With the rapid growth of biological technology, large amount of biological data can be produced in few days or months. Many of common-used tools become computation-consuming in analyzing such big biological data. Cloud computing has emerged to provide the huge amount of computing power and play important role in development of bioinformatics tools. We propose a cloud computing framework that is able to easily deploy the bioinformatics tools on cloud virtualization platform based on Hadoop. This framework can work on the public cloud platform vendor such as Amazon EC2 and also private cloud platform. All the tools performed by cloud computing framework as Bioinformatics as a Services are available at http://bioinfo.cs.pu.edu.tw/CBBTS. The tools deployed on cloud platform by the proposed framework are tested in Providence University cloud platform and provided good proportional acceleration when scaled out onto many computational units. In the big biological data era, cloud computing based solutions are important role to develop bioinformatics services over internet. In the work, the proposed framework is able to simply deploy several well-known bioinformatics tools on cloud virtualization platform and as web services. This framework can work on the public cloud platform vendor such as Amazon EC2 and also private cloud platform.

Nowadays, with the explosive growth of the network technologies many new applications and services have been developed on Internet. World Wide Web can provide these services provided without the limitation of time and location. Obviously, the number of user is dramatically increasing from amount of the visitations of web pages. In our previous work, we proposed an algorithm to discover more significant information from visited web pages to provide this information to web designers or policy makers to adjust the presentation of their Web contents. However, this algorithm is time-consuming approach due to it needs to scan the whole database many times. Therefore, we propose a GPGPU-based Preference Utility algorithm to enhance the performance by GPGPU parallel model. The proposed algorithm is developed on NVIDIA CUDA architecture. The experimental results show that the proposed method can achieve about 7x times over CPU-based method. The proposed algorithm can used to mine the information from web log data efficiently.

Currently, cloud computing has been applied to share computing resources to achieve coherence and economies of scale similar to a utility over a network. Hadoop is an widely-used open-source cloud computing environment that implements the Google MapReduce framework. Many bioinformatics tools have been developed to provide cloud services by using Hadoop. This paper proposes approaches in providing a pairwise 3D protein structure alignment; our web service takes advantage of the MapReduce paradigm as means of management and parallelizing tools under massive number of protein pairs examined under the experiment. It shows that our previously proposed sequential combinatorial algorithms are well parallelized under the map/reduce platform. These methods are tested on the real-world data obtained in from the RCSB PDB data set; the computation efficiency can be effectively improved proportional to the number of processors being used.

Recently, many new next-generation sequencing techniques have been proposed. These techniques can produce lot of short reads rapidly. Hence, a number of tools have been developed to map these short reads to the genome. However, with more and more reads sequenced and the length of reads increases, these tools require high memory usage and huge computational cost and are also impractical for utilization. As the GPU has become increasingly more powerful and ubiquitous, many scientific applications have been implemented to enhance the computational performance on GPU platform. In this paper, we proposed a method, CUDA-FRESCO, to map the short reads to the genome by using CUDA on GPU platform. The experimental results present that CUDA-FRESCO can achieve dramatic speed up than other tools. CUDA-FRESCO can be alternative tool for biologists to map the short reads fast.

With the rapid growth of network technologies, many new web services have been developed to provide various applications and computing functions. These services rely deeply on the internet. Therefore, packet classification is an important issue of network security that typically adopts a flexible packet filtering system to classify each processed packet. Traditional packet classification requires hung computing time to process large amount of internet packets. Hence, we propose a GPGPU-based parallel packet classification method to decrease the computational cost. We also evaluate the performance of the proposed method with implementation on various memory architectures of CUDA device. The experiment results demonstrate that the proposed method can achieve significant speed up over the sequential packet classification algorithms on single CPU.

This research paper discusses performance issues of cluster and grid computing platforms, and reasons to support the implementation of these computing infrastructures. A number of benchmark programs are executed in these computing systems, in order to perform performance analysis of experimental results. We are able to show that cluster platforms are excellent alternatives to access to supercomputing, due to its cost/performance, scalability and commodity components factors. In addition, we also show that grid technology is viable by increasing total system performance at no additional cost.

Alternative splicing of a single pre-mRNA can give rise to different mRNA transcripts. Alternative splicing of pre-messenger RNA is an important layer of gene expression regulation in eukaryotic cell. Consequently, alternative splicing is an important mechanism for generating protein diversity from a single gene. Although alternative splicing is an important biological process, standard molecular biology techniques have only identified several hundred alternative splicing variants and create a bottleneck in terms of experimental validation. In this paper, we propose methods of obtaining models of weighted alternative splicing graphs and ways of generating all alternative splicing forms from a weighted alternative splicing graph and formulate linear programming models and use the popular linear programming solver to obtain the quantitative distributions of various alternative splicing forms. Basically, the method uses the UniGene clusters of human expressed sequence tags (ESTs) to identify alternative splicing sites. Furthermore, we propose linear time algorithms that correctly produce all possible alternative splicing variants with their corresponding probabilities. Using these methods, we infer several sets of putative alternative splicing forms; these results are then compared with methods proposed by others. Then by aligning sequences of EST database to the genomic data, we identify locations of exons as well as the alternative splicing sites. To quantify these putative alternative splicing forms, we choose segments in genome to count the EST number, and combine the information of EST and alternative splicing form by constructing the suitable linear programming model.

In the past several years, the analysis of alternative splicing and its application to identify the origins of disease has been gaining momentum. Therefore, we developed a value added transcriptome database (Avatar). We mapped EST (expressed sequence tag) and mRNA sequences onto whole human genomic sequence. We identified 9,937 alternative splicing relationships genes through an analysis of large-scale ESTs. Besides, we provided alternative splicing information for six organisms: Homo sapiens, Mus musculus, Rattus norvegiens, Caenorhabditis elegans, Drosophila melanogaster and Arabidopsis thaliana.

SUMMARY: MAVG is a software tool for finding k non-overlapping maximum-average segments that are sufficiently long in a given sequence of real numbers, for any k > 0. It has applications in several areas of biomolecular sequence analysis including locating GC-rich regions and CpG islands in a genomic sequence, and annotating multiple sequence alignments. AVAILABILITY: http://iubio.bio.indiana.edu/soft/molbio/pattern/cpg_islands/.

The nth power$( n \geq 1)$ of a graph $G = ( V,E )$, written $G^n $, is defined to be the graph having V as its vertex set with two vertices u, $v $ adjacent in $G^n $ if and only if there exists a path of length at most n between them. Similarly, graph H has an nth rootG if $G^n = H$. For the case of $n = 2$, $G^2 $ is the square of G and G is the square root of $G^2 $. This paper presents a linear time algorithm for finding the tree square roots of a given graph and a linear time algorithm for finding the square roots of planar graphs. A polynomial time algorithm for finding the square roots of subdivision graphs, which is equivalent to the problem of the inversion of total graphs, is also presented. Further, the authors give a linear time algorithm for finding a Hamiltonian cycle in a cubic graph and prove the NP-completeness of finding the maximum cliques in powers of graphs and the chordality of powers of trees.