International Journal of Genomics Novel Computational Technologies for Next-Generation Sequencing Data Analysis and Their Applications Guest Editors: Chuan Yi Tang, Che-Lun Hung, Huiru Zheng, Chun-Yuan Lin, and Hai Jiang Novel Computational Technologies for Next-Generation Sequencing Data Analysis and Their Applications International Journal of Genomics Novel Computational Technologies for Next-Generation Sequencing Data Analysis and Their Applications Guest Editors: Chuan Yi Tang, Che-Lun Hung, Huiru Zheng, Chun-Yuan Lin, and Hai Jiang Copyright © òýÔ Hindawi Publishing Corporation. All rights reserved. is is a special issue published in “International Journal of Genomics.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board Jacques Camonis, France Sylvia Hagemann, Austria Elena Pasyukova, Russia Shen Liang Chen, Taiwan Henry Heng, USA Graziano Pesole, Italy Prabhakara V. Choudary, USA Eivind Hovig, Norway Giulia Piaggio, Italy Martine A. Collart, Switzerland Peter Little, Australia Mohamed Salem, USA Ian Dunham, United Kingdom Shalima Nair, Australia Brian Wigdahl, USA Soraya E. Gutierrez, Chile Giuliana Napolitano, Italy Jinfa Zhang, USA M. Hadzopoulou-Cladaras, Greece Ferenc Olasz, Hungary Contents Novel Computational Technologies for Next-Generation Sequencing Data Analysis and eir Applications, Chuan Yi Tang, Che-Lun Hung, Huiru Zheng, Chun-Yuan Lin, and Hai Jiang Volume òýÔ , Article ID ò ¥â , ç pages Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency, Rodrigo Aniceto, Rene Xavier, Valeria Guimarães, Fernanda Hondo, Maristela Holanda, Maria Emilia Walter, and Sérgio Lifschitz Volume òýÔ , Article ID ýòÞÀ , Þ pages RECORD: Reference-Assisted Genome Assembly for Closely Related Genomes, Krisztian Buza, Bartek Wilczynski, and Norbert Dojer Volume òýÔ , Article ID âç¥ò, Ôý pages Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System, Yu Liu, Yang Hong, Chun-Yuan Lin, and Che-Lun Hung Volume òýÔ , Article ID ÞâÔýâç, Ôò pages SimpLiFiCPM: A Simple and Lightweight Filter-Based Algorithm for Circular Pattern Matching, Md. Aashikur Rahman Azim, Costas S. Iliopoulos, M. Sohel Rahman, and M. Samiruzzaman Volume òýÔ , Article ID ò Àçòý, Ôý pages A New Binning Method for Metagenomics by One-Dimensional Cellular Automata, Ying-Chih Lin Volume òýÔ , Article ID ÔÀÞÀ , â pages Spaced Seed Data Structures for De Novo Assembly, Inanç Birol, Justin Chu, Hamid Mohamadi, Shaun D. Jackman, Karthika Raghavan, Benjamin P. Vandervalk, Anthony Raymond, and René L. Warren Volume òýÔ , Article ID ÔÀâ ÀÔ, pages Accelerating Multiple Compound Comparison Using LINGO-Based Load-Balancing Strategies on Multi-GPUs, Chun-Yuan Lin, Chung-Hung Wang, Che-Lun Hung, and Yu-Shiang Lin Volume òýÔ , Article ID À ýÀý , À pages Hindawi Publishing Corporation International Journal of Genomics Volume 2015, Article ID 254685, 3 pages http://dx.doi.org/10.1155/2015/254685 Editorial Novel Computational Technologies for Next-Generation Sequencing Data Analysis and Their Applications Chuan Yi Tang,1 Che-Lun Hung,2 Huiru Zheng,3 Chun-Yuan Lin,4 and Hai Jiang5 1 Department of Computer Science and Information Engineering, Providence University, 200 Sec. 7, Taiwan Boulevard, Shalu District, Taichung City 43301, Taiwan 2Department of Computer Science and Communication Engineering, Providence University, 200 Sec. 7, Taiwan Boulevard, Shalu District, Taichung City 43301, Taiwan 3School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Jordanstown Campus, Shore Road, Newtownabbey, County Antrim BT37 0QB, UK 4Department of Computer Science and Information Engineering, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, Taoyuan City 33302, Taiwan 5Department of Computer Science, Arkansas State University, 2105 Aggie Road, Jonesboro, AR 72401, USA Correspondence should be addressed to Che-Lun Hung; [email protected] Received 20 September 2015; Accepted 29 September 2015 Copyright © 2015 Chuan Yi Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction NGS sequence data. Also, many successful bioinformatics applications with NGS data through these methods have Next-generation sequencing (NGS) technologies, such as unveiled a lot of scientific results, which encourage biologists Illumina/Solexa, ABI/SOLiD, and Roche/454 Pyrosequenc- to adopt novel computing technologies. The research papers ing, are revolutionizing the acquisition of genomic data at selected for this special issue represent recent progress in relatively low cost. NGS technologies are rapidly changing the aspects, including theoretical studies, novel algorithms, the approach to complex genomic studies, opening a way high performance computing technologies, and method and to the development of personalized drugs and personal- algorithm improvement. All of these papers not only provide ized medicine. NGS technologies use massive throughput sequencing to obtain relatively short reads. NGS technolo- novel ideas and state-of-the-art technologies in the field but gies will generate enormous datasets, in which even small also stimulate future research for next-generation sequencing genomic projects may generate terabytes of data. Therefore, data analysis and their applications. new computational methods are needed to analyze a wide range of genetic information and to assist data interpretation 2. Computational Genomics and downstream applications, including high-throughput polymorphism detections, comparative genomics, prediction Development of efficient algorithms for processing short of gene function and protein structure, transcriptome analy- nucleotide sequences has played a key role in enabling the sis, mutation detection and confirmation, genome mapping, uptake of DNA sequencing technologies in life sciences. and drug design. The creation of large-scale datasets now In particular, reassembly of human genomes (de novo or poses a great computational challenge. It will be imperative reference guided) from short DNA sequence reads has had to improve software pipelines, so that we can analyze genome a substantial impact on health research. De novo assembly data more efficiently. of the genome of a species is essential in the absence of Until now, many new computational methods have been a reference genome sequence. The paper by I. Birol et al. proposed to cope with the big biological data, especially entitled “Spaced Seed Data Structures for De Novo Assembly” 2 International Journal of Genomics introduces the data structure designs for spaced seeds in Compound comparison is an important task for the com- the form of paired -mers to solve the limitation of the de putational chemistry. By the comparison results, potential Bruijn graph (DBG) paradigm for long reads. First, Bloom inhibitorscanbefoundandthenusedforthepharmacy filter is used to store spaced seeds, and it can be tolerant experiments. The time complexity of a pairwise compound 2 of sequencing errors. Then they used a data structure for comparison is O( ), where is the maximal length of tracking the frequencies of observed spaced seeds. compounds. The intrinsic time complexity of multiple com- 2 2 Next-generation sequencing technologies are now pro- pound comparison problem is O( )with compounds of ducing multiple times the genome size in total reads from maximal length .ThepaperbyC.-Y.Linetal.entitled“Accel- a single experiment. This is enough information to recon- erating Multiple Compound Comparison Using LINGO- struct at least some of the differences between the individ- Based Load-Balancing Strategies on Multi-GPUs” proposes ual genome studied in the experiment and the reference a GPU-based algorithm for MCC problem, called CUDA- genome of the species. However, in most typical protocols MCC, on single- and multi-GPUs. Four LINGO-based load- this information is disregarded and the reference genome is used. The paper by K. Buza et al. entitled “RECORD: balancing strategies are considered in CUDA-MCC in order Reference-Assisted Genome Assembly for Closely Related to accelerate the computation speed among thread blocks on Genomes” proposes a new approach that allows researchers GPUs. to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the 5. Genomics reads used in the experiment. Their approach applies de novo assembly software, called “RECORD,” to experimental reads The Circular Pattern Matching (CPM) problem appears as andsocalledpseudoreadsandusestheresultingcontigsto an interesting problem in many biological contexts. CPM generate a modified reference sequence. In this way, it can consists in finding all occurrences of the rotations of a very quickly, and at no additional sequencing cost, generate pattern of length in a text of length .Thepaper new, modified reference sequence that is closer to the actual by Md. A. R. Azim et al. entitled “SimpLiFiCPM: A Simple sequenced genome and has a full coverage. and Lightweight Filter-Based Algorithm for Circular Pattern Matching” presents SimpLiFiCPM, a simple and lightweight 3. Metagenomics filter-based algorithm