Virtual Environment for Next Generation Sequencing Analysis

Next Generation Sequencing technology, on the one hand, allows a more accurate analysis, and, on the other hand, increases the amount of data to process. A new protocol for sequencing the messenger RNA in a cell, known as RNA- Seq, generates millions of short sequence fragments in a single run. These fragments, or reads, can be used to measure levels of gene expression and to identify novel splice variants of genes. The proposed solution is a distributed architecture consisting of a Grid Environment and a Virtual Grid Environment, in order to reduce processing time by making the system scalable and flexibl

A Cloud Infrastructure for Optimization of a Massive Parallel Sequencing Workflow

Massive Parallel Sequencing is a term used to describe several revolutionary approaches to DNA sequencing, the so-called Next Generation Sequencing technologies. These technologies generate millions of short sequence fragments in a single run and can be used to measure levels of gene expression and to identify novel splice variants of genes allowing more accurate analysis. The proposed solution provides novelty on two fields, firstly an optimization of the read mapping algorithm has been designed, in order to parallelize processes, secondly an implementation of an architecture that consists of a Grid platform, composed of physical nodes, a Virtual platform, composed of virtual nodes set up on demand, and a scheduler that allows to integrate the two platform

Optimizing Splicing Junction Detection in Next Generation Sequencing Data on a Virtual-GRID Infrastructure

The new protocol for sequencing the messenger RNA in a cell, named RNA-seq produce millions of short sequence fragments. Next Generation Sequencing technology allows more accurate analysis but increase needs in term of computational resources. This paper describes the optimization of a RNA-seq analysis pipeline devoted to splicing variants detection, aimed at reducing computation time and providing a multi-user/multisample environment. This work brings two main contributions. First, we optimized a well-known algorithm called TopHat by parallelizing some sequential mapping steps. Second, we designed and implemented a hybrid virtual GRID infrastructure allowing to efficiently execute multiple instances of TopHat running on different samples or on behalf of different users, thus optimizing the overall execution time and enabling a flexible multi-user environmen

Sam2bam: High-Performance Framework for NGS Data Preprocessing Tools

This paper introduces a high-throughput software tool framework called {\it sam2bam} that enables users to significantly speedup pre-processing for next-generation sequencing data. The sam2bam is especially efficient on single-node multi-core large-memory systems. It can reduce the runtime of data pre-processing in marking duplicate reads on a single node system by 156-186x compared with de facto standard tools. The sam2bam consists of parallel software components that can fully utilize the multiple processors, available memory, high-bandwidth of storage, and hardware compression accelerators if available. The sam2bam provides file format conversion between well-known genome file formats, from SAM to BAM, as a basic feature. Additional features such as analyzing, filtering, and converting the input data are provided by {\it plug-in} tools, e.g., duplicate marking, which can be attached to sam2bam at runtime. We demonstrated that sam2bam could significantly reduce the runtime of NGS data pre-processing from about two hours to about one minute for a whole-exome data set on a 16-core single-node system using up to 130 GB of memory. The sam2bam could reduce the runtime for whole-genome sequencing data from about 20 hours to about nine minutes on the same system using up to 711 GB of memory

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilisticmodels, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.Ministerio de Economía y Competitividad TIN2012-37434Junta de Andalucía P08-TIC-0420

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor