@article {38383, title = {The minimum information about a genome sequence (MIGS) specification}, journal = {Nature biotechnologyNature biotechnology}, volume = {26}, year = {2008}, note = {http://www.ncbi.nlm.nih.gov/pubmed/18464787?dopt=Abstract}, type = {10.1038/nbt1360}, abstract = {With the quantity of genomic data increasing at an exponential rate, it is imperative that these data be captured electronically, in a standard format. Standardization activities must proceed within the auspices of open-access and international working bodies. To tackle the issues surrounding the development of better descriptions of genomic investigations, we have formed the Genomic Standards Consortium (GSC). Here, we introduce the minimum information about a genome sequence (MIGS) specification with the intent of promoting participation in its development and discussing the resources that will be required to develop improved mechanisms of metadata capture and exchange. As part of its wider goals, the GSC also supports improving the {\textquoteright}transparency{\textquoteright} of the information contained in existing genomic databases.}, keywords = {Chromosome mapping, Databases, Factual, information dissemination, Information Storage and Retrieval, Information Theory, Internationality}, author = {Field, Dawn and Garrity, George and Gray, Tanya and Morrison, Norman and J. Selengut and Sterk, Peter and Tatusova, Tatiana and Thomson, Nicholas and Allen, Michael J. and Angiuoli, Samuel V. and Ashburner, Michael and Axelrod, Nelson and Baldauf, Sandra and Ballard, Stuart and Boore, Jeffrey and Cochrane, Guy and Cole, James and Dawyndt, Peter and De Vos, Paul and DePamphilis, Claude and Edwards, Robert and Faruque, Nadeem and Feldman, Robert and Gilbert, Jack and Gilna, Paul and Gl{\"o}ckner, Frank Oliver and Goldstein, Philip and Guralnick, Robert and Haft, Dan and Hancock, David and Hermjakob, Henning and Hertz-Fowler, Christiane and Hugenholtz, Phil and Joint, Ian and Kagan, Leonid and Kane, Matthew and Kennedy, Jessie and Kowalchuk, George and Kottmann, Renzo and Kolker, Eugene and Kravitz, Saul and Kyrpides, Nikos and Leebens-Mack, Jim and Lewis, Suzanna E. and Li, Kelvin and Lister, Allyson L. and Lord, Phillip and Maltsev, Natalia and Markowitz, Victor and Martiny, Jennifer and Methe, Barbara and Mizrachi, Ilene and Moxon, Richard and Nelson, Karen and Parkhill, Julian and Proctor, Lita and White, Owen and Sansone, Susanna-Assunta and Spiers, Andrew and Stevens, Robert and Swift, Paul and Taylor, Chris and Tateno, Yoshio and Tett, Adrian and Turner, Sarah and Ussery, David and Vaughan, Bob and Ward, Naomi and Whetzel, Trish and San Gil, Ingio and Wilson, Gareth and Wipat, Anil} } @article {49560, title = {MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements.}, volume = {28}, year = {2006}, month = {2006 Dec}, pages = {1960-72}, abstract = {

In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.

}, keywords = {algorithms, Artificial Intelligence, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique}, issn = {0162-8828}, doi = {10.1109/TPAMI.2006.247}, author = {Khan, Zia and Balch, Tucker and Dellaert, Frank} } @article {38294, title = {Genome Properties: a system for the investigation of prokaryotic genetic content for microbiology, genome annotation and comparative genomics}, journal = {Bioinformatics (Oxford, England)Bioinformatics (Oxford, England)}, volume = {21}, year = {2005}, note = {http://www.ncbi.nlm.nih.gov/pubmed/15347579?dopt=Abstract}, type = {10.1093/bioinformatics/bti015}, abstract = {MOTIVATION: The presence or absence of metabolic pathways and structures provide a context that makes protein annotation far more reliable. Compiling such information across microbial genomes improves the functional classification of proteins and provides a valuable resource for comparative genomics. RESULTS: We have created a Genome Properties system to present key aspects of prokaryotic biology using standardized computational methods and controlled vocabularies. Properties reflect gene content, phenotype, phylogeny and computational analyses. The results of searches using hidden Markov models allow many properties to be deduced automatically, especially for families of proteins (equivalogs) conserved in function since their last common ancestor. Additional properties are derived from curation, published reports and other forms of evidence. Genome Properties system was applied to 156 complete prokaryotic genomes, and is easily mined to find differences between species, correlations between metabolic features and families of uncharacterized proteins, or relationships among properties. AVAILABILITY: Genome Properties can be found at http://www.tigr.org/Genome_Properties SUPPLEMENTARY INFORMATION: http://www.tigr.org/tigr-scripts/CMR2/genome_properties_references.spl.}, keywords = {Chromosome mapping, database management systems, Databases, Genetic, documentation, Gene Expression Profiling, Gene Expression Regulation, Genomics, Information Storage and Retrieval, Microbiological Techniques, natural language processing, Prokaryotic Cells, Proteome, signal transduction, software, User-Computer Interface, Vocabulary, Controlled}, author = {Haft, Daniel H. and J. Selengut and Brinkac, Lauren M. and Zafar, Nikhat and White, Owen} } @article {49562, title = {MCMC-based particle filtering for tracking a variable number of interacting targets.}, volume = {27}, year = {2005}, month = {2005 Nov}, pages = {1805-19}, abstract = {

We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.

}, keywords = {algorithms, Animals, Artificial Intelligence, Computer simulation, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Markov chains, Models, Biological, Models, Statistical, Monte Carlo Method, Motion, Movement, Pattern Recognition, Automated, Subtraction Technique, Video Recording}, issn = {0162-8828}, doi = {10.1109/TPAMI.2005.223}, author = {Khan, Zia and Balch, Tucker and Dellaert, Frank} } @article {49683, title = {Reducing storage requirements for biological sequence comparison.}, journal = {Bioinformatics}, volume = {20}, year = {2004}, month = {2004 Dec 12}, pages = {3363-9}, abstract = {

MOTIVATION: Comparison of nucleic acid and protein sequences is a fundamental tool of modern bioinformatics. A dominant method of such string matching is the {\textquoteright}seed-and-extend{\textquoteright} approach, in which occurrences of short subsequences called {\textquoteright}seeds{\textquoteright} are used to search for potentially longer matches in a large database of sequences. Each such potential match is then checked to see if it extends beyond the seed. To be effective, the seed-and-extend approach needs to catalogue seeds from virtually every substring in the database of search strings. Projects such as mammalian genome assemblies and large-scale protein matching, however, have such large sequence databases that the resulting list of seeds cannot be stored in RAM on a single computer. This significantly slows the matching process.

RESULTS: We present a simple and elegant method in which only a small fraction of seeds, called {\textquoteright}minimizers{\textquoteright}, needs to be stored. Using minimizers can speed up string-matching computations by a large factor while missing only a small fraction of the matches found using all seeds.

}, keywords = {algorithms, Databases, Genetic, Information Storage and Retrieval, Numerical Analysis, Computer-Assisted, sequence alignment, Sequence Analysis}, issn = {1367-4803}, doi = {10.1093/bioinformatics/bth408}, author = {Roberts, Michael and Hayes, Wayne and Hunt, Brian R and Mount, Stephen M and Yorke, James A} }