Precise prediction of the antigens that bind to major histocompatibility complex (MHC) molecules is one of the most important steps for many immune response-related studies including identification of neoantigen capable of recognizing cancer cells for the implementation of personalized immunotherapy as well as vaccine/therapeutic protein development. To overcome the limitations of the limited prediction performance of the existing models, we developed a new convolutional neural network (CNN)-based epitope prediction model by incorporating new features including protein sequence vectors (Prot2Vec), chemical and structural characteristics of an epitope and MHC molecule. Our new model is optimized to achieve better prediction performance than the conventional methods when validated with a benchmark dataset from the Immune Epitope Database (IEDB). Along with the new prediction model, we also developed a web-based framework, called Kepre, to provide interfaces to our novel CNN-based epitope prediction functionalities. Kepre provides a user-friendly and automated interface for epitope prediction. Upon the researcher's submission of a sequence of interest, Kepre predicts the binding probability between the sequence and each type of the MHC molecules. Kepre runs the prediction analysis on high-throughput computing infrastructure and provides the prediction results in a user-interactable GUI and also in a pdf formatted report file.

Genome sequence alignment is one of the basic procedure of genome sequencing analysis pipeline and also one of the most time-consuming parts. Including BigBWA, a number of tools were proposed to accelerate genome sequence alignment by parallelizing computation with Hadoop technologies. However, HDFS incurs considerable I/O overhead. In this research, we propose a new sequence alignment tool adopting Hadoop on Lustre. Based on BigBWA, we removed data transfer overhead caused by HDFS and parallelized whole I/O steps. Experimental result shows that our solution is five times faster than original BigBWA in a ten-node Lustre based Hadoop cluster.

Differential Co-Expression Networks (DCENs) are graphical representations of genes showing differential co-expression pattern in response to experimental conditions or genetic changes. They have been successfully applied to identify condition-specific modules and provide a picture of the dynamic changes in gene regulatory networks. DCENs analysis investigates the differences among gene interconnections by calculating the expression correlation change of each gene pair between conditions. In this study, we collected many different datasets from NCBI GEO including 25 RNA-seq and 2,102 microarray samples derived from human brain and blood in Alzheimer's disease and performed differential co-expression analyses to identify functional modules responsible for the characterization of Alzheimer's disease. The DCENs were generated using Pearson correlation coefficient and meta-analysis was conducted using rank-based method. The preliminary results show that the structural characteristics of DCENs can provide new insights into the underlying gene regulatory dynamics in Alzheimer's disease. There is low size overlap between microarray- and RNA-seq-derived DCENs however, DCENs from RNA-seq would complement ones from microarray due to the higher coverage and dynamic range of RNA-seq.

MOTIVATION: Advances in sequencing technologies have remarkably lowered the detection limit of somatic variants to a low frequency. However, calling mutations at this range is still confounded by many factors including environmental contamination. Vector contamination is a continuously occurring issue and is especially problematic since vector inserts are hardly distinguishable from the sample sequences. Such inserts, which may harbor polymorphisms and engineered functional mutations, can result in calling false variants at corresponding sites. Numerous vector-screening methods have been developed, but none could handle contamination from inserts because they are focusing on vector backbone sequences alone. RESULTS: We developed a novel method-Vecuum-that identifies vector-originated reads and resultant false variants. Since vector inserts are generally constructed from intron-less cDNAs, Vecuum identifies vector-originated reads by inspecting the clipping patterns at exon junctions. False variant calls are further detected based on the biased distribution of mutant alleles to vector-originated reads. Tests on simulated and spike-in experimental data validated that Vecuum could detect 93% of vector contaminants and could remove up to 87% of variant-like false calls with 100% precision. Application to public sequence datasets demonstrated the utility of Vecuum in detecting false variants resulting from various types of external contamination. AVAILABILITY AND IMPLEMENTATION: Java-based implementation of the method is available at http://vecuum.sourceforge.net/ CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

Recent advances in DNA sequencing technology have enabled Next Generation Sequencing (NGS) instruments to accelerate generating billions of DNA reads in a few days. However, the management of enormous NGS data and the concurrent analysis of these vast amount of data requires a great deal of computing power and memory as well as huge disk storage. Current popular job scheduling systems provide efficient ways for managing and scheduling vast amount of analysis based on the available computing resources but don't consider the maximum amount of Input/Output (I/O). Thus, when executing large number of genome analysis on a large scale cluster system, the maximum bandwidth of storage I/O is insufficient to utilize all computing resources so the analysis jobs are frequently suspended. Here we developed a disk I/O aware job submission scheduler to maximize disk I/O usage but not hampering previously running jobs due to the heavy disk I/O of a new job. And we constructed a cancer genome analysis pipeline by using our I/O aware scheduler and HPC resources in National Institute of Supercomputing and Networking (NISN) to overcome the obstacles of concurrent analysis for vast amount of NGS data. Based on our I/O aware job submission scheduler, we performed major genome analyses on over 50 case-control pairs of chromophobe renal cell carcinoma patients whole genome samples sequenced by The Cancer Genome Atlas (TCGA) and successfully completed all analysis jobs while maintaining no jobs to be suspended by I/O bottleneck.

This paper describes a workflow-based problem solving environment (PSE) for supporting collaborative aerospace engineering process on the grid. The focus of our work is on the design of a workflow system that supports computational activities that are to be executed on the grid and manual activities that are to be performed by humans. According to our investigation on the aerospace engineering research process through interviews with aerospace researchers and engineers, it is essential to incorporate the grid-enabled execution of large-scale simulation runs and the support for manual activities into the workflow system. Most existing scientific workflow systems are aware of the importance of the support for the execution of applications on the grid, but not concerned with the support for manual activities. On the other hand, most existing business workflow systems are designed to have human intervention in mind, but not concerned with the grid-enabled execution. Based on the analysis of the e-AIRS system which is about a collaborative PSE for aerospace engineering, we have identified a collaborative research scenario and requirements. As a proof of concept system, we have prototyped a grid-enabled workflow system that realizes the scenario and meets the requirements.

In e-science environments, scientists need to execute a scientific application with various parameters multiple times to simulate and experiment complicated problems on the grid. For this, they should write every single job description with distinct parameters even if this is a troublesome task. To provide the flexibility and adaptability for parameter study, we propose an application parameter description language (APDL) and a service oriented parameter study scheme, called a parametric study service (PSS), for parameterized simulations on the grid. The APDL extends the job submission description language (JSDL) to generate parameters for multiple jobs. The proposed PSS provides a unified interface to submit jobs into various middleware platforms such as gLite, Globus, etc. The problem solving environment (PSE) assists a parameter study for their applications and every research fields tend to construct individual own PSE., The proposed PSS can be easily adapted into the specific PSE because of being implemented as Web services. In practice, we apply the APDL and the PSS into aerospace research PSE which carry out the three-dimensional turbulent analysis for compressible flow.