Current Bioinformatics - Volume 4, Issue 3, 2009

Phenotype at crop level is the result of the interaction among many genes whose expression is often dependent on environmental conditions and developmental stage. Multilevel, computer-based, data integration thus plays a fundamental role in the understanding of many important agronomic traits such as yield and resource use efficiencies. Bioinformatics is the key for realizing the full potential of post-genomic revolution moving plant science toward crop systems biology. This manuscript will explore the benefit of bioinformatics application to plant research and, particularly, to crop science. Plant biologists and information technology specialists can contribute equally to such a task by organizing their work in a collaborative and interdisciplinary manner, thus applying in the most effective way their different technical skills to solve agricultural problems.

Protein subcellular localization is one of the key characteristic to understand its biological function. Proteins are transported to specific organelles and suborganelles after they are synthesized. They take part in cell activity and function efficiently when correctly localized. Inaccurate subcellular localization will have great impact on cellular function. Prediction of protein subcellular localization is one of the important areas in protein function research. Now it becomes the hot issue in bioinformatics. In this review paper, the recent progress on bioinformatics research of protein subcellular localization and its prospect are discussed.

Understanding the genetic machinery of plant growth and development is of fundamental importance in agriculture and biology. Recently, a novel statistical framework, coined functional mapping, has been developed to study the genetic architecture of the dynamic pattern of phenotypic development at different levels of organization. By integrating mathematical aspects of cellular and biological processes, functional mapping provides a quantitative platform in which a seemingly unlimited number of hypotheses about the interplay between genes and development can be asked and tested. However, plant development involves a series of multi-hierarchical, sequential pathways from DNA to mRNA to proteins to metabolites and finally to high-order phenotypes, and thus it is unlikely that the control mechanisms of plant development can be understood using genetic knowledge alone. Here, we describe a network biology approach for functional mapping of phenotypic formation and progression through their underlying biochemical pathways. The integration of functional mapping with information-rich spectroscopic data sets including transcriptome, proteome, and metabolome can be used to model and predict physiological variation and plant development, and will pave the way for future genetic studies capable of addressing the complex nature of growth and development.

Traditional Chinese Medicine (TCM) is characterized by regulating the integrity of the human body and has accumulated thousand-year experience in the use of Herb Formula (“Fu-Fang“ in Chinese) for managing complicated TCM Syndrome (“ZHENG” in Chinese). In recent years, there has been increasing concern about the application of bioinformatics and systems biology approaches for deciphering the scientific basis and the systematic features of TCM. Based on the new trends in such an interdisciplinary field, which we termed TCM systems bioinformatics (TCMSB), we propose for the first time a map of “Phenotype network-Biological network-Herb network” with an attempt to uncover the network systems underlying, and identify network biomarkers for, TCM Syndrome and Herb Formula. This multilayer map can serve as a start point towards the systematic interpretation of TCM theory and practice, and give promise to bridge the gap between the ancient TCM and the coming systems biology-based medicine in both system and molecular levels. Moreover, TCMSB approaches, which combine the use of computational modeling and experimental studies, may not only help catch the traditional features of TCM in view of complex biological systems and lead to the step by step modernization for TCM, but may also educe new concept for the future integrative medicine and systems medicine.

The identification of catalytic residues of an enzyme is one of the most important steps towards understanding its biological roles and exploring its applications. Thus far, a range of catalytic residue prediction methods have been developed, which play an increasingly important role in complementing the experimental characterization of enzymatic functions. The available approaches can be split into two broad categories: i) similarity-based catalytic residue annotation and ii) de novo catalytic residue prediction. In this article, we review the existing research strategies, recently developed bioinformatics tools, and future perspectives in the topic of de novo catalytic residue prediction. In particular, we review the various residue properties that have been used to distinguish catalytic and non-catalytic residues. We also detail how these residue properties can be combined into a prediction system with the assistance of different statistical or machine learning methods. Since in many respects de novo prediction of catalytic residues is still in its infancy, in this review we also propose some hints that are likely to result in novel prediction methods or increased performance.

Network biology is conceptualized as an interdisciplinary field, lying at the intersection among graph theory, statistical mechanics and biology. Great efforts have been made to promote the concept of network biology and its various applications in life science. In this review, we focus on the modules that are functional entities and building blocks of a complex network. We first introduce the basic concepts and hot spots of network biology, including several network models, general design principles of complex networks in the module point of view, module discovery approaches and the conservation and evolvability of modules. We then present several cases in which the important mechanisms underlying the cellular behavior are revealed in the framework of network, especially, in the concept of module.

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Protein inference is the final but very important step to identify proteins in biological complex from tandem mass spectrometry analysis. Assembling peptides identified from tandem mass spectra is one of the most widely used methods for protein inference in proteomics studies. The “bottom-up” approaches cut off the connection between peptides and proteins and thus significantly complicate the protein inference. First, the existence of peptides shared by multiple parent proteins leads to ambiguities in identifying proteins. Second, it is tough to develop an effective model which can recover the connection between peptides and proteins. To address these issues, three new concepts have been proposed, which are parsimony principle, proteotypic peptides, and peptide detectability. In this review we survey the advantages and disadvantages of several state-of-the-art statistical models which applied these concepts. In order to keep in line with proteomics experiments, we first give a brief introduction of peptide identification. Finally, we point out further requirements for improvements and future perspectives.

MicroRNAs (miRNAs) are small, non-coding RNAs that participate in the post-transcriptional regulation of messenger RNAs (mRNAs) by degrading or inhibiting translation. Some of the topical studies strongly suggest that the disorders in the normal activities of miRNAs might cause many diseases. Generally, such studies concern patient-specific expression profiles for the purposes like pruning, clustering or classification. This paper describes a novel relative coexpression measure to compute deviation in microarray expression profiles of diseased people over a set of people. This measure is used by an unsupervised algorithm of complexity O (n3 log n), where n denotes the number of miRNAs, to locate the group of miRNAs responsible for the specific disease. The results taken over the expression data of schizophrenic patients show efficiency in locating brain-enriched miRNAs, which have earlier established support to be associated with schizophrenia neuropathology.

Molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing many kinds of alterations in the expression levels. To identify disease-altered genes better, we have developed an approach that searches for the genes which present a significant rank alteration in the rank of their expression profiles, by comparing an altered rank with another gene. The approach provides groups of genes and assigns a statistical measure of significance to each pair of genes selected. The method is evaluated using two experimental sets of microarrays. We investigated the possibility of inferring the rank reverse pairs and its possibility in diagnostic application when there is no prior knowledge of the genes belonging to disease, nor about the structure of gene regulatory network. We also show that rank reverse is a very powerful technique to diagnostic disease in the practical application and could be used prior to traditional biomarker discovery.