Search results for: ion torrent personal genome machine (PGM)

Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

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Authors: Anny Lucrece Nlend Nkott, Ludovic Temple

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The appearance in 2012 of the CRISPR-CaS9 genome editing technique marks a turning point in the field of genetics. This technique would make it possible to create new varieties quickly and cheaply. Although some consider CRISPR-CaS9 to be revolutionary, others consider it a potential societal threat. To document the controversy, we explain the socioeconomic conditions under which this technique could be accepted for the creation of a rainfed rice variety in Madagascar. The methodological framework is based on 38 individual and semistructured interviews, a multistakeholder forum with 27 participants, and a survey of 148 rice producers. Results reveal that the acceptability of genome editing requires (i) strengthening the seed system through the operationalization of regulatory structures and the upgrading of stakeholders' knowledge of genetically modified organisms, (ii) assessing the effects of the edited variety on biodiversity and soil nitrogen dynamics, and (iii) strengthening the technical and human capacities of the biosafety body. Structural mechanisms for regulating the seed system are necessary to ensure safe experimentation of genome editing techniques. Organizational innovation also appears to be necessary. The study documents how collective learning between communities of scientists and nonscientists is a component of systemic processes of varietal innovation. This study was carried out with the financial support of the GENERICE project (Generation and Deployment of Genome-Edited, Nitrogen-use-Efficient Rice Varieties), funded by the Agropolis Foundation.

Keywords: CRISPR-CaS9, varietal innovation, seed system, innovation system

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Nermin Gozukirmizi, Buket Cakmak, Sevgi Marakli

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Sireviruses are genera of copia LTR retrotransposons with a unique genome structure among retrotransposons. Barley (Hordeum vulgare L.) is an economically important plant and has been studied as a model plant regarding its short annual life cycle and seven chromosome pairs. In this study, we used mature barley embryos, 10-day-old roots and 10-day-old leaves derived from the same barley plant to investigate SIRE1 retrotransposon movements by Inter-Retrotransposon Amplified Polymorphism (IRAP) technique. We found polymorphism rates between 0-64% among embryos, roots and leaves. Polymorphism rates were detected to be 0-27% among embryos, 8-60% among roots, and 11-50% among leaves. Polymorphisms were observed not only among the parts of different individuals, but also on the parts of the same plant (23-64%). The internal domains of SIRE1 (gag, env and rt) were also analyzed in the embryos, roots and leaves. Analysis of band profiles showed no polymorphism for gag, however, different band patterns were observed among samples for rt and env. The sequencing of SIRE1 gag, env and rt domains revealed 79% similarity for gag, 95% for env and 84% for rt to Ty1-copia retrotransposons. SIRE1 retrotransposon was identified in the soybean genome and has been studied on other plants (maize, rice, tomatoe etc.). This study is the first detailed investigation of SIRE1 in barley genome. The obtained findings are expected to contribute to the comprehension of SIRE1 retrotransposon and its role in barley genome.

Keywords: barley, polymorphism, retrotransposon, SIRE1 virus

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Authors: N. A. Badrul

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People are concerned that they are vulnerable as a result of what is exposed about them on the internet. Users are increasingly aware of their privacy and are making various efforts to protect their personal information. However, besides individuals themselves, organisations are also exposing personal information of their staff to the general public by publishing it on their official website. This practice may put individuals at risk and particularly vulnerable to threats. This preliminary study explores explicitly the amount and types of personal information disclosure from organisational websites. Threats and risks related to the disclosures are discussed. In general, all the examined organisational websites discloses personal information with varies identifiable degree of data.

Keywords: personal information, privacy, e-government, information disclosure

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Authors: Abdul Amir Hazbavi

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The present study is an attempt to provide a relatively comprehensive preview of the Iranian English translators’ perception on Machine Translation. Furthermore, the study tries to shed light on the status of implementation of Machine Translation among the Iranian English Translators. To reach the aforementioned objectives, the Localization Industry Standards Association’s questioner for measuring perceptions with regard to the adoption of a technology innovation was adapted and used to investigate three parameter among the participants of the study, namely familiarity with Machine Translation, general perception on Machine Translation and implementation of Machine Translation systems in translation tasks. The participants of the study were 224 last-year undergraduate Iranian students of English translation at 10 universities across the country. The study revealed a very low level of adoption and a very high level of willingness to get familiar with and learn about Machine Translation, as well as a positive perception of and attitude toward Machine Translation by the Iranian English translators.

Keywords: translation technology, machine translation, perception, implementation

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Authors: Rabia Faridi, Rizwana Maqbool, Umara Sahar Rana, Zaheer Ahmad

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Climate change impacts agriculture, notably in Germany, where spring faba beans predominate. However, improved winter hardiness aligns with milder winters, enabling autumn-sown varieties. Genetic resistance to Ascochyta blight is vital for crop integration. Traditional breeding faces challenges due to complex inheritance. This study assessed 224 homozygous faba bean lines for Ascochyta resistance traits. To achieve h²>70%, 12 replicates were required (realized h²=87%). Genetic variation and strong trait correlations were observed. Five lines outperformed 29H, while three were highly susceptible. A genome-wide association study (GWAS) with 188 inbred lines and 2058 markers, including 17 guide SNP markers, identified 12 markers associated with resistance traits, potentially indicating new resistance genes. One guide marker (Vf-Mt1g014230-001) on chromosome III validated a known QTL. The guided marker approach complemented GWAS, facilitating marker-assisted selection for Ascochyta resistance. The Göttingen Winter Bean Population offers promise for resistance breeding.

Keywords: genome wide association studies, marker assisted breeding, faba bean, ascochyta blight

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Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

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Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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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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Authors: Yina A. Cifuentes Triana, Andrés M. Pinzón Velásco, Marío E. Velásquez Lozano

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In this work the sequencing and genome characterization of a natural isolate of Saccharomyces cerevisiae yeast (strain 202-3), identified with potential for the production of second generation ethanol from sugarcane bagasse hydrolysates is presented. This strain was selected because its capability to consume xylose during the fermentation of sugarcane bagasse hydrolysates, taking into account that many strains of S. cerevisiae are incapable of processing this sugar. This advantage and other prominent positive aspects during fermentation profiles evaluated in bagasse hydrolysates made the strain 202-3 a candidate strain to improve the production of second-generation ethanol, which was proposed as a first step to study the strain at the genomic level. The molecular characterization was carried out by genome sequencing with the Illumina HiSeq 2000 platform paired end; the assembly was performed with different programs, finally choosing the assembler ABYSS with kmer 89. Gene prediction was developed with the approach of hidden Markov models with Augustus. The genes identified were scored based on similarity with public databases of nucleotide and protein. Records were organized from ontological functions at different hierarchical levels, which identified central metabolic functions and roles of the S. cerevisiae strain 202-3, highlighting the presence of four possible new proteins, two of them probably associated with the positive consumption of xylose.

Keywords: cellulosic ethanol, Saccharomyces cerevisiae, genome sequencing, xylose consumption

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Authors: Zohar Manber, Ran Elkon

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Accumulation of somatic mutations (SMs) in the genome is a major driving force of cancer development. Most SMs in the tumor's genome are functionally neutral; however, some cause damage to critical processes and provide the tumor with a selective growth advantage (termed cancer driver mutations). Current research on functional significance of SMs is mainly focused on finding alterations in protein coding sequences. However, the exome comprises only 3% of the human genome, and thus, SMs in the noncoding genome significantly outnumber those that map to protein-coding regions. Although our understanding of noncoding driver SMs is very rudimentary, it is likely that disruption of regulatory elements in the genome is an important, yet largely underexplored mechanism by which somatic mutations contribute to cancer development. The expression of most human genes is controlled by multiple enhancers, and therefore, it is conceivable that regulatory SMs are distributed across different enhancers of the same target gene. Yet, to date, most statistical searches for regulatory SMs have considered each regulatory element individually, which may reduce statistical power. The first challenge in considering the cumulative activity of all the enhancers of a gene as a single unit is to map enhancers to their target promoters. Such mapping defines for each gene its set of regulating enhancers (termed "set of regulatory elements" (SRE)). Considering multiple enhancers of each gene as one unit holds great promise for enhancing the identification of driver regulatory SMs. However, the success of this approach is greatly dependent on the availability of comprehensive and accurate enhancer-promoter (E-P) maps. To date, the discovery of driver regulatory SMs has been hindered by insufficient sample sizes and statistical analyses that often considered each regulatory element separately. In this study, we analyzed more than 2,500 whole-genome sequence (WGS) samples provided by The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) in order to identify such driver regulatory SMs. Our analyses took into account the combinatorial aspect of gene regulation by considering all the enhancers that control the same target gene as one unit, based on E-P maps from three genomics resources. The identification of candidate driver noncoding SMs is based on their recurrence. We searched for SREs of genes that are "hotspots" for SMs (that is, they accumulate SMs at a significantly elevated rate). To test the statistical significance of recurrence of SMs within a gene's SRE, we used both global and local background mutation rates. Using this approach, we detected - in seven different cancer types - numerous "hotspots" for SMs. To support the functional significance of these recurrent noncoding SMs, we further examined their association with the expression level of their target gene (using gene expression data provided by the ICGC and TCGA for samples that were also analyzed by WGS).

Keywords: cancer genomics, enhancers, noncoding genome, regulatory elements

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Authors: Neelma Ashraf

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Actinomycetes, particularly genus Streptomyces is of great importance due to their role in the discovery of new natural products, particularly antimicrobial secondary metabolites in the medicinal science and biotechnology industry. Different Streptomyces strains were isolated from Helianthus annuus plants and tested for antibacterial and antifungal activities. The most promising five strains were chosen for further investigation, and growth conditions for antibiotic synthesis were optimised. The supernatants were extracted in different solvents, and the extracted products were analyzed using liquid chromatography-mass spectrometry (LC-MS) and biological testing. From one of the potent strains Streptomyces globusus sp. BR123, a compound lavendamycin was identified using these analytical techniques. In addition, this potent strain also produces a strong antifungal polyene compound with a quasimolecular ion of 2072. Streptomyces sp. BR123 was genome sequenced because of its promising antimicrobial potential in order to identify the gene cluster responsible for analyzed compound “lavendamycin”. The genome analysis yielded candidate genes responsible for the production of this potent compound. The genome sequence of 8.15 Mb of Streptomyces sp. isolate BR123 with a GC content of 72.63% and 8103 protein coding genes was attained. Many antimicrobial, antiparasitic, and anticancerous compounds were detected through multiple biosynthetic gene clusters predicted by in-Silico analysis. Though, the novelty of metabolites was determined through the insignificant resemblance with known biosynthetic gene clusters. The current study gives insight into the bioactive potential of Streptomyces sp. isolate BR123 with respect to the synthesis of bioactive secondary metabolites through genomic and spectrometric analysis. Moreover, the comparative genome study revealed the connection of isolate BR123 with other Streptomyces strains, which could expand the knowledge of this genus and the mechanism involved in the discovery of new antimicrobial metabolites.

Keywords: streptomyces, secondary metabolites, genome, biosynthetic gene clusters, high performance liquid chromatography, mass spectrometry

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Authors: Iman Farasat, Howard M. Salis

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The CRISPR/Cas9 system has revolutionized genome engineering by enabling site-directed and high-throughput genome editing, genome insertion, and gene knockdowns in several species, including bacteria, yeast, flies, worms, and human cell lines. This technology has the potential to enable human gene therapy to treat genetic diseases and cancer at the molecular level; however, the current CRISPR/Cas9 system suffers from seemingly sporadic off-target genome mutagenesis that prevents its use in gene therapy. A comprehensive mechanistic model that explains how the CRISPR/Cas9 functions would enable the rational design of the guide-RNAs responsible for target site selection while minimizing unexpected genome mutagenesis. Here, we present the first quantitative model of the CRISPR/Cas9 genome mutagenesis system that predicts how guide-RNA sequences (crRNAs) control target site selection and cleavage activity. We used statistical thermodynamics and law of mass action to develop a five-step biophysical model of cas9 cleavage, and examined it in vivo and in vitro. To predict a crRNA's binding specificities and cleavage rates, we then compiled a nearest neighbor (NN) energy model that accounts for all possible base pairings and mismatches between the crRNA and the possible genomic DNA sites. These calculations correctly predicted crRNA specificity across 5518 sites. Our analysis reveals that cas9 activity and specificity are anti-correlated, and, the trade-off between them is the determining factor in performing an RNA-mediated cleavage with minimal off-targets. To find an optimal solution, we first created a scheme of safe-design criteria for Cas9 target selection by systematic analysis of available high throughput measurements. We then used our biophysical model to determine the optimal Cas9 expression levels and timing that maximizes on-target cleavage and minimizes off-target activity. We successfully applied this approach in bacterial and mammalian cell lines to reduce off-target activity to near background mutagenesis level while maintaining high on-target cleavage rate.

Keywords: biophysical model, CRISPR, Cas9, genome editing

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Authors: L. Lamola, E. Manolas, A. Krause

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Background: The incidence of childhood cancer incidence is increasing gradually in low-middle income countries, such as South Africa. Globally, there is an extensive range of familial- and hereditary-cancer syndromes, where underlying germline variants increase the likelihood of developing cancer in childhood. Next-Generation Sequencing (NGS) technologies have been key in determining the occurrence and genetic contribution of germline variants to paediatric cancer development. We aimed to design and evaluate a candidate gene panel specific to inherited cancer-predisposing genes to provide a comprehensive insight into the contribution of germline variants to childhood cancer. Methods: 32 paediatric patients (aged 0-18 years) diagnosed with a malignant tumour were recruited, and biological samples were obtained. After quality control, DNA was sequenced using an ion Ampliseq 50 candidate gene panel design and Ion Torrent S5 technologies. Sequencing variants were called using Ion Torrent Suite software and were subsequently annotated using Ion Reporter and Ensembl's VEP. High priority variants were manually analysed using tools such as MutationTaster, SIFT-INDEL and VarSome. Putative identified candidates were validated via Sanger Sequencing. Results: The patients studied had a variety of cancers, the most common being nephroblastoma (13), followed by osteosarcoma (4) and astrocytoma (3). We identified 10 pathogenic / likely pathogenic variants in 10 patients, most of which were novel. Conclusions: According to the literature, we expected ~10% of our patient population to harbour pathogenic or likely pathogenic germline variants, however, we reported about 3 times (~30%) more than we expected. Majority of the identified variants are novel; this may be because this is the first study of its kind in an understudied South African population.

Keywords: Africa, genetics, germline-variants, paediatric-cancer

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Authors: M. Suwaibah, S. W. Tan, I. Aiini, K. Yusoff, A. R. Omar

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Respiratory diseases are the most important infectious diseases affecting poultry worldwide. One of the avian respiratory virus of global importance causing significant economic losses is Infectious Bronchitis Virus (IBV). The virus causes a wide spectrum disease known as Infectious Bronchitis (IB), affecting not only the respiratory system but also the kidney and the reproductive system, depending on its strain. IB and Newcastle disease are two of the most prevalent diseases affecting poultry in Malaysia. However, a study on the molecular characterization of Malaysian IBV is lacking. In this study, an IBV strain IBS130 which was isolated in 2015 was fully sequenced using next-gene sequencing approach. Sequence analysis of IBS130 based on the complete genome, polyprotein 1ab and S1 genes were compared with other IBV sequences available in Genbank, National Center for Biotechnology Information (NCBI). IBV strain IBS130 is characterised as QX-like strain based on whole genome and S1 gene sequence analysis. Comparisons of the virus with other IBV strains showed that the nucleotide identity ranged from 67% to 99.2%, depending on the region analysed. The similarity in whole genome nucleotide ranging from 84.9% to 90.7% with the least similar was from Singapore strains (84.9%) and highly similar with China QX-like strains. Meanwhile, the similarity in polyprotein 1ab ranging from 85.3% to 89.9% with the least similar to Singapore strains (85.3%) and highly similar with Mass strains from USA.

Keywords: infectious bronchitis virus, phylogenetic analysis, chicken, Malaysia

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Authors: Afsaneh Mohammadnejad, Marianne Nygaard, Jan Baumbach, Shuxia Li, Weilong Li, Jesper Lund, Jacob v. B. Hjelmborg, Lene Christensen, Qihua Tan

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Cognitive impairment in the elderly is a key issue affecting the quality of life. Despite a strong genetic background in cognition, only a limited number of single nucleotide polymorphisms (SNPs) have been found. These explain a small proportion of the genetic component of cognitive function, thus leaving a large proportion unaccounted for. We hypothesize that one reason for this missing heritability is the misspecified modeling in data analysis concerning phenotype distribution as well as the relationship between SNP dosage and the phenotype of interest. In an attempt to overcome these issues, we introduced a model-free method based on the generalized correlation coefficient (GCC) in a genome-wide association study (GWAS) of cognitive function in twin samples and compared its performance with two popular linear regression models. The GCC-based GWAS identified two genome-wide significant (P-value < 5e-8) SNPs; rs2904650 near ZDHHC2 on chromosome 8 and rs111256489 near CD6 on chromosome 11. The kinship model also detected two genome-wide significant SNPs, rs112169253 on chromosome 4 and rs17417920 on chromosome 7, whereas no genome-wide significant SNPs were found by the linear mixed model (LME). Compared to the linear models, more meaningful biological pathways like GABA receptor activation, ion channel transport, neuroactive ligand-receptor interaction, and the renin-angiotensin system were found to be enriched by SNPs from GCC. The GCC model outperformed the linear regression models by identifying more genome-wide significant genetic variants and more meaningful biological pathways related to cognitive function. Moreover, GCC-based GWAS was robust in handling genetically related twin samples, which is an important feature in handling genetic confounding in association studies.

Keywords: cognition, generalized correlation coefficient, GWAS, twins

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Authors: Surabhi Maiti, Prajakta Tamhankar, Prachi Uttam Mehta

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Since the accomplishment of the Human Genome Project, there has been an unparalled escalation in the sequencing of genomic data. This project has been the first major vault in the field of medical research, especially in genomics. This project won accolades by using a concept called Bigdata which was earlier, extensively used to gain value for business. Bigdata makes use of data sets which are generally in the form of files of size terabytes, petabytes, or exabytes and these data sets were traditionally used and managed using excel sheets and RDBMS. The voluminous data made the process tedious and time consuming and hence a stronger framework called Hadoop was introduced in the field of genetic sciences to make data processing faster and efficient. This paper focuses on using SPARK which is gaining momentum with the advancement of BigData technologies. Cloud Storage is an effective medium for storage of large data sets which is generated from the genetic research and the resultant sets produced from SPARK analysis.

Keywords: human genome project, Bigdata, genomic data, SPARK, cloud storage, Hadoop

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Authors: Haroon Jan Khan, Xian-Feng Xu, Syed Nasir Shah, Anooshay Niazi

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CNC machines are not only used on a large scale but also now become a prominent necessity among households and smaller businesses. Printed Circuit Boards manufactured by the chemical process are not only risky and unsafe but also expensive and time-consuming. A 3-axis precise CNC machine has been developed, which not only fabricates PCB but has also been used for multi-tasks just by changing the materials used and tools, making it versatile. The advanced CNC machine takes data from CAM software. The TB-6560 controller is used in the CNC machine to adjust variation in the X, Y, and Z axes. The advanced machine is efficient in automatic drilling, engraving, and cutting.

Keywords: CNC, G-code, CAD, CAM, Proteus, FLATCAM, Easel

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Authors: Borys Stegniy, Anton Gerilovych, Oleksii Solodiankin, Vitaliy Bolotin, Anton Stegniy, Denys Muzyka, Claudio Afonso

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New APMV was isolated from white fronted goose in Ukraine. This isolate was tested serologically using monoclonal antibodies in haemagglutination-inhibition tests against APMV1-9. As the results obtained isolate showed cross reactions with APMV7. Following investigations were provided for the full genome sequencing using random primers and cloning into pCRII-TOPO. Analysis of 100 transformed colonies of E.coli using traditional sequencing gave us possibilities to find only 3 regions, which could identify by BLAST. The first region with the length of 367 bp had 70 % nucleotide sequence identity to the APMV 12 isolate Wigeon/Italy/3920_1/2005 at genome position 2419-2784. Next region (344 bp) had 66 % identity to the same APMV 12 isolate at position 4760-5103. The last region (365 bp) showed 71 % identity to Newcastle disease virus strain M4 at position 12569-12928.

Keywords: APMV, Newcastle disease virus, Ukraine, full genome sequencing

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Authors: Bilal Wajid, Erchin Serpedin

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The health industry is aiming towards personalized medicine. If the patient’s genome needs to be sequenced it is important that the entire analysis be completed quickly. This paper explores use of parallel computing to analyze very large sequences. Two cases have been considered. In the first case, the sequence is kept constant and the effect of increasing the number of MPI-based processes is evaluated in terms of execution time, speed and efficiency. In the second case the number of MPI-based processes have been kept constant whereas, the length of the sequence was increased.

Keywords: parallel computing, alignment, genome assembly, alignment

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Authors: Ogwueleka Francisca Nonyelum, Jiya Muhammad

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Poor communication between the victims of the burglaries, road and fire accidents and the agencies, and lack of quick emergency response by the agencies is solved through Machine-to-Machine (M2M) communication. A distress caller is expected to make a call through a network to the respective agency for emergency response but due to some challenges, this often becomes arduous and futile. This research puts forth an Internet-based architecture for Machine-to-Machine (M2M) communication to enhance information dissemination in National Public Security Communication System (NPSCS) network. M2M enables the flow of data between machines and machines and ultimately machines and people with information flowing from a machine over a network, and then through a gateway to a system where it is reviewed and acted on. The research findings showed that Internet-based architecture for M2M communication is most suitable for deployment of a public security network which will allow machines to use Internet to talk to each other.

Keywords: machine-to-machine (M2M), internet-based architecture, network, gateway

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Authors: Lu Xi, Li Pan, Wen Mengmeng

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The stiffness of each component has different influences on the stiffness of the machine tool. Taking the five-axis gantry machining center as an example, we made the modal analysis of the machine tool, followed by raising and lowering the stiffness of the pillar, slide plate, beam, ram and saddle so as to study the stiffness matching among these components on the standard of whether the stiffness of the modified machine tool changes more than 50% relative to the stiffness of the original machine tool. The structural optimization of the machine tool can be realized by changing the stiffness of the components whose stiffness is mismatched. For example, the stiffness of the beam is mismatching. The natural frequencies of the first six orders of the beam increased by 7.70%, 0.38%, 6.82%, 7.96%, 18.72% and 23.13%, with the weight increased by 28Kg, leading to the natural frequencies of several orders which had a great influence on the dynamic performance of the whole machine increased by 1.44%, 0.43%, 0.065%, which verified the correctness of the optimization method based on stiffness matching proposed in this paper.

Keywords: machine tool, optimization, modal analysis, stiffness matching

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Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

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In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

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Authors: Joseph C. Chen

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Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use.

Keywords: DMAIC, machine vision system, process capability, Taguchi Parameter Design

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Authors: Aishik Deb, Rituparna Sinha

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We have developed an algorithm to detect the abnormal segment/"structural variation in the genome across a number of samples. We have worked on simulated as well as real data from the BAM Files and have designed a segmentation algorithm where abnormal segments are detected. This algorithm aims to improve the accuracy and performance of the existing CNV-CH algorithm. The next-generation sequencing (NGS) approach is very fast and can generate large sequences in a reasonable time. So the huge volume of sequence information gives rise to the need for Big Data and parallel approaches of segmentation. Therefore, we have designed a map-reduce approach for the existing CNV-CH algorithm where a large amount of sequence data can be segmented and structural variations in the human genome can be detected. We have compared the efficiency of the traditional and map-reduce algorithms with respect to precision, sensitivity, and F-Score. The advantages of using our algorithm are that it is fast and has better accuracy. This algorithm can be applied to detect structural variations within a genome, which in turn can be used to detect various genetic disorders such as cancer, etc. The defects may be caused by new mutations or changes to the DNA and generally result in abnormally high or low base coverage and quantification values.

Keywords: cancer detection, convex hull segmentation, map reduce, next generation sequencing

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Authors: Farahnaz Sadat Golestan Hashemi, Mohd Razi Ismail, Mohd Y. Rafii

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Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein (Cas) system can facilitate targeted genome editing in organisms. Dual or single guide RNA (gRNA) can program the Cas9 nuclease to cut target DNA in particular areas; thus, introducing concise mutations either via error-prone non-homologous end-joining repairing or via incorporating foreign DNAs by homologous recombination between donor DNA and target area. In spite of high demand of such promising technology, developing a well-organized procedure in order for reliable mining of potential target sites for gRNAs in large genomic data is still challenging. Hence, we aimed to perform high-throughput detection of target sites by specific PAMs for not only common Streptococcus pyogenes (SpCas9) but also for Neisseria meningitides (NmCas9) CRISPR-Cas systems. Previous research confirmed the successful application of such RNA-guided Cas9 orthologs for effective gene targeting and subsequently genome manipulation. However, Cas9 orthologs need their particular PAM sequence for DNA cleavage activity. Activity levels are based on the sequence of the protospacer and specific combinations of favorable PAM bases. Therefore, based on the specific length and sequence of PAM followed by a constant length of the target site for the two orthogonals of Cas9 protein, we created a reliable procedure to explore possible gRNA sequences. To mine CRISPR target sites, four different searching modes of sgRNA binding to target DNA strand were applied. These searching modes are as follows i) coding strand searching, ii) anti-coding strand searching, iii) both strand searching, and iv) paired-gRNA searching. Finally, a complete list of all potential gRNAs along with their locations, strands, and PAMs sequence orientation can be provided for both SpCas9 as well as another potential Cas9 ortholog (NmCas9). The artificial design of potential gRNAs in a genome of interest can accelerate functional genomic studies. Consequently, the application of such novel genome editing tool (CRISPR/Cas technology) will enhance by presenting increased versatility and efficiency.

Keywords: CRISPR/Cas9 genome editing, gRNA mining, SpCas9, NmCas9

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Authors: Ghulam Muhammad Ali

Abstract:

Unveiling of genes involved in drought and root architecture using transcriptomic analyses remained fragmented for further improvement of wheat through genome editing. The purpose of this research endeavor was to unveil the variations in different genes implicated in drought tolerance and root architecture in wheat through RNA-seq data analysis. In this study seedlings of 8 days old, 6 cultivars of wheat namely, Batis, Blue Silver, Local White, UZ888, Chakwal 50 and Synthetic wheat S22 were subjected to transcriptomic analysis for root and shoot genes. Total of 12 RNA samples was sequenced by Illumina. Using updated wheat transcripts from Ensembl and IWGC references with 54,175 gene models, we found that 49,621 out of 54,175 (91.5%) genes are expressed at an RPKM of 0.1 or more (in at least 1 sample). The number of genes expressed was higher in Local White than Batis. Differentially expressed genes (DEG) were higher in Chakwal 50. Expression-based clustering indicated conserved function of DRO1and RPK1 between Arabidopsis and wheat. Dendrogram showed that Local White is sister to Chakwal 50 while Batis is closely related to Blue Silver. This study flaunts transcriptomic sequence variations in different cultivars that showed mutations in genes associated with drought that may directly contribute to drought tolerance. DRO1 and RPK1 genes were fetched/isolated for genome editing. These genes are being edited in wheat through CRISPR-Cas9 for yield enhancement.

Keywords: transcriptomic, wheat, genome editing, drought, CRISPR-Cas9, yield enhancement

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Authors: Vinod Kumar Gupta, Narendrakumar M. Chaudhari, Suchismitha Iskepalli, Chitra Dutta

Abstract:

Background-The community composition of the human microbiome is known to vary at distinct anatomical niches. But little is known about the nature of variations if any, at the genome/sub-genome levels of a specific microbial community across different niches. The present report aims to explore, as a case study, the variations in gene repertoire of 28 Prevotella reference draft genomes derived from different body-sites of human, as reported earlier by the Human Microbiome Consortium. Results-The analysis reveals the exclusive presence of 11798, 3673, 3348 and 934 gene families and exclusive absence of 17, 221, 115 and 645 gene families in Prevotella genomes derived from the human oral cavity, gastro-intestinal tracts (GIT), urogenital tract (UGT) and skin, respectively. The pan-genome for Prevotella remains “open”. Distribution of various functional COG categories differs appreciably among the habitat-specific genes, within Prevotella pan-genome and between the GIT-derived Bacteroides and Prevotella. The skin and GIT isolates of Prevotella are enriched in singletons involved in Signal transduction mechanisms, while the UGT and oral isolates show higher representation of the Defense mechanisms category. No niche-specific variations could be observed in the distribution of KEGG pathways. Conclusion-Prevotella may have developed distinct genetic strategies for adaptation to different anatomical habitats through selective, niche-specific acquisition and elimination of suitable gene-families. In addition, individual microorganisms tend to develop their own distinctive adaptive stratagems through large repertoires of singletons. Such in situ, habitat-driven refurbishment of the genetic makeup can impart substantial intra-lineage genome diversity within the microbes without perturbing their general taxonomic heritage.

Keywords: body niche adaptation, human microbiome, pangenome, Prevotella

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Authors: Adel M. Zakri, Mohammed A. Al-Saleh, Judith. K. Brown, Ali M. Idris

Abstract:

Betasatellites are small circular single stranded DNA molecules found associated with begomoviruses on field symptomatic plants. Their genome size is about half that of the helper begomovirus, ranging between 1.3 and 1.4 kb. The helper begomoviruses are usually members of the family Geminiviridae. Okra leaves showing vein thickening were collected from okra plants growing in Jazan, Saudi Arabia. Total DNA was extracted from leaves and used as a template to amplify circular DNA using rolling circle amplification (RCA) technology. Products were digested with PstI to linearize the helper viral genome(s), and associated DNA satellite(s), yielding a 2.8kbp and 1.4kbp fragment, respectively. The linearized fragments were cloned into the pGEM-5Zf (+) vector and subjected to DNA sequencing. The 2.8 kb fragment was identified as Cotton leaf curl Gezira virus genome, at 2780bp, an isolate closely related to strains reported previously from Saudi Arabia. A clone obtained from the 1.4 kb fragments he 1.4kb was blasted to GeneBank database found to be a betasatellite. The genome of betasatellite was 1357-bp in size. It was found to be a recombinant containing one fragment (877-bp) that shared 91% nt identity with Cotton leaf curl Gezira betasatellite [KM279620], and a smaller fragment [133--bp) that shared 86% nt identity with Tomato leaf curl Sudan virus [JX483708]. This satellite is thus a recombinant between a malvaceous-infecting satellite and a solanaceous-infecting begomovirus.

Keywords: begomovirus, betasatellites, cotton leaf curl Gezira virus, okra plants

Procedia PDF Downloads 310

Authors: R. Kanthavel, R. Yogesh Kumar, T. Narendrakumar, B. Santhosh, S. Surya Prakash

Abstract:

This paper primarily focuses on developing an affordable personal assistant and the implementation of it in the field of Artificial Intelligence (AI) to create a virtual assistant/friend. The problem in existing home automation techniques is that it requires the usage of exact command words present in the database to execute the corresponding task. Our proposed work is ATOM a.k.a ‘Artificial intelligence Talking Omnipresent Machine’. Our inspiration came from an unlikely source- the movie ‘Iron Man’ in which a character called J.A.R.V.I.S has omnipresence, and device controlling capability. This device can control household devices in real time and send the live information to the user. This device does not require the user to utter the exact commands specified in the database as it can capture the keywords from the uttered commands, correlates the obtained keywords and perform the specified task. This ability to compare and correlate the keywords gives the user the liberty to give commands which are not necessarily the exact words provided in the database. The proposed work has a higher flexibility (due to its keyword extracting ability from the user input) comparing to the existing work Intelligent Home automation System (IHAS), is more accurate, and is much more affordable as it makes use of WI-FI module and raspberry pi 2 instead of ZigBee and a computer respectively.

Keywords: home automation, speech recognition, voice control, personal assistant, artificial intelligence

Procedia PDF Downloads 310