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international Symposium on Computer Architecture and High Performance computing (SBAC-PAD)

作者： N.F. Almeida E.N. Caceres C.E.R. Alves S.W. Song University Federal de Mato Grosso do Sul Brazil Universidade São Judas Tadeu Brazil Universidade de Sáo Paulo Brazil

Comparison of the dna sequences and genes of two genomes can be useful to investigate the common functionalities of the corresponding organisms and get a better understanding of how the genes or groups of genes are organized and involved in several functions. We use high-performance parallel computing to compare the whole genomes of two organisms, namely Xanthomonas axonopodis pv. citri and Xanthomonas campestris pv. campestris, each with more than five million base pairs. Our purpose is two-fold. First we intend to exploit the high-performance power of a cluster of low-cost microcomputers, propose a parallel solution to this problem, and show its feasibility with implementation and performance results. Second we do additional comparisons of the two genomes by locating and compare not only the homologous genes (expressed in terms of the 20-letter amino acids) but also compare the regions or gaps (in terms of the 4-letter dna nucleotides) between the corresponding homologous genes. We have implemented the proposed comparison strategy to compare the two genomes Xanthomonas axonopodis pv. citri (Xac) and Xanthomonas campestris pv. campestris (Xcc). the parallel platform used is a Beowulf cluster of 64 nodes consisting of low cost microcomputers. Xac has 5175554 base pairs and 4313 protein-coding genes while Xcc has 5076187 base pairs and 4182 protein-coding genes. the parallel solution is based on the dynamic programming approach and presents not only less processing time, but also better quality results as compared to approaches based on Blast and EGG.

关键词： Genomics Bioinformatics Parallel processing dna Organisms Microcomputers Proteins Sequences Amino acids Costs

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Fabrication of a Carbon Chain Based Nanosensor for Maximizing Spatial Resolution in dna Sequencing

Fabrication of a Carbon Chain Based Nanosensor for Maximizin...

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IEEE international conference on Nano/Micro Engineered and molecular Systems (NEMS)

作者： Bo Ma Steve Tung University of Arkansas USA

ISBN: (数字)9781728172309

ISBN: (纸本)9781728172316

Using a carbon chain as the sensing element can significantly improve the spatial resolution in nanodevice based single molecule dna sequencing. In this report, we demonstrate a top-down approach to reduce a single monolayer graphene attached to a silicon microelectrode chip to a sub-nanometer wide carbon chain suitable for dna sequencing. this approach consists three major processing steps to first transfer the graphene to the chip, dice the attached graphene into graphene nanoribbons using Focused Ion Beam (FIB), and finally reduce the ribbon width by knocking off carbon atoms on the ribbon edge using Transmission Electron Microscopy (TEM). A final carbon chain of 0.8 nm wide and 5 nm long is accomplished. the measured electrical properties of the reduced graphene agree well with previous studies.

关键词： conferences

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Order-sensitive clustering for remote homologous protein detection

Order-sensitive clustering for remote homologous protein det...

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Jin Chen Wynne Hsu Mong Li Lee School of Computing National University of Singapore Singapore

ISBN: (纸本)0769520383

Traditional sequence alignment methods are effective in identifying homologous proteins that are highly similar. However, these approaches do not perform well for remote homologous proteins, that is, proteins whose 3D structures are similar but their sequences are not. Recent biological research reveals that protein sequences contain residues that determine the 3D structure of proteins. In this work, we investigate incorporating this information to aid in the clustering of protein databases. We capture protein residues in the form of patterns with fixed order among them. First, the significant patterns are extracted from the protein sequences. Based on the extracted patterns, we perform sequence mining to generate the order among them. Finally, we adopt a partition-based method to cluster protein sequences using the patterns and order features. Experiments on COG and SCOP40 datasets show that our new approach is able to generate high quality clusters that are similar to those determined manually by the biologists.

关键词： Data mining Clustering algorithms Protein sequence Spatial databases dna Cells (biology) Artificial intelligence

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A Low-drift Extended-Gate Field Effect Transistor (EGFET) with Differential Amplifier for Cordyceps Sinensis dna Detection Optimized by gm/ID theory

A Low-drift Extended-Gate Field Effect Transistor (EGFET) wi...

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IEEE international conference on Nano/Micro Engineered and molecular Systems (NEMS)

作者： Yifan Xu Hadi Tavakkoli Jingting Xu Yi-Kuen Lee Hong Kong University of Science and Technology HKSAR China HKUST HKSAR China

ISBN: (数字)9781728172309

ISBN: (纸本)9781728172316

An extended-gate field effect transistor (EGFET) integrated with a differential MOSFET amplifier and an open-source Arduino Yun MCU system was realized for detection of Cordyceps Sinensis dna molecules. A gold microelectrode chip coated with a single-stranded dna probe, as the extended gate was fabricated by MEMS fabrication processes. the differential MOSFET amplifier and additional coating of alkyl-thiol reduced the drifting by one order of magnitude. Moreover, generalized g m /I D theory was used to study the optimized working regime of the EGFET sensor. the highest electric-electrochemical sensitivity could be achieved in the Moderate Inversion (MI) regime. the sensitivity and limit of detection (LOD) of the EGFET sensor were obtained to be 13.85mV/dec and 10nM, respectively. this low-cost low-drift EGFET sensor system is promising for Internet of Living things in the near future.

关键词： dna Probes Sensitivity Gold MOSFET Logic gates Fabrication

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Carcinogenesis Induced by Aflatoxin B1: A Computational Study

Carcinogenesis Induced by Aflatoxin B1: A Computational Stud...

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IEEE international conference on Bioinformatics and Bioengineering

作者： Tibor Kuna Urban Bren Laboratory for Physical Chemistry and Chemical Thermodynamics Faculty of Chemistry and Chemical Technology University of Maribor

ISBN: (纸本)9781467379847

the reported study represents a nice example of how state-of-the-art molecular modeling techniques can be used to study complicated macromolecular complexes, whose structures have not yet been experimentally determined, and to validate these against the available experimental data. the proposed structure will facilitate future studies on the rational design of successful Aflatoxin B1 (AFB1) modulators or on human subpopulations characterized by specific CYP3A4 polymorphisms that are especially sensitive to AFB1 - the most potent natural carcinogen known to men. We also report a series of ab initio and density functional theory simulations of guanine alkylation by aflatoxin B1 exo-8,9-epoxide and predict that the preference of aflatoxin B1 exo-8,9-epoxide over the endo stereoisomer for the reaction with guanine exists in the aqueous solution and is only further amplified in the dna duplex. Finally, through comparison with an analogous reaction between 3a,6a-dihydrofuro[2,3-b]furan exo-4,5-epoxide and guanine, we show that the large planar body of aflatoxin B1 does not enhance its reactivity and related carcinogenicity. this explains why the planar region of related mycotoxins sterigmatocystin and aflatoxin G1 could have been evolutionarily optimized in a different way.

关键词： dna Data models Chemicals Modulation Density functional theory Protocols Kinetic theory

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A sequence-based multiple kernel model for identifying dna-binding proteins

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BMC BIOINFORMATICS 2021年第SUPPL 3期22卷 291-291页

作者： Qian, Yuqing Jiang, Limin Ding, Yijie Tang, Jijun Guo, Fei Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol 1068 Xueyuan Ave Shenzhen Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

Background: dna-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting dna-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. the integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. Results: In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 (84.19%) and PDB186 (83.7%), respectively. Conclusion: Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

关键词： dna-binding proteins Feature extraction Multiple kernel learning Support vector machine Centered kernel alignment

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Exploring I/O Strategies for Parallel Sequence-Search Tools with S3aSim

Exploring I/O Strategies for Parallel Sequence-Search Tools ...

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international Symposium on High Performance Distributed computing

作者： A. Ching Wu-chun Feng Heshan Lin Xiaosong Ma A. Choudhary Department of EECS. Northwestern University South Korea Department of Computer Science Virginia Technology USA Department of Computer Science North Carolina State University USA Computer Science and Mathematics Division Oak Ridge National Laboratory USA

Parallel sequence-search tools are rising in popularity among computational biologists. With the rapid growth of sequence databases, database segmentation is the trend of the future for such search tools. While I/O currently is not a significant bottleneck for parallel sequence-search tools, future technologies including faster processors, customized computational hardware such as FPGAs, improved search algorithms, and exponentially growing databases emphasize an increasing need for efficient parallel I/O in future parallel sequence-search tools. Our paper focuses on examining different I/O strategies for these future tools in a modern parallel file system (PVFS2). Because implementing and comparing various I/O algorithms in every search tool is labor-intensive and time-consuming, we introduce S3aSim, a general simulation framework for sequence-search which allows us to quickly implement, test, and profile various I/O strategies. We examine a variety of I/O strategies (e.g., master-writing and various worker-writing strategies using individual and collective I/O methods) for storing result data in sequence-search tools such as mpiBLAST, pioBLAST, and parallel HMMer. Our experiments fully detail the interaction of computing and I/O within a full application simulation as opposed to typical I/O-only benchmarks

关键词： Databases Sequences Computer science Biology computing Concurrent computing File systems Hidden Markov models dna Hardware Field programmable gate arrays

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Accelerating motif finding problem using grid computing with enhanced Brute Force

Accelerating motif finding problem using grid computing with...

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international conference on Advanced Communication Technology (ICACT)

作者： H.M. Faheem Computer Systems Department Faculty of Computer and Information. Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781424454273;9788955191455

Motif finding problem is a major task to understand the mechanisms of gene expression regulation. Motif is generally defined as a recurring pattern in the sequence of nucleotides or amino acids. In the dna sequence, it is usually a short segment that occurs frequently, but not required to be an exact copy for each occurrence. this property of motif makes motif mining very difficult. In fact, motif finding problem is proven to be NP-Complete. Many algorithms fail to solve the well-known challenge problem of finding a motif of length 15 and has at most 4 mutations (15, 4) due to the huge runtime needed. Others fail to distinguish the motif from background sequences. Brute Force is one of the exact algorithms that never fail to find the motif, but it suffers from an intractable running time. In this paper, we present a novel approach to accelerate the motif finding problem using grid computing. As grid computing makes available low-cost high-performance computing (HPC) infrastructures, the best candidates for using such infrastructures are applications that require high computational power, large storage capacity, or fast and high-throughput networking. the nature of motif finding problem is considered one of the perfect scenarios for grid computing. We deployed an enhanced version of the Brute Force; skip-Brute Force running on EUMEDGRID infrastructure (Co-Funded project by the European Commission in the framework of FP6, with the aim of supporting the development of a Grid e-Infrastructure in the Mediterranean Area and promoting the porting of computationally intensive applications on the Grid platform). the idea behind our skip Brute Force algorithm is that it skips all the iterations that will not lead to a correct solution. the message passing programming paradigm is deployed since it assumes a partitioned address space and supports explicit parallelization. Our experimental results showed boosting in the performance without sacrificing the exactness of the Brute

关键词： Acceleration Grid computing Sequences Computer networks Gene expression Amino acids dna Genetic mutations Runtime Computer applications

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Fabrication of Small-Scale Solid-State Nanopores by Dielectric Breakdown

Fabrication of Small-Scale Solid-State Nanopores by Dielectr...

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IEEE international conference on Nano/Micro Engineered and molecular Systems (NEMS)

作者： Zengdao Gu Dexian Ma Zhicheng Zhang Yin Zhang Jingjie Sha School of Mechanical Engineering Southeast University Nanjing P. R. China

ISBN: (数字)9781728172309

ISBN: (纸本)9781728172316

As a tool for next-generation dna sequencing, solid-state nanopores have significant advantages in terms of stability and process integration. However, traditional manufacturing methods such as FIB and TEM are pretty expensive and require higher-skilled operators. In this paper, we explored the effect of the initial leakage current on the dielectric breakdown time and then demonstrated a low-cost and rapid method of fabricating SiNx nanopores. We prepared nanopores of appropriate size by changing the voltage and cut-off current, and finally realized the preparation of nanopores as small as a few nanometers and as large as tens of nanometers. We also found that the thinned area of silicon nitride film affects the value of the initial leakage current, thereby impacting the time of dielectric breakdown. the foundation of these problems helps us to perfect the fabrication of nanopores by dielectric breakdown.

关键词： Nanobioscience Leakage currents Dielectric breakdown Fabrication Current measurement Breakdown voltage dna

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Discovering clusters in gene expression data using evolutionary approach

Discovering clusters in gene expression data using evolution...

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international conference on Tools for Artificial Intelligence (ICTAI)

作者： P.C.H. Ma K.C.C. Chan Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong China

the combined interpretation of gene expression data and gene sequences offers a valuable approach to investigate the intricate relationships involving gene transcriptional regulation. the highly interactive expression data produced by microarray hybridization experiments allow us to find the clusters of coexpressed genes. By analyzing the upstream regions of the identified coexpressed genes, we can discover the regulatory patterns characterized by transcription factor binding sites, which govern the process of transcriptional regulation. this paper presents a generic clustering algorithm that uses a Hybrid GA approach to discover clusters in gene expression data. the advantage of this method is that large search space can be effectively explored by utilizing the evolutionary algorithm techniques. Moreover, it is able to discover underlying patterns in noisy gene expression data for meaningful data groupings, and also statistically significant patterns hidden in each cluster can be extracted at the same time. Since the proposed method can handle both continuous-and discrete-valued data, it can be used with different microarray and biomedical data. To test its effectiveness, we have used it on real expression data. the experimental results reveal meaningful groupings and uncover many known transcription factor binding sites.

关键词： Gene expression Clustering algorithms dna Partitioning algorithms Data mining Genetics Working environment noise Sequences Pattern analysis Evolutionary computation

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