CAMSA: a Tool for Comparative Analysis and Merging of Scaffold Assemblies

MotivationDespite the recent progress in genome sequencing and assembly, many of the currently available assembled genomes come in a draft form. Such draft genomes consist of a large number of genomic fragments (scaffolds), whose positions and orientations along the genome are unknown. While there exists a number of methods for reconstruction of the genome from its scaffolds, utilizing various computational and wet-lab techniques, they often can produce only partial error-prone scaffold assemblies. It therefore becomes important to compare and merge scaffold assemblies produced by different methods, thus combining their advantages and highlighting present conflicts for further investigation. These tasks may be labor intensive if performed manually.ResultsWe present CAMSA—a tool for comparative analysis and merging of two or more given scaffold assemblies. The tool (i) creates an extensive report with several comparative quality metrics; (ii) constructs the most confident merged scaffold assembly; and (iii) provides an interactive framework for a visual comparative analysis of the given assemblies. Among the CAMSA features, only scaffold merging can be evaluated in comparison to existing methods. Namely, it resembles the functionality of assembly reconciliation tools, although their primary targets are somewhat different. Our evaluations show that CAMSA produces merged assemblies of comparable or better quality than existing assembly reconciliation tools while being the fastest in terms of the total running time.AvailabilityCAMSA is distributed under the MIT license and is available at http://cblab.org/camsa/.

Clique-Based Neural Associative Memories with Local Coding and Precoding

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

Research of Combined Optimum Grey Model to Mid and Long Term Electric Load Forecasting

It is well known that mid and long term electric load forecasting has many uncertain factors that influence the forecasting precision greatly, so every forecasting method has its limitation. Considering limitations of basic grey model and conventional improved models, a new practical method called combined optimum grey model for mid and long term load forecasting is introduced. The combined model is composed of partial error optimum grey model (GM) as well as equa-l dimension and new-information grey model. The forecasting algorithm can estimate model parameters, meet the requirements of dynamic power load and overcome random disturbances. Example analysis shows that the forecasting error is below 3 percent. Compared with conventional theoretical methods, the proposed scheme has the characters of simple computation, high forecasting precision and good applicability.

Calibration of a six-DOF parallel manipulator for chromosome dissection

This article proposes a calibration method for a six-PPPS parallel manipulator which is the macro-part of a dual-drive chromosome dissection system. The calibration model is built with respect to Denavit–Hartenberg notation. Certain adjustment matrices are utilized to provide independent error parameters. The pose of the moving platform as while as relative motion of all prismatic joints and spherical joints are measured by the latest API T3 laser tracker. Other than general methods that need reconstruction of the location and orientation of the platform with point coordinates, these data can be obtained directly within one single measurement in the proposed method. For data processing, a step-by-step calculation method has been presented to select initial error parameters. With this method, partial error parameters can be derived in advance to be substituted into the calibration model providing calculation efficiency superior to simultaneous computation. Simulation based on the geometry of this manipulator shows a decent convergence performance of the calibration model. The experimental results also validate the efficiency and the convenience of the proposed method.