The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.

A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.

Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services

Cloud offloading is an indispensable solution to supporting computationally demanding applications on resource constrained mobile devices. In this paper, we introduce the concept of wireless aware joint scheduling and computation offloading (JSCO) for multi-component applications, where an optimal decision is made on which components need to be offloaded as well as the scheduling order of these components. The JSCO approach allows for more degrees of freedom in the solution by moving away from a compiler pre-determined scheduling order for the components towards a more wireless aware scheduling order. For some component dependency graph structures, the proposed algorithm can shorten execution times by parallel processing appropriate components in the mobile and cloud. We define a net utility that trades-off the energy saved by the mobile, subject to constraints on the communication delay, overall application execution time, and component precedence ordering. The linear optimization problem is solved using real data measurements obtained from running multi-component applications on an HTC smartphone and the Amazon EC2, using WiFi for cloud offloading. The performance is further analyzed using various component dependency graph topologies and sizes. Results show that the energy saved increases with longer application runtime deadline, higher wireless rates, and smaller offload data sizes.

Optimal Joint Scheduling and Cloud Offloading for Mobile Applications

The rapidly reducing cost of bacterial genome sequencing has lead to its routine use in large-scale microbial analysis. Though mapping approaches can be used to find differences relative to the reference, many bacteria are subject to constant evolutionary pressures resulting in events such as the loss and gain of mobile genetic elements, horizontal gene transfer through recombination and genomic rearrangements. De novo assembly is the reconstruction of the underlying genome sequence, an essential step to understanding bacterial genome diversity. Here we present a high-throughput bacterial assembly and improvement pipeline that has been used to generate nearly 20 000 annotated draft genome assemblies in public databases. We demonstrate its performance on a public data set of 9404 genomes. We find all the genes used in multi-locus sequence typing schema present in 99.6 % of assembled genomes. When tested on low-, neutral- and high-GC organisms, more than 94 % of genes were present and completely intact. The pipeline has been proven to be scalable and robust with a wide variety of datasets without requiring human intervention. All of the software is available on GitHub under the GNU GPL open source license.

/pdf/robust-high-throughput-prokaryote-de-novo-assembly-and-1g6zvkl556.pdf

Robust high-throughput prokaryote de novo assembly and improvement pipeline for Illumina data.

CARE Resource Broker: A framework for scheduling and supporting virtual resource management

To reduce the energy consumption in mobile devices, intricate applications are divided into several interconnected partitions like Task Interaction Graph (TIG) and are of floaded to cloud resources or nearby surrogates. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to reduce the power consumption during mapping and scheduling stages. Most of the existing research works proposed several task scheduling solutions by considering the voltage/frequency scaling at the scheduling stage alone. But, the efficacy of these solutions can be improved by applying the DVFS in both mapping as well as scheduling stages. This research work attempts to apply DVFS in mapping as well as scheduling stages by combining both the task-resource and resource-frequency assignments in a single problem. The idea is to estimate the worst-case global slack time for each task-resource assignment, distributes it over the TIG and slowing down the execution of tasks using dynamic voltage and frequency scaling. This optimal slowdown increases the computation time of TIG without exceeding its worst-case completion time. Further, the proposed work models the code offloading as a Quadratic Assignment Problem (QAP) in Matlab-R2012b and solves it using two-level Genetic Algorithm (GA) of the global optimization toolbox. The effectiveness of the proposed model is assessed by a simulation and the results conclude that there is an average energy savings of 35% in a mobile device.

Energy-Efficient Mapping and Scheduling of Task Interaction Graphs for Code Offloading in Mobile Cloud Computing

Recently, large bio-projects dealing with the release of different genomes have transpired. Most of these projects use next-generation sequencing platforms. As a consequence, many de novo assembly tools have evolved to assemble the reads generated by these platforms. Each tool has its own inherent advantages and disadvantages, which make the selection of an appropriate tool a challenging task. We have evaluated the performance of frequently used de novo assemblers namely ABySS, IDBA-UD, Minia, SOAP, SPAdes, Sparse, and Velvet. These assemblers are assessed based on their output quality during the assembly process conducted over fungal data. We compared the performance of these assemblers by considering both computational as well as quality metrics. By analyzing these performance metrics, the assemblers are ranked and a procedure for choosing the candidate assembler is illustrated. In this study, we propose an assessment method for the selection of de novo assemblers by considering their computational as well as quality metrics at the draft genome level. We divide the quality metrics into three groups: g1 measures the goodness of the assemblies, g2 measures the problems of the assemblies, and g3 measures the conservation elements in the assemblies. Our results demonstrate that the assemblers ABySS and IDBA-UD exhibit a good performance for the studied data from fungal genomes in terms of running time, memory, and quality. The results suggest that whole genome shotgun sequencing projects should make use of different assemblers by considering their merits.

/pdf/assessment-of-de-novo-assemblers-for-draft-genomes-a-case-2u8lt9alzl.pdf

P. Balakrishnan

Papers

CARE Resource Broker: A framework for scheduling and supporting virtual resource management

Energy-Efficient Mapping and Scheduling of Task Interaction Graphs for Code Offloading in Mobile Cloud Computing

Assessment of de novo assemblers for draft genomes: a case study with fungal genomes

A resource provisioning framework for bioinformatics applications in multi-cloud environments

Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing