Search results for: genome sequencing

Authors: M. Sadeghi, H. Pezeshk, R. Tusserkani, A. Sharifi Zarchi, A. Malekpour, M. Foroughmand, S. Goliaei, M. Totonchi, N. Ansari–Pour

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The role and relative importance of intrinsic and extrinsic factors in the development of complex diseases such as cancer still remains a controversial issue. Determining the amount of variation explained by these factors needs experimental data and statistical models. These models are nevertheless based on the occurrence and accumulation of random mutational events during stem cell division, thus rendering cancer development a stochastic outcome. We demonstrate that not only individual genome sequencing is uninformative in determining cancer risk, but also assigning a unique genome sequence to any given individual (healthy or affected) is not meaningful. Current whole-genome sequencing approaches are therefore unlikely to realize the promise of personalized medicine. In conclusion, since genome sequence differs from cell to cell and changes over time, it seems that determining the risk factor of complex diseases based on genome sequence is somewhat unrealistic, and therefore, the resulting data are likely to be inherently uninformative.

Keywords: Cancer risk, extrinsic factors, genome sequencing, intrinsic factors.

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Authors: Chien-Ju Li, Chun-Hui Yu, Chi-Chuan Hwang, Tsunglin Liu , Darby Tien-Hao Chang

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De novo genome assembly is always fragmented. Assembly fragmentation is more serious using the popular next generation sequencing (NGS) data because NGS sequences are shorter than the traditional Sanger sequences. As the data throughput of NGS is high, the fragmentations in assemblies are usually not the result of missing data. On the contrary, the assembled sequences, called contigs, are often connected to more than one other contigs in a complicated manner, leading to the fragmentations. False connections in such complicated connections between contigs, named a contig graph, are inevitable because of repeats and sequencing/assembly errors. Simplifying a contig graph by removing false connections directly improves genome assembly. In this work, we have developed a tool, SIMGraph, to resolve ambiguous connections between contigs using NGS data. Applying SIMGraph to the assembly of a fungus and a fish genome, we resolved 27.6% and 60.3% ambiguous contig connections, respectively. These results can reduce the experimental efforts in resolving contig connections.

Keywords: Contig graph, NGS, de novo assembly, scaffold.

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Authors: E. Al Daoud

Abstract:

The purpose of computing the similarity and the diversity in the species is to trace the process of evolution and to find the relationship between the species and discover the unique, the special, the common and the universal proteins. The proteins of the whole genome of 40 species are compared with the cronobacter genome which is used as reference genome. More than 3 billion pairwise alignments are performed using blastp. Several findings are introduced in this study, for example, we found 172 proteins in cronobacter genome which have insignificant hits in other species, 116 significant proteins in the all tested species with very high score value and 129 common proteins in the plants but have insignificant hits in mammals, birds, fishes, and insects.

Keywords: Genome, species, blastp, conserved genes, cronobacter.

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Authors: Somayyeh Azizi, Saeed Kaboli, Atsushi Yagi

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Whole genome duplication (WGD) increased the number of yeast Saccharomyces cerevisiae chromosomes from 8 to 16. In spite of retention the number of chromosomes in the genome of this organism after WGD to date, chromosomal rearrangement events have caused an evolutionary distance between current genome and its ancestor. Studies under evolutionary-based approaches on eukaryotic genomes have shown that the rearrangement distance is an approximable problem. In the case of S. cerevisiae, we describe that rearrangement distance is accessible by using dedoubled adjacency graph drawn for 55 large paired chromosomal regions originated from WGD. Then, we provide a program extracted from a C program database to draw a dedoubled genome adjacency graph for S. cerevisiae. From a bioinformatical perspective, using the duplicated blocks of current genome in S. cerevisiae, we infer that genomic organization of eukaryotes has the potential to provide valuable detailed information about their ancestrygenome.

Keywords: Whole-genome duplication, Evolution, Double-cutand- join operation, Yeast.

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Authors: Essam Al Daoud

Abstract:

Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit distance is proposed. The main idea is to convert the unsigned permutation to signed permutation and evaluate the ants by using Kaplan algorithm. Two new operations are added to the standard ant colony algorithm: Replacing the worst ants by re-sampling the ants from a new probability distribution and applying the crossover operations on the best ants. The proposed algorithm is tested and compared with the improved breakpoint reversal sort algorithm by using three datasets. The results indicate that the proposed algorithm achieves better accuracy ratio than the previous methods.

Keywords: Ant colony algorithm, Edit distance, Genome breakpoint, Genome rearrangement, Reversal sort.

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Authors: Hossein Ahmadi, Ogholgol Nazari

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The necessity of grading and sequencing tasks has led to the development of different criteria in this regard. However, appropriateness of these criteria in different situations is less discussed. This paper attempts to shed more light on the priority of different criteria in relation with different factors including learners, teachers, educational, and cultural factors.

Keywords: Criteria, Grading, Sequencing, Language learning tasks.

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: Metagenomics, phenotype prediction, deep learning, embeddings, multiple instance learning.

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Authors: N. Manavizadeh, A. Dehghani, M. Rabbani

Abstract:

Mixed model assembly lines (MMAL) are a type of production line where a variety of product models similar in product characteristics are assembled. The effective design of these lines requires that schedule for assembling the different products is determined. In this paper we tried to fit the sequencing problem with the main characteristics of make to order (MTO) environment. The problem solved in this paper is a multiple objective sequencing problem in mixed model assembly lines sequencing using weighted Sum Method (WSM) using GAMS software for small problem and an effective GA for large scale problems because of the nature of NP-hardness of our problem and vast time consume to find the optimum solution in large problems. In this problem three practically important objectives are minimizing: total utility work, keeping a constant production rate variation, and minimizing earliness and tardiness cost which consider the priority of each customer and different due date which is a real situation in mixed model assembly lines and it is the first time we consider different attribute to prioritize the customers which help the company to reduce the cost of earliness and tardiness. This mechanism is a way to apply an advance available to promise (ATP) in mixed model assembly line sequencing which is the main contribution of this paper.

Keywords: Available to promise, Earliness & Tardiness, GA, Mixed-Model assembly line Sequencing.

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Authors: Swapnoneel Roy, Minhazur Rahman, Ashok Kumar Thakur

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Bioinformatics and computational biology involve the use of techniques including applied mathematics, informatics, statistics, computer science, artificial intelligence, chemistry, and biochemistry to solve biological problems usually on the molecular level. Research in computational biology often overlaps with systems biology. Major research efforts in the field include sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and proteinprotein interactions, and the modeling of evolution. Various global rearrangements of permutations, such as reversals and transpositions,have recently become of interest because of their applications in computational molecular biology. A reversal is an operation that reverses the order of a substring of a permutation. A transposition is an operation that swaps two adjacent substrings of a permutation. The problem of determining the smallest number of reversals required to transform a given permutation into the identity permutation is called sorting by reversals. Similar problems can be defined for transpositions and other global rearrangements. In this work we perform a study about some genome rearrangement primitives. We show how a genome is modelled by a permutation, introduce some of the existing primitives and the lower and upper bounds on them. We then provide a comparison of the introduced primitives.

Keywords: Sorting Primitives, Genome Rearrangements, Transpositions, Block Interchanges, Strip Exchanges.

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Authors: P. Pasomboon, P. Chumnanpuen, T. E-kobon

Abstract:

Hyaluronic acid (HA) consists of linear heteropolysaccharides repeat of D-glucuronic acid and N-acetyl-D-glucosamine. HA has various useful properties to maintain skin elasticity and moisture, reduce inflammation, and lubricate the movement of various body parts without causing immunogenic allergy. HA can be found in several animal tissues as well as in the capsule component of some bacteria including Pasteurella multocida. This study aimed to modify a genome-scale metabolic model of Escherichia coli using computational simulation and flux analysis methods to predict HA productivity under different carbon sources and nitrogen supplement by the addition of two enzymes (GLMU2 and HYAD) from P. multocida to improve the HA production under the specified amount of carbon sources and nitrogen supplements. Result revealed that threonine and aspartate supplement raised the HA production by 12.186%. Our analyses proposed the genome-scale metabolic model is useful for improving the HA production and narrows the number of conditions to be tested further.

Keywords: Pasteurella multocida, Escherichia coli, hyaluronic acid, genome-scale metabolic model, bioinformatics.

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Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

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Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: Human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing.

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Authors: Shamim Ahmed Koichi Nishigaki

Abstract:

Genome profiling (GP), a genotype based technology, which exploits random PCR and temperature gradient gel electrophoresis, has been successful in identification/classification of organisms. In this technology, spiddos (Species identification dots) and PaSS (Pattern similarity score) were employed for measuring the closeness (or distance) between genomes. Based on the closeness (PaSS), we can buildup phylogenetic trees of the organisms. We noticed that the topology of the tree is rather robust against the experimental fluctuation conveyed by spiddos. This fact was confirmed quantitatively in this study by computer-simulation, providing the limit of the reliability of this highly powerful methodology. As a result, we could demonstrate the effectiveness of the GP approach for identification/classification of organisms.

Keywords: Fluctuation, Genome profiling (GP), Pattern similarity score (PaSS), Robustness, Spiddos-shift.

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Authors: Ali W. Alattabi, Khalid S. Hashim, Hassnen M. Jafer, Ali Alzeyadi

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This study was performed to optimise the react time (RT) and study its effects on the removal rates of nitrogen compounds in a sequencing batch reactor (SBR) treating synthetic industrial wastewater. The results showed that increasing the RT from 4 h to 10, 16 and 22 h significantly improved the nitrogen compounds’ removal efficiency, it was increased from 69.5% to 95%, 75.7 to 97% and from 54.2 to 80.1% for NH₃-N, NO₃-N and NO₂-N respectively. The results obtained from this study showed that the RT of 22 h was the optimum for nitrogen compounds removal efficiency.

Keywords: Ammonia-nitrogen, retention time, nitrate, nitrite, sequencing batch reactor, sludge characteristics.

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Authors: Fereshteh Shacheraghi, Mohammad Reza Shakibaie, Hanieh Noveiri

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Fourty one strains of ESBL producing P.aeruginosa which were previously isolated from burn patients in Kerman University general hospital, Iran were subjected to PCR, RFLP and sequencing in order to determine the type of extended spectrum β- lactamases (ESBL), the restriction digestion pattern and possibility of mutation among detected genes. DNA extraction was carried out by phenol chloroform method. PCR for detection of bla genes was performed using specific primer for each gene. Restriction Fragment Length Polymorphism (RFLP) for ESBL genes was carried out using EcoRI, NheI, PVUII, EcoRV, DdeI, and PstI restriction enzymes. The PCR products were subjected to direct sequencing of both the strands for identification of the ESBL genes.The blaCTX-M, blaVEB-1, blaPER-1, blaGES-1, blaOXA-1, blaOXA-4 and blaOXA-10 genes were detected in the (n=1) 2.43%, (n=41)100%, (n=28) 68.3%, (n=10) 24.4%, (n=29) 70.7%, (n=7)17.1% and (n=38) 92.7% of the ESBL producing isolates respectively. The RFLP analysis showed that each ESBL gene has identical pattern of digestion among the isolated strains. Sequencing of the ESBL genes confirmed the genuinety of PCR products and revealed no mutation in the restriction sites of the above genes. From results of the present investigation it can be concluded that blaVEB-1 and blaCTX-M were the most and the least frequently isolated ESBL genes among the P.aeruginosa strains isolated from burn patients. The RFLP and sequencing analysis revealed that same clone of the bla genes were indeed existed among the antibiotic resistant strains.

Keywords: ESBL genes, PCR, RFLP, Sequencing, P.aeruginosa

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Authors: S. Moghaddaszadeh-Ahrabi, S. Farajnia, Gh. Rahimi-Mianji, A. Nejati-Javaremi

Abstract:

In recent years, there has been an increasing interest toward the use of bovine genotyped embryos for commercial embryo transfer programs. Biopsy of a few cells in morulla stage is essential for preimplantation genetic diagnosis (PGD). Low amount of DNA have limited performing the several molecular analyses within PGD analyses. Whole genome amplification (WGA) promises to eliminate this problem. We evaluated the possibility and performance of an improved primer extension preamplification (I-PEP) method with a range of starting bovine genomic DNA from 1-8 cells into the WGA reaction. We optimized a short and simple I-PEP (ssI-PEP) procedure (~3h). This optimized WGA method was assessed by 6 loci specific polymerase chain reactions (PCRs), included restriction fragments length polymorphism (RFLP). Optimized WGA procedure possesses enough sensitivity for molecular genetic analyses through the few input cells. This is a new era for generating characterized bovine embryos in preimplantation stage.

Keywords: Whole genome amplification (WGA), Genotyping, Bovine, Preimplantation genetic diagnosis (PGD)

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Authors: Amin Mojiri, Akiyoshi Ohashi, Tomonori Kindaichi

Abstract:

Synthetic domestic wastewater was treated via combining treatment methods, including electrochemical oxidation, adsorption, and sequencing batch reactor (SBR). In the upper part of the reactor, an anode and a cathode (Ti/RuO₂-IrO₂) were organized in parallel for the electrochemical oxidation procedure. Sodium sulfate (Na₂SO₄) with a concentration of 2.5 g/L was applied as the electrolyte. The voltage and current were fixed on 7.50 V and 0.40 A, respectively. Then, 15% working value of the reactor was filled by activated sludge, and 85% working value of the reactor was added with synthetic wastewater. Powdered cockleshell, 1.5 g/L, was added in the reactor to do ion-exchange. Response surface methodology was employed for statistical analysis. Reaction time (h) and pH were considered as independent factors. A total of 97.0% biochemical oxygen demand, 99.9% phosphorous and 88.6% cadmium were eliminated at the optimum reaction time (80.0 min) and pH (6.4).

Keywords: Adsorption, electrochemical oxidation, metals, sequencing batch reactor.

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Authors: O. Yavuz, L. Ozyilmaz

Abstract:

HIV-1 genome is highly heterogeneous. Due to this variation, features of HIV-I genome is in a wide range. For this reason, the ability to infection of the virus changes depending on different chemokine receptors. From this point of view, R5 HIV viruses use CCR5 coreceptor while X4 viruses use CXCR5 and R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the experiments on HIV-1 genome. In this study, R5X4 type of HIV viruses were classified using Auto Regressive (AR) model through Artificial Neural Networks (ANNs). The statistical data of R5X4, R5 and X4 viruses was analyzed by using signal processing methods and ANNs. Accessible residues of these virus sequences were obtained and modeled by AR model since the dimension of residues is large and different from each other. Finally the pre-processed data was used to evolve various ANN structures for determining R5X4 viruses. Furthermore ROC analysis was applied to ANNs to show their real performances. The results indicate that R5X4 viruses successfully classified with high sensitivity and specificity values training and testing ROC analysis for RBF, which gives the best performance among ANN structures.

Keywords: Auto-Regressive Model, HIV, Neural Networks, ROC Analysis.

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Authors: Martin Bader

Abstract:

During the last years, the genomes of more and more species have been sequenced, providing data for phylogenetic recon- struction based on genome rearrangement measures. A main task in all phylogenetic reconstruction algorithms is to solve the median of three problem. Although this problem is NP-hard even for the sim- plest distance measures, there are exact algorithms for the breakpoint median and the reversal median that are fast enough for practical use. In this paper, this approach is extended to the transposition median as well as to the weighted reversal and transposition median. Although there is no exact polynomial algorithm known even for the pairwise distances, we will show that it is in most cases possible to solve these problems exactly within reasonable time by using a branch and bound algorithm.

Keywords: Comparative genomics, genome rearrangements, me-dian, reversals, transpositions.

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Authors: Chang Kug Kim, Dong Suk Park, Young Joo Seol, Jang Ho Hahn

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The National Agricultural Biotechnology Information Center (NABIC) plays a leading role in the biotechnology information database for agricultural plants in Korea. Since 2002, we have concentrated on functional genomics of major crops, building an integrated biotechnology database for agro-biotech information that focuses on bioinformatics of major agricultural resources such as rice, Chinese cabbage, and microorganisms. In the NABIC, integration-based biotechnology database provides useful information through a user-friendly web interface that allows analysis of genome infrastructure, multiple plants, microbial resources, and living modified organisms.

Keywords: biotechnology, database, genome information

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Authors: Maryam Saeidi, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Zymomonas mobilis is known as an example of the uncoupled growth phenomenon. This microorganism also has a unique metabolism that degrades glucose by the Entner–Doudoroff (ED) pathway. In this paper, a genome-scale metabolic model including 434 genes, 757 reactions and 691 metabolites was reconstructed to simulate uncoupled growth and study its effect on flux distribution in the central metabolism. The model properly predicted that ATPase was activated in experimental growth yields of Z. mobilis. Flux distribution obtained from model indicates that the major carbon flux passed through ED pathway that resulted in the production of ethanol. Small amounts of carbon source were entered into pentose phosphate pathway and TCA cycle to produce biomass precursors. Predicted flux distribution was in good agreement with experimental data. The model results also indicated that Z. mobilis metabolism is able to produce biomass with maximum growth yield of 123.7 g (mol glucose)-1 if ATP synthase is coupled with growth and produces 82 mmol ATP gDCW-1h-1. Coupling the growth and energy reduced ethanol secretion and changed the flux distribution to produce biomass precursors.

Keywords: Genome-scale metabolic model, Zymomonas mobilis, uncoupled growth, flux distribution, ATP dissipation.

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Authors: Abdulrahim Shamayleh

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In this work, the surgical theater of a local hospital in KSA was analyzed using simulation. The focus was on attempting to answer questions related to how many Operating Rooms (ORs) to open and to analyze the performance of the surgical theater in general and mainly the Post Anesthesia Care Unit (PACU) to assist making decisions regarding PACU staffing. The surgical theater consists of ten operating rooms and the PACU unit which has a maximum capacity of fifteen beds. Different sequencing rules to sequence the surgical cases were tested and the Longest Case First (LCF) were superior to others. The results of the different alternatives developed and tested can be used by the manager as a tool to plan and manage the OR and PACU

Keywords: Operating room, post anesthesia care unit, PACUstaffing, sequencing, healthcare

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Authors: Benjamin Y. M. Kwan, Jennifer Y. Y. Kwan, Hon Keung Kwan

Abstract:

In the paper, the relative performances on spectral classification of short exon and intron sequences of the human and eleven model organisms is studied. In the simulations, all combinations of sixteen one-sequence numerical representations, four threshold values, and four window lengths are considered. Sequences of 150-base length are chosen and for each organism, a total of 16,000 sequences are used for training and testing. Results indicate that an appropriate combination of one-sequence numerical representation, threshold value, and window length is essential for arriving at top spectral classification results. For fixed-length sequences, the precisions on exon and intron classification obtained for different organisms are not the same because of their genomic differences. In general, precision increases as sequence length increases.

Keywords: Exons and introns classification, Human genome, Model organism genome, Spectral analysis

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Authors: Jafar Ahmadi, Zhohreh Asiaban, Sedigheh Fabriki Ourang

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Brassinosteroids (BRs) regulate cell elongation, vascular differentiation, senescence, and stress responses. BRs signal through the BES1/BZR1 family of transcription factors, which regulate hundreds of target genes involved in this pathway. In this research a comprehensive genome-wide analysis was carried out in BES1/BZR1 gene family in Arabidopsis thaliana, Cucumis sativus, Vitis vinifera, Glycin max and Brachypodium distachyon. Specifications of the desired sequences, dot plot and hydropathy plot were analyzed in the protein and genome sequences of five plant species. The maximum amino acid length was attributed to protein sequence Brdic3g with 374aa and the minimum amino acid length was attributed to protein sequence Gm7g with 163aa. The maximum Instability index was attributed to protein sequence AT1G19350 equal with 79.99 and the minimum Instability index was attributed to protein sequence Gm5g equal with 33.22. Aliphatic index of these protein sequences ranged from 47.82 to 78.79 in Arabidopsis thaliana, 49.91 to 57.50 in Vitis vinifera, 55.09 to 82.43 in Glycin max, 54.09 to 54.28 in Brachypodium distachyon 55.36 to 56.83 in Cucumis sativus. Overall, data obtained from our investigation contributes a better understanding of the complexity of the BES1/BZR1 gene family and provides the first step towards directing future experimental designs to perform systematic analysis of the functions of the BES1/BZR1 gene family.

Keywords: BES1/BZR1, Brassinosteroids, Phylogenetic analysis, Transcription factor.

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Authors: Zeyneb Kurt, Oguzhan Yavuz

Abstract:

The feature of HIV genome is in a wide range because of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point, R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to classify by using the coreceptors of HIV genome. The aim of this study is to develop the optimal Multilayer Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model (AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4 viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.

Keywords: Multilayer Perceptron, Auto-Regressive Model, HIV, ROC Analysis

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Authors: Nadezhda M. Usmanova, Nikolai V. Tomilin

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Mammalian genomes contain large number of retroelements (SINEs, LINEs and LTRs) which could affect expression of protein coding genes through associated transcription factor binding sites (TFBS). Activity of the retroelement-associated TFBS in many genes is confirmed experimentally but their global functional impact remains unclear. Human SINEs (Alu repeats) and mouse SINEs (B1 and B2 repeats) are known to be clustered in GCrich gene rich genome segments consistent with the view that they can contribute to regulation of gene expression. We have shown earlier that Alu are involved in formation of cis-regulatory modules (clusters of TFBS) in human promoters, and other authors reported that Alu located near promoter CpG islands have an increased frequency of CpG dinucleotides suggesting that these Alu are undermethylated. Human Alu and mouse B1/B2 elements have an internal bipartite promoter for RNA polymerase III containing conserved sequence motif called B-box which can bind basal transcription complex TFIIIC. It has been recently shown that TFIIIC binding to B-box leads to formation of a boundary which limits spread of repressive chromatin modifications in S. pombe. SINEassociated B-boxes may have similar function but conservation of TFIIIC binding sites in SINEs located near mammalian promoters has not been studied earlier. Here we analysed abundance and distribution of retroelements (SINEs, LINEs and LTRs) in annotated sequences of the Database of mammalian transcription start sites (DBTSS). Fractions of SINEs in human and mouse promoters are slightly lower than in all genome but >40% of human and mouse promoters contain Alu or B1/B2 elements within -1000 to +200 bp interval relative to transcription start site (TSS). Most of these SINEs is associated with distal segments of promoters (-1000 to -200 bp relative to TSS) indicating that their insertion at distances >200 bp upstream of TSS is tolerated during evolution. Distribution of SINEs in promoters correlates negatively with the distribution of CpG sequences. Using analysis of abundance of 12-mer motifs from the B1 and Alu consensus sequences in genome and DBTSS it has been confirmed that some subsegments of Alu and B1 elements are poorly conserved which depends in part on the presence of CpG dinucleotides. One of these CpG-containing subsegments in B1 elements overlaps with SINE-associated B-box and it shows better conservation in DBTSS compared to genomic sequences. It has been also studied conservation in DBTSS and genome of the B-box containing segments of old (AluJ, AluS) and young (AluY) Alu repeats and found that CpG sequence of the B-box of old Alu is better conserved in DBTSS than in genome. This indicates that Bbox- associated CpGs in promoters are better protected from methylation and mutation than B-box-associated CpGs in genomic SINEs. These results are consistent with the view that potential TFIIIC binding motifs in SINEs associated with human and mouse promoters may be functionally important. These motifs may protect promoters from repressive histone modifications which spread from adjacent sequences. This can potentially explain well known clustering of SINEs in GC-rich gene rich genome compartments and existence of unmethylated CpG islands.

Keywords: Retroelement, promoter, CpG island, DNAmethylation.

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Authors: Tarek Aboueldah, Hanan Farag

Abstract:

Parallel Job Shop Scheduling Problem (JSSP) is a multi-objective and multi constrains NP-optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution. Thus, we propose a hybrid Artificial Intelligence (AI) model with Discrete Breeding Swarm (DBS) added to traditional AI to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.

Keywords: Parallel Job Shop Scheduling Problem, Artificial Intelligence, Discrete Breeding Swarm, Car Sequencing and Operator Allocation, cost minimization.

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Authors: N. Manavizadeh , M. Rabbani , H. Sotudian , F. Jolai

Abstract:

Mixed Model Production is the practice of assembling several distinct and different models of a product on the same assembly line without changeovers and then sequencing those models in a way that smoothes the demand for upstream components. In this paper, we consider an objective function which minimizes total stoppage time and total idle time and it is presented sequence dependent set up time. Many studies have been done on the mixed model assembly lines. But in this paper we specifically focused on reducing the idle times. This is possible through various help policies. For improving the solutions, some cases developed and about 40 tests problem was considered. We use scatter search for optimization and for showing the efficiency of our algorithm, experimental results shows behavior of method. Scatter search and help policies can produce high quality answers, so it has been used in this paper.

Keywords: mixed model assembly lines, Scatter search, help policies, idle time, Stoppage time

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Authors: O. Grinchuk, E. Motakis, V. Kuznetsov

Abstract:

Sense-antisense gene pair (SAGP) is a pair of two oppositely transcribed genes sharing a common region on a chromosome. In the mammalian genomes, SAGPs can be organized in more complex sense-antisense gene architectures (CSAGA) in which at least one gene could share loci with two or more antisense partners. Many dozens of CSAGAs can be found in the human genome. However, CSAGAs have not been systematically identified and characterized in context of their role in human diseases including cancers. In this work we characterize the structural-functional properties of a cluster of 5 genes –TMEM97, IFT20, TNFAIP1, POLDIP2 and TMEM199, termed TNFAIP1 / POLDIP2 module. This cluster is organized as CSAGA in cytoband 17q11.2. Affymetrix U133A&B expression data of two large cohorts (410 atients, in total) of breast cancer patients and patient survival data were used. For the both studied cohorts, we demonstrate (i) strong and reproducible transcriptional co-regulatory patterns of genes of TNFAIP1/POLDIP2 module in breast cancer cell subtypes and (ii) significant associations of TNFAIP1/POLDIP2 CSAGA with amplification of the CSAGA region in breast cancer, (ii) cancer aggressiveness (e.g. genetic grades) and (iv) disease free patient-s survival. Moreover, gene pairs of this module demonstrate strong synergetic effect in the prognosis of time of breast cancer relapse. We suggest that TNFAIP1/ POLDIP2 cluster can be considered as a novel type of structural-functional gene modules in the human genome.

Keywords: Sense-antisense gene pair, complex genome architecture, TMEM97, IFT20, TNFAIP1, POLDIP2, TMEM199, 17q11.2, breast cancer, transcription regulation, survival analysis, prognosis.

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Authors: Patcharee Intanoo, Jittipan Chavadej, Sumaeth Chavadej

Abstract:

The objective of this study was to investigate hydrogen production from alcohol wastewater by anaerobic sequencing batch reactor (ASBR) under thermophillic operation. The ASBR unit used in this study had a liquid holding volume of 4 L and was operated at 6 cycles per day. The seed sludge taken from an upflow anaerobic sludge blanket unit treating the same wastewater was boiled at 95 °C for 15 min before being fed to the ASBR unit. The ASBR system was operated at different COD loading rates at a thermophillic temperature (55 °C), and controlled pH of 5.5. When the system was operated under optimum conditions (providing maximum hydrogen production performance) at a feed COD of 60 000 mg/l, and a COD loading rate of 68 kg/m3 d, the produced gas contained 43 % H2 content in the produced gas. Moreover, the hydrogen yield and the specific hydrogen production rate (SHPR) were 130 ml H2/g COD removed and 2100 ml H2/l d, respectively.

Keywords: Biohydrogen, Alcohol wastewater, Anaerobic sequencing batch reactor (ASBR), Thermophillic operation

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

Abstract:

Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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