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194 Publications

Showing 191-194 of 194 results
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    Bock Lab
    06/18/13 | The Open Connectome Project Data Cluster: Scalable analysis and vision for high-throughput neuroscience.
    Burns R, Roncal WG, Kleissas D, Lillaney K, Manavalan P, Perlman E, Berger DR, Bock DD, Chung K, Grosenick L, Kasthuri N, Weiler NC, Deisseroth K, Kazhdan M, Lichtman J, Reid RC, Smith SJ, Szalay AS, Vogelstein JT, Vogelstein RJ
    Scientific and Statistical Database Management: International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management. 2013 Jun 18:. doi: 10.1145/2484838.2484870

    We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

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    01/01/13 | The Oxytricha trifallax macronuclear genome: a complex eukaryotic genome with 16,000 tiny chromosomes.
    Swart EC, Bracht JR, Magrini V, Minx P, Chen X, Zhou Y, Khurana JS, Goldman AD, Nowacki M, Schotanus K, Jung S, Fulton RS, Ly A, McGrath S, Haub K, Wiggins JL, Storton D, Matese JC, Parsons L, Chang W, Bowen MS, Stover NA, Jones TA, Eddy SR, Herrick GA, Doak TG, Wilson RK, Mardis ER, Landweber LF
    PLoS Biology. 2013 Jan;11(1):e1001473. doi: 10.1371/journal.pbio.1001473

    The macronuclear genome of the ciliate Oxytricha trifallax displays an extreme and unique eukaryotic genome architecture with extensive genomic variation. During sexual genome development, the expressed, somatic macronuclear genome is whittled down to the genic portion of a small fraction (\~{}5%) of its precursor "silent" germline micronuclear genome by a process of "unscrambling" and fragmentation. The tiny macronuclear "nanochromosomes" typically encode single, protein-coding genes (a small portion, 10%, encode 2-8 genes), have minimal noncoding regions, and are differentially amplified to an average of \~{}2,000 copies. We report the high-quality genome assembly of \~{}16,000 complete nanochromosomes (\~{}50 Mb haploid genome size) that vary from 469 bp to 66 kb long (mean \~{}3.2 kb) and encode \~{}18,500 genes. Alternative DNA fragmentation processes \~{}10% of the nanochromosomes into multiple isoforms that usually encode complete genes. Nucleotide diversity in the macronucleus is very high (SNP heterozygosity is \~{}4.0%), suggesting that Oxytricha trifallax may have one of the largest known effective population sizes of eukaryotes. Comparison to other ciliates with nonscrambled genomes and long macronuclear chromosomes (on the order of 100 kb) suggests several candidate proteins that could be involved in genome rearrangement, including domesticated MULE and IS1595-like DDE transposases. The assembly of the highly fragmented Oxytricha macronuclear genome is the first completed genome with such an unusual architecture. This genome sequence provides tantalizing glimpses into novel molecular biology and evolution. For example, Oxytricha maintains tens of millions of telomeres per cell and has also evolved an intriguing expansion of telomere end-binding proteins. In conjunction with the micronuclear genome in progress, the O. trifallax macronuclear genome will provide an invaluable resource for investigating programmed genome rearrangements, complementing studies of rearrangements arising during evolution and disease.

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    Singer Lab
    01/01/13 | Time-integrated fluorescence cumulant analysis and its application in living cells.
    Wu B, Singer RH, Mueller JD
    Methods in Enzymology. 2013;518:99-119. doi: 10.1016/B978-0-12-388422-0.00005-4

    Time-integrated fluorescence cumulant analysis (TIFCA) is a data analysis technique for fluorescence fluctuation spectroscopy (FFS) that extracts information from the cumulants of the integrated fluorescence intensity. It is the first exact theory that describes the effect of sampling time on FFS experiment. Rebinning of data to longer sampling times helps to increase the signal/noise ratio of the experimental cumulants of the photon counts. The sampling time dependence of the cumulants encodes both brightness and diffusion information of the sample. TIFCA analysis extracts this information by fitting the cumulants to model functions. Generalization of TIFCA to multicolor FFS experiment is straightforward. Here, we present an overview of the theory, its implementation, as well as the benefits and requirements of TIFCA. The questions of why, when, and how to use TIFCA will be discussed. We give several examples of practical applications of TIFCA, particularly focused on measuring molecular interaction in living cells.

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    Cardona Lab
    01/01/13 | Towards semi-automatic reconstruction of neural circuits.
    Cardona A
    Neuroinformatics. 2013 Jan;11(1):31-3. doi: 10.1007/s12021-012-9166-x