Data mining in bioinformatics using Weka

The Weka machine learning workbench provides a general purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it

Jumble Java Byte Code to Measure the Effectiveness of Unit Tests

Jumble is a byte code level mutation testing tool for Java which inter-operates with JUnit. It has been designed to operate in an industrial setting with large projects. Heuristics have been included to speed the checking of mutations, for example, noting which test fails for each mutation and running this first in subsequent mutation checks. Significant effort has been put into ensuring that it can test code which uses custom class loading and reflection. This requires careful attention to class path handling and coexistence with foreign class-loaders. Jumble is currently used on a continuous basis within an agile programming environment with approximately 370,000 lines of Java code under source control. This checks out project code every fifteen minutes and runs an incremental set of unit tests and mutation tests for modified classes. Jumble is being made available as open source

Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone

The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu.National Institutes of Health (U.S.) (Grant 1R01GM081871)National Science Foundation (U.S.) (Grant MCB-0347203)National Science Foundation (U.S.) (Grant 0821391

Divergent modulation of nociception by glutamatergic and GABAergic neuronal subpopulations in the periaqueductal gray

The ventrolateral periaqueductal gray (vlPAG) constitutes a major descending pain modulatory system and is a crucial site for opioid-induced analgesia. A number of previous studies have demonstrated that glutamate and GABA play critical opposing roles in nociceptive processing in the vlPAG. It has been suggested that glutamatergic neurotransmission exerts antinociceptive effects, whereas GABAergic neurotransmission exert pronociceptive effects on pain transmission, through descending pathways. The inability to exclusively manipulate subpopulations of neurons in the PAG has prevented direct testing of this hypothesis. Here, we demonstrate the different contributions of genetically defined glutamatergic and GABAergic vlPAG neurons in nociceptive processing by employing cell type-specific chemogenetic approaches in mice. Global chemogenetic manipulation of vlPAG neuronal activity suggests that vlPAG neural circuits exert tonic suppression of nociception, consistent with previous pharmacological and electrophysiological studies. However, selective modulation of GABAergic or glutamatergic neurons demonstrates an inverse regulation of nociceptive behaviors by these cell populations. Selective chemogenetic activation of glutamatergic neurons, or inhibition of GABAergic neurons, in vlPAG suppresses nociception. In contrast, inhibition of glutamatergic neurons, or activation of GABAergic neurons, in vlPAG facilitates nociception. Our findings provide direct experimental support for a model in which excitatory and inhibitory neurons in the PAG bidirectionally modulate nociception

Microheated substrates for patterning cells and controlling development

Here, we seek to control cellular development by devising a means through which cells can be subjected to a microheated environment in standard culture conditions. Numerous techniques have been devised for controlling cellular function and development via manipulation of surface environmental cues at the micro- and nanoscale. It is well understood that temperature plays a significant role in the rate of cellular activities, migratory behavior (thermotaxis), and in some cases, protein expression. Yet, the effects and possible utilization of micrometer-scale temperature fields in cell cultures have not been explored. Toward this end, two types of thermally isolated microheated substrates were designed and fabricated, one with standard backside etching beneath a dielectric film and another with a combination of surface and bulk micromachining and backside etching. The substrates were characterized with infrared microscopy, finite element modeling, scanning electron microscopy, stylus profilometry, and electrothermal calibrations. Neuron culture studies were conducted on these substrates to 1) examine the feasibility of using a microheated environment to achieve patterned cell growth and 2) selectively accelerate neural development on regions less than 100$mu m$wide. Results show that attached neurons, grown on microheated regions set at 37$~^circ C$, extended processes substantially faster than those incubated at 25$~^circ C$on the same substrate. Further, unattached neurons were positioned precisely along the length of the heater filament (operating at 45$~^circ C$) using free convection currents. These preliminary findings indicate that microheated substrates may be used to direct cellular development spatially in a practical manner.$hfillhbox[1414]

Perspectives on open access high resolution digital elevation models to produce global flood hazard layers

Global flood hazard models have recently become a reality thanks to the release of open access global digital elevation models, the development of simplified and highly efficient flow algorithms, and the steady increase in computational power. In this commentary we argue that although the availability of open access global terrain data has been critical in enabling the development of such models, the relatively poor resolution and precision of these data now limit significantly our ability to estimate flood inundation and risk for the majority of the planet’s surface. The difficulty of deriving an accurate ‘bare-earth’ terrain model due to the interaction of vegetation and urban structures with the satellite-based remote sensors means that global terrain data are often poorest in the areas where people, property (and thus vulnerability) are most concentrated. Furthermore, the current generation of open access global terrain models are over a decade old and many large floodplains, particularly those in developing countries, have undergone significant change in this time. There is therefore a pressing need for a new generation of high resolution and high vertical precision open access global digital elevation models to allow significantly improved global flood hazard models to be developed

CrY2H-seq: a massively multiplexed assay for deep-coverage interactome mapping.

Broad-scale protein-protein interaction mapping is a major challenge given the cost, time, and sensitivity constraints of existing technologies. Here, we present a massively multiplexed yeast two-hybrid method, CrY2H-seq, which uses a Cre recombinase interaction reporter to intracellularly fuse the coding sequences of two interacting proteins and next-generation DNA sequencing to identify these interactions en masse. We applied CrY2H-seq to investigate sparsely annotated Arabidopsis thaliana transcription factors interactions. By performing ten independent screens testing a total of 36 million binary interaction combinations, and uncovering a network of 8,577 interactions among 1,453 transcription factors, we demonstrate CrY2H-seq's improved screening capacity, efficiency, and sensitivity over those of existing technologies. The deep-coverage network resource we call AtTFIN-1 recapitulates one-third of previously reported interactions derived from diverse methods, expands the number of known plant transcription factor interactions by three-fold, and reveals previously unknown family-specific interaction module associations with plant reproductive development, root architecture, and circadian coordination