BACKGROUND: Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline. RESULTS: We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods. CONCLUSION: Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

BACKGROUND: Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data. RESULTS: Our methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data. CONCLUSION: Identifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.

BACKGROUND: Gene families are sets of structurally and evolutionarily related genes - in one or multiple species - that typically share a conserved biological function. As such, the identification and subsequent analyses of entire gene families are widely employed in the fields of evolutionary and functional genomics of both well established and newly sequenced plant genomes. Currently, plant gene families are typically identified using one of two major ways: 1) HMM-profile based searches using models built on Arabidopsis thaliana genes or 2) coding sequence homology searches using curated databases. Integrated databases containing functionally annotated genes and gene families have been developed for model organisms and several important crops; however, a comprehensive methodology for gene family annotation is currently lacking, preventing automated annotation of newly sequenced genomes. RESULTS: This paper proposes a combined measure of homology identification, motif conservation, phylogenomic and integrated gene expression analyses to define gene family structures in multiple plant species. The MAP3K gene families in seven plant species, including two currently unexamined species Gossypium hirsutum, and Zostera marina, were characterized to reveal new insights into their collective function and evolution and demonstrate the effectiveness of our novel methodology. CONCLUSION: Compared with recent reports, this methodology performs significantly better for the identification and analysis of gene family members in several monocots/dicots, diploid as well as polyploid plant species.

Scalable data distribution in large-scale dynamic collaborative systems requires efficient, low overhead communication control. We propose efficient algorithms for clustering network nodes dynamically based on their communication interest. Several group communication architectures have been proposed to date without considering the constraints imposed by the communication infrastructure. Among these, distributed hash tables are scalable and resilient data structures used for data dissemination control. However DHT's are not optimized for high dynamics of network node interest and real-time end-to-end performance requirements. This paper proposes efficient control algorithms for large-scale collaborative systems optimized for scalability as well as end-to-end data dissemination. Network node communication interest is modeled as a multi-dimensional attribute space partitioned into interest cells mapped to multicast communication groups. The proposed control algorithms use proximity-based clustering of network nodes and hierarchical communication interest aggregation. We show that network overlay control algorithms achieve scalability and low overhead with a controlled degradation of end-to-end data path performance

We consider heterogeneous group communication in which users have different preference for information related to various topics. The problem of disseminating such information to interested set of users in an overlay network is challenging when both low end-to-end latency and low incurred overlay traffic are desired. We investigate heuristic based overlay topology generation algorithm that exploits both network proximity in underlying network and similarity between users' preferences to reduce both the overlay latency and the amount of data forwarding done by nodes that do not prefer the data. Our results show that while these requirements conflict, an application specific tradeoff can be made to improve the overall system performance.

Group communication in large-scale interactive applications such as real-time conferencing and collaboration, networked virtual environments (e.g. massively multi-player games) requires efficient, low-overhead group communication mechanisms. In this paper we consider the application-level (or end-system) multicast and propose a stateless group communication mechanism together with its tree building algorithms. Stateless multicast reduces the control signaling of dynamic multicast groups linearly with the group size. To support interactive applications involving a large number of dynamic multicast groups, the application level multicast uses stateless forwarding within clusters of network nodes and hierarchical aggregation of multicast group membership. We show that dynamic tree construction achieve low computation overhead with a controlled degradation of the end-to-end data path performance.

The Grid provides a new and unrivaled technology for large scale distributed simulation as it enables collaboration and the use of distributed computing resources. This panel paper presents the views of four researchers in the area of Distributed Simulation and the Grid. Together we try to identify the main research issues involved in applying Grid technology to distributed simulation and the key future challenges that need to be solved to achieve this goal. Such challenges include not only technical challenges, but also political ones such as management methodology for the Grid and the development of standards. The benefits of the Grid to end-user simulation modelers also are discussed.

Distributed interactive applications such as multiplayer games will become increasingly popular in wide area distributed systems. To provide the response time desired by users despite high and unpredictable communication latency in such systems, shared objects will be replicated or cached by clients that participate in the applications. Any updates to the shared objects will have to be disseminated to clients that actually use the objects to maintain consistency. We address the problem of efficient and scalable update dissemination in an environment where client interests can change dynamically and the number of multicast channels available for update dissemination is limited. We present a heuristic based algorithm that can group objects and clients in a way that it handles limited bandwidth resources. We show that our algorithm can produce better results than several algorithms that have been developed in the past for update dissemination.

Predicted availability of broadband access will enable deployment of network virtual environments over the Internet. Online virtual worlds will require efficient runtime data replication solutions. This paper presents a quality of service (QoS) architecture for just-in-time data replication in network virtual environments. Quality of service is achieved by predicting the load and adapting to network traffic variations. Data is prefetched at the client based on network traffic estimates and viewpoint navigation prediction. QoS negotiation allows the server to control the network resources allocated per client. Experimental results show that QoS data replication can be implemented with reasonably small network and sever overload.