and in situ hybridization images. All three methodologies are incorporated into a Matlab tool to visualize the results in a user friendly way and save them for future work.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"32 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124536007","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":"Poster: Sequence classification of homopolymer emissions (SCOPE)","authors":"James T. Morton, Patricia Abrudan, C. Liang, J. Karro","doi":"10.1109/ICCABS.2012.6182655","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182655","url":null,"abstract":"In this poster we will describe SCOPE: a tool for the identification and removal of homopolymer sequences embedded in sequenced mRNA transcript fragments as a result of the Eukaryotic polyadenylation process, allowing for the identification and cleaning of next generation sequence transcriptome data of fragments added by post-transcriptional processes. By making use of Hidden Markov models trained on-the-fly we are able to detect the embedded homopolymers and accurately identify boundaries in the presence of high rates of sequencing error that might otherwise obscure them. Compared to the SeqClean tool [1], we see comparable to considerably improved sensitivity and specificity in identification, and a significantly improved ability to correctly detect homopolymer boundaries. SCOPE is being developed as an open-source tool, with a preliminary version that will be distributed to interested users upon request to the authors.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129407758","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":"Invited: A protein domain-centric approach to translational bioinformatics","authors":"M. Kann","doi":"10.1109/ICCABS.2012.6182622","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182622","url":null,"abstract":"Identifying the functional context for key molecular disruptions in complex diseases is a major goal of modern medicine that will lead to improved preventive clinical approaches, earlier diagnosis and more effective personalized disease therapies. Most available resources for visualization and analysis of disease mutations centered on gene analysis and do not leverage information about similarity on the functional context of the mutation. In addition, gene-centric approaches are confounded because genes may share some functional sub-units, or protein domains, but not others. We have built a resource for domain mapping of disease mutations, DMDM, a protein domain database in which each disease mutation can be displayed by its protein domain location. DMDM provides a unique domain-level view where human coding mutations are mapped to protein domains by highlighting molecular relationships among mutations from different diseases that might not have been discovered with traditional gene-centric visualization tools. We have also developed a statistical method, the domain significance scores (DSScores), to assess the significance of disease mutation clusters on protein domains. When we applied the DS-Scores to human data and identified domain hotspots in oncogenes, tumor suppressors, and genes associated with Mendelian diseases. Since most proteins need to interact to perform their function, the identification domains of clinical relevance needs to be complemented by the identification of domain-domain interactions to provide a better understanding about the molecular underpinning of disease. Computational tools to predict domain-domain interactions provide a detailed molecular view of the protein interactions and complements expensive and laborious experimental techniques to identify such interactions. The evolutionary distances of interacting proteins often display a higher level of similarity than those of non-interacting proteins. This finding indicates that interacting proteins are subject to common evolutionary constraints and constitute the basis of a method to predict protein interactions known as mirrortree. It has been difficult, however, to identify the direct cause of the observed similarities between evolutionary trees. One possible explanation is the existence of compensatory mutations between partners binding sites to maintain proper binding. This explanation, however, has been recently challenged. It has been suggested that the signal of correlated evolution uncovered by the mirrortree method is unrelated to any correlated evolution between binding sites. We have addressed this controversial debate in the field by studying the contribution of binding sites to the correlation between evolutionary trees of interacting domains. We showed that binding neighborhoods of interacting proteins have, on average, higher co-evolutionary signal compared to the regions outside binding sites; although when the binding neighborhood was removed, the rem","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"40 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132973514","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":"Accelerating gene regulatory networks inference through GPU/CUDA programming","authors":"Fabrizio F. Borelli, R. Camargo, D. Martins, B. Stransky, L. Rozante","doi":"10.1109/ICCABS.2012.6182628","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182628","url":null,"abstract":"Gene regulatory networks (GRN) inference is an important bioinformatics problem in which the gene interactions need to be deduced from gene expression data, such as microarray data. Feature selection methods can be applied to this problem. A feature selection technique is composed by two parts: a search algorithm and a criterion function. Among the search algorithms already proposed, there is the exhaustive search where the best feature subset is returned, although its computational complexity is unfeasible in almost all situations. The objective of this work is the development of a low cost parallel solution based on GPU architectures for exhaustive search with a viable cost-benefit. CUDA™ is a general purpose parallel architecture with a new parallel programming model allowing that the NVIDIA® GPUs solve complex problems in an efficient way. We developed a parallel algorithm for GRN inference based on the GPU/CUDA and encouraging speedups (60x) were achieved when assuming that each target gene has two predictors. The idea behind the proposed method can be applied considering three or more predictors for each target gene as well.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126075523","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":"Workshop: Calling Structural Variation with low-coverage sequencing data by mapping to focal region","authors":"Jin Zhang, Y. Wu","doi":"10.1109/ICCABS.2012.6182679","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182679","url":null,"abstract":"In the problem of Structural Variations (SV) finding, split-read mapping methods which search gaps upto a pre-defined maximum deletion size can be time consuming and less accurate. In this paper, we present a split read mapping method that calls SVs with low-coverage sequencing data by mapping to focal region. Simulation results suggest that our method is more efficient and accurate than several alternative methods.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121656113","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":"Poster title (mass spectrometric protein profiling analyses of pathological and physiological hypertrophy cardiac muscle tissues)","authors":"H. Kwon, J. Chun, Z. Park, Dong-Yu Kim","doi":"10.1109/ICCABS.2012.6182652","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182652","url":null,"abstract":"The mammalian heart is a muscular organ that can adjust dynamic alterations in its workload. In response to physiological or pathological stimuli and development, the heart changes hypertrophic enlargement which increase in the size of individual cardiac myocytes. A comparative proteomic survey is needed to acquire systemic insights into mammalian cardiac hypertrophy. In this study, using a shotgun proteomics approach based on MudPIT and tandem mass spectrometry, we have gained global protein profiling for each of pathological and physiological cardiac hypertrophy models and applied a statistical method by normalization of spectral counts. We analyzed relative protein expression abundance and studied their biological functions with bioinformatics tools. Interestingly, between physiological and pathological cardiac hypertrophy there were little overlap in the expression profiles. More specifically, there was a significant, negative correlation in biological functions of two models. Thus, different models of pathological and physiological cardiac hypertrophy are associated with distinct proteins controlled each signaling pathway. These data demonstrate key biological functions which describe each type of cardiac hypertrophy.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133735811","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":"Poster: Parameter estimation for a reduced order spiking neuron model with relevance to in vitro embryonic rat motoneuronal data","authors":"Lingfei Zhi, Zhao Wang, A. Behal","doi":"10.1109/ICCABS.2012.6182663","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182663","url":null,"abstract":"The problem of finding parameters of an adaptive quadratic spiking neuron model in order to fit data derived from in vitro embryonic rat motoneurons is addressed. Since the model does not fit the shape of the experimental action potential, we utilize a correlation based technique to extract input-independent spike areas, and pre-distort these areas such that they conform to the model assumptions. Simulation results show that the spike train is accurately predicted.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130298701","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":"Workshop: Novel transcript reconstruction from paired-end RNA-Seq reads using fragment length distribution","authors":"S. Mangul, A. Caciula, I. Măndoiu, A. Zelikovsky","doi":"10.1109/ICCABS.2012.6182673","DOIUrl":"https://doi.org/10.1109/ICCABS.2012.6182673","url":null,"abstract":"In this work, we propose a novel statistical \"genome- guided\" method called \"Transciptome Reconstruction us- ing Integer Programing\" (TRIP) that incorporates fragment length distribution into novel transcript reconstruction from paired-end RNA-Seq reads. To reconstruct novel transcipts, we create a splice graph based an exact annotation of exon boundaries and RNA-Seq reads. The exact annotation of exons can be obtained from annotation databases (e.g., Ensembl) or can be inferred from aligned RNA-Seq reads. A splice graph is a directed acyclic graph (DAG), whose vertices represent exons and edges represent splicing events. We enumerate all maximal paths in the splice graph using adepth-first-search (DFS) algorithm. These paths correspond to putative transcripts and are the input for the TRIP algorithm.","PeriodicalId":335863,"journal":{"name":"2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116035543","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}