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General fields of bioinformatics and its applications in cli

    General fields of bioinformatics and its applications in clinical

    Table of Contents
    Summary. 1
    Introduction. 1
    General Fields of Bioinformatics. 2
    Sequence Analysis. 2
    Prediction of Protein Structure. 2
    High-throughput image analysis. 3
    Genome Annotation. 3
    Computational Evolutionary Biology and Comparative Genomics. 3
    Analysis of Gene Expression, Regulation, and Molecular Interaction. 4
    Specific Applications of Bioinformatics. 4
    Virtual Screening for Drug Candidates. 4
    Immunoinformatics and Personalized Medicine. 5
    Neurodegenerative Diseases and Neuroimaging. 6
    Conclusion. 7
    Reference. 8
    Appendix. 11
     

    Summary

    General fields of bioinformatics and its applications in clinical 代写
    Bioinformatics has become an important area of biology for many years. Applications of bioinformatics are involved in the various fields, including molecular biology, chemistry, immunology and neurobiology. This review at first summarizes general fields of bioinformatics. And then three specific examples from different fields are discussed respectively. Though bioinformatics has wide applications, it still faces with many challenges, which encourage researchers to study it further.

    Introduction

    As early as the beginning of the 1970s, the term “bioinformatics” has been used by Paulien Hogeweg and Ben Hesper, when it was defined as the research of informatization processes in biosystems (Hogeweg, 2011). Information processing is one of properties of organisms, which includes diverse forms like evolution causing information accumulation, and information interpretation at multiple levels (Hogeweg, 2011). These information processing is a good channel to study living systems.
    Now the definition of bioinformatics has been changed. It is defined as the use of computational methods to study biology data (Higgs & Attwood, 2009). In addition, the area of bioinformatics also becomes wider and the application of it has been put into use on multiple fields. In recent 10 years, various bioinformatics sub-disciplines appear. Besides molecular biology which is the most original utilization of bioinformatics, it can be applied to chemistry, neurobiology, immunology, toxicology and so forth. Actually, some new terms like cheminformatics, neuroinformatics and immunoinformatics emerge (Perez-Iratxeta, Andrade-Navarro & Wren, 2007).
    General fields of bioinformatics and its applications in clinical 代写

    General Fields of Bioinformatics

    Bioinformatics is widely used in almost every aspects of biology, including molecular biology, biochemistry, neurology, immunology, evolutionary biology and so forth. Furthermore, it can analyze various data like DNA sequences, protein 3-D structures, neuroimages, antigen epitopes, sequence alignments, etc. The following is general fields bioinformatics is able to be applied to.

    Sequence Analysis

    General fields of bioinformatics and its applications in clinical 代写
    Hundreds of thousands of DNA sequences from various organisms have been sequenced and decoded since the earliest organism was sequenced in 1977 (Sanger et al., 1977). It was estimated that GenBank has contained more than 190 billion nucleotides from over 250,000 organisms (Benson et al., 2012). These sequences can be used to analyze RNA sequences, proteins, domains, motifs, and repetitive sequences.

    Prediction of Protein Structure

    Primary structure of protein is determined by the DNA sequence, and secondary and 3-dimensional structures are determined by the primary structure. However, how to get secondary or 3-dimensional structures from primary structure is an important application of bioinformatics. Though different databases and tools have been implemented, but the accuracy is still questionable. In fact, assessing the quality of protein structures is an essential part of protein structure prediction, and different quality scores have been reported, like QMEAN Z-score (Benkert, Biasini & Schwede, 2011).

    High-throughput image analysis

    Biomedical images contain plenty of information, which can be processed, quantified and analyzed through computers. Biomedical imaging has become more and more vital for diagnosis and research. Examples of image analysis are clinical image analysis (which will be mentioned in the following by discussing neuroimaging), cytohistopathology and high-content screening, behavioral observations of animals, etc.

    Genome Annotation

    Biology has entered the post-genome era. Marking genes or other features in DNA sequences is essential to understand the function of genes. Prediction of protein-coding genes, alignment of cDNAs and alignments of proteins can be used to annotate genomes (Pop & Salzberg, 2008). According to these three information, genome annotation softwares are able to find genes, tRNAs and predict the functions of genes. Actually, the annotation can help researchers identify SNPs, unknown mutations of cancer and other genetic disorders. However, the accuracy of these softwares is questioned. Errors in genome annotation can emerge during sequencing, gene-calling process, and even gene functions assignment (Poptsova & Gogarten, 2010).

    Computational Evolutionary Biology and Comparative Genomics

    Multiple evolutionary events occurred in the history. Individual nucleotides are changed by point mutations; chromosomes are altered by duplication, transposition, inversion, horizontal transfer; genomes undergo hybridizing and polyploidizing. Evolution happens. Therefore, comparing genes and genome can trace the evolutionary processes. How two species are diverged or how new gene is produced can be answered with the results of bioinformatics. Furthermore, comparative genomics is also applied to find missing genes, new pathways or new protein complexes (Ferrer, Shearer & Karp, 2011). There are numerous softwares, online tools, algorithms and databases to help researchers to study evolutionary processes.

    Analysis of Gene Expression, Regulation, and Molecular Interaction

    Gene expression can be probed by measuring levels of mRNA. The data of gene expression can be applied to infer gene regulation. Nevertheless gene regulation is more complex. For example, motifs of DNA sequences, which can be recognized by transcription factors, are experimentally resolved or computationally inferred (Badis et al., 2009). The motifs and transcription factors subsequently are used to predict expression levels and the genes they regulate (Wunderlich & Mirny, 2009). Molecular interaction can be simulated by computational algorithms and docking algorithms is peculiar to research molecular interaction.

    Specific Applications of Bioinformatics

    Three specific examples of bioinformatics application from different sub-disciplines of cheminformatics, neuroinformatics and immunoinformatics, are illustrated in the following.

    Virtual Screening for Drug Candidates

    Cheminformatics which can be regarded as a sub-discipline of bioinformatics focus on analyzing chemical structure and derived aspects of chemical structure. Biomacromolecules are important component parts. Cheminformatics have been extensively used in novel drug discovery. High-throughput X-ray crystallography in part results in the increase of structural targets and lots of target proteins have been regarded as therapeutic potential (Kitchen, Decornez, Furr & Bajorath, 2004). Under such a background, more novel compound classes need to be applied in clinical. Due to millions of compounds, it is necessary to use cheminformatics for prescreening. The filter methods used in virtual screening include detecting reactive or toxic groups, aqueous solubility and passive absorption, drug-like characters, blood–brain barrier penetration and CNS activity, and other factors determining the in vivo impact of drugs (Bajorath, 2002).
    An example of anti-malarial drugs virtual screening will be recounted in the following. Malaria is an infectious disease and endemic to South America, Africa and South East Asia. Global deaths caused by malaria were more than 1,000,000 in 2010 (Murray et al., 2012) and affected almost 300 – 500 million people every year. Anti-malarial drugs have been studied for more than 70 years, but drug resistance still restrains the effects. Therefore, novel targets or new compound classes are studying to increase the potency and efficacy. It was reported that using cheminformatics virtual libraries were generated and prioritize compounds were selected (Guha et al., 2010). The authors virtually screened drug candidates from Ugi reaction, which might contain millions of compounds. First, a virtual library was generated by the use of an amine, ketone, carboxylic acid and other required molecules. All possible combinations were predicted. Second, string manipulation methods were used to generate a final library by validating structures and substructures. These two steps required researchers were master of cheminformatics. Now, this virtual library still includes nearly 100,000 compounds, which was impossible to synthesize and screen in the cell lines. Third, some compounds were prioritized and a small number of compounds were synthesized. In this step, crystal structures were predicted and compounds were identified whether they were inhibitors of falcipain-2 enzyme, which was a cystiene protease and played a key role in the development of malarial parasites (Sijwali, Koo, Singh & Rosenthal, 2006). At last, a ranking of these compounds were got and ten compounds were synthesized and tested in cell lines. The results of cell-based assay showed that four compounds were valid to malaria ex vivo, as well as in vivo (Guha et al., 2010).
    In conclusion, virtual screening is the inexorable trend owing to the millions of compounds. And the application of cheminformatics can undoubtedly improve the potent drugs discovery.

    Immunoinformatics and Personalized Medicine

    General fields of bioinformatics and its applications in clinical 代写
    Biology has entered the postgenomic era. How to use more than 30,000 genes in the human genome has become a multifaceted problem. Personalized medicine is a new orientation, requiring targeting variations of genome, especially variation of the immune system. The immune system plays a vital part in personalized medicines which may cure various diseases like cancer, autoimmune diseases and even infectious diseases. Numerous molecules participate in the dynamic network of the immune system.
    Immunoinformatics really contributes to the personalized medicine. It can be used to identify the relation between a certain disease and genetic variations, which is one of the most important factors in the development of personalized medicine (Yan, 2008). Immunoinformatics studies the genetic variations between individuals in response to medicines. The variations of allele frequencies of immune molecules are extremely important, because according to these patients can be divided into subgroups with different drug responses. For instance, S427T is a SNP in interferon regulatory factor 3 (IRF) which belongs to innate immune genes. This SNP is associated with high risk of cervical cancer and HPV persistence (Wang et al., 2009).

    Conclusion

    Bioinformatics has been applied in various fields and improve clinical including drug virtual screening. However, there are still countless problems. For example, the accuracy of numerous predictions from bioinformatics needs to be tested, which has led to quality assessment standard. Personalized medicines are hardly popularized and the factors influencing the variation are needed to study. In addition, when bioinformatics comes to neurobiology, there are more challenges. For brain is the most complex organ, much more information may contain and need to be integrated. Facing neuro-diseases, therapies that can affect the etiology are always lacking. Therefore, it is essential to study bioinformatics further, and more efforts are needed to apply it to clinical.

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    Appendix

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    Figure. 1This figure is replicated from papers of Trojanowski et al. (2010). It shows the relationship between AD stage and biomarkers. Before disease onset, the only predictive biomarkers are genetic mutations which are nearly impossible to detect for every people. And later, Aβ, neurodegeneration, neuronal loss and memory loss or cognitive decline can be biomarkers of AD.