Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases.

Rapid advances in gene expression microarray technology have enabled to discover molecular markers used for cancer diagnosis, prognosis, and prediction. One computational challenge with using microarray data analysis to create cancer classifiers is how to effectively deal with microarray data which are composed of high-dimensional attributes (p) and low-dimensional instances (n). Gene selection and classifier construction are two key issues concerned with this topics. In this article, we reviewed major methods for computational identification of cancer marker genes. We concluded that simple methods should be preferred to complicated ones for their interpretability and applicability.

Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transactional regulators, and play important roles in cancer. One essential step to understand the regulatory effect of miRNAs is the reliable prediction of their target mRNAs. Typically, the predictions are solely based on the sequence information, which unavoidably have high false detection rates. Recently, some novel approaches are developed to predict miRNA targets by integrating the typical algorithm with the paired expression profiles of miRNA and mRNA. Here we review and discuss these integrative approaches and propose a new algorithm called HCTarget. Applying HCtarget to the expression data in multiple myeloma, we predict target genes for ten specific miRNAs. The experimental verification and a loss of function study validate our predictions. Therefore, the integrative approach is a reliable and effective way to predict miRNA targets, and could improve our comprehensive understanding of gene regulation.

Malaria is a serious infectious disease that causes over one million deaths yearly. It is caused by a group of protozoan parasites in the genus . No effective vaccine is currently available and the elevated levels of resistance to drugs in use underscore the pressing need for novel antimalarial targets. In this review, we survey omics centered developments in biology, which have set the stage for a quantum leap in our understanding of the fundamental processes of the parasite life cycle and mechanisms of drug resistance and immune evasion.

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

The process of human phonation involves a complex interaction between the physical domains of structural dynamics, fluid flow, and acoustic sound production and radiation. Given the high degree of nonlinearity of these processes, even small anatomical or physiological disturbances can significantly affect the voice signal. In the worst cases, patients can lose their voice and hence the normal mode of speech communication. To improve medical therapies and surgical techniques it is very important to understand better the physics of the human phonation process. Due to the limited experimental access to the human larynx, alternative strategies, including artificial vocal folds, have been developed. The following review gives an overview of experimental investigations of artificial vocal folds within the last 30 years. The models are sorted into three groups: static models, externally driven models, and self-oscillating models. The focus is on the different models of the human vocal folds and on the ways in which they have been applied.

In recent years, increasingly sophisticated computational and bioinformatics tools have evolved for the analyses of protein structure, function, ligand interactions, modeling and energetics. This includes the development of algorithms to recursively evaluate side-chain rotamer permutations, identify regions in a 3D structure that meet some set of search parameters, calculate and minimize energy values, and provide high-resolution visual tools for theoretical modeling. Here we discuss the interdependency between different areas of bioinformatics, the evolution of different algorithm design approaches, and finally the transition from theoretical models to real-world design and application as they relate to Ca(2+)-binding proteins. Within this context, it has become evident that significant pre-experimental design and calculations can be modeled through computational methods, thus eliminating potentially unproductive research and increasing our confidence in the correlation between real and theoretical models. Moving from prediction to production, it is anticipated that bioinformatics tools will play an increasingly significant role in research and development, improving our ability to both understand the physiological roles of Ca(2+) and other metals and to extend that knowledge to the design of function-specific synthetic proteins capable of fulfilling different roles in medical diagnostics and therapeutics.

Olfactory receptors, in addition to being involved in first step of the physiological processes that leads to olfaction, occupy an important place in mammalian genomes. ORs constitute super families in these genomes. Elucidating ol-factory receptor function at a molecular level can be aided by a computationally derived structure and an understanding of its interactions with odor molecules. Experimental functional analyses of olfactory receptors in conjunction with computational studies serve to validate findings and generate hypotheses. We present here a review of the research efforts in: creating computational models of olfactory receptors, identifying binding strategies for these receptors with odorant molecules, performing medium to long range simulation studies of odor ligands in the receptor binding region, and identifying amino acid positions within the receptor that are responsible for ligand-binding and olfactory receptor activation. Written as a primer and a teaching tool, this review will help researchers extend the methodologies described herein to other GPCRs.

The International HapMap Project provides a key resource of genotypic data on human samples including lymphoblastoid cell lines derived from individuals of four major world populations of African, European, Japanese and Chinese ancestry. Researchers have utilized this resource to identify genetic elements that correlate with various phenotypes such as risks of common diseases, individual drug response and gene expression variation. However, recent comparative studies have suggested that the currently available HapMap genotypic data may not capture a substantial proportion of rare or untyped SNPs in these populations, implying that the HapMap SNPs may not be sufficient for comprehensive association studies. In this paper, three large-scale deep resequencing projects covering the HapMap samples: ENCODE (Encyclopedia of DNA Elements), SeattleSNPs and NIEHS (National Institute of Environmental Health Sciences) Environmental Genome Project are discussed. Prospectively, once integrated with the HapMap resource, these efforts will greatly benefit the next wave of association studies and data mining using these cell lines.

Experiments on human and on animal excised specimens as well as animal preparations are so far the most realistic approaches to simulate the process of human phonation. These experiments do not have the disadvantage of limited space within the neck and enable studies of the actual organ necessary for phonation, i.e., the larynx. The studies additionally allow the analysis of flow, vocal fold dynamics, and resulting acoustics in relation to well-defined laryngeal alterations.

The identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine data analysis.

Over the past two decades, studies have discovered a special form of alternative splicing (AS) that produces a circular form of RNA. This stands in contrast to normal AS, which produces a linear form of RNA. Although these circRNAs have garnered considerable attention in the scientific community for their biogenesis and functions, the focus of these studies has been on the regulatory role of circRNAs with the assumption that circRNAs are non-coding. As non-coding RNAs, they may regulate mRNA transcription, tumor initiation, and translation by sponging miRNAs and RNA-binding proteins (RBPs). In addition to these regulatory roles of circRNAs, however, recent studies have provided strong evidence for their translation. The translation of circRNAs is expected to have an important role in promoting cancer cell growth and activating molecular pathways related to cancer development. In some cases, the translation of circRNAs is shown to be efficiently driven by an internal ribosome entry site (IRES). The development of a computational tool for identifying and characterizing the translation of circRNAs using high-throughput sequencing and IRES increases identifiable proteins translated from circRNAs. In turn, it has a substantial impact on helping researchers understand the functional role of proteins derived from circRNAs. New web resources for aggregating, cataloging, and visualizing translational information of circRNAs derived from previous studies have been developed. In this paper, general concepts of circRNA, circRNA biogenesis, translation of circRNA, and existing circRNA tools and databases are summarized to provide new insight into circRNA studies.