Nanoinformatics - A newly introduced tool for research

Research in the field of nanotechnology has witnessed rapid increase in the synthesis of Engineered nanoparticles (ENPs). This has even led to development of new discipline of Nanotoxicology. Advances in the field of Nanotoxicology further led to development of new domain-nanoinformatics. This new domain of nanoinformatics provides a computational perspective to biology and nanotechnology addressing multi level integration. Nanoinformatics not only helps in predicting nanoparticle structure, composition and behaviour but also covers raw data management, analysis of data derived from biomedical applications and simulation of nanoparticle interactions with biological systems. In addition, it accelerates nano-related research and applications into clinical practice. There are various computational models developed to study the key steps in nano-medicine like drug encapsulation and release, nanoparticle targeting, delivery and uptake and nanoparticle effects on cells and tissues. These prospects have opened up a large domain enabling possibilities of nanomedicine and frontiers for clinical practice and biomedical research in a cost-effective manner along with various applications including studies in clinical trials, toxicity assays, drug delivery systems. This review highlights new approaches for Engineered nanoparticles (ENP) risk assessment and regulation.

Computational Prediction of miR130a Target Genes in Cancer

@#Introduction: Cancer is one of the main causes of mortality globally and the incidence has been rising over the years. Studies have shown that miRNAs have the potential as cancer biomarkers. The miR-130a has been reported to be upregulated in several types of cancer, which indicate the important roles of miR-130a in cancer development and metastasis. The aim of this study is to identify potential target genes and to predict the regulatory function of miR130a-3p and 5p in cancer. Methods: Three bioinformatics platforms namely miRWalk, the Database for annotations, visualization and integrated discovery (DAVID) Gene Functional Classification Tool and miRanda-miRSVR analysis tools were used to identify possible interaction between miR-130a and its target. Protein-protein interaction (PPI) network for the predicted target genes was then constructed. Results: The analyses have identified nine predicted target genes for miR-130a-3p (RAPGEF4, SOS2, NRP1, RPS6KB1, MET, IL15, ACVR1, RYR2 and ITPR1), and ten for miR-130a-5p (BCL11A, SPOPL, NLK, PPARGC1A, POU4F2, CPEB4, ST18, RSBN1L, ELF5 and ARID4B), that might play an important role in the development of cancer. Findings from this report suggest that miR-130a may involves in controlling cancer related genes; MET, ACVR1 and BCL11A. miR-130a-3p may regulates MET which involves in apoptosis and metastasis, and ACVR1 which involves in metastasis and angiogenesis. miR-130a-5p may regulates BCL11A which involves in apoptosis, proliferation and tumorigenesis. Conclusion: This study has highlighted the molecular interaction of miR-130a with associated genes and pathways, suggesting therapeutic potential of miR130a as personalised targeted therapy for cancer.

Structure Prediction of ncRNA genes: A review on Computational Intelligence Algorithms

Noncoding RNAs (ncRNAs) are an important part of genes and having an important role in human cellular activities and serious diseases. To predict ncRNAs structure, there are many computational intelligence algorithms (CIAs) that are developed in past studies. However, many studies suggested that there were still many structures that are still unpredictable by researchers. In this paper, CIAs algorithms were comprehensively reviewed to predict ncRNAs structures. The advantages and disadvantages of CIA algorithms are briefly mentioned related to ncRNA genes. Moreover, the latest software tools are also compared and reviewed to identify the structure of ncRNAs for mining deep sequencing data. In this study, conventional machine learning algorithms are mainly focused and future trends are also described to predict ncRNAs structure. This paper concludes that there is a need for improving CIA algorithms by using deep learning architectures in terms of layers and computational complexity to predict ncRNAs structures.

Genome wide identification of nucleotide-binding site leucine-rich repeat (NBS-LRR) gene encoding regions in Solanum lycopersicum resistant to environmental pathogens by computational methods

Aim: The nucleotide binding site leucine rich repeat (NBS-LRR) genes are responsive to pathogen strike in plants. This study focused on identifying the loci specific NBS-LRR gene encoding regions in tomato at whole genome level. Methodology: Major computational challenges in analyzing large genomic data using existing analytical tools were limited by the amount of memory used for reading large data. In this study, a specific algorithm was developed to identify a signature pattern associated with stress tolerant coding regions using stream readers for reading whole tomato genome, chromosome wise, to locate NBS-LRR coding sequences. Results: The computer program reads chromosome wise data and extracts the potential stress tolerant coding regions. It was found that more than 300 disease resistance protein coding regions were found across all chromosome, specifically in chromosome 12 more NBS-LRR concentration were found and their respective locus were identified. Interpretation: The identified disease resistance protein coding regions, specifically the NBS-LRR coding regions and their loci can be useful for plant breeders to select parental lines for developing plants tolerant to disease and pest invasion

Current Challenges of Computational Intelligent Techniques for Functional Annotation of ncRNA Genes

The limited understanding of functional annotation of non-coding RNAs (ncRNAs) has been largely due to the complex functionalities of ncRNAs. They perform a vital part in the operation of the cell. There are many ncRNAs available such as micro RNAs or long non-coding RNAs that play important functions in the cell. In practice, there is a strong binding of the function of RNAs that must be considered to develop computational intelligent techniques. Comprehensive modeling of the structure of an ncRNA is essential that may provide the first clue towards an understanding of its functions. Many computational techniques have been developed to predict ncRNAs structures but few of them focused on the functions of ncRNA genes. Nevertheless, the accuracy of the functional annotation of ncRNAs is still facing computational challenges and results are not satisfactory. Here, many computational intelligent methods were described in this paper to predict the functional annotation of ncRNAs. The current literature review is suggested that there is still a dire need to develop advanced computational techniques for functional annotating of ncRNA genes in terms of accuracy and computational time.

Stereotypes About Enterotype: the Old and New Ideas / 基因组蛋白质组与生物信息学报·英文版

In 2011, the term "enterotype" first appeared to the general public in Nature, which refers to stratification of human gut microbiota. However, with more studies on enterotypes conducted nowadays, doubts about the existence and robustness of enterotypes have also emerged. Here we reviewed current opinions about enterotypes from both conceptual and analytical points of view. We firstly illustrated the definition of the enterotype and various factors influencing enterotypes, such as diet, administration of antibiotics, and age. Then we summarized lines of evidence that pose the concept against the enterotype, and described the current methods for enterotype analysis. Finally, we showed that the concept of enterotype has been extended to other ecological niches. Based on current studies on enterotypes, it has been clear that more studies with larger sample sizes are needed to characterize the enterotypes. Improved computational methods are also required to build sophisticated models, reflecting the dynamics and resilience of enterotypes.