Genomics made easier: An introductory tutorial to genome datamining

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.

Expression and characterization of thermostable glycogen branching enzyme from Geobacillus mahadia Geo-05

The glycogen branching enzyme (EC 2.4.1.18), which catalyses the formation of α-1,6-glycosidic branch points in glycogen structure, is often used to enhance the nutritional value and quality of food and beverages. In order to be applicable in industries, enzymes that are stable and active at high temperature are much desired. Using genome mining, the nucleotide sequence of the branching enzyme gene (glgB) was extracted from the Geobacillus mahadia Geo-05 genome sequence provided by the Malaysia Genome Institute. The size of the gene is 2013 bp, and the theoretical molecular weight of the protein is 78.43 kDa. The gene sequence was then used to predict the thermostability, function and the three dimensional structure of the enzyme. The gene was cloned and overexpressed in E. coli to verify the predicted result experimentally. The purified enzyme was used to study the effect of temperature and pH on enzyme activity and stability, and the inhibitory effect by metal ion on enzyme activity. This thermostable glycogen branching enzyme was found to be most active at 55 °C, and the half-life at 60 °C and 70 °C was 24 h and 5 h, respectively. From this research, a thermostable glycogen branching enzyme was successfully isolated from Geobacillus mahadia Geo-05 by genome mining together with molecular biology technique.

Identification of sequences common to more than one therapeutic target to treat complex diseases: simulating the high variance in sequence interactivity evolved to modulate robust phenotypes

BACKGROUND

Genome-wide association studies show that most human traits and diseases are caused by a combination of environmental and genetic causes, with each one of these having a relatively small effect. In contrast, most therapies based on macromolecules like antibodies, antisense oligonucleotides or peptides focus on a single gene product. On the other hand, complex organisms seem to have a plethora of functional molecules able to bind specifically to multiple genes or genes products based on their sequences but the mechanisms that lead organisms to recruit these multispecific regulators remain unclear.

RESULTS

The mutational biases inferred from the genomic sequences of six organisms show an increase in the variance of sequence interactivity in complex organisms. The high variance in the interactivity of sequences presents an ideal evolutionary substrate to recruit sequence-specific regulators able to target multiple gene products. For example, here it is shown how the 3'UTR can fluctuate between sequences likely to be complementary to other sites in the genome in the search for advantageous interactions. A library of nucleotide- and peptide-based tools was built using a script to search for candidates (e.g. peptides, antigens to raise antibodies or antisense oligonucleotides) to target sequences shared by key pathways in human disorders, such as cancer and immune diseases. This resource will be accessible to the community at www.wikisequences.org .

CONCLUSIONS

This study describes and encourages the adoption of the same multitarget strategy (e.g., miRNAs, Hsp90) that has evolved in organisms to modify complex traits to treat diseases with robust pathological phenotypes. The increase in the variance of sequence interactivity detected in the human and mouse genomes when compared with less complex organisms could have expedited the evolution of regulators able to interact to multiple gene products and modulate robust phenotypes. The identification of sequences common to more than one therapeutic target carried out in this study could facilitate the design of new multispecific methods able to modify simultaneously key pathways to treat complex diseases.

CompaGB: An open framework for genome browsers comparison

Background

Tools to visualize and explore genomes hold a central place in genomics and the diversity of genome browsers has increased dramatically over the last few years. It often turns out to be a daunting task to compare and choose a well-adapted genome browser, as multidisciplinary knowledge is required to carry out this task and the number of tools, functionalities and features are overwhelming.

Findings

To assist in this task, we propose a community-based framework based on two cornerstones: (i) the implementation of industry promoted software qualification method (QSOS) adapted for genome browser evaluations, and (ii) a web resource providing numerous facilities either for visualizing comparisons or performing new evaluations. We formulated 60 criteria specifically for genome browsers, and incorporated another 65 directly from QSOS's generic section. Those criteria aim to answer versatile needs, ranging from a biologist whose interest primarily lies into user-friendly and informative functionalities, a bioinformatician who wants to integrate the genome browser into a wider framework, or a computer scientist who might choose a software according to more technical features. We developed a dedicated web application to enrich the existing QSOS functionalities (weighting of criteria, user profile) with features of interest to a community-based framework: easy management of evolving data, user comments...

Conclusions

The framework is available at http://genome.jouy.inra.fr/CompaGB. It is open to anyone who wishes to participate in the evaluations. It helps the scientific community to (1) choose a genome browser that would better fit their particular project, (2) visualize features comparatively with easily accessible formats, such as tables or radar plots and (3) perform their own evaluation against the defined criteria. To illustrate the CompaGB functionalities, we have evaluated seven genome browsers according to the implemented methodology. A summary of the features of the compared genome browsers is presented and discussed.

Materiomics: biological protein materials, from nano to macro

Materiomics is an emerging field of science that provides a basis for multiscale material system characterization, inspired in part by natural, for example, protein-based materials. Here we outline the scope and explain the motivation of the field of materiomics, as well as demonstrate the benefits of a materiomic approach in the understanding of biological and natural materials as well as in the design of de novo materials. We discuss recent studies that exemplify the impact of materiomics - discovering Nature's complexity through a materials science approach that merges concepts of material and structure throughout all scales and incorporates feedback loops that facilitate sensing and resulting structural changes at multiple scales. The development and application of materiomics is illustrated for the specific case of protein-based materials, which constitute the building blocks of a variety of biological systems such as tendon, bone, skin, spider silk, cells, and tissue, as well as natural composite material systems (a combination of protein-based and inorganic constituents) such as nacre and mollusk shells, and other natural multiscale systems such as cellulose-based plant and wood materials. An important trait of these materials is that they display distinctive hierarchical structures across multiple scales, where molecular details are exhibited in macroscale mechanical responses. Protein materials are intriguing examples of materials that balance multiple tasks, representing some of the most sustainable material solutions that integrate structure and function despite severe limitations in the quality and quantity of material building blocks. However, up until now, our attempts to analyze and replicate Nature's materials have been hindered by our lack of fundamental understanding of these materials' intricate hierarchical structures, scale-bridging mechanisms, and complex material components that bestow protein-based materials their unique properties. Recent advances in analytical tools and experimental methods allow a holistic view of such a hierarchical biological material system. The integration of these approaches and amalgamation of material properties at all scale levels to develop a complete description of a material system falls within the emerging field of materiomics. Materiomics is the result of the convergence of engineering and materials science with experimental and computational biology in the context of natural and synthetic materials. Through materiomics, fundamental advances in our understanding of structure-property-process relations of biological systems contribute to the mechanistic understanding of certain diseases and facilitate the development of novel biological, biologically inspired, and completely synthetic materials for applications in medicine (biomaterials), nanotechnology, and engineering.

Heterogeneous distribution of SNPs in the human genome: microsatellites as predictors of nucleotide diversity and divergence

Understanding the forces that govern the distribution of single nucleotide polymorphisms is vital for many of their applications. Here we conducted a systematic search to quantify how both SNP density and human-chimpanzee divergence vary around different repetitive sequences. We uncovered a highly complicated picture in which these quantities often differ significantly from the genome-wide average in regions extending more than 20 kb, the direction of the deviation varying with repeat number and motif. AT microsatellites in particular are potent predictors of SNP density, long (AT)(n) repeat tracts tending to be found in regions of significantly reduced SNP density and low GC content. Although the causal relationships remain difficult to determine, our results indicate a strong relationship between microsatellites and the DNA that flanks them. Our results help to explain the mixed picture that emerges from other studies and have important implications for the way in which genetic diversity is distributed in our genomes.